Evaluation of Imputed Gene Expression Risk Scores

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1 Introduction

This study evaluates the predictive utility of GeRS calculated using several strategies. The predictive utility of models containing polygenic scores and GeRS is also investigated.

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2 Aims

• Evaluate the predictive utility of GeRS based on a single eQTL data source.
• Evaluate models combining GeRS derived eQTL data from multiple tissues.
• Determine whether GeRS can improve prediction when in combination with polygenic scores.

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3 Methods

3.1 Samples

• UK Biobank
• TEDS

3.2 Outcomes

• UK Biobank
  • Depression (binary)
  • Intelligence (continuous)
  • Body mass index (BMI - continuous)
  • Height (continuous)
  • Coronary Artery Disease (CAD - Binary)
  • Type II Diabetes (T2D - Binary)
  • Inflammatory Bowel Disorder (IBD - Binary)
  • Rheumatoid arthritis (RheuArth - Binary)
• TEDS
  • ADHD traits (continuous)
  • Height (continuous)
  • Body mass index (BMI - continuous)
  • GCSE scores (continuous)

Note. Multiple Slerosis in UK Biobank was not included due to insufficent SNP data in the corresponding GWAS for TWAS.

3.3 Genotypic data

Both target sample underwent stringent quality control prior to imputation using the HRC-reference. After imputation, genotypes were converted to PLINK hard-calls, and only HapMap3 SNPs were retained. Eureopean individuals within the target samples were identified and retained if they were within the 3SD of the 1KG European mean of the first 100 principal components.

3.4 GWAS summary statistics

For each target phenotype, the largest independent phenotype-matched GWAS was selected for calculating polygenic scores. More information can be found in the table below.

Preparing GWAS sumstats table for UK Biobank phenotypes

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')

library(data.table)

pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_031218.csv')

ukb_pheno=c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth')
ukb_gwas=c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','SCLE02','RHEU01')
ukb_prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005)
ukb_dat<-data.frame(pheno=ukb_pheno,gwas=ukb_gwas,prev=ukb_prev)

pheno_ukb<-pheno[(pheno$Code %in% ukb_gwas),]
pheno_ukb<-pheno_ukb[,c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2 observed','h2 se','lambda GC','intercept','intercept se')]
names(pheno_ukb)<-c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_se','lambda','intercept','intercept_se')

pheno_ukb$pop_prev<-ukb_dat$prev[match(pheno_ukb$Code, ukb_dat$gwas)]
pheno_ukb$Target_Phenotype<-ukb_dat$pheno[match(pheno_ukb$Code, ukb_dat$gwas)]
pheno_ukb$Ncases<-as.numeric(gsub(',','',pheno_ukb$Ncases))
pheno_ukb$Ncontrols<-as.numeric(gsub(',','',pheno_ukb$Ncontrols))
pheno_ukb$samp_prev<-pheno_ukb$Ncases/(pheno_ukb$Ncases+pheno_ukb$Ncontrols)

h2l_R2 <- function(k, r2, p) {
  # K baseline disease risk
  # r2 from a linear regression model attributable to genomic profile risk score
  # P proportion of sample that are cases
  # calculates proportion of variance explained on the liability scale
  #from ABC at http://www.complextraitgenomics.com/software/
  #Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
  x= qnorm(1-k)
  z= dnorm(x)
  i=z/k
  C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
  theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
  h2l_R2 = C*r2 / (1 + C*theta*r2)
}

se_h2l_R2 <- function(k,h2,se, p) {
  # K baseline disease risk
  # r2 from a linear regression model attributable to genomic profile risk score
  # P proportion of sample that are cases
  # calculates proportion of variance explained on the liability scale
  #from ABC at http://www.complextraitgenomics.com/software/
  #Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.

  #SE on the liability (From a Taylor series expansion)
  #var(h2l_r2) = [d(h2l_r2)/d(R2v)]^2*var(R2v) with d being calculus differentiation
  x= qnorm(1-k)
  z= dnorm(x)
  i=z/k
  C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
  theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
  se_h2l_R2 = C*(1-h2*theta)*se
}

pheno_ukb$h2_obs<-as.numeric(pheno_ukb$h2_obs)
pheno_ukb$h2_se<-as.numeric(pheno_ukb$h2_se)

pheno_ukb$h2_liab<-round(h2l_R2(k=pheno_ukb$pop_prev, r2=pheno_ukb$h2_obs, p=pheno_ukb$samp_prev),3)
pheno_ukb$h2_liab_se<-round(se_h2l_R2(k=pheno_ukb$pop_prev,h2=pheno_ukb$h2_obs,se=pheno_ukb$h2_se, p=pheno_ukb$samp_prev),3)

pheno_ukb$h2_obs<-paste0(pheno_ukb$h2_obs," (", pheno_ukb$h2_se,")")
pheno_ukb$h2_se<-NULL
pheno_ukb$h2_liab[!is.na(pheno_ukb$Ncases)]<-paste0(pheno_ukb$h2_liab[!is.na(pheno_ukb$Ncases)]," (", pheno_ukb$h2_liab_se[!is.na(pheno_ukb$Ncases)],")")
pheno_ukb$h2_liab_se<-NULL
pheno_ukb$intercept<-paste0(pheno_ukb$intercept," (", pheno_ukb$intercept_se,")")
pheno_ukb$intercept_se<-NULL

pheno_ukb$trait<-c('BMI','CAD','College Completion',"Crohn's Disease",'Major Depression','T2D','Height','RheuArth','MultiScler')
pheno_ukb<-pheno_ukb[match(ukb_dat$gwas,pheno_ukb$Code),]

pheno_ukb<-pheno_ukb[,c('Target_Phenotype','Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_liab','intercept','lambda')]
names(pheno_ukb)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')

write.csv(pheno_ukb, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv', row.names=F, quote=F)

Show GWAS for UK Biobank phenotypes

GWAS used for each UK Biobank phenotype

Target Phenotype	Code	GWAS Phenotype	Year	PMID	Ncase	Ncontrol	N	h2-obs (SE)	h2-liab (SE)	Intercept	Lambda
Major Depression	DEPR06	major depressive disorder	2018	29700475	116404	314990	431394	0.0551 (0.0023)	0.085 (0.004)	1.0169 (0.0088)	1.3894
Intelligence	COLL01	college completion	2013	23722424	NA	NA	95427	0.1048 (0.0075)	NA	1.0214 (0.0086)	1.1940
BMI	BODY04	BMI	2015	25673413	NA	NA	322154	0.1297 (0.0056)	NA	0.6729 (0.0076)	1.0772
Height	HEIG03	height	2014	25282103	NA	NA	253288	0.312 (0.0141)	NA	1.3254 (0.0185)	2.0007
T2D	DIAB05	diabetes type 2	2017	28566273	26676	132532	159208	0.0781 (0.0053)	0.124 (0.008)	0.9984 (0.0083)	1.1459
CAD	COAD01	coronary artery disease	2015	26343387	60801	123504	184305	0.0661 (0.0045)	0.057 (0.004)	0.8875 (0.0074)	1.0466
Crohn’s Disease	CROH01	crohn disease	2015	26192919	5956	14927	20883	0.4751 (0.0538)	0.542 (0.059)	1.0316 (0.0092)	1.1396
MultiScler	SCLE03	multiple sclerosis	2011	21833088	9772	17376	27148	0.0493 (0.0262)	0.021 (0.012)	1.0599 (0.0085)	1.0375
RheuArth	RHEU02	rheumatoid arthritis	2014	24390342	14361	43923	58284	0.1406 (0.0176)	0.102 (0.013)	0.9511 (0.0076)	1.0466
Breast Cancer	BRCA01	breast cancer	2017	29059683	122977	105974	228951	0.1326 (0.0108)	0.157 (0.013)	1.1001 (0.0125)	1.3581
Prostate Cancer	PRCA01	prostate cancer	2017	29892016	79148	61106	140254	0.1635 (0.0238)	0.198 (0.029)	1.0804 (0.0153)	1.2201

Preparing GWAS sumstats table for TEDS phenotypes

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')

library(data.table)

pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_031218.csv')

teds_pheno=c('Height21', 'BMI21', 'GCSE', 'ADHD')
teds_gwas=c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
teds_prev=c(NA,NA,NA,0.05)
teds_dat<-data.frame(pheno=teds_pheno,gwas=teds_gwas,prev=teds_prev)

pheno_teds<-pheno[(pheno$Code %in% teds_gwas),]
pheno_teds<-pheno_teds[,c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2 observed','h2 se','lambda GC','intercept','intercept se')]
names(pheno_teds)<-c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_se','lambda','intercept','intercept_se')

pheno_teds$pop_prev<-teds_dat$prev[match(pheno_teds$Code, teds_dat$gwas)]
pheno_teds$Target_Phenotype<-teds_dat$pheno[match(pheno_teds$Code, teds_dat$gwas)]
pheno_teds$Ncases<-as.numeric(gsub(',','',pheno_teds$Ncases))
pheno_teds$Ncontrols<-as.numeric(gsub(',','',pheno_teds$Ncontrols))
pheno_teds$samp_prev<-pheno_teds$Ncases/(pheno_teds$Ncases+pheno_teds$Ncontrols)

h2l_R2 <- function(k, r2, p) {
  # K baseline disease risk
  # r2 from a linear regression model attributable to genomic profile risk score
  # P proportion of sample that are cases
  # calculates proportion of variance explained on the liability scale
  #from ABC at http://www.complextraitgenomics.com/software/
  #Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
  x= qnorm(1-k)
  z= dnorm(x)
  i=z/k
  C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
  theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
  h2l_R2 = C*r2 / (1 + C*theta*r2)
}

se_h2l_R2 <- function(k,h2,se, p) {
  # K baseline disease risk
  # r2 from a linear regression model attributable to genomic profile risk score
  # P proportion of sample that are cases
  # calculates proportion of variance explained on the liability scale
  #from ABC at http://www.complextraitgenomics.com/software/
  #Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.

  #SE on the liability (From a Taylor series expansion)
  #var(h2l_r2) = [d(h2l_r2)/d(R2v)]^2*var(R2v) with d being calculus differentiation
  x= qnorm(1-k)
  z= dnorm(x)
  i=z/k
  C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
  theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
  se_h2l_R2 = C*(1-h2*theta)*se
}

pheno_teds$h2_obs<-as.numeric(pheno_teds$h2_obs)
pheno_teds$h2_se<-as.numeric(pheno_teds$h2_se)

pheno_teds$h2_liab<-round(h2l_R2(k=pheno_teds$pop_prev, r2=pheno_teds$h2_obs, p=pheno_teds$samp_prev),3)
pheno_teds$h2_liab_se<-round(se_h2l_R2(k=pheno_teds$pop_prev,h2=pheno_teds$h2_obs,se=pheno_teds$h2_se, p=pheno_teds$samp_prev),3)

pheno_teds$h2_obs<-paste0(pheno_teds$h2_obs," (", pheno_teds$h2_se,")")
pheno_teds$h2_se<-NULL
pheno_teds$h2_liab[!is.na(pheno_teds$Ncases)]<-paste0(pheno_teds$h2_liab[!is.na(pheno_teds$Ncases)]," (", pheno_teds$h2_liab_se[!is.na(pheno_teds$Ncases)],")")
pheno_teds$h2_liab_se<-NULL
pheno_teds$intercept<-paste0(pheno_teds$intercept," (", pheno_teds$intercept_se,")")
pheno_teds$intercept_se<-NULL

pheno_teds$trait<-c("ADHD (mixed ancestry)",'BMI','Educational Attainment','Height')
pheno_teds<-pheno_teds[match(teds_dat$gwas,pheno_teds$Code),]

pheno_teds<-pheno_teds[,c('Target_Phenotype','Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_liab','intercept','lambda')]
names(pheno_teds)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')

pheno_teds$PMID[2]<-30124842
pheno_teds$PMID[4]<-30478444

write.csv(pheno_teds, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv', row.names=F, quote=F)

Show GWAS for TEDS phenotypes

GWAS used for each TEDS phenotype

Target Phenotype	Code	GWAS Phenotype	Year	PMID	Ncase	Ncontrol	N	h2-obs (SE)	h2-liab (SE)	Intercept	Lambda
Height	HEIG03	height	2014	25282103	NA	NA	253288	0.312 (0.0141)	NA	1.3254 (0.0185)	2.0007
BMI	BODY11	BMI	2018	30124842	NA	NA	681275	0.1908 (0.0053)	NA	1.1864 (0.0206)	2.7872
GCSE	EDUC03	years educational attainment	2018	30038396	NA	NA	766345	0.1066 (0.0026)	NA	1.0301 (0.0137)	2.0940
ADHD symptoms	ADHD04	ADHD	2017	30478444	20183	35191	55374	0.3635 (0.0227)	0.426 (0.026)	1.0295 (0.0096)	1.2431

3.5 TWAS

Preparing table showing SNP-weight sets used

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')

library(data.table)
weights<-fread(paste0(TWAS_rep, '/snp_weight_list.txt'), header=F)$V1

pos_unique<-fread('/users/k1806347/brc_scratch/Data/1KG/Phase3/Predicted_expression/Tissue_specific.pos')
pos_unique$WGT<-paste0(pos_unique$PANEL,'/',gsub('.*/','',pos_unique$WGT))

weights_info<-NULL
pos_all<-NULL
for(weights_i in weights){
  pos<-fread(paste0(FUSION_dir,'/SNP-weights/',weights_i,'/',weights_i,'.pos'))
  
  if(grepl('CMC', weights_i) == T){
    sample_i<-'CMC'
    tissue_i<-'Brain:DLPFC'
  }
  if(grepl('NTR', weights_i) == T){
    sample_i<-'NTR'
    tissue_i<-'Peripheral Blood'
  }
  if(grepl('YFS', weights_i) == T){
    sample_i<-'YFS'
    tissue_i<-'Whole Blood'
  }
  if(grepl('METSIM', weights_i) == T){
    sample_i<-'METSIM'
    tissue_i<-'Adipose'
  }
  if(grepl('CMC|NTR|YFS|METSIM', weights_i) == F){
    sample_i<-'GTEx'
    tissue_i<-gsub('_',' ',weights_i)
  }

  weights_info<-rbind(weights_info, data.frame(Set=weights_i,
                                               Sample=sample_i,
                                               Tissue=tissue_i,
                                               Type='Expression',
                                               N_indiv=pos$N[1],
                                               N_feat=dim(pos)[1],
                                               N_feat_spec=sum(pos$WGT %in% pos_unique$WGT)))
  
  pos_all<-rbind(pos_all, pos)
}

dim(pos_all) # 260598
length(unique(pos_all$ID)) # 26434

weights_info$Type<-as.character(weights_info$Type)
weights_info$Type[weights_info$Set=='CMC.BRAIN.RNASEQ_SPLICING']<-'Splicing'

write.csv(weights_info, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv', row.names=F, quote=F)

Show SNP-weight characteristics

SNP-weight set characteristics

Set	Sample	Tissue	Type	N_indiv	N_feat	N_feat_spec
Adipose_Subcutaneous	GTEx	Adipose Subcutaneous	Expression	385	8192	3917
Adipose_Visceral_Omentum	GTEx	Adipose Visceral Omentum	Expression	313	6173	2693
Adrenal_Gland	GTEx	Adrenal Gland	Expression	175	4520	1988
Artery_Aorta	GTEx	Artery Aorta	Expression	267	6462	2949
Artery_Coronary	GTEx	Artery Coronary	Expression	152	3247	1310
Artery_Tibial	GTEx	Artery Tibial	Expression	388	8223	4041
Brain_Amygdala	GTEx	Brain Amygdala	Expression	88	1836	794
Brain_Anterior_cingulate_cortex_BA24	GTEx	Brain Anterior cingulate cortex BA24	Expression	109	2709	1286
Brain_Caudate_basal_ganglia	GTEx	Brain Caudate basal ganglia	Expression	144	3660	1778
Brain_Cerebellar_Hemisphere	GTEx	Brain Cerebellar Hemisphere	Expression	125	4407	2238
Brain_Cerebellum	GTEx	Brain Cerebellum	Expression	154	5854	2991
Brain_Cortex	GTEx	Brain Cortex	Expression	136	4011	1932
Brain_Frontal_Cortex_BA9	GTEx	Brain Frontal Cortex BA9	Expression	118	3143	1531
Brain_Hippocampus	GTEx	Brain Hippocampus	Expression	111	2294	1051
Brain_Hypothalamus	GTEx	Brain Hypothalamus	Expression	108	2314	1071
Brain_Nucleus_accumbens_basal_ganglia	GTEx	Brain Nucleus accumbens basal ganglia	Expression	130	3239	1584
Brain_Putamen_basal_ganglia	GTEx	Brain Putamen basal ganglia	Expression	111	2817	1316
Brain_Spinal_cord_cervical_c-1	GTEx	Brain Spinal cord cervical c-1	Expression	83	2005	853
Brain_Substantia_nigra	GTEx	Brain Substantia nigra	Expression	80	1603	711
Breast_Mammary_Tissue	GTEx	Breast Mammary Tissue	Expression	251	5043	2199
Cells_EBV-transformed_lymphocytes	GTEx	Cells EBV-transformed lymphocytes	Expression	117	2757	1004
Cells_Transformed_fibroblasts	GTEx	Cells Transformed fibroblasts	Expression	300	7352	3458
CMC.BRAIN.RNASEQ	CMC	Brain:DLPFC	Expression	452	5419	2637
CMC.BRAIN.RNASEQ_SPLICING	CMC	Brain:DLPFC	Splicing	452	7771	3413
Colon_Sigmoid	GTEx	Colon Sigmoid	Expression	203	4873	2161
Colon_Transverse	GTEx	Colon Transverse	Expression	246	5315	2310
Esophagus_Gastroesophageal_Junction	GTEx	Esophagus Gastroesophageal Junction	Expression	213	4887	2148
Esophagus_Mucosa	GTEx	Esophagus Mucosa	Expression	358	8060	3794
Esophagus_Muscularis	GTEx	Esophagus Muscularis	Expression	335	7772	3711
Heart_Atrial_Appendage	GTEx	Heart Atrial Appendage	Expression	264	5670	2606
Heart_Left_Ventricle	GTEx	Heart Left Ventricle	Expression	272	5081	2207
Liver	GTEx	Liver	Expression	153	2913	1231
Lung	GTEx	Lung	Expression	383	7775	3514
METSIM.ADIPOSE.RNASEQ	METSIM	Adipose	Expression	563	4671	1560
Minor_Salivary_Gland	GTEx	Minor Salivary Gland	Expression	85	1821	712
Muscle_Skeletal	GTEx	Muscle Skeletal	Expression	491	7408	3515
Nerve_Tibial	GTEx	Nerve Tibial	Expression	361	9656	5014
NTR.BLOOD.RNAARR	NTR	Peripheral Blood	Expression	1247	2454	724
Ovary	GTEx	Ovary	Expression	122	2808	1302
Pancreas	GTEx	Pancreas	Expression	220	5093	2270
Pituitary	GTEx	Pituitary	Expression	157	4401	2102
Prostate	GTEx	Prostate	Expression	132	2796	1222
Skin_Not_Sun_Exposed_Suprapubic	GTEx	Skin Not Sun Exposed Suprapubic	Expression	335	7457	3637
Skin_Sun_Exposed_Lower_leg	GTEx	Skin Sun Exposed Lower leg	Expression	414	8878	4486
Small_Intestine_Terminal_Ileum	GTEx	Small Intestine Terminal Ileum	Expression	122	2878	1115
Spleen	GTEx	Spleen	Expression	146	4496	1690
Stomach	GTEx	Stomach	Expression	237	4455	1865
Testis	GTEx	Testis	Expression	225	9252	5860
Thyroid	GTEx	Thyroid	Expression	399	9825	5257
Uterus	GTEx	Uterus	Expression	101	2134	892
Vagina	GTEx	Vagina	Expression	106	2012	783
Whole_Blood	GTEx	Whole Blood	Expression	369	6006	1834
YFS.BLOOD.RNAARR	YFS	Whole Blood	Expression	1264	4700	1688

Preparing table showing TWAS descriptives

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')

library(data.table)
weights<-fread(paste0(TWAS_rep, '/snp_weight_list.txt'), header=F)$V1
pheno=c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth','BMI','Educational Attainment','ADHD symptoms')
sample=c('UKB','UKB','UKB','Both','UKB','UKB','UKB','UKB','UKB','TEDS','TEDS','TEDS')
gwas=c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','SCLE02','RHEU01','BODY11', 'EDUC03', 'ADHD04')

twas_descript<-NULL
for(i in 1:length(gwas)){
  twas<-fread(paste0(TWAS_rep,'/',gwas[i],'_withCOLOC/',gwas[i],'_res_GW.txt'))
  
  twas_descript<-rbind(twas_descript, data.frame(Sample=sample[i],
                                                 Target_Phenotype=pheno[i],
                                                 GWAS=gwas[i],
                                                 Nfeat=dim(twas)[1],
                                                 Nfeat_imp=sum(!is.na(twas$TWAS.P))))
}

twas_descript<-twas_descript[order(twas_descript$Sample),]

write.csv(twas_descript, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/twas_descript_table.csv', row.names=F, quote=F)

Show TWAS descriptives

TWAS descriptives

Sample	Target_Phenotype	GWAS	Nfeat	Nfeat_imp
UKB	Depression	DEPR06	260598	259237
UKB	Intelligence	COLL01	260598	254147
UKB	BMI	BODY03	260598	256091
UKB	T2D	DIAB05	260598	260115
UKB	CAD	COAD01	260598	259656
UKB	IBD	CROH01	260598	258900
UKB	MultiScler	SCLE02	260598	1194
UKB	RheuArth	RHEU01	260598	259008
Both	Height	HEIG03	260598	256037
TEDS	BMI	BODY11	260598	256109
TEDS	Educational Attainment	EDUC03	260598	260171
TEDS	ADHD symptoms	ADHD04	260598	254736

3.6 Gene expression risk scores (GeRS)

TWAS integrates GWAS summary statistics with multi-SNP predictors of gene expression (SNP-weights) to infer gene expression associations. Multi-SNP predictors in combination with individual-level genotype data can also be used to predict the expression level of genes within an each individual. GeRS are calculated as the TWAS-effect size weighted sum of predicted gene expression levels in each individual.

This study used SNP-weights derived from multiple panels capturing eQTL effects across a range of adult tissues. SNP-weights were downloaded from the FUSION website. TWAS was performed using FUSION and SNP-weights were used to calculated predicted expression levels using PLINK. GeRS for each panel were then calculated in R. To account for the correlation between the predicted expression of nearby features due to LD, feature clumping was used to remove features within 5Mb of lead features with a predicted expression r^2 of >0.9. Due to the complex LD structure within the MHC region, only the lead feature within this region was retained.

TWAS, gene expression prediction and GeRS calculations were carried out using LD and MAF estimations from an ancestry matched reference genotype dataset. The same SNP-weights are used to predict expression levels regardless of the target samples, using MAF imputation to account for missing variation. Predicted expression levels for each gene are then standardised based on the ancestry-matched mean and standard deviation of expression. Clumping of features is performed using predicted expression level in the reference sample.

The code used to prepare the reference data required for calculating GeRS can be found here. The code used for calculating predicted expression levels in the target samples can be found here

Calculating GeRS in samples

###
# UKBB
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}

sleep 20
done

###
# TEDS
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)

for i in $(seq 1 4);do
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}

sleep 5
done

Calculating GeRS in samples using PP4 to filter features

###
# UKBB
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}

sleep 20
done

###
# TEDS
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)

for i in $(seq 1 4);do
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}

sleep 5
done

Calculating GeRS in samples using tissue specific features

###
# UKBB
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt

# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.TissueSpecific.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt
    fi
  done
done

for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.TissueSpecific.${weights}.${gwas}

sleep 20
done

###
# TEDS
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt

# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)

for i in $(seq 1 4);do
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.TissueSpecific.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.TissueSpecific.${weights}.${gwas}

sleep 5
done

Calculating GeRS in samples using colocalised features

###
# UKBB
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}

sleep 20
done

###
# TEDS
###

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt

# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)

for i in $(seq 1 4);do
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
    if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
      echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt
    fi
  done
  
done

for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt)

sbatch -p brc,shared --mem 10G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
  --targ_feature_pred ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
  --ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
  --ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
  --pheno_name ${gwas} \
  --n_cores 1 \
  --pigz ${pigz_binary} \
  --output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}

sleep 5
done

3.7 Functionally-informed polygenic scoring

Functionally informed polygenic scores were derived as follows:

1. TWAS SNP-weight-stratified p-value thresholding and clumping (eQTL pT+clump)

Calculating GeRS in samples

# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

#######
# UKBB
#######
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)

# Calculate polygenic scores using 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  sbatch --mem 10G -p brc,shared -J pT_clump /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
    --target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
    --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}_withCOLOC/1KGPhase3.w_hm3.${gwas_i} \
    --ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}_withCOLOC/1KGPhase3.w_hm3.${gwas_i}.EUR.scale \
    --ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
    --plink ${plink1_9} \
    --pheno_name ${gwas_i} \
    --output ${UKBB_output}/PRS_for_comparison/1KG_ref_withCOLOC/pt_clump_stratified_TWAS_Gene/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}
done

#######
# TEDS
#######
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)

# Calculate polygenic scores using 1KG reference
for i in $(seq 1 4);do
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

  sbatch --mem 10G -p brc,shared -J pT_clump /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
    --target_plink_chr ${TEDS_output_dir}/Genotype/Harmonised/TEDS.w_hm3.QCd.AllSNP.chr \
    --target_keep ${TEDS_output_dir}/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
    --ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.EUR.scale \
    --ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
    --plink ${plink1_9} \
    --pheno_name ${gwas_i} \
    --output ${TEDS_output_dir}/PolygenicScores_stratified_TWAS_Gene/${gwas_i}/TEDS.subset.w_hm3.${gwas_i}
done

3.8 Functionally-agnostic polygenic scoring

Polygenic scores were derived using PRScs-auto, a Bayesian shrinkage method that I have shown to perform well previously.

Polygenic scores were derived using a reference standardised pipeline. The European subset of the 1KG reference was used (described here). In brief, all scores were derived using HapMap3 SNPs only, modelling LD based on the reference. Any HapMap3 missing in the target sample are imputed using the reference estimated allele frequency.

Polygenic scoring in target samples has been previously documented here

3.8.1 Derive pT+clump polygenic scores without retaining single variant in the MHC

Only do this as a sensitivity analysis for Rheumatoid arthritis

Show code

# Generate scoring files
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config

sbatch -p shared,brc --mem=6G /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/polygenic_score_file_creator/polygenic_score_file_creator.R \
  --ref_plink_chr ${Geno_1KG_dir}/1KGPhase3.w_hm3.chr \
  --ref_keep ${Geno_1KG_dir}/keep_files/EUR_samples.keep \
  --sumstats ${gwas_rep}/RHEU01.sumstats.gz \
  --plink ${plink1_9} \
  --memory 3000 \
  --prune_hla F \
  --output ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump \
  --ref_pop_scale ${Geno_1KG_dir}/super_pop_keep.list

# Calculate scores in UKB
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

sbatch --mem 10G -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
    --target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
    --target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.RheuArth.txt \
    --ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump \
    --ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump.EUR.scale \
    --ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
    --plink ${plink1_9} \
    --pheno_name RHEU01 \
    --output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/RHEU01.noMHCClump/UKBB.subset.w_hm3.RHEU01.noMHCClump

3.9 Estimating predictive ability

Models containing a single predictor were derived using generalised linear model (GLM). Models containing multiple predictors were derived using elastic-net regularisation to reduce the likelihood of overfitting and account for multicollinearity when modelling highly correlated predictors. Nested cross validation was used to estimate the variance explained by models to avoid overfitting. This involves an outer loop, splitting the data into training and test datasets, deriving the model using 10-fold cross validation in the training dataset, saving model predictions for the test dataset, combining the test predictions for each outer loop and estimating the variance explained.

Model building and evaluation was performed using an Rscript called Model_builder_V2_nested.R (more information here).

3.9.1 UK Biobank

Single-pT vs. Multi-pT

##############################
# Evaluating predictive utility of GeRS across multiple pTs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
for weight in ${weights};do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

cat > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups <<EOF
predictors 
${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile
EOF

done
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

# 1KG reference
for i in $(seq 1 8);do
for weight in ${weights};do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs \
    --n_core 2 \
    --compare_predictors T \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups
done
sleep 200
done

Single-tissue vs. Multi-tissue

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=YFS.BLOOD.RNAARR

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
done

Multi-tissue per pT

##############################
# Evaluating predictive utility of GeRS across multiple tissues for each pT seperately
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Split the GeRS files by pT
module add apps/R
R

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')

gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

weights<-read.table('~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', stringsAsFactors=F)$V1

library(data.table)

for(gwas_i in gwas){
print(gwas_i)
  for(weights_i in weights){

    GeRS<-fread(paste0(UKBB_output,'/GeRS_for_comparison/1KG_ref/EUR/',weights_i,'/UKBB.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile'))
  
    GeRS_pT<-gsub('.*_','',names(GeRS)[-1:-2])
    
    for(pT_i in GeRS_pT){
      write.table(GeRS[,c('FID','IID',paste0(gwas_i,'_',pT_i)), with=F], paste0(UKBB_output,'/GeRS_for_comparison/1KG_ref/EUR/',weights_i,'/UKBB.w_hm3.EUR.',weights_i,'.',gwas_i,'.pT_',pT_i,'.fiprofile'),col.names=T, row.names=F, quote=F)
    }
  }
}

q()
n

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
pT=$(echo 1e-06 1e-05 1e-04 0.001 0.01 0.05 0.1 0.5 1)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
  
  for pT_i in ${pT};do
    for weight in ${weights}; do
        if [ -f ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ]; then

        echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ${pT_i} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
      
        fi
    done
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 20G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
done

Single-tissue vs. Multi-tissue (PP4+clump)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=YFS.BLOOD.RNAARR

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 20G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4 \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups
done

Single-tissue vs. Multi-tissue (TissueSpecific)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.TissueSpecific.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=YFS.BLOOD.RNAARR

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 20G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups
done

Single-tissue vs. Multi-tissue (colocalised)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups

  for weight in ${weights}; do

    if [ -f ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ]; then
  
      echo ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups
  
    fi
  
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 20G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups
done

Multi-tissue per pT (colocalised)

##############################
# Evaluating predictive utility of GeRS across multiple tissues for each pT seperately
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Split the GeRS files by pT
module add apps/R
R

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')

gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

weights<-read.table('~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', stringsAsFactors=F)$V1

library(data.table)

for(gwas_i in gwas){
print(gwas_i)
  for(weights_i in weights){

    if(file.exists(paste0(UKBB_output,'/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/',weights_i,'/UKBB.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile')) == T){
      
      GeRS<-fread(paste0(UKBB_output,'/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/',weights_i,'/UKBB.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile'))
    
      GeRS_pT<-gsub('.*_','',names(GeRS)[-1:-2])
      
      for(pT_i in GeRS_pT){
        write.table(GeRS[,c('FID','IID',paste0(gwas_i,'_',pT_i)), with=F], paste0(UKBB_output,'/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/',weights_i,'/UKBB.w_hm3.EUR.',weights_i,'.',gwas_i,'.pT_',pT_i,'.fiprofile'),col.names=T, row.names=F, quote=F)
      }
    }
  }
}

q()
n

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
pT=$(echo 1e-06 1e-05 1e-04 0.001 0.01 0.05 0.1 0.5 1)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
  
  for pT_i in ${pT};do
    for weight in ${weights}; do
        if [ -f ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ]; then

        echo ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ${pT_i} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
      
        fi
    done
  done
  
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 20G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
done

GeRS + PRS

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

# Run for no mhc clump rheumarth
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.noMHCClump.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

for weight in ${weights}; do
  echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.RHEU01.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.noMHCClump.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

echo ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/RHEU01.noMHCClump/UKBB.subset.w_hm3.RHEU01.noMHCClump.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.noMHCClump.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01 RHEU01.noMHCClump)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005 0.005)

# 1KG reference
for i in $(seq 1 9);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

GeRS PP4 + PRS

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
done

GeRS TissueSpecific + PRS

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.TissueSpecific.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

GeRS + PRScs

##############################
# Evaluating predictive utility of GeRS and PRScs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
  done

    echo ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.PRScs_profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 4 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs \
    --n_core 4 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
done

Functionally agnostic polygenic score

##############################
# Evaluating predictive utility of pT + clump PRSs across multiple pTs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 SCLE02 RHEU01)

for i in $(seq 1 9);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

cat > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups <<EOF
predictors 
${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles
EOF

done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 SCLE02 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# pT + clump (sparse)
for i in $(seq 1 9);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs \
    --n_core 2 \
    --compare_predictors T \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups
done

TWAS gene stratified polygenic score

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done

# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups

  for weight in ${weights}; do
    echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
  done

    echo ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_stratified_TWAS_Gene/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles strat_PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)

# 1KG reference
for i in $(seq 1 8);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
    --keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified \
    --n_core 2 \
    --compare_predictors F \
    --assoc F \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
done

3.9.2 TEDS

Single-pT vs. Multi-pT

##############################
# Evaluating predictive utility of GeRS across multiple pTs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
for weight in ${weights};do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

cat > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups <<EOF
predictors 
${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile
EOF

done
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

# 1KG reference
for i in $(seq 1 4);do
for weight in ${weights};do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.${weight}.${gwas_i}.EUR-GeRSs \
    --n_core 2 \
    --compare_predictors T \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups
done
sleep 60
done

Single-tissue vs. Multi-tissue

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
done

Multi-tissue per pT

##############################
# Evaluating predictive utility of GeRS across multiple tissues for each pT seperately
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Split the GeRS files by pT
module add apps/R
R

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')

gwas<-c('HEIG03','BODY11','EDUC03','ADHD04')

weights<-read.table('~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', stringsAsFactors=F)$V1

library(data.table)

for(gwas_i in gwas){
print(gwas_i)
  for(weights_i in weights){

    GeRS<-fread(paste0(TEDS_output_dir,'/FunctionallyInformedPolygenicScores/EUR/',weights_i,'/TEDS.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile'))
  
    GeRS_pT<-gsub('.*_','',names(GeRS)[-1:-2])
    
    for(pT_i in GeRS_pT){
      write.table(GeRS[,c('FID','IID',paste0(gwas_i,'_',pT_i)), with=F], paste0(TEDS_output_dir,'/FunctionallyInformedPolygenicScores/EUR/',weights_i,'/TEDS.w_hm3.EUR.',weights_i,'.',gwas_i,'.pT_',pT_i,'.fiprofile'),col.names=T, row.names=F, quote=F)
    }
  }
}

q()
n

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
pT=$(echo 1e-06 1e-05 1e-04 0.001 0.01 0.05 0.1 0.5 1)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
  
  for pT_i in ${pT};do
    for weight in ${weights}; do
        if [ -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ]; then

        echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ${pT_i} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
      
        fi
    done
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.per_PT.predictor_groups
    
done

Single-tissue vs. Multi-tissue (PP4+clump)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4 \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.predictor_groups
done

Single-tissue vs. Multi-tissue (Tissue Specific)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.TissueSpecific.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.predictor_groups
done

Single-tissue vs. Multi-tissue (colocalised)

##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.predictor_groups
done

Multi-tissue per pT (colocalised)

##############################
# Evaluating predictive utility of GeRS across multiple tissues for each pT seperately
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Split the GeRS files by pT
module add apps/R
R

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')

gwas<-c('HEIG03','BODY11','EDUC03','ADHD04')

weights<-read.table('~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', stringsAsFactors=F)$V1

library(data.table)

for(gwas_i in gwas){
print(gwas_i)
  for(weights_i in weights){
    if(file.exists(paste0(TEDS_output_dir,'/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/',weights_i,'/TEDS.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile')) == T){
      GeRS<-fread(paste0(TEDS_output_dir,'/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/',weights_i,'/TEDS.w_hm3.EUR.',weights_i,'.',gwas_i,'.fiprofile'))
    
      GeRS_pT<-gsub('.*_','',names(GeRS)[-1:-2])
      
      for(pT_i in GeRS_pT){
        write.table(GeRS[,c('FID','IID',paste0(gwas_i,'_',pT_i)), with=F], paste0(TEDS_output_dir,'/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/',weights_i,'/TEDS.w_hm3.EUR.',weights_i,'.',gwas_i,'.pT_',pT_i,'.fiprofile'),col.names=T, row.names=F, quote=F)
      }
    }
  }
}

q()
n

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
pT=$(echo 1e-06 1e-05 1e-04 0.001 0.01 0.05 0.1 0.5 1)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
  
  for pT_i in ${pT};do
    for weight in ${weights}; do
        if [ -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ]; then

        echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.pT_${pT_i}.fiprofile ${pT_i} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
      
        fi
    done
  done
  
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_pT_withColoc.per_PT.predictor_groups
    
done

GeRS + PRS

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${TEDS_output_dir}/PolygenicScores/EUR/${gwas_i}/TEDS.w_hm3.${gwas_i}.EUR.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

GeRS + PRS (PP4+clump)

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${TEDS_output_dir}/PolygenicScores/EUR/${gwas_i}/TEDS.w_hm3.${gwas_i}.EUR.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.predictor_groups
done

GeRS + PRS (Tissue Specific)

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.TissueSpecific.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
  done

    echo ${TEDS_output_dir}/PolygenicScores/EUR/${gwas_i}/TEDS.w_hm3.${gwas_i}.EUR.profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.${gwas_i}.EUR-GeRSs.EUR-PRSs.pt_clump.predictor_groups
done

GeRS + PRScs

##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
  done

    echo ${TEDS_output_dir}/PolygenicScores_PRScs/EUR/${gwas_i}/TEDS.w_hm3.${gwas_i}.EUR.PRScs_profiles PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.EUR-PRSs.PRScs.predictor_groups
done

pT + clump comparison

##############################
# Evaluating predictive utility of pT + clump PRSs across multiple pTs individually and in combination
##############################

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)

for i in $(seq 1 4);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')

cat > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups <<EOF
predictors 
/users/k1806347/brc_scratch/Data/TEDS/PolygenicScores/EUR/${gwas_i}/TEDS.w_hm3.${gwas_i}.EUR.profiles
EOF

done

pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# pT+clump (sparse)
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno /users/k1806347/brc_scratch/Data/TEDS/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs \
    --n_core 2 \
    --compare_predictors T \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups
done

TWAS gene stratified polygenic score

. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config

# Make required directories
for pheno_i in $(echo Height21 BMI21 GCSE ADHD);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done

# Create a file listing the predictors files
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)

for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  
  echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups

  for weight in ${weights}; do
    echo ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weight}/TEDS.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
  done

    echo ${TEDS_output_dir}/PolygenicScores_stratified_TWAS_Gene/${gwas_i}/TEDS.subset.w_hm3.${gwas_i}.profiles strat_PRS >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
done

# Derive and evaluate models
pheno=$(echo Height21 BMI21 GCSE ADHD)
gwas=$(echo HEIG03 BODY11 EDUC03 ADHD04)
prev=$(echo NA NA NA NA)

# 1KG reference
for i in $(seq 1 4);do
  pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
  pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
  gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
  prev_i=$(echo ${prev} | cut -f ${i} -d ' ')

sbatch --mem 10G -n 2 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
    --pheno ${TEDS_output_dir}/Phenotypic/Derived_outcomes/TEDS_${pheno_i}.txt \
      --keep /users/k1806347/brc_scratch/Data/TEDS/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
    --out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified \
    --n_core 2 \
    --compare_predictors F \
    --assoc T \
    --outcome_pop_prev ${prev_i} \
    --predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/${pheno_i}/Association_withPRSs/TEDS.w_hm3.${gwas_i}.EUR-PRSs-TWAS_gene_stratified.predictor_groups
done

3.10 Estimate proportion of SNP-based heritability explained by GeRS, PRS and stratified PRS

3.10.1 UK Biobank

Estimate using GeRS

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv")

library(avengeme)

GeRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.per_PT.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  GeRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_poylygenic_stratified_TWAS_Gene/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-GeRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-GeRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_AVENGME_res.csv', row.names=F)

Estimate using GeRS (colocalised)

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv")

library(avengeme)

GeRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.per_PT.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  GeRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_poylygenic_stratified_TWAS_Gene/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-GeRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-GeRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_coloc_AVENGME_res.csv', row.names=F)

Estimate using PRS

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv")

library(avengeme)

PRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/UKBB.w_hm3.',gwas[i],'.EUR-PRSs.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  PRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_polygenic/pt_clump/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-PRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=PRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-8,1e-06,1e-04,1e-02,0.1,0.2,0.3,0.4,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-PRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-PRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-PRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=PRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-8,1e-06,1e-04,1e-02,0.1,0.2,0.3,0.4,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/PRS_AVENGME_res.csv', row.names=F)

---

3.10.2 TEDS

Estimate using GeRS

pheno<-c('Height21','BMI21','GCSE','ADHD')

gwas<-c('HEIG03','BODY11','EDUC03','ADHD04')

prev=c(NA,NA,NA,NA)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv")

library(avengeme)

GeRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.per_PT.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  GeRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_poylygenic_stratified_TWAS_Gene/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-GeRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-GeRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_AVENGME_res.csv', row.names=F)

Estimate using GeRS (colocalised)

pheno<-c('Height21','BMI21','GCSE','ADHD')

gwas<-c('HEIG03','BODY11','EDUC03','ADHD04')

prev=c(NA,NA,NA,NA)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv")

library(avengeme)

GeRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.per_PT.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  GeRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_poylygenic_stratified_TWAS_Gene/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-GeRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-GeRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-GeRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=GeRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-06,1e-05,1e-04,0.001,0.01,0.05,0.1,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_coloc_AVENGME_res.csv', row.names=F)

Estimate using PRS

pheno<-c('Height21','BMI21','GCSE','ADHD')

gwas<-c('HEIG03','BODY11','EDUC03','ADHD04')

prev=c(NA,NA,NA,NA)

gwas_desc<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv")

library(avengeme)

PRS_res<-list()
nsnp_logs<-list()
for(i in 1:length(gwas)){
  res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/TEDS.w_hm3.',gwas[i],'.EUR-PRSs.pred_eval.txt'), header=T, stringsAsFactors=F)
  
  res_i<-res_i[1:dim(res_i)[1]-1,]
  res_i$Indep_Z<-res_i$R/res_i$SE
  
  PRS_res[[gwas[i]]]<-res_i
  
  nsnp_log<-read.table(paste0('/users/k1806347/brc_scratch/Data/1KG/Phase3/Score_files_for_poylygenic/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.NSNP_per_pT'), header=T)
  
  nsnp_logs[[gwas[i]]]<-nsnp_log
}

mod_res_all<-NULL
for(i in 1:length(gwas)){
  if(is.na(prev[i])){
    targ_N<-PRS_res[[gwas[i]]]$N[1]
  
    mod_res<-estimatePolygenicModel(p=PRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-8,1e-06,1e-04,1e-02,0.1,0.2,0.3,0.4,0.5,1),
                           binary = c(FALSE, FALSE), 
                           prevalence = c(NA, NA), 
                           sampling = c(NA, NA), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  } else {
    targ_N<-PRS_res[[gwas[i]]]$N[1]
    targ_N_Ca<-PRS_res[[gwas[i]]]$Ncase[1]
    targ_N_Co<-PRS_res[[gwas[i]]]$Ncont[1]

    mod_res<-estimatePolygenicModel(p=PRS_res[[gwas[i]]]$Indep_Z, 
                           nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                           n=c(gwas_desc$N[gwas_desc$Code == gwas[i]], targ_N), 
                           pupper = c(0,1e-8,1e-06,1e-04,1e-02,0.1,0.2,0.3,0.4,0.5,1),
                           binary = c(T, T), 
                           prevalence = prev[i], 
                           sampling = c(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]/(gwas_desc$Ncase[gwas_desc$Code == gwas[i]]+gwas_desc$Ncontrol[gwas_desc$Code == gwas[i]]), targ_N_Ca/(targ_N_Ca+targ_N_Co)), 
                           fixvg2pi02 = T,
                           alpha = 0.05)
    
    mod_res_all<-rbind(mod_res_all,data.frame(Phenotype=pheno[i],
                                              GWAS=gwas[i],
                                                                          nsnp=nsnp_logs[[gwas[i]]]$NSNP[length(nsnp_logs[[gwas[i]]]$NSNP)], 
                                              vg_est=mod_res$vg[1],
                                              vg_lowCI=mod_res$vg[2],
                                              vg_highCI=mod_res$vg[3],
                                              pi0_est=mod_res$pi0[1],
                                              pi0_lowCI=mod_res$pi0[2],
                                              pi0_highCI=mod_res$pi0[3]))
    
  }
}

write.csv(mod_res_all, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/PRS_AVENGME_res.csv', row.names=F)

---

4 Results

4.1 UK Biobank

Plot per pT GeRS results

#####
# Compare results across pTs for each phenotype
#####
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')
weights=read.table('/users/k1806347/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', header=F)$V1

weights_clean<-gsub('_',' ',weights)
weights_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',weights_clean)
weights_clean<-gsub('SPLICING','Splicing',weights_clean)
weights_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',weights_clean)
weights_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',weights_clean)
weights_clean<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',weights_clean)
weights_clean[!grepl('CMC|NTR|YFS|METSIM', weights)]<-paste0('GTEx ',weights_clean[!grepl('CMC|NTR|YFS|METSIM', weights)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
weights_clean<-gsub('Brain', '', weights_clean)
weights_clean <- gsub('Anterior cingulate cortex', 'ACC', weights_clean)
weights_clean <- gsub('basal ganglia', '', weights_clean)
weights_clean <- gsub('BA9', '', weights_clean)
weights_clean <- gsub('BA24', '', weights_clean)
weights_clean <- gsub('  ', ' ', weights_clean)
weights_clean_short<-substr(weights_clean, start = 1, stop = 15)  #start the name at the first character and stop at the 25th
weights_clean_short[nchar(weights_clean) > 15]<-paste0(weights_clean_short[nchar(weights_clean) > 15], "...")

res<-NULL
res_best<-NULL
for(i in 1:length(gwas)){
  for(weight in 1:length(weights)){
    res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.',weights[weight],'.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

    res_i$Phenotype<-pheno[i]
    res_i$Weight<-weights[weight]
    
    if(sum(grepl('R2l',names(res_i)))>0){
        res_i<-res_i[,c('Phenotype','Weight','Model','R','SE','P','R2l')]
        names(res_i)<-c('Phenotype','Weight','Model','R','SE','P','R2')
        res_i$Binary<-T
    } else {
        res_i<-res_i[,c('Phenotype','Weight','Model','R','SE','P','R2o')]
        names(res_i)<-c('Phenotype','Weight','Model','R','SE','P','R2')
        res_i$Binary<-F
    }
    
    res_i$Model<-gsub('_group','',gsub(paste0(gwas[i],'.'),'',res_i$Model))
    res_i$Model<-factor(res_i$Model, levels=res_i$Model)
    
    res_i_best<-res_i[res_i$R == max(res_i$R),]
  
    res<-rbind(res, res_i)
    res_best<-rbind(res_best, res_i_best)
  }
}

res_brief<-res[,c('Phenotype','Weight','Model','R','SE','P')]
res_best_brief<-res_best[,c('Phenotype','Weight','Model','R','SE','P')]

write.csv(res_brief, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_per_pT.csv', row.names=F, quote=F)
write.csv(res_best_brief, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_best_pT.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)

res$Model<-gsub('e.0','*x*10^-', res$Model)
res$Model<-factor(res$Model, levels=unique(res$Model))
res$P[res$P < 1e-300]<-0
res$P<-format(res$P, scientific = TRUE, digits = 1)
res$P<-gsub('e-','*x*10^-',res$P)
res$P[res$P == ' 0e+00']<-paste0("'<1'*x*10^-300")

res_plot<-list()
for(i in 1:length(gwas)){
  # Extract result for 5 most predictive tissue
  tmp<-res_best[res_best$Phenotype == pheno[i],]
  tmp<-tmp[order(-tmp$R2),]
  tmp<-tmp[1:3,]
  best_weights<-tmp$Weight
  
  res_tmp<-res[res$Phenotype == pheno[i] & (res$Weight %in% best_weights),]
  ylim_max<-max(res_tmp$R2)
  ylim_max<-ylim_max+ylim_max*1.5
  if(res[res$Phenotype == pheno[i],]$Binary[1] == T){
    ylab<-'Liability R-squared'
  } else {
    ylab<-'R-squared'
  }
  
  res_plot_tmp<-list()
  for(weight in best_weights){
    weights_index<-which(weights == weight)
    print(weight)
  res_plot_tmp[[as.character(weights[weights_index])]]<-ggplot(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R2)) +
                                    geom_bar(stat="identity", position=position_dodge(), fill='#3399FF') +
                                    labs(y=ylab, x='pT', title=paste0('\n\n',weights_clean_short[weights_index])) +
                                    theme_half_open() +
                                    ylim(0,ylim_max) +
                                    geom_text(data=res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R2, label=P), vjust=0.5, hjust= -0.15, angle=90, size=4, parse=T) +
                                    theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust=1), plot.title = element_text(hjust = 0.5, size=12)) +
                                    background_grid(major = 'y', minor = 'y') +
                                    scale_x_discrete(labels = parse(text = as.character(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],]$Model))) +
                                    coord_cartesian(clip='off')

  }
  res_plot[[pheno[i]]]<-plot_grid(plotlist=res_plot_tmp, nrow = 1)
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_per_pT_UKBB.png', units='px', res=300, width=3000, height=7000)
  plot_grid(plotlist=res_plot, ncol = 1, labels = paste0(pheno))
dev.off()

#######
# Recreate plots using R on the y axis and full SNP-weight set names
#######

res_plot<-list()
for(i in 1:length(gwas)){
  # Extract result for 5 most predictive tissue
  tmp<-res_best[res_best$Phenotype == pheno[i],]
  tmp<-tmp[order(-tmp$R),]
  tmp<-tmp[1:3,]
  best_weights<-tmp$Weight
  
  res_tmp<-res[res$Phenotype == pheno[i] & (res$Weight %in% best_weights),]
  ylim_max<-max(res_tmp$R)
  ylim_max<-ylim_max+ylim_max*1.5
  if(min(res_tmp$R) < 0){
    ylim_min<-min(res_tmp$R)
    ylim_min<-ylim_min-max(res_tmp$SE)
  } else {
    ylim_min<-NA
  }

  res_plot_tmp<-list()
  for(weight in best_weights){
    weights_index<-which(weights == weight)
    print(weight)
  res_plot_tmp[[as.character(weights[weights_index])]]<-ggplot(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R)) +
                                    geom_bar(stat="identity", position=position_dodge(), fill='#3399FF') +
                                    geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2, position=position_dodge(.9)) +
                                    labs(y='Correlation', x='pT', title=paste0('\n\n',weights_clean[weights_index])) +
                                    theme_half_open() +
                                    ylim(ylim_min,ylim_max) +
                                    geom_text(data=res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R+SE, label=P), vjust=0.3, hjust= -0.15, angle=90, size=4, parse=T) +
                                    theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust=1), plot.title = element_text(hjust = 0.4, size=12)) +
                                    background_grid(major = 'y', minor = 'y') +
                                    scale_x_discrete(labels = parse(text = as.character(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],]$Model))) +
                                    coord_cartesian(clip='off')

  }
  res_plot[[pheno[i]]]<-plot_grid(plotlist=res_plot_tmp, nrow = 1)
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_per_pT_UKBB_R.png', units='px', res=300, width=3000, height=7000)
  plot_grid(plotlist=res_plot, ncol = 1, labels = paste0(pheno))
dev.off()

Plot comparison results

#####
# Compare results from each approach
#####
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

res<-list()
for(i in 1:length(gwas)){
res_1<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.per_PT.pred_comp.txt'), header=T, stringsAsFactors=F)
res_1<-res_1[res_1$Model_1 == 'All',]
res_1<-res_1[res_1$Model_2 != 'All',]
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)
res_4<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/UKBB.w_hm3.',gwas[i],'.EUR-PRSs-TWAS_gene_stratified.pred_comp.txt'), header=T, stringsAsFactors=F)
res_5<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.EUR-PRSs.PRScs.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_pT','GeRS_multi_tissue','PRS_and_GeRS','Strat_PRS','PRScs_and_GeRS'),        
                        do.call(rbind,list( res_1[res_1$Model_2_R == max(res_1$Model_2_R),],
                                                    res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,],
                                                    res_4[8,],
                                                    res_5[8,])))
}

res_table<-do.call(rbind, res)

# Calculate percentage difference
res_table$R_diff_perc<-res_table$R_diff/res_table$Model_1_R*100

res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
  tmp_res<-res[[pheno[i]]]
  
  tmp_res$R_diff_pval_num<-tmp_res$R_diff_pval
  
  tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
  tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)
  
  tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
  names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval','R_diff_pval_num')
  tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
  names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval','R_diff_pval_num')
  tmp_res_Model_2$R_diff<-NA
  tmp_res_Model_2$R_diff_pval<-NA
  
  tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
  tmp_res_plot$Phenotype<-pheno[i]
  
  res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_pT']<-'GeRS Best pT   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_pT']<-'GeRS Multi pT'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS Best Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS Multi Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'Strat_PRS']<-'Strat_PRS only'
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'Strat_PRS']<-'Strat_PRS + GeRS'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRScs_and_GeRS']<-'PRScs only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRScs_and_GeRS']<-'PRScs + GeRS'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS Best pT   ","GeRS Multi pT", "GeRS Best Tissue   ","GeRS Multi Tissue","PRS only   ","PRS + GeRS", "Strat_PRS only", "Strat_PRS + GeRS","PRScs only   ","PRScs + GeRS"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_1<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
              ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_4<-ggplot(All_res_plot[All_res_plot$Test == 'Strat_PRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_5<-ggplot(All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_tests_summary_UKBB.png', units='px', res=300, width=3000, height=3500)
  plot_grid(Plot_1,Plot_2,Plot_3, Plot_5, labels = "AUTO")
dev.off()

####
# Recreate in black and white
####


# Plot results
Plot_1<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          scale_fill_grey() +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
              ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          scale_fill_grey() +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          scale_fill_grey() +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_4<-ggplot(All_res_plot[All_res_plot$Test == 'Strat_PRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          scale_fill_grey() +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_5<-ggplot(All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          scale_fill_grey() +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_tests_summary_UKBB_bw.png', units='px', res=300, width=3000, height=3500)
  plot_grid(Plot_1,Plot_2,Plot_3, Plot_5, labels = "AUTO")
dev.off()

####
# Recreate plot higlighting significant results
####
# Note. I am not going to add error bars as this information is not output with the results files used here. This would either require the difficult job of pulling SE from the pred_eval file or editing the model_builder script and re-running all analyses. There is also quite a lot going on in this figure already as well.

All_res_plot$Sig<-'NS'
All_res_plot$Sig[All_res_plot$R_diff_pval_num < 0.05 & All_res_plot$R_diff > 0]<-'Pos'
All_res_plot$Sig[All_res_plot$R_diff_pval_num < 0.05 & All_res_plot$R_diff < 0]<-'Neg'
All_res_plot$Sig<-factor(All_res_plot$Sig, levels=c('NS','Pos','Neg'))

scale_colour_op <- function(...){
    ggplot2:::manual_scale(
        'colour', 
        values = setNames(c("#000000", "#009933","#FF0000"), c('NS', 'Pos', 'Neg')), 
        ...
    )
}

# Plot results
Plot_1<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_4<-ggplot(All_res_plot[All_res_plot$Test == 'Strat_PRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_5<-ggplot(All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_tests_summary_UKBB.png', units='px', res=300, width=3000, height=3500)
  plot_grid(Plot_1,Plot_2,Plot_3, Plot_5, labels = "AUTO")
dev.off()

Plot Rheumatoid Arthritis sensitivity analysis

# Read in the pT+clump (with MHC clump) results
res_3<-read.table('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt', header=T, stringsAsFactors=F)
res_4<-read.table('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.noMHCClump.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt', header=T, stringsAsFactors=F)
res_5<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/RheuArth/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.RHEU01.EUR-GeRSs.EUR-PRSs.PRScs.pred_comp.txt'), header=T, stringsAsFactors=F)

res_table<-data.frame(Test=c('PRS_and_GeRS','PRS_noMHCClump_and_GeRS','PRScs_and_GeRS'),        
                        do.call(rbind,list( res_3[8,],
                                                    res_4[8,],
                                                    res_5[8,])))

# Calculate percentage difference
res_table$R_diff_perc<-res_table$R_diff/res_table$Model_1_R*100

res_table$Phenotype<-'RheuArth'
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]

write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_tests_RheuArth_withnoMHCClump.csv', row.names=F, quote=F)

Show Rheumatoid Arthritis TWAS results

library(data.table)

source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config')

res<-fread(paste0(TWAS_rep,'/RHEU01/RHEU01_res_GW.txt'))

# Sort by abs(TWAS.Z
res<-res[order(-abs(res$TWAS.Z)),]
res<-res[,c('FILE','CHR','P0','P1','PANEL','ID','TWAS.Z','TWAS.P')]
res$FILE<-gsub('.*/','',res$FILE)

# Restrict to top 10% of TWAS.Z
res<-res[abs(res$TWAS.Z) >= quantile(abs(res$TWAS.Z), probs=0.99,na.rm=T),]

res$TWAS.P<-as.character(res$TWAS.P)
res$TWAS.P[res$TWAS.P == '0']<-'<1e-320'

write.csv(res, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/Top_TWAS_res_RheuArth.csv', row.names=F, quote=F)

Plot comparison results (PP4+clump)

#####
# Compare results from each approach
#####

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

res<-list()
for(i in 1:length(gwas)){
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_tissue','PRS_and_GeRS'),     
                        do.call(rbind,list( res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,])))
}

res_table<-do.call(rbind, res)
res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_PP4_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
tmp_res<-res[[pheno[i]]]

tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)

tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2$R_diff<-NA
tmp_res_Model_2$R_diff_pval<-NA

tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
tmp_res_plot$Phenotype<-pheno[i]

res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS PP4 Best Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS PP4 Multi Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS PP4'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS PP4 Best Tissue   ","GeRS PP4 Multi Tissue","PRS only   ","PRS + GeRS PP4"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_PP4_tests_summary_UKBB.png', units='px', res=300, width=3500, height=2000)
  plot_grid(Plot_2,Plot_3, labels = "AUTO")
dev.off()

Plot comparison results (Tissue Specific)

#####
# Compare results from each approach
#####

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

res<-list()
for(i in 1:length(gwas)){
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_tissue','PRS_and_GeRS'),     
                        do.call(rbind,list( res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,])))
}

res_table<-do.call(rbind, res)
res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_TissueSpecific_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
tmp_res<-res[[pheno[i]]]

tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)

tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2$R_diff<-NA
tmp_res_Model_2$R_diff_pval<-NA

tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
tmp_res_plot$Phenotype<-pheno[i]

res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS TissueSpecific\nBest Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS TissueSpecific\nMulti Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS TissueSpecific'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS TissueSpecific\nBest Tissue   ","GeRS TissueSpecific\nMulti Tissue","PRS only   ","PRS + GeRS TissueSpecific"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.6) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_TissueSpecific_tests_summary_UKBB.png', units='px', res=300, width=3500, height=2000)
  plot_grid(Plot_2,Plot_3, labels = "AUTO")
dev.off()

Compare stratified PRS to multi-tissue GeRS

# Plot the results of the stratified PRS against Multi-tissue GeRS
# And look at the variance exaplained by each tissue
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

res<-list()
crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS<-res_GeRS[dim(res_GeRS)[1],]

res_GeRS_coloc<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_coloc<-res_GeRS_coloc[dim(res_GeRS_coloc)[1],]

res_stratPRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/UKBB.w_hm3.',gwas[i],'.EUR-PRSs-TWAS_gene_stratified.pred_eval.txt'), header=T, stringsAsFactors=F)

res_stratPRS<-res_stratPRS[2,]

res_GWPRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/UKBB.w_hm3.',gwas[i],'.EUR-PRSs.pred_eval.txt'), header=T, stringsAsFactors=F)
res_GWPRS<-res_GWPRS[dim(res_GWPRS)[1],]

res_all<-do.call(rbind, list(res_GeRS, res_GeRS_coloc, res_stratPRS, res_GWPRS))
res_all$Method<-c('GeRS',"GeRS (coloc)","PRS (Gene)",'PRS')
res_all$Phenotype<-pheno[i]

res_all<-res_all[,c('Model','R','SE','P','N','Method','Phenotype')]

res[[pheno[i]]]<-res_all
}

res_table<-do.call(rbind, res)
res_table<-res_table[,c('Phenotype','Method','R','SE')]

write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/StratPRS_comp_summary.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)
# Plot comparison across PRS, stratified PRS and GeRS
res_table$Phenotype<-factor(res_table$Phenotype, level=unique(res_table$Phenotype))

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/StratPRS_comp_UKBB.png', units='px', res=300, width=1500, height=1000)

ggplot(res_table, aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.4) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

# Make black and white version
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/StratPRS_comp_UKBB_bw.png', units='px', res=300, width=1500, height=1000)

ggplot(res_table, aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          scale_fill_grey() +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.4) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

# Make the plot without the coloc result
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/StratPRS_comp_nocoloc_UKBB.png', units='px', res=300, width=1500, height=1000)

ggplot(res_table[(res_table$Method %in% c('GeRS','PRS',"PRS (Gene)")),], aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.4) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

# Make the plot with just PRS and GeRS
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/StratPRS_comp_justPRSGeRS_UKBB.png', units='px', res=300, width=1500, height=1000)

ggplot(res_table[(res_table$Method %in% c('GeRS','PRS')),], aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.4) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

Compare GeRS to PP4 and TissueSpecific GeRS

# Plot the results of the stratified PRS against Multi-tissue GeRS
# And look at the variance exaplained by each tissue
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

res<-list()
crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_all<-res_GeRS[dim(res_GeRS)[1],]
res_GeRS<-res_GeRS[-dim(res_GeRS)[1],]
res_GeRS_best<-res_GeRS[which(res_GeRS$R == max(res_GeRS$R)),]

res_GeRS_coloc<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_coloc_all<-res_GeRS_coloc[dim(res_GeRS_coloc)[1],]
res_GeRS_coloc<-res_GeRS_coloc[-dim(res_GeRS_coloc)[1],]
res_GeRS_coloc_best<-res_GeRS_coloc[which(res_GeRS_coloc$R == max(res_GeRS_coloc$R)),]

res_GeRS_TS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_TS_all<-res_GeRS_TS[dim(res_GeRS_TS)[1],]
res_GeRS_TS<-res_GeRS_TS[-dim(res_GeRS_TS)[1],]
res_GeRS_TS_best<-res_GeRS_TS[which(res_GeRS_TS$R == max(res_GeRS_TS$R)),]

res_all<-do.call(rbind, list(res_GeRS_best,res_GeRS_all,res_GeRS_coloc_best,res_GeRS_coloc_all,res_GeRS_TS_best, res_GeRS_TS_all))
res_all$Method<-c("GeRS (best)","GeRS (all)","GeRS coloc (best)","GeRS coloc (all)","GeRS TS (best)","GeRS TS (all)")

res_all$Phenotype<-pheno[i]

res_all<-res_all[,c('Model','R','SE','P','N','Method','Phenotype')]

res[[pheno[i]]]<-res_all
}

res_table<-do.call(rbind, res)
res_table<-res_table[,c('Phenotype','Method','R','SE')]

write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_coloc_TissueSpecific_comp_summary.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)
# Plot comparison across GeRS
res_table$Phenotype<-factor(res_table$Phenotype, level=unique(res_table$Phenotype))

res_table$Method<-factor(res_table$Method, levels=c("GeRS (best)","GeRS (all)","GeRS coloc (best)","GeRS coloc (all)","GeRS TS (best)","GeRS TS (all)"))
  
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_coloc_TissueSpecific_comp_UKBB.png', units='px', res=300, width=2000, height=1000)

ggplot(res_table, aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.3) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

Plot GeRS across tissues

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)


crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)
crossTissue_i$Panel<-crossTissue_i$Model

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue_i$R_scaled<-scale(crossTissue_i$R)

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE','Panel','R_scaled')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_Tissue_comp_UKBB.png', units='px', res=300, width=3000, height=10000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Estimate the correlation between SNP-weight set sample size, number of features and predictive utility
weight_info<-fread('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv')
weight_info$Set<-gsub('_','.',weight_info$Set)
weight_info$Set<-gsub('-','.',weight_info$Set)

crossTissue_table<-merge(crossTissue_table, weight_info, by.x='Panel', by.y='Set')

# Check correlation across 
cor(crossTissue_table$R, crossTissue_table$N_indiv) # 0.1516963
feat_cor<-cor(crossTissue_table$R, crossTissue_table$N_feat) # 0.2879782
cor(crossTissue_table$N_indiv, crossTissue_table$N_feat) # 0.3263912

summary(lm(R ~ N_feat + N_indiv, data=crossTissue_table)) # R2 = 0.08666
# N_indiv effect is non significant when moddeling N_feat
crossTissue_table$R_resid<-resid(lm(R ~ N_feat, data=crossTissue_table))

plot_list<-list()
for(i in 1:length(gwas)){
  crossTissue_table$R_resid[crossTissue_table$Phenotype == pheno[i]]<-scale(crossTissue_table$R_resid[crossTissue_table$Phenotype == pheno[i]])
  tmp<-crossTissue_table[crossTissue_table$Phenotype == pheno[i],]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R_resid))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R_resid, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          labs(y="Residual Correlation", x='', title=pheno[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_Tissue_comp_resid_UKBB.png', units='px', res=300, width=3000, height=10000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Plot relationship between N_feat and R2 scaled for each phenotype
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_Tissue_comp_Nfeat_UKBB.png', units='px', res=300, width=1500, height=1000)
ggplot(crossTissue_table, aes(x=N_feat, R_scaled)) +
  labs(y="Relative prediction", x='Number of features') +
  geom_smooth(method='lm') +
  annotate("text", x=7500, y=-2, label = paste0("italic('r') == ",round(feat_cor,2)), parse=T) +
  geom_point(data=crossTissue_table, aes(x=N_feat, R_scaled, colour=Phenotype)) +
  theme_half_open()
dev.off()

Plot GeRS (PP4+clump) across tissues

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.pred_eval.txt'), header=T, stringsAsFactors=F)

crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)
crossTissue_i$Panel<-crossTissue_i$Model

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE','Panel')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_PP4_Tissue_comp_UKBB.png', units='px', res=300, width=3000, height=10000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Estimate the correlation between SNP-weight set sample size, number of features and predictive utility
weight_info<-fread('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv')
weight_info$Set<-gsub('_','.',weight_info$Set)
weight_info$Set<-gsub('-','.',weight_info$Set)

crossTissue_table<-merge(crossTissue_table, weight_info, by.x='Panel', by.y='Set')

# Check correlation across 
cor(crossTissue_table$R, crossTissue_table$N_indiv) # 0.1289168
cor(crossTissue_table$R, crossTissue_table$N_feat) # 0.2561282
cor(crossTissue_table$N_indiv, crossTissue_table$N_feat) # 0.3263912

summary(lm(R ~ N_feat + N_indiv, data=crossTissue_table)) # R2 = 0.0679
crossTissue_table$R_resid<-resid(lm(R ~ N_feat + N_indiv, data=crossTissue_table))

Plot GeRS (TissueSpecific) across tissues

pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','RheuArth')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/UKBB.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/GeRS_TissueSpecific_Tissue_comp_UKBB.png', units='px', res=300, width=3000, height=10000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

Show GeRS prediction across p-value thresholds

Predictive utility: R2 y-axis

Predictive utility: R y-axis

Correlation between GeRS model predictions and observed values in UKBB

Phenotype	Weight	Model	R (SE)
Depression	Adipose_Subcutaneous	1e.06	0.023 (0.004)
Depression	Adipose_Subcutaneous	1e.05	0.022 (0.004)
Depression	Adipose_Subcutaneous	1e.04	0.028 (0.004)
Depression	Adipose_Subcutaneous	0.001	0.031 (0.004)
Depression	Adipose_Subcutaneous	0.01	0.038 (0.004)
Depression	Adipose_Subcutaneous	0.05	0.046 (0.004)
Depression	Adipose_Subcutaneous	0.1	0.046 (0.004)
Depression	Adipose_Subcutaneous	0.5	0.048 (0.004)
Depression	Adipose_Subcutaneous	1	0.048 (0.004)
Depression	Adipose_Subcutaneous	All	0.05 (0.004)
Depression	Adipose_Visceral_Omentum	1e.06	0.008 (0.004)
Depression	Adipose_Visceral_Omentum	1e.05	0.012 (0.004)
Depression	Adipose_Visceral_Omentum	1e.04	0.02 (0.004)
Depression	Adipose_Visceral_Omentum	0.001	0.021 (0.004)
Depression	Adipose_Visceral_Omentum	0.01	0.033 (0.004)
Depression	Adipose_Visceral_Omentum	0.05	0.04 (0.004)
Depression	Adipose_Visceral_Omentum	0.1	0.041 (0.004)
Depression	Adipose_Visceral_Omentum	0.5	0.043 (0.004)
Depression	Adipose_Visceral_Omentum	1	0.043 (0.004)
Depression	Adipose_Visceral_Omentum	All	0.043 (0.004)
Depression	Adrenal_Gland	1e.06	0.011 (0.004)
Depression	Adrenal_Gland	1e.05	0.017 (0.004)
Depression	Adrenal_Gland	1e.04	0.026 (0.004)
Depression	Adrenal_Gland	0.001	0.036 (0.004)
Depression	Adrenal_Gland	0.01	0.045 (0.004)
Depression	Adrenal_Gland	0.05	0.049 (0.004)
Depression	Adrenal_Gland	0.1	0.048 (0.004)
Depression	Adrenal_Gland	0.5	0.044 (0.004)
Depression	Adrenal_Gland	1	0.046 (0.004)
Depression	Adrenal_Gland	All	0.049 (0.004)
Depression	Artery_Aorta	1e.06	0.021 (0.004)
Depression	Artery_Aorta	1e.05	0.023 (0.004)
Depression	Artery_Aorta	1e.04	0.031 (0.004)
Depression	Artery_Aorta	0.001	0.031 (0.004)
Depression	Artery_Aorta	0.01	0.039 (0.004)
Depression	Artery_Aorta	0.05	0.046 (0.004)
Depression	Artery_Aorta	0.1	0.047 (0.004)
Depression	Artery_Aorta	0.5	0.047 (0.004)
Depression	Artery_Aorta	1	0.047 (0.004)
Depression	Artery_Aorta	All	0.051 (0.004)
Depression	Artery_Coronary	1e.06	0.008 (0.004)
Depression	Artery_Coronary	1e.05	0.012 (0.004)
Depression	Artery_Coronary	1e.04	0.019 (0.004)
Depression	Artery_Coronary	0.001	0.023 (0.004)
Depression	Artery_Coronary	0.01	0.031 (0.004)
Depression	Artery_Coronary	0.05	0.033 (0.004)
Depression	Artery_Coronary	0.1	0.035 (0.004)
Depression	Artery_Coronary	0.5	0.036 (0.004)
Depression	Artery_Coronary	1	0.036 (0.004)
Depression	Artery_Coronary	All	0.035 (0.004)
Depression	Artery_Tibial	1e.06	0.012 (0.004)
Depression	Artery_Tibial	1e.05	0.027 (0.004)
Depression	Artery_Tibial	1e.04	0.03 (0.004)
Depression	Artery_Tibial	0.001	0.034 (0.004)
Depression	Artery_Tibial	0.01	0.038 (0.004)
Depression	Artery_Tibial	0.05	0.046 (0.004)
Depression	Artery_Tibial	0.1	0.047 (0.004)
Depression	Artery_Tibial	0.5	0.048 (0.004)
Depression	Artery_Tibial	1	0.049 (0.004)
Depression	Artery_Tibial	All	0.05 (0.004)
Depression	Brain_Amygdala	1e.06	0.013 (0.004)
Depression	Brain_Amygdala	1e.05	0.017 (0.004)
Depression	Brain_Amygdala	1e.04	0.022 (0.004)
Depression	Brain_Amygdala	0.001	0.024 (0.004)
Depression	Brain_Amygdala	0.01	0.032 (0.004)
Depression	Brain_Amygdala	0.05	0.034 (0.004)
Depression	Brain_Amygdala	0.1	0.033 (0.004)
Depression	Brain_Amygdala	0.5	0.033 (0.004)
Depression	Brain_Amygdala	1	0.033 (0.004)
Depression	Brain_Amygdala	All	0.034 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.013 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.013 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.02 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	0.001	0.026 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	0.01	0.035 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	0.05	0.036 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	0.1	0.038 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	0.5	0.038 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	1	0.038 (0.004)
Depression	Brain_Anterior_cingulate_cortex_BA24	All	0.04 (0.004)
Depression	Brain_Caudate_basal_ganglia	1e.06	0.02 (0.004)
Depression	Brain_Caudate_basal_ganglia	1e.05	0.019 (0.004)
Depression	Brain_Caudate_basal_ganglia	1e.04	0.025 (0.004)
Depression	Brain_Caudate_basal_ganglia	0.001	0.025 (0.004)
Depression	Brain_Caudate_basal_ganglia	0.01	0.033 (0.004)
Depression	Brain_Caudate_basal_ganglia	0.05	0.033 (0.004)
Depression	Brain_Caudate_basal_ganglia	0.1	0.034 (0.004)
Depression	Brain_Caudate_basal_ganglia	0.5	0.038 (0.004)
Depression	Brain_Caudate_basal_ganglia	1	0.038 (0.004)
Depression	Brain_Caudate_basal_ganglia	All	0.041 (0.004)
Depression	Brain_Cerebellar_Hemisphere	1e.06	0.025 (0.004)
Depression	Brain_Cerebellar_Hemisphere	1e.05	0.026 (0.004)
Depression	Brain_Cerebellar_Hemisphere	1e.04	0.029 (0.004)
Depression	Brain_Cerebellar_Hemisphere	0.001	0.031 (0.004)
Depression	Brain_Cerebellar_Hemisphere	0.01	0.037 (0.004)
Depression	Brain_Cerebellar_Hemisphere	0.05	0.039 (0.004)
Depression	Brain_Cerebellar_Hemisphere	0.1	0.043 (0.004)
Depression	Brain_Cerebellar_Hemisphere	0.5	0.045 (0.004)
Depression	Brain_Cerebellar_Hemisphere	1	0.045 (0.004)
Depression	Brain_Cerebellar_Hemisphere	All	0.047 (0.004)
Depression	Brain_Cerebellum	1e.06	0.021 (0.004)
Depression	Brain_Cerebellum	1e.05	0.018 (0.004)
Depression	Brain_Cerebellum	1e.04	0.028 (0.004)
Depression	Brain_Cerebellum	0.001	0.03 (0.004)
Depression	Brain_Cerebellum	0.01	0.03 (0.004)
Depression	Brain_Cerebellum	0.05	0.04 (0.004)
Depression	Brain_Cerebellum	0.1	0.041 (0.004)
Depression	Brain_Cerebellum	0.5	0.042 (0.004)
Depression	Brain_Cerebellum	1	0.043 (0.004)
Depression	Brain_Cerebellum	All	0.044 (0.004)
Depression	Brain_Cortex	1e.06	0.017 (0.004)
Depression	Brain_Cortex	1e.05	0.018 (0.004)
Depression	Brain_Cortex	1e.04	0.02 (0.004)
Depression	Brain_Cortex	0.001	0.03 (0.004)
Depression	Brain_Cortex	0.01	0.033 (0.004)
Depression	Brain_Cortex	0.05	0.039 (0.004)
Depression	Brain_Cortex	0.1	0.04 (0.004)
Depression	Brain_Cortex	0.5	0.048 (0.004)
Depression	Brain_Cortex	1	0.048 (0.004)
Depression	Brain_Cortex	All	0.048 (0.004)
Depression	Brain_Frontal_Cortex_BA9	1e.06	0.008 (0.004)
Depression	Brain_Frontal_Cortex_BA9	1e.05	0.008 (0.004)
Depression	Brain_Frontal_Cortex_BA9	1e.04	0.019 (0.004)
Depression	Brain_Frontal_Cortex_BA9	0.001	0.026 (0.004)
Depression	Brain_Frontal_Cortex_BA9	0.01	0.035 (0.004)
Depression	Brain_Frontal_Cortex_BA9	0.05	0.036 (0.004)
Depression	Brain_Frontal_Cortex_BA9	0.1	0.038 (0.004)
Depression	Brain_Frontal_Cortex_BA9	0.5	0.036 (0.004)
Depression	Brain_Frontal_Cortex_BA9	1	0.036 (0.004)
Depression	Brain_Frontal_Cortex_BA9	All	0.038 (0.004)
Depression	Brain_Hippocampus	1e.06	0.016 (0.004)
Depression	Brain_Hippocampus	1e.05	0.02 (0.004)
Depression	Brain_Hippocampus	1e.04	0.025 (0.004)
Depression	Brain_Hippocampus	0.001	0.029 (0.004)
Depression	Brain_Hippocampus	0.01	0.032 (0.004)
Depression	Brain_Hippocampus	0.05	0.031 (0.004)
Depression	Brain_Hippocampus	0.1	0.031 (0.004)
Depression	Brain_Hippocampus	0.5	0.032 (0.004)
Depression	Brain_Hippocampus	1	0.033 (0.004)
Depression	Brain_Hippocampus	All	0.037 (0.004)
Depression	Brain_Hypothalamus	1e.06	0.002 (0.004)
Depression	Brain_Hypothalamus	1e.05	0.011 (0.004)
Depression	Brain_Hypothalamus	1e.04	0.015 (0.004)
Depression	Brain_Hypothalamus	0.001	0.02 (0.004)
Depression	Brain_Hypothalamus	0.01	0.031 (0.004)
Depression	Brain_Hypothalamus	0.05	0.033 (0.004)
Depression	Brain_Hypothalamus	0.1	0.035 (0.004)
Depression	Brain_Hypothalamus	0.5	0.036 (0.004)
Depression	Brain_Hypothalamus	1	0.037 (0.004)
Depression	Brain_Hypothalamus	All	0.037 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.016 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.014 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.015 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.023 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.033 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.033 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.035 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.036 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	1	0.037 (0.004)
Depression	Brain_Nucleus_accumbens_basal_ganglia	All	0.039 (0.004)
Depression	Brain_Putamen_basal_ganglia	1e.06	0.017 (0.004)
Depression	Brain_Putamen_basal_ganglia	1e.05	0.017 (0.004)
Depression	Brain_Putamen_basal_ganglia	1e.04	0.018 (0.004)
Depression	Brain_Putamen_basal_ganglia	0.001	0.026 (0.004)
Depression	Brain_Putamen_basal_ganglia	0.01	0.033 (0.004)
Depression	Brain_Putamen_basal_ganglia	0.05	0.035 (0.004)
Depression	Brain_Putamen_basal_ganglia	0.1	0.039 (0.004)
Depression	Brain_Putamen_basal_ganglia	0.5	0.039 (0.004)
Depression	Brain_Putamen_basal_ganglia	1	0.039 (0.004)
Depression	Brain_Putamen_basal_ganglia	All	0.04 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	1e.06	0.019 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	1e.05	0.015 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	1e.04	0.022 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	0.001	0.026 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	0.01	0.03 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	0.05	0.031 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	0.1	0.032 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	0.5	0.035 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	1	0.035 (0.004)
Depression	Brain_Spinal_cord_cervical_c-1	All	0.036 (0.004)
Depression	Brain_Substantia_nigra	1e.06	0.006 (0.004)
Depression	Brain_Substantia_nigra	1e.05	0.006 (0.004)
Depression	Brain_Substantia_nigra	1e.04	0.017 (0.004)
Depression	Brain_Substantia_nigra	0.001	0.02 (0.004)
Depression	Brain_Substantia_nigra	0.01	0.028 (0.004)
Depression	Brain_Substantia_nigra	0.05	0.031 (0.004)
Depression	Brain_Substantia_nigra	0.1	0.03 (0.004)
Depression	Brain_Substantia_nigra	0.5	0.031 (0.004)
Depression	Brain_Substantia_nigra	1	0.031 (0.004)
Depression	Brain_Substantia_nigra	All	0.033 (0.004)
Depression	Breast_Mammary_Tissue	1e.06	0.013 (0.004)
Depression	Breast_Mammary_Tissue	1e.05	0.015 (0.004)
Depression	Breast_Mammary_Tissue	1e.04	0.017 (0.004)
Depression	Breast_Mammary_Tissue	0.001	0.027 (0.004)
Depression	Breast_Mammary_Tissue	0.01	0.036 (0.004)
Depression	Breast_Mammary_Tissue	0.05	0.04 (0.004)
Depression	Breast_Mammary_Tissue	0.1	0.04 (0.004)
Depression	Breast_Mammary_Tissue	0.5	0.042 (0.004)
Depression	Breast_Mammary_Tissue	1	0.042 (0.004)
Depression	Breast_Mammary_Tissue	All	0.043 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	1e.06	0.014 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	1e.05	0.015 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	1e.04	0.013 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	0.001	0.021 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	0.01	0.025 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	0.05	0.032 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	0.1	0.034 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	0.5	0.041 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	1	0.041 (0.004)
Depression	Cells_EBV-transformed_lymphocytes	All	0.041 (0.004)
Depression	Cells_Transformed_fibroblasts	1e.06	0.01 (0.004)
Depression	Cells_Transformed_fibroblasts	1e.05	0.014 (0.004)
Depression	Cells_Transformed_fibroblasts	1e.04	0.022 (0.004)
Depression	Cells_Transformed_fibroblasts	0.001	0.026 (0.004)
Depression	Cells_Transformed_fibroblasts	0.01	0.035 (0.004)
Depression	Cells_Transformed_fibroblasts	0.05	0.042 (0.004)
Depression	Cells_Transformed_fibroblasts	0.1	0.043 (0.004)
Depression	Cells_Transformed_fibroblasts	0.5	0.046 (0.004)
Depression	Cells_Transformed_fibroblasts	1	0.047 (0.004)
Depression	Cells_Transformed_fibroblasts	All	0.046 (0.004)
Depression	CMC.BRAIN.RNASEQ	1e.06	0.012 (0.004)
Depression	CMC.BRAIN.RNASEQ	1e.05	0.017 (0.004)
Depression	CMC.BRAIN.RNASEQ	1e.04	0.029 (0.004)
Depression	CMC.BRAIN.RNASEQ	0.001	0.031 (0.004)
Depression	CMC.BRAIN.RNASEQ	0.01	0.038 (0.004)
Depression	CMC.BRAIN.RNASEQ	0.05	0.046 (0.004)
Depression	CMC.BRAIN.RNASEQ	0.1	0.048 (0.004)
Depression	CMC.BRAIN.RNASEQ	0.5	0.052 (0.004)
Depression	CMC.BRAIN.RNASEQ	1	0.052 (0.004)
Depression	CMC.BRAIN.RNASEQ	All	0.051 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	1e.06	-0.01 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.003 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.018 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.028 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.04 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.047 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.048 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.045 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	1	0.047 (0.004)
Depression	CMC.BRAIN.RNASEQ_SPLICING	All	0.048 (0.004)
Depression	Colon_Sigmoid	1e.06	0.001 (0.004)
Depression	Colon_Sigmoid	1e.05	0.003 (0.004)
Depression	Colon_Sigmoid	1e.04	0.018 (0.004)
Depression	Colon_Sigmoid	0.001	0.024 (0.004)
Depression	Colon_Sigmoid	0.01	0.036 (0.004)
Depression	Colon_Sigmoid	0.05	0.04 (0.004)
Depression	Colon_Sigmoid	0.1	0.041 (0.004)
Depression	Colon_Sigmoid	0.5	0.043 (0.004)
Depression	Colon_Sigmoid	1	0.043 (0.004)
Depression	Colon_Sigmoid	All	0.044 (0.004)
Depression	Colon_Transverse	1e.06	0.005 (0.004)
Depression	Colon_Transverse	1e.05	0.001 (0.004)
Depression	Colon_Transverse	1e.04	0.024 (0.004)
Depression	Colon_Transverse	0.001	0.036 (0.004)
Depression	Colon_Transverse	0.01	0.038 (0.004)
Depression	Colon_Transverse	0.05	0.041 (0.004)
Depression	Colon_Transverse	0.1	0.044 (0.004)
Depression	Colon_Transverse	0.5	0.047 (0.004)
Depression	Colon_Transverse	1	0.047 (0.004)
Depression	Colon_Transverse	All	0.05 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	1e.06	0.005 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	1e.05	0.014 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	1e.04	0.028 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	0.001	0.028 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	0.01	0.032 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	0.05	0.035 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	0.1	0.036 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	0.5	0.042 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	1	0.042 (0.004)
Depression	Esophagus_Gastroesophageal_Junction	All	0.043 (0.004)
Depression	Esophagus_Mucosa	1e.06	0.009 (0.004)
Depression	Esophagus_Mucosa	1e.05	0.016 (0.004)
Depression	Esophagus_Mucosa	1e.04	0.023 (0.004)
Depression	Esophagus_Mucosa	0.001	0.033 (0.004)
Depression	Esophagus_Mucosa	0.01	0.043 (0.004)
Depression	Esophagus_Mucosa	0.05	0.043 (0.004)
Depression	Esophagus_Mucosa	0.1	0.044 (0.004)
Depression	Esophagus_Mucosa	0.5	0.049 (0.004)
Depression	Esophagus_Mucosa	1	0.049 (0.004)
Depression	Esophagus_Mucosa	All	0.05 (0.004)
Depression	Esophagus_Muscularis	1e.06	0.013 (0.004)
Depression	Esophagus_Muscularis	1e.05	0.023 (0.004)
Depression	Esophagus_Muscularis	1e.04	0.03 (0.004)
Depression	Esophagus_Muscularis	0.001	0.031 (0.004)
Depression	Esophagus_Muscularis	0.01	0.043 (0.004)
Depression	Esophagus_Muscularis	0.05	0.05 (0.004)
Depression	Esophagus_Muscularis	0.1	0.049 (0.004)
Depression	Esophagus_Muscularis	0.5	0.049 (0.004)
Depression	Esophagus_Muscularis	1	0.05 (0.004)
Depression	Esophagus_Muscularis	All	0.053 (0.004)
Depression	Heart_Atrial_Appendage	1e.06	0.012 (0.004)
Depression	Heart_Atrial_Appendage	1e.05	0.022 (0.004)
Depression	Heart_Atrial_Appendage	1e.04	0.027 (0.004)
Depression	Heart_Atrial_Appendage	0.001	0.031 (0.004)
Depression	Heart_Atrial_Appendage	0.01	0.033 (0.004)
Depression	Heart_Atrial_Appendage	0.05	0.039 (0.004)
Depression	Heart_Atrial_Appendage	0.1	0.04 (0.004)
Depression	Heart_Atrial_Appendage	0.5	0.043 (0.004)
Depression	Heart_Atrial_Appendage	1	0.044 (0.004)
Depression	Heart_Atrial_Appendage	All	0.045 (0.004)
Depression	Heart_Left_Ventricle	1e.06	0.011 (0.004)
Depression	Heart_Left_Ventricle	1e.05	0.013 (0.004)
Depression	Heart_Left_Ventricle	1e.04	0.022 (0.004)
Depression	Heart_Left_Ventricle	0.001	0.026 (0.004)
Depression	Heart_Left_Ventricle	0.01	0.035 (0.004)
Depression	Heart_Left_Ventricle	0.05	0.039 (0.004)
Depression	Heart_Left_Ventricle	0.1	0.036 (0.004)
Depression	Heart_Left_Ventricle	0.5	0.043 (0.004)
Depression	Heart_Left_Ventricle	1	0.043 (0.004)
Depression	Heart_Left_Ventricle	All	0.043 (0.004)
Depression	Liver	1e.06	0.012 (0.004)
Depression	Liver	1e.05	0.017 (0.004)
Depression	Liver	1e.04	0.017 (0.004)
Depression	Liver	0.001	0.021 (0.004)
Depression	Liver	0.01	0.027 (0.004)
Depression	Liver	0.05	0.032 (0.004)
Depression	Liver	0.1	0.033 (0.004)
Depression	Liver	0.5	0.033 (0.004)
Depression	Liver	1	0.034 (0.004)
Depression	Liver	All	0.033 (0.004)
Depression	Lung	1e.06	0.018 (0.004)
Depression	Lung	1e.05	0.027 (0.004)
Depression	Lung	1e.04	0.029 (0.004)
Depression	Lung	0.001	0.037 (0.004)
Depression	Lung	0.01	0.04 (0.004)
Depression	Lung	0.05	0.048 (0.004)
Depression	Lung	0.1	0.05 (0.004)
Depression	Lung	0.5	0.05 (0.004)
Depression	Lung	1	0.05 (0.004)
Depression	Lung	All	0.052 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	1e.06	0.016 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	1e.05	0.014 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	1e.04	0.024 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	0.001	0.025 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	0.01	0.036 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	0.05	0.039 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	0.1	0.041 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	0.5	0.041 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	1	0.041 (0.004)
Depression	METSIM.ADIPOSE.RNASEQ	All	0.043 (0.004)
Depression	Minor_Salivary_Gland	1e.06	0.014 (0.004)
Depression	Minor_Salivary_Gland	1e.05	0.019 (0.004)
Depression	Minor_Salivary_Gland	1e.04	0.012 (0.004)
Depression	Minor_Salivary_Gland	0.001	0.022 (0.004)
Depression	Minor_Salivary_Gland	0.01	0.024 (0.004)
Depression	Minor_Salivary_Gland	0.05	0.022 (0.004)
Depression	Minor_Salivary_Gland	0.1	0.023 (0.004)
Depression	Minor_Salivary_Gland	0.5	0.028 (0.004)
Depression	Minor_Salivary_Gland	1	0.027 (0.004)
Depression	Minor_Salivary_Gland	All	0.03 (0.004)
Depression	Muscle_Skeletal	1e.06	-0.013 (0.004)
Depression	Muscle_Skeletal	1e.05	0.019 (0.004)
Depression	Muscle_Skeletal	1e.04	0.025 (0.004)
Depression	Muscle_Skeletal	0.001	0.028 (0.004)
Depression	Muscle_Skeletal	0.01	0.033 (0.004)
Depression	Muscle_Skeletal	0.05	0.038 (0.004)
Depression	Muscle_Skeletal	0.1	0.041 (0.004)
Depression	Muscle_Skeletal	0.5	0.041 (0.004)
Depression	Muscle_Skeletal	1	0.041 (0.004)
Depression	Muscle_Skeletal	All	0.041 (0.004)
Depression	Nerve_Tibial	1e.06	0.011 (0.004)
Depression	Nerve_Tibial	1e.05	0.021 (0.004)
Depression	Nerve_Tibial	1e.04	0.03 (0.004)
Depression	Nerve_Tibial	0.001	0.039 (0.004)
Depression	Nerve_Tibial	0.01	0.042 (0.004)
Depression	Nerve_Tibial	0.05	0.046 (0.004)
Depression	Nerve_Tibial	0.1	0.046 (0.004)
Depression	Nerve_Tibial	0.5	0.049 (0.004)
Depression	Nerve_Tibial	1	0.049 (0.004)
Depression	Nerve_Tibial	All	0.052 (0.004)
Depression	NTR.BLOOD.RNAARR	1e.06	-0.004 (0.004)
Depression	NTR.BLOOD.RNAARR	1e.05	0.01 (0.004)
Depression	NTR.BLOOD.RNAARR	1e.04	0.012 (0.004)
Depression	NTR.BLOOD.RNAARR	0.001	0.019 (0.004)
Depression	NTR.BLOOD.RNAARR	0.01	0.026 (0.004)
Depression	NTR.BLOOD.RNAARR	0.05	0.03 (0.004)
Depression	NTR.BLOOD.RNAARR	0.1	0.033 (0.004)
Depression	NTR.BLOOD.RNAARR	0.5	0.037 (0.004)
Depression	NTR.BLOOD.RNAARR	1	0.037 (0.004)
Depression	NTR.BLOOD.RNAARR	All	0.036 (0.004)
Depression	Ovary	1e.06	0.017 (0.004)
Depression	Ovary	1e.05	0.023 (0.004)
Depression	Ovary	1e.04	0.027 (0.004)
Depression	Ovary	0.001	0.026 (0.004)
Depression	Ovary	0.01	0.032 (0.004)
Depression	Ovary	0.05	0.031 (0.004)
Depression	Ovary	0.1	0.032 (0.004)
Depression	Ovary	0.5	0.034 (0.004)
Depression	Ovary	1	0.035 (0.004)
Depression	Ovary	All	0.04 (0.004)
Depression	Pancreas	1e.06	0.017 (0.004)
Depression	Pancreas	1e.05	0.021 (0.004)
Depression	Pancreas	1e.04	0.033 (0.004)
Depression	Pancreas	0.001	0.036 (0.004)
Depression	Pancreas	0.01	0.041 (0.004)
Depression	Pancreas	0.05	0.043 (0.004)
Depression	Pancreas	0.1	0.044 (0.004)
Depression	Pancreas	0.5	0.045 (0.004)
Depression	Pancreas	1	0.044 (0.004)
Depression	Pancreas	All	0.048 (0.004)
Depression	Pituitary	1e.06	0.013 (0.004)
Depression	Pituitary	1e.05	0.013 (0.004)
Depression	Pituitary	1e.04	0.024 (0.004)
Depression	Pituitary	0.001	0.027 (0.004)
Depression	Pituitary	0.01	0.031 (0.004)
Depression	Pituitary	0.05	0.035 (0.004)
Depression	Pituitary	0.1	0.033 (0.004)
Depression	Pituitary	0.5	0.037 (0.004)
Depression	Pituitary	1	0.037 (0.004)
Depression	Pituitary	All	0.037 (0.004)
Depression	Prostate	1e.06	0.011 (0.004)
Depression	Prostate	1e.05	0.011 (0.004)
Depression	Prostate	1e.04	0.015 (0.004)
Depression	Prostate	0.001	0.02 (0.004)
Depression	Prostate	0.01	0.025 (0.004)
Depression	Prostate	0.05	0.027 (0.004)
Depression	Prostate	0.1	0.027 (0.004)
Depression	Prostate	0.5	0.029 (0.004)
Depression	Prostate	1	0.031 (0.004)
Depression	Prostate	All	0.031 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.011 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.015 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.022 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.034 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.041 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.044 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.046 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.05 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	1	0.051 (0.004)
Depression	Skin_Not_Sun_Exposed_Suprapubic	All	0.051 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	1e.06	0.008 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	1e.05	0.015 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	1e.04	0.028 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	0.001	0.035 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	0.01	0.04 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	0.05	0.044 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	0.1	0.045 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	0.5	0.048 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	1	0.048 (0.004)
Depression	Skin_Sun_Exposed_Lower_leg	All	0.049 (0.004)
Depression	Small_Intestine_Terminal_Ileum	1e.06	0.004 (0.004)
Depression	Small_Intestine_Terminal_Ileum	1e.05	0.016 (0.004)
Depression	Small_Intestine_Terminal_Ileum	1e.04	0.02 (0.004)
Depression	Small_Intestine_Terminal_Ileum	0.001	0.026 (0.004)
Depression	Small_Intestine_Terminal_Ileum	0.01	0.032 (0.004)
Depression	Small_Intestine_Terminal_Ileum	0.05	0.036 (0.004)
Depression	Small_Intestine_Terminal_Ileum	0.1	0.036 (0.004)
Depression	Small_Intestine_Terminal_Ileum	0.5	0.04 (0.004)
Depression	Small_Intestine_Terminal_Ileum	1	0.04 (0.004)
Depression	Small_Intestine_Terminal_Ileum	All	0.04 (0.004)
Depression	Spleen	1e.06	0.017 (0.004)
Depression	Spleen	1e.05	0.021 (0.004)
Depression	Spleen	1e.04	0.026 (0.004)
Depression	Spleen	0.001	0.031 (0.004)
Depression	Spleen	0.01	0.033 (0.004)
Depression	Spleen	0.05	0.039 (0.004)
Depression	Spleen	0.1	0.039 (0.004)
Depression	Spleen	0.5	0.04 (0.004)
Depression	Spleen	1	0.041 (0.004)
Depression	Spleen	All	0.043 (0.004)
Depression	Stomach	1e.06	0.015 (0.004)
Depression	Stomach	1e.05	0.025 (0.004)
Depression	Stomach	1e.04	0.024 (0.004)
Depression	Stomach	0.001	0.03 (0.004)
Depression	Stomach	0.01	0.034 (0.004)
Depression	Stomach	0.05	0.038 (0.004)
Depression	Stomach	0.1	0.038 (0.004)
Depression	Stomach	0.5	0.038 (0.004)
Depression	Stomach	1	0.038 (0.004)
Depression	Stomach	All	0.044 (0.004)
Depression	Testis	1e.06	0.017 (0.004)
Depression	Testis	1e.05	0.019 (0.004)
Depression	Testis	1e.04	0.028 (0.004)
Depression	Testis	0.001	0.035 (0.004)
Depression	Testis	0.01	0.046 (0.004)
Depression	Testis	0.05	0.046 (0.004)
Depression	Testis	0.1	0.049 (0.004)
Depression	Testis	0.5	0.053 (0.004)
Depression	Testis	1	0.053 (0.004)
Depression	Testis	All	0.054 (0.004)
Depression	Thyroid	1e.06	0.027 (0.004)
Depression	Thyroid	1e.05	0.028 (0.004)
Depression	Thyroid	1e.04	0.039 (0.004)
Depression	Thyroid	0.001	0.044 (0.004)
Depression	Thyroid	0.01	0.048 (0.004)
Depression	Thyroid	0.05	0.053 (0.004)
Depression	Thyroid	0.1	0.054 (0.004)
Depression	Thyroid	0.5	0.057 (0.004)
Depression	Thyroid	1	0.056 (0.004)
Depression	Thyroid	All	0.058 (0.004)
Depression	Uterus	1e.05	0.018 (0.004)
Depression	Uterus	1e.04	0.017 (0.004)
Depression	Uterus	0.001	0.021 (0.004)
Depression	Uterus	0.01	0.026 (0.004)
Depression	Uterus	0.05	0.025 (0.004)
Depression	Uterus	0.1	0.025 (0.004)
Depression	Uterus	0.5	0.03 (0.004)
Depression	Uterus	1	0.03 (0.004)
Depression	Uterus	All	0.032 (0.004)
Depression	Vagina	1e.06	0.017 (0.004)
Depression	Vagina	1e.05	0.012 (0.004)
Depression	Vagina	1e.04	0.018 (0.004)
Depression	Vagina	0.001	0.02 (0.004)
Depression	Vagina	0.01	0.029 (0.004)
Depression	Vagina	0.05	0.031 (0.004)
Depression	Vagina	0.1	0.032 (0.004)
Depression	Vagina	0.5	0.032 (0.004)
Depression	Vagina	1	0.032 (0.004)
Depression	Vagina	All	0.033 (0.004)
Depression	Whole_Blood	1e.06	0.023 (0.004)
Depression	Whole_Blood	1e.05	0.025 (0.004)
Depression	Whole_Blood	1e.04	0.026 (0.004)
Depression	Whole_Blood	0.001	0.033 (0.004)
Depression	Whole_Blood	0.01	0.039 (0.004)
Depression	Whole_Blood	0.05	0.045 (0.004)
Depression	Whole_Blood	0.1	0.044 (0.004)
Depression	Whole_Blood	0.5	0.046 (0.004)
Depression	Whole_Blood	1	0.047 (0.004)
Depression	Whole_Blood	All	0.049 (0.004)
Depression	YFS.BLOOD.RNAARR	1e.06	0.006 (0.004)
Depression	YFS.BLOOD.RNAARR	1e.05	0.015 (0.004)
Depression	YFS.BLOOD.RNAARR	1e.04	0.019 (0.004)
Depression	YFS.BLOOD.RNAARR	0.001	0.024 (0.004)
Depression	YFS.BLOOD.RNAARR	0.01	0.029 (0.004)
Depression	YFS.BLOOD.RNAARR	0.05	0.04 (0.004)
Depression	YFS.BLOOD.RNAARR	0.1	0.042 (0.004)
Depression	YFS.BLOOD.RNAARR	0.5	0.043 (0.004)
Depression	YFS.BLOOD.RNAARR	1	0.043 (0.004)
Depression	YFS.BLOOD.RNAARR	All	0.043 (0.004)
Intelligence	Adipose_Subcutaneous	1e.05	0.023 (0.004)
Intelligence	Adipose_Subcutaneous	1e.04	0.03 (0.004)
Intelligence	Adipose_Subcutaneous	0.001	0.04 (0.004)
Intelligence	Adipose_Subcutaneous	0.01	0.043 (0.004)
Intelligence	Adipose_Subcutaneous	0.05	0.049 (0.004)
Intelligence	Adipose_Subcutaneous	0.1	0.051 (0.004)
Intelligence	Adipose_Subcutaneous	0.5	0.054 (0.004)
Intelligence	Adipose_Subcutaneous	1	0.053 (0.004)
Intelligence	Adipose_Subcutaneous	All	0.056 (0.004)
Intelligence	Adipose_Visceral_Omentum	1e.05	0.019 (0.004)
Intelligence	Adipose_Visceral_Omentum	1e.04	0.022 (0.004)
Intelligence	Adipose_Visceral_Omentum	0.001	0.038 (0.004)
Intelligence	Adipose_Visceral_Omentum	0.01	0.041 (0.004)
Intelligence	Adipose_Visceral_Omentum	0.05	0.038 (0.004)
Intelligence	Adipose_Visceral_Omentum	0.1	0.039 (0.004)
Intelligence	Adipose_Visceral_Omentum	0.5	0.043 (0.004)
Intelligence	Adipose_Visceral_Omentum	1	0.043 (0.004)
Intelligence	Adipose_Visceral_Omentum	All	0.048 (0.004)
Intelligence	Adrenal_Gland	1e.05	0.004 (0.004)
Intelligence	Adrenal_Gland	1e.04	0.025 (0.004)
Intelligence	Adrenal_Gland	0.001	0.031 (0.004)
Intelligence	Adrenal_Gland	0.01	0.036 (0.004)
Intelligence	Adrenal_Gland	0.05	0.038 (0.004)
Intelligence	Adrenal_Gland	0.1	0.043 (0.004)
Intelligence	Adrenal_Gland	0.5	0.044 (0.004)
Intelligence	Adrenal_Gland	1	0.045 (0.004)
Intelligence	Adrenal_Gland	All	0.046 (0.004)
Intelligence	Artery_Aorta	1e.06	0.01 (0.004)
Intelligence	Artery_Aorta	1e.05	0.013 (0.004)
Intelligence	Artery_Aorta	1e.04	0.021 (0.004)
Intelligence	Artery_Aorta	0.001	0.033 (0.004)
Intelligence	Artery_Aorta	0.01	0.037 (0.004)
Intelligence	Artery_Aorta	0.05	0.041 (0.004)
Intelligence	Artery_Aorta	0.1	0.043 (0.004)
Intelligence	Artery_Aorta	0.5	0.045 (0.004)
Intelligence	Artery_Aorta	1	0.043 (0.004)
Intelligence	Artery_Aorta	All	0.045 (0.004)
Intelligence	Artery_Coronary	1e.05	0.023 (0.004)
Intelligence	Artery_Coronary	1e.04	0.026 (0.004)
Intelligence	Artery_Coronary	0.001	0.033 (0.004)
Intelligence	Artery_Coronary	0.01	0.033 (0.004)
Intelligence	Artery_Coronary	0.05	0.032 (0.004)
Intelligence	Artery_Coronary	0.1	0.033 (0.004)
Intelligence	Artery_Coronary	0.5	0.035 (0.004)
Intelligence	Artery_Coronary	1	0.037 (0.004)
Intelligence	Artery_Coronary	All	0.041 (0.004)
Intelligence	Artery_Tibial	1e.06	0.017 (0.004)
Intelligence	Artery_Tibial	1e.05	0.032 (0.004)
Intelligence	Artery_Tibial	1e.04	0.039 (0.004)
Intelligence	Artery_Tibial	0.001	0.037 (0.004)
Intelligence	Artery_Tibial	0.01	0.042 (0.004)
Intelligence	Artery_Tibial	0.05	0.046 (0.004)
Intelligence	Artery_Tibial	0.1	0.048 (0.004)
Intelligence	Artery_Tibial	0.5	0.053 (0.004)
Intelligence	Artery_Tibial	1	0.053 (0.004)
Intelligence	Artery_Tibial	All	0.055 (0.004)
Intelligence	Brain_Amygdala	1e.05	0.02 (0.004)
Intelligence	Brain_Amygdala	1e.04	0.027 (0.004)
Intelligence	Brain_Amygdala	0.001	0.025 (0.004)
Intelligence	Brain_Amygdala	0.01	0.029 (0.004)
Intelligence	Brain_Amygdala	0.05	0.027 (0.004)
Intelligence	Brain_Amygdala	0.1	0.032 (0.004)
Intelligence	Brain_Amygdala	0.5	0.038 (0.004)
Intelligence	Brain_Amygdala	1	0.038 (0.004)
Intelligence	Brain_Amygdala	All	0.04 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.028 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.032 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	0.001	0.028 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	0.01	0.036 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	0.05	0.038 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	0.1	0.04 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	0.5	0.035 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	1	0.035 (0.004)
Intelligence	Brain_Anterior_cingulate_cortex_BA24	All	0.041 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	1e.05	0.004 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	1e.04	0.022 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	0.001	0.023 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	0.01	0.036 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	0.05	0.041 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	0.1	0.043 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	0.5	0.037 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	1	0.037 (0.004)
Intelligence	Brain_Caudate_basal_ganglia	All	0.044 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	1e.06	0.005 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	1e.05	0.019 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	1e.04	0.027 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	0.001	0.032 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	0.01	0.034 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	0.05	0.038 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	0.1	0.042 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	0.5	0.041 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	1	0.04 (0.004)
Intelligence	Brain_Cerebellar_Hemisphere	All	0.045 (0.004)
Intelligence	Brain_Cerebellum	1e.06	0.003 (0.004)
Intelligence	Brain_Cerebellum	1e.05	0.019 (0.004)
Intelligence	Brain_Cerebellum	1e.04	0.023 (0.004)
Intelligence	Brain_Cerebellum	0.001	0.036 (0.004)
Intelligence	Brain_Cerebellum	0.01	0.041 (0.004)
Intelligence	Brain_Cerebellum	0.05	0.043 (0.004)
Intelligence	Brain_Cerebellum	0.1	0.044 (0.004)
Intelligence	Brain_Cerebellum	0.5	0.044 (0.004)
Intelligence	Brain_Cerebellum	1	0.043 (0.004)
Intelligence	Brain_Cerebellum	All	0.047 (0.004)
Intelligence	Brain_Cortex	1e.06	0.006 (0.004)
Intelligence	Brain_Cortex	1e.05	0.007 (0.004)
Intelligence	Brain_Cortex	1e.04	0.029 (0.004)
Intelligence	Brain_Cortex	0.001	0.031 (0.004)
Intelligence	Brain_Cortex	0.01	0.033 (0.004)
Intelligence	Brain_Cortex	0.05	0.034 (0.004)
Intelligence	Brain_Cortex	0.1	0.036 (0.004)
Intelligence	Brain_Cortex	0.5	0.037 (0.004)
Intelligence	Brain_Cortex	1	0.037 (0.004)
Intelligence	Brain_Cortex	All	0.041 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	1e.06	0.005 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	1e.05	0.005 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	1e.04	0.029 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	0.001	0.027 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	0.01	0.031 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	0.05	0.032 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	0.1	0.034 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	0.5	0.036 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	1	0.036 (0.004)
Intelligence	Brain_Frontal_Cortex_BA9	All	0.038 (0.004)
Intelligence	Brain_Hippocampus	1e.05	0 (0.004)
Intelligence	Brain_Hippocampus	1e.04	0.025 (0.004)
Intelligence	Brain_Hippocampus	0.001	0.026 (0.004)
Intelligence	Brain_Hippocampus	0.01	0.027 (0.004)
Intelligence	Brain_Hippocampus	0.05	0.028 (0.004)
Intelligence	Brain_Hippocampus	0.1	0.034 (0.004)
Intelligence	Brain_Hippocampus	0.5	0.036 (0.004)
Intelligence	Brain_Hippocampus	1	0.036 (0.004)
Intelligence	Brain_Hippocampus	All	0.037 (0.004)
Intelligence	Brain_Hypothalamus	1e.05	0.024 (0.004)
Intelligence	Brain_Hypothalamus	1e.04	0.029 (0.004)
Intelligence	Brain_Hypothalamus	0.001	0.031 (0.004)
Intelligence	Brain_Hypothalamus	0.01	0.033 (0.004)
Intelligence	Brain_Hypothalamus	0.05	0.034 (0.004)
Intelligence	Brain_Hypothalamus	0.1	0.037 (0.004)
Intelligence	Brain_Hypothalamus	0.5	0.037 (0.004)
Intelligence	Brain_Hypothalamus	1	0.036 (0.004)
Intelligence	Brain_Hypothalamus	All	0.037 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.019 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.03 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.029 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.033 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.036 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.038 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.038 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	1	0.038 (0.004)
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	All	0.04 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	1e.05	0.023 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	1e.04	0.033 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	0.001	0.029 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	0.01	0.034 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	0.05	0.038 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	0.1	0.039 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	0.5	0.04 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	1	0.04 (0.004)
Intelligence	Brain_Putamen_basal_ganglia	All	0.043 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	1e.05	-0.002 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	1e.04	0.021 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	0.001	0.017 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	0.01	0.025 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	0.05	0.03 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	0.1	0.031 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	0.5	0.029 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	1	0.029 (0.004)
Intelligence	Brain_Spinal_cord_cervical_c-1	All	0.034 (0.004)
Intelligence	Brain_Substantia_nigra	1e.04	0.031 (0.004)
Intelligence	Brain_Substantia_nigra	0.001	0.021 (0.004)
Intelligence	Brain_Substantia_nigra	0.01	0.025 (0.004)
Intelligence	Brain_Substantia_nigra	0.05	0.025 (0.004)
Intelligence	Brain_Substantia_nigra	0.1	0.028 (0.004)
Intelligence	Brain_Substantia_nigra	0.5	0.026 (0.004)
Intelligence	Brain_Substantia_nigra	1	0.025 (0.004)
Intelligence	Brain_Substantia_nigra	All	0.033 (0.004)
Intelligence	Breast_Mammary_Tissue	1e.05	0.003 (0.004)
Intelligence	Breast_Mammary_Tissue	1e.04	0.024 (0.004)
Intelligence	Breast_Mammary_Tissue	0.001	0.035 (0.004)
Intelligence	Breast_Mammary_Tissue	0.01	0.04 (0.004)
Intelligence	Breast_Mammary_Tissue	0.05	0.039 (0.004)
Intelligence	Breast_Mammary_Tissue	0.1	0.039 (0.004)
Intelligence	Breast_Mammary_Tissue	0.5	0.044 (0.004)
Intelligence	Breast_Mammary_Tissue	1	0.044 (0.004)
Intelligence	Breast_Mammary_Tissue	All	0.045 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	1e.04	0.019 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	0.001	0.025 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	0.01	0.026 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	0.05	0.036 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	0.1	0.034 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	0.5	0.034 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	1	0.034 (0.004)
Intelligence	Cells_EBV-transformed_lymphocytes	All	0.034 (0.004)
Intelligence	Cells_Transformed_fibroblasts	1e.05	0.012 (0.004)
Intelligence	Cells_Transformed_fibroblasts	1e.04	0.022 (0.004)
Intelligence	Cells_Transformed_fibroblasts	0.001	0.033 (0.004)
Intelligence	Cells_Transformed_fibroblasts	0.01	0.039 (0.004)
Intelligence	Cells_Transformed_fibroblasts	0.05	0.044 (0.004)
Intelligence	Cells_Transformed_fibroblasts	0.1	0.045 (0.004)
Intelligence	Cells_Transformed_fibroblasts	0.5	0.048 (0.004)
Intelligence	Cells_Transformed_fibroblasts	1	0.048 (0.004)
Intelligence	Cells_Transformed_fibroblasts	All	0.049 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	1e.06	0.009 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	1e.05	0.019 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	1e.04	0.023 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	0.001	0.032 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	0.01	0.033 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	0.05	0.038 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	0.1	0.042 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	0.5	0.046 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	1	0.044 (0.004)
Intelligence	CMC.BRAIN.RNASEQ	All	0.048 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.009 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.011 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.017 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.026 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.039 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.039 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.038 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.04 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	1	0.04 (0.004)
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	All	0.046 (0.004)
Intelligence	Colon_Sigmoid	1e.05	0.004 (0.004)
Intelligence	Colon_Sigmoid	1e.04	0.03 (0.004)
Intelligence	Colon_Sigmoid	0.001	0.034 (0.004)
Intelligence	Colon_Sigmoid	0.01	0.034 (0.004)
Intelligence	Colon_Sigmoid	0.05	0.042 (0.004)
Intelligence	Colon_Sigmoid	0.1	0.041 (0.004)
Intelligence	Colon_Sigmoid	0.5	0.043 (0.004)
Intelligence	Colon_Sigmoid	1	0.043 (0.004)
Intelligence	Colon_Sigmoid	All	0.045 (0.004)
Intelligence	Colon_Transverse	1e.05	0.021 (0.004)
Intelligence	Colon_Transverse	1e.04	0.031 (0.004)
Intelligence	Colon_Transverse	0.001	0.035 (0.004)
Intelligence	Colon_Transverse	0.01	0.037 (0.004)
Intelligence	Colon_Transverse	0.05	0.037 (0.004)
Intelligence	Colon_Transverse	0.1	0.04 (0.004)
Intelligence	Colon_Transverse	0.5	0.043 (0.004)
Intelligence	Colon_Transverse	1	0.042 (0.004)
Intelligence	Colon_Transverse	All	0.046 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	1e.04	0.031 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	0.001	0.03 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	0.01	0.034 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	0.05	0.035 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	0.1	0.04 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	0.5	0.044 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	1	0.044 (0.004)
Intelligence	Esophagus_Gastroesophageal_Junction	All	0.046 (0.004)
Intelligence	Esophagus_Mucosa	1e.05	0.024 (0.004)
Intelligence	Esophagus_Mucosa	1e.04	0.028 (0.004)
Intelligence	Esophagus_Mucosa	0.001	0.036 (0.004)
Intelligence	Esophagus_Mucosa	0.01	0.037 (0.004)
Intelligence	Esophagus_Mucosa	0.05	0.039 (0.004)
Intelligence	Esophagus_Mucosa	0.1	0.039 (0.004)
Intelligence	Esophagus_Mucosa	0.5	0.042 (0.004)
Intelligence	Esophagus_Mucosa	1	0.042 (0.004)
Intelligence	Esophagus_Mucosa	All	0.044 (0.004)
Intelligence	Esophagus_Muscularis	1e.06	0.002 (0.004)
Intelligence	Esophagus_Muscularis	1e.05	0.017 (0.004)
Intelligence	Esophagus_Muscularis	1e.04	0.019 (0.004)
Intelligence	Esophagus_Muscularis	0.001	0.035 (0.004)
Intelligence	Esophagus_Muscularis	0.01	0.038 (0.004)
Intelligence	Esophagus_Muscularis	0.05	0.043 (0.004)
Intelligence	Esophagus_Muscularis	0.1	0.049 (0.004)
Intelligence	Esophagus_Muscularis	0.5	0.051 (0.004)
Intelligence	Esophagus_Muscularis	1	0.051 (0.004)
Intelligence	Esophagus_Muscularis	All	0.053 (0.004)
Intelligence	Heart_Atrial_Appendage	1e.05	0.007 (0.004)
Intelligence	Heart_Atrial_Appendage	1e.04	0.019 (0.004)
Intelligence	Heart_Atrial_Appendage	0.001	0.032 (0.004)
Intelligence	Heart_Atrial_Appendage	0.01	0.044 (0.004)
Intelligence	Heart_Atrial_Appendage	0.05	0.045 (0.004)
Intelligence	Heart_Atrial_Appendage	0.1	0.048 (0.004)
Intelligence	Heart_Atrial_Appendage	0.5	0.048 (0.004)
Intelligence	Heart_Atrial_Appendage	1	0.049 (0.004)
Intelligence	Heart_Atrial_Appendage	All	0.051 (0.004)
Intelligence	Heart_Left_Ventricle	1e.05	0.023 (0.004)
Intelligence	Heart_Left_Ventricle	1e.04	0.027 (0.004)
Intelligence	Heart_Left_Ventricle	0.001	0.03 (0.004)
Intelligence	Heart_Left_Ventricle	0.01	0.038 (0.004)
Intelligence	Heart_Left_Ventricle	0.05	0.039 (0.004)
Intelligence	Heart_Left_Ventricle	0.1	0.043 (0.004)
Intelligence	Heart_Left_Ventricle	0.5	0.044 (0.004)
Intelligence	Heart_Left_Ventricle	1	0.043 (0.004)
Intelligence	Heart_Left_Ventricle	All	0.042 (0.004)
Intelligence	Liver	1e.05	0.012 (0.004)
Intelligence	Liver	1e.04	0.029 (0.004)
Intelligence	Liver	0.001	0.034 (0.004)
Intelligence	Liver	0.01	0.03 (0.004)
Intelligence	Liver	0.05	0.031 (0.004)
Intelligence	Liver	0.1	0.032 (0.004)
Intelligence	Liver	0.5	0.033 (0.004)
Intelligence	Liver	1	0.033 (0.004)
Intelligence	Liver	All	0.04 (0.004)
Intelligence	Lung	1e.06	0.022 (0.004)
Intelligence	Lung	1e.05	0.021 (0.004)
Intelligence	Lung	1e.04	0.033 (0.004)
Intelligence	Lung	0.001	0.034 (0.004)
Intelligence	Lung	0.01	0.042 (0.004)
Intelligence	Lung	0.05	0.043 (0.004)
Intelligence	Lung	0.1	0.045 (0.004)
Intelligence	Lung	0.5	0.046 (0.004)
Intelligence	Lung	1	0.047 (0.004)
Intelligence	Lung	All	0.05 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	1e.05	0.02 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	1e.04	0.027 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	0.001	0.032 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	0.01	0.035 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	0.05	0.035 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	0.1	0.039 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	0.5	0.048 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	1	0.047 (0.004)
Intelligence	METSIM.ADIPOSE.RNASEQ	All	0.049 (0.004)
Intelligence	Minor_Salivary_Gland	1e.04	0.017 (0.004)
Intelligence	Minor_Salivary_Gland	0.001	0.027 (0.004)
Intelligence	Minor_Salivary_Gland	0.01	0.03 (0.004)
Intelligence	Minor_Salivary_Gland	0.05	0.029 (0.004)
Intelligence	Minor_Salivary_Gland	0.1	0.03 (0.004)
Intelligence	Minor_Salivary_Gland	0.5	0.032 (0.004)
Intelligence	Minor_Salivary_Gland	1	0.033 (0.004)
Intelligence	Minor_Salivary_Gland	All	0.035 (0.004)
Intelligence	Muscle_Skeletal	1e.06	-0.011 (0.004)
Intelligence	Muscle_Skeletal	1e.05	-0.008 (0.004)
Intelligence	Muscle_Skeletal	1e.04	0.03 (0.004)
Intelligence	Muscle_Skeletal	0.001	0.032 (0.004)
Intelligence	Muscle_Skeletal	0.01	0.042 (0.004)
Intelligence	Muscle_Skeletal	0.05	0.051 (0.004)
Intelligence	Muscle_Skeletal	0.1	0.052 (0.004)
Intelligence	Muscle_Skeletal	0.5	0.054 (0.004)
Intelligence	Muscle_Skeletal	1	0.052 (0.004)
Intelligence	Muscle_Skeletal	All	0.053 (0.004)
Intelligence	Nerve_Tibial	1e.05	0.018 (0.004)
Intelligence	Nerve_Tibial	1e.04	0.034 (0.004)
Intelligence	Nerve_Tibial	0.001	0.036 (0.004)
Intelligence	Nerve_Tibial	0.01	0.045 (0.004)
Intelligence	Nerve_Tibial	0.05	0.046 (0.004)
Intelligence	Nerve_Tibial	0.1	0.05 (0.004)
Intelligence	Nerve_Tibial	0.5	0.048 (0.004)
Intelligence	Nerve_Tibial	1	0.049 (0.004)
Intelligence	Nerve_Tibial	All	0.052 (0.004)
Intelligence	NTR.BLOOD.RNAARR	1e.04	0.025 (0.004)
Intelligence	NTR.BLOOD.RNAARR	0.001	0.031 (0.004)
Intelligence	NTR.BLOOD.RNAARR	0.01	0.029 (0.004)
Intelligence	NTR.BLOOD.RNAARR	0.05	0.033 (0.004)
Intelligence	NTR.BLOOD.RNAARR	0.1	0.029 (0.004)
Intelligence	NTR.BLOOD.RNAARR	0.5	0.032 (0.004)
Intelligence	NTR.BLOOD.RNAARR	1	0.034 (0.004)
Intelligence	NTR.BLOOD.RNAARR	All	0.038 (0.004)
Intelligence	Ovary	1e.04	0.014 (0.004)
Intelligence	Ovary	0.001	0.024 (0.004)
Intelligence	Ovary	0.01	0.033 (0.004)
Intelligence	Ovary	0.05	0.037 (0.004)
Intelligence	Ovary	0.1	0.037 (0.004)
Intelligence	Ovary	0.5	0.039 (0.004)
Intelligence	Ovary	1	0.038 (0.004)
Intelligence	Ovary	All	0.039 (0.004)
Intelligence	Pancreas	1e.05	0.004 (0.004)
Intelligence	Pancreas	1e.04	0.026 (0.004)
Intelligence	Pancreas	0.001	0.029 (0.004)
Intelligence	Pancreas	0.01	0.035 (0.004)
Intelligence	Pancreas	0.05	0.038 (0.004)
Intelligence	Pancreas	0.1	0.041 (0.004)
Intelligence	Pancreas	0.5	0.046 (0.004)
Intelligence	Pancreas	1	0.046 (0.004)
Intelligence	Pancreas	All	0.049 (0.004)
Intelligence	Pituitary	1e.05	0.007 (0.004)
Intelligence	Pituitary	1e.04	0.029 (0.004)
Intelligence	Pituitary	0.001	0.034 (0.004)
Intelligence	Pituitary	0.01	0.035 (0.004)
Intelligence	Pituitary	0.05	0.038 (0.004)
Intelligence	Pituitary	0.1	0.037 (0.004)
Intelligence	Pituitary	0.5	0.04 (0.004)
Intelligence	Pituitary	1	0.04 (0.004)
Intelligence	Pituitary	All	0.043 (0.004)
Intelligence	Prostate	1e.06	0.01 (0.004)
Intelligence	Prostate	1e.05	0.017 (0.004)
Intelligence	Prostate	1e.04	0.026 (0.004)
Intelligence	Prostate	0.001	0.034 (0.004)
Intelligence	Prostate	0.01	0.035 (0.004)
Intelligence	Prostate	0.05	0.037 (0.004)
Intelligence	Prostate	0.1	0.039 (0.004)
Intelligence	Prostate	0.5	0.038 (0.004)
Intelligence	Prostate	1	0.038 (0.004)
Intelligence	Prostate	All	0.042 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.013 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.026 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.039 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.039 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.043 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.046 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.049 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	1	0.048 (0.004)
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	All	0.051 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	1e.05	0.006 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	1e.04	0.026 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	0.001	0.03 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	0.01	0.035 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	0.05	0.036 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	0.1	0.041 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	0.5	0.045 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	1	0.044 (0.004)
Intelligence	Skin_Sun_Exposed_Lower_leg	All	0.046 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	1e.05	0.012 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	1e.04	0.022 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	0.001	0.027 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	0.01	0.026 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	0.05	0.029 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	0.1	0.033 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	0.5	0.033 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	1	0.034 (0.004)
Intelligence	Small_Intestine_Terminal_Ileum	All	0.035 (0.004)
Intelligence	Spleen	1e.04	0.028 (0.004)
Intelligence	Spleen	0.001	0.038 (0.004)
Intelligence	Spleen	0.01	0.039 (0.004)
Intelligence	Spleen	0.05	0.04 (0.004)
Intelligence	Spleen	0.1	0.041 (0.004)
Intelligence	Spleen	0.5	0.043 (0.004)
Intelligence	Spleen	1	0.042 (0.004)
Intelligence	Spleen	All	0.047 (0.004)
Intelligence	Stomach	1e.05	0.025 (0.004)
Intelligence	Stomach	1e.04	0.029 (0.004)
Intelligence	Stomach	0.001	0.037 (0.004)
Intelligence	Stomach	0.01	0.042 (0.004)
Intelligence	Stomach	0.05	0.044 (0.004)
Intelligence	Stomach	0.1	0.045 (0.004)
Intelligence	Stomach	0.5	0.046 (0.004)
Intelligence	Stomach	1	0.045 (0.004)
Intelligence	Stomach	All	0.048 (0.004)
Intelligence	Testis	1e.05	0.004 (0.004)
Intelligence	Testis	1e.04	0.026 (0.004)
Intelligence	Testis	0.001	0.035 (0.004)
Intelligence	Testis	0.01	0.043 (0.004)
Intelligence	Testis	0.05	0.044 (0.004)
Intelligence	Testis	0.1	0.047 (0.004)
Intelligence	Testis	0.5	0.047 (0.004)
Intelligence	Testis	1	0.048 (0.004)
Intelligence	Testis	All	0.051 (0.004)
Intelligence	Thyroid	1e.06	0.003 (0.004)
Intelligence	Thyroid	1e.05	0.033 (0.004)
Intelligence	Thyroid	1e.04	0.032 (0.004)
Intelligence	Thyroid	0.001	0.031 (0.004)
Intelligence	Thyroid	0.01	0.042 (0.004)
Intelligence	Thyroid	0.05	0.045 (0.004)
Intelligence	Thyroid	0.1	0.047 (0.004)
Intelligence	Thyroid	0.5	0.051 (0.004)
Intelligence	Thyroid	1	0.05 (0.004)
Intelligence	Thyroid	All	0.052 (0.004)
Intelligence	Uterus	1e.06	0.022 (0.004)
Intelligence	Uterus	1e.05	0.029 (0.004)
Intelligence	Uterus	1e.04	0.026 (0.004)
Intelligence	Uterus	0.001	0.024 (0.004)
Intelligence	Uterus	0.01	0.029 (0.004)
Intelligence	Uterus	0.05	0.032 (0.004)
Intelligence	Uterus	0.1	0.031 (0.004)
Intelligence	Uterus	0.5	0.037 (0.004)
Intelligence	Uterus	1	0.036 (0.004)
Intelligence	Uterus	All	0.04 (0.004)
Intelligence	Vagina	1e.04	0.032 (0.004)
Intelligence	Vagina	0.001	0.023 (0.004)
Intelligence	Vagina	0.01	0.027 (0.004)
Intelligence	Vagina	0.05	0.027 (0.004)
Intelligence	Vagina	0.1	0.029 (0.004)
Intelligence	Vagina	0.5	0.033 (0.004)
Intelligence	Vagina	1	0.032 (0.004)
Intelligence	Vagina	All	0.039 (0.004)
Intelligence	Whole_Blood	1e.05	-0.003 (0.004)
Intelligence	Whole_Blood	1e.04	0.03 (0.004)
Intelligence	Whole_Blood	0.001	0.032 (0.004)
Intelligence	Whole_Blood	0.01	0.036 (0.004)
Intelligence	Whole_Blood	0.05	0.035 (0.004)
Intelligence	Whole_Blood	0.1	0.036 (0.004)
Intelligence	Whole_Blood	0.5	0.037 (0.004)
Intelligence	Whole_Blood	1	0.038 (0.004)
Intelligence	Whole_Blood	All	0.043 (0.004)
Intelligence	YFS.BLOOD.RNAARR	1e.05	0.002 (0.004)
Intelligence	YFS.BLOOD.RNAARR	1e.04	0.021 (0.004)
Intelligence	YFS.BLOOD.RNAARR	0.001	0.029 (0.004)
Intelligence	YFS.BLOOD.RNAARR	0.01	0.026 (0.004)
Intelligence	YFS.BLOOD.RNAARR	0.05	0.033 (0.004)
Intelligence	YFS.BLOOD.RNAARR	0.1	0.038 (0.004)
Intelligence	YFS.BLOOD.RNAARR	0.5	0.047 (0.004)
Intelligence	YFS.BLOOD.RNAARR	1	0.047 (0.004)
Intelligence	YFS.BLOOD.RNAARR	All	0.049 (0.004)
BMI	Adipose_Subcutaneous	1e.06	0.043 (0.004)
BMI	Adipose_Subcutaneous	1e.05	0.05 (0.004)
BMI	Adipose_Subcutaneous	1e.04	0.06 (0.004)
BMI	Adipose_Subcutaneous	0.001	0.073 (0.004)
BMI	Adipose_Subcutaneous	0.01	0.092 (0.004)
BMI	Adipose_Subcutaneous	0.05	0.109 (0.004)
BMI	Adipose_Subcutaneous	0.1	0.113 (0.004)
BMI	Adipose_Subcutaneous	0.5	0.127 (0.004)
BMI	Adipose_Subcutaneous	1	0.127 (0.004)
BMI	Adipose_Subcutaneous	All	0.128 (0.004)
BMI	Adipose_Visceral_Omentum	1e.06	0.046 (0.004)
BMI	Adipose_Visceral_Omentum	1e.05	0.049 (0.004)
BMI	Adipose_Visceral_Omentum	1e.04	0.055 (0.004)
BMI	Adipose_Visceral_Omentum	0.001	0.071 (0.004)
BMI	Adipose_Visceral_Omentum	0.01	0.086 (0.004)
BMI	Adipose_Visceral_Omentum	0.05	0.1 (0.004)
BMI	Adipose_Visceral_Omentum	0.1	0.108 (0.004)
BMI	Adipose_Visceral_Omentum	0.5	0.118 (0.004)
BMI	Adipose_Visceral_Omentum	1	0.118 (0.004)
BMI	Adipose_Visceral_Omentum	All	0.119 (0.004)
BMI	Adrenal_Gland	1e.06	0.036 (0.004)
BMI	Adrenal_Gland	1e.05	0.04 (0.004)
BMI	Adrenal_Gland	1e.04	0.05 (0.004)
BMI	Adrenal_Gland	0.001	0.058 (0.004)
BMI	Adrenal_Gland	0.01	0.073 (0.004)
BMI	Adrenal_Gland	0.05	0.085 (0.004)
BMI	Adrenal_Gland	0.1	0.091 (0.004)
BMI	Adrenal_Gland	0.5	0.101 (0.004)
BMI	Adrenal_Gland	1	0.102 (0.004)
BMI	Adrenal_Gland	All	0.103 (0.004)
BMI	Artery_Aorta	1e.06	0.032 (0.004)
BMI	Artery_Aorta	1e.05	0.038 (0.004)
BMI	Artery_Aorta	1e.04	0.042 (0.004)
BMI	Artery_Aorta	0.001	0.056 (0.004)
BMI	Artery_Aorta	0.01	0.075 (0.004)
BMI	Artery_Aorta	0.05	0.092 (0.004)
BMI	Artery_Aorta	0.1	0.097 (0.004)
BMI	Artery_Aorta	0.5	0.108 (0.004)
BMI	Artery_Aorta	1	0.109 (0.004)
BMI	Artery_Aorta	All	0.116 (0.004)
BMI	Artery_Coronary	1e.06	0.035 (0.004)
BMI	Artery_Coronary	1e.05	0.04 (0.004)
BMI	Artery_Coronary	1e.04	0.05 (0.004)
BMI	Artery_Coronary	0.001	0.057 (0.004)
BMI	Artery_Coronary	0.01	0.075 (0.004)
BMI	Artery_Coronary	0.05	0.089 (0.004)
BMI	Artery_Coronary	0.1	0.095 (0.004)
BMI	Artery_Coronary	0.5	0.1 (0.004)
BMI	Artery_Coronary	1	0.101 (0.004)
BMI	Artery_Coronary	All	0.103 (0.004)
BMI	Artery_Tibial	1e.06	0.046 (0.004)
BMI	Artery_Tibial	1e.05	0.052 (0.004)
BMI	Artery_Tibial	1e.04	0.06 (0.004)
BMI	Artery_Tibial	0.001	0.074 (0.004)
BMI	Artery_Tibial	0.01	0.091 (0.004)
BMI	Artery_Tibial	0.05	0.111 (0.004)
BMI	Artery_Tibial	0.1	0.116 (0.004)
BMI	Artery_Tibial	0.5	0.127 (0.004)
BMI	Artery_Tibial	1	0.128 (0.004)
BMI	Artery_Tibial	All	0.13 (0.004)
BMI	Brain_Amygdala	1e.06	0.029 (0.004)
BMI	Brain_Amygdala	1e.05	0.032 (0.004)
BMI	Brain_Amygdala	1e.04	0.039 (0.004)
BMI	Brain_Amygdala	0.001	0.044 (0.004)
BMI	Brain_Amygdala	0.01	0.057 (0.004)
BMI	Brain_Amygdala	0.05	0.066 (0.004)
BMI	Brain_Amygdala	0.1	0.07 (0.004)
BMI	Brain_Amygdala	0.5	0.078 (0.004)
BMI	Brain_Amygdala	1	0.079 (0.004)
BMI	Brain_Amygdala	All	0.083 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.03 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.036 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.039 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	0.001	0.049 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	0.01	0.068 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	0.05	0.081 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	0.1	0.088 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	0.5	0.095 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	1	0.096 (0.004)
BMI	Brain_Anterior_cingulate_cortex_BA24	All	0.1 (0.004)
BMI	Brain_Caudate_basal_ganglia	1e.06	0.032 (0.004)
BMI	Brain_Caudate_basal_ganglia	1e.05	0.041 (0.004)
BMI	Brain_Caudate_basal_ganglia	1e.04	0.05 (0.004)
BMI	Brain_Caudate_basal_ganglia	0.001	0.058 (0.004)
BMI	Brain_Caudate_basal_ganglia	0.01	0.071 (0.004)
BMI	Brain_Caudate_basal_ganglia	0.05	0.083 (0.004)
BMI	Brain_Caudate_basal_ganglia	0.1	0.088 (0.004)
BMI	Brain_Caudate_basal_ganglia	0.5	0.098 (0.004)
BMI	Brain_Caudate_basal_ganglia	1	0.099 (0.004)
BMI	Brain_Caudate_basal_ganglia	All	0.102 (0.004)
BMI	Brain_Cerebellar_Hemisphere	1e.06	0.045 (0.004)
BMI	Brain_Cerebellar_Hemisphere	1e.05	0.046 (0.004)
BMI	Brain_Cerebellar_Hemisphere	1e.04	0.056 (0.004)
BMI	Brain_Cerebellar_Hemisphere	0.001	0.065 (0.004)
BMI	Brain_Cerebellar_Hemisphere	0.01	0.076 (0.004)
BMI	Brain_Cerebellar_Hemisphere	0.05	0.091 (0.004)
BMI	Brain_Cerebellar_Hemisphere	0.1	0.094 (0.004)
BMI	Brain_Cerebellar_Hemisphere	0.5	0.103 (0.004)
BMI	Brain_Cerebellar_Hemisphere	1	0.105 (0.004)
BMI	Brain_Cerebellar_Hemisphere	All	0.105 (0.004)
BMI	Brain_Cerebellum	1e.06	0.046 (0.004)
BMI	Brain_Cerebellum	1e.05	0.049 (0.004)
BMI	Brain_Cerebellum	1e.04	0.057 (0.004)
BMI	Brain_Cerebellum	0.001	0.064 (0.004)
BMI	Brain_Cerebellum	0.01	0.077 (0.004)
BMI	Brain_Cerebellum	0.05	0.095 (0.004)
BMI	Brain_Cerebellum	0.1	0.099 (0.004)
BMI	Brain_Cerebellum	0.5	0.108 (0.004)
BMI	Brain_Cerebellum	1	0.108 (0.004)
BMI	Brain_Cerebellum	All	0.11 (0.004)
BMI	Brain_Cortex	1e.06	0.031 (0.004)
BMI	Brain_Cortex	1e.05	0.037 (0.004)
BMI	Brain_Cortex	1e.04	0.043 (0.004)
BMI	Brain_Cortex	0.001	0.05 (0.004)
BMI	Brain_Cortex	0.01	0.068 (0.004)
BMI	Brain_Cortex	0.05	0.085 (0.004)
BMI	Brain_Cortex	0.1	0.089 (0.004)
BMI	Brain_Cortex	0.5	0.098 (0.004)
BMI	Brain_Cortex	1	0.098 (0.004)
BMI	Brain_Cortex	All	0.102 (0.004)
BMI	Brain_Frontal_Cortex_BA9	1e.06	0.032 (0.004)
BMI	Brain_Frontal_Cortex_BA9	1e.05	0.042 (0.004)
BMI	Brain_Frontal_Cortex_BA9	1e.04	0.045 (0.004)
BMI	Brain_Frontal_Cortex_BA9	0.001	0.055 (0.004)
BMI	Brain_Frontal_Cortex_BA9	0.01	0.068 (0.004)
BMI	Brain_Frontal_Cortex_BA9	0.05	0.078 (0.004)
BMI	Brain_Frontal_Cortex_BA9	0.1	0.086 (0.004)
BMI	Brain_Frontal_Cortex_BA9	0.5	0.091 (0.004)
BMI	Brain_Frontal_Cortex_BA9	1	0.092 (0.004)
BMI	Brain_Frontal_Cortex_BA9	All	0.098 (0.004)
BMI	Brain_Hippocampus	1e.06	0.026 (0.004)
BMI	Brain_Hippocampus	1e.05	0.032 (0.004)
BMI	Brain_Hippocampus	1e.04	0.041 (0.004)
BMI	Brain_Hippocampus	0.001	0.049 (0.004)
BMI	Brain_Hippocampus	0.01	0.058 (0.004)
BMI	Brain_Hippocampus	0.05	0.071 (0.004)
BMI	Brain_Hippocampus	0.1	0.076 (0.004)
BMI	Brain_Hippocampus	0.5	0.081 (0.004)
BMI	Brain_Hippocampus	1	0.082 (0.004)
BMI	Brain_Hippocampus	All	0.084 (0.004)
BMI	Brain_Hypothalamus	1e.06	0.032 (0.004)
BMI	Brain_Hypothalamus	1e.05	0.035 (0.004)
BMI	Brain_Hypothalamus	1e.04	0.04 (0.004)
BMI	Brain_Hypothalamus	0.001	0.046 (0.004)
BMI	Brain_Hypothalamus	0.01	0.059 (0.004)
BMI	Brain_Hypothalamus	0.05	0.072 (0.004)
BMI	Brain_Hypothalamus	0.1	0.075 (0.004)
BMI	Brain_Hypothalamus	0.5	0.08 (0.004)
BMI	Brain_Hypothalamus	1	0.082 (0.004)
BMI	Brain_Hypothalamus	All	0.082 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.034 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.043 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.049 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.058 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.068 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.08 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.088 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.093 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	1	0.093 (0.004)
BMI	Brain_Nucleus_accumbens_basal_ganglia	All	0.095 (0.004)
BMI	Brain_Putamen_basal_ganglia	1e.06	0.033 (0.004)
BMI	Brain_Putamen_basal_ganglia	1e.05	0.038 (0.004)
BMI	Brain_Putamen_basal_ganglia	1e.04	0.047 (0.004)
BMI	Brain_Putamen_basal_ganglia	0.001	0.054 (0.004)
BMI	Brain_Putamen_basal_ganglia	0.01	0.066 (0.004)
BMI	Brain_Putamen_basal_ganglia	0.05	0.077 (0.004)
BMI	Brain_Putamen_basal_ganglia	0.1	0.085 (0.004)
BMI	Brain_Putamen_basal_ganglia	0.5	0.093 (0.004)
BMI	Brain_Putamen_basal_ganglia	1	0.094 (0.004)
BMI	Brain_Putamen_basal_ganglia	All	0.096 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	1e.06	0.035 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	1e.05	0.041 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	1e.04	0.049 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	0.001	0.057 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	0.01	0.069 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	0.05	0.078 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	0.1	0.084 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	0.5	0.089 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	1	0.09 (0.004)
BMI	Brain_Spinal_cord_cervical_c-1	All	0.09 (0.004)
BMI	Brain_Substantia_nigra	1e.06	0.031 (0.004)
BMI	Brain_Substantia_nigra	1e.05	0.049 (0.004)
BMI	Brain_Substantia_nigra	1e.04	0.052 (0.004)
BMI	Brain_Substantia_nigra	0.001	0.055 (0.004)
BMI	Brain_Substantia_nigra	0.01	0.065 (0.004)
BMI	Brain_Substantia_nigra	0.05	0.074 (0.004)
BMI	Brain_Substantia_nigra	0.1	0.078 (0.004)
BMI	Brain_Substantia_nigra	0.5	0.083 (0.004)
BMI	Brain_Substantia_nigra	1	0.082 (0.004)
BMI	Brain_Substantia_nigra	All	0.083 (0.004)
BMI	Breast_Mammary_Tissue	1e.06	0.042 (0.004)
BMI	Breast_Mammary_Tissue	1e.05	0.049 (0.004)
BMI	Breast_Mammary_Tissue	1e.04	0.054 (0.004)
BMI	Breast_Mammary_Tissue	0.001	0.067 (0.004)
BMI	Breast_Mammary_Tissue	0.01	0.079 (0.004)
BMI	Breast_Mammary_Tissue	0.05	0.093 (0.004)
BMI	Breast_Mammary_Tissue	0.1	0.1 (0.004)
BMI	Breast_Mammary_Tissue	0.5	0.11 (0.004)
BMI	Breast_Mammary_Tissue	1	0.11 (0.004)
BMI	Breast_Mammary_Tissue	All	0.111 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	1e.06	0.037 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	1e.05	0.041 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	1e.04	0.047 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	0.001	0.06 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	0.01	0.071 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	0.05	0.085 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	0.1	0.09 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	0.5	0.097 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	1	0.097 (0.004)
BMI	Cells_EBV-transformed_lymphocytes	All	0.097 (0.004)
BMI	Cells_Transformed_fibroblasts	1e.06	0.042 (0.004)
BMI	Cells_Transformed_fibroblasts	1e.05	0.05 (0.004)
BMI	Cells_Transformed_fibroblasts	1e.04	0.056 (0.004)
BMI	Cells_Transformed_fibroblasts	0.001	0.071 (0.004)
BMI	Cells_Transformed_fibroblasts	0.01	0.088 (0.004)
BMI	Cells_Transformed_fibroblasts	0.05	0.104 (0.004)
BMI	Cells_Transformed_fibroblasts	0.1	0.109 (0.004)
BMI	Cells_Transformed_fibroblasts	0.5	0.119 (0.004)
BMI	Cells_Transformed_fibroblasts	1	0.121 (0.004)
BMI	Cells_Transformed_fibroblasts	All	0.124 (0.004)
BMI	CMC.BRAIN.RNASEQ	1e.06	0.063 (0.004)
BMI	CMC.BRAIN.RNASEQ	1e.05	0.067 (0.004)
BMI	CMC.BRAIN.RNASEQ	1e.04	0.071 (0.004)
BMI	CMC.BRAIN.RNASEQ	0.001	0.079 (0.004)
BMI	CMC.BRAIN.RNASEQ	0.01	0.092 (0.004)
BMI	CMC.BRAIN.RNASEQ	0.05	0.109 (0.004)
BMI	CMC.BRAIN.RNASEQ	0.1	0.115 (0.004)
BMI	CMC.BRAIN.RNASEQ	0.5	0.125 (0.004)
BMI	CMC.BRAIN.RNASEQ	1	0.125 (0.004)
BMI	CMC.BRAIN.RNASEQ	All	0.126 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.046 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.053 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.063 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.073 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.086 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.094 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.099 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.106 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	1	0.106 (0.004)
BMI	CMC.BRAIN.RNASEQ_SPLICING	All	0.109 (0.004)
BMI	Colon_Sigmoid	1e.06	0.033 (0.004)
BMI	Colon_Sigmoid	1e.05	0.039 (0.004)
BMI	Colon_Sigmoid	1e.04	0.041 (0.004)
BMI	Colon_Sigmoid	0.001	0.055 (0.004)
BMI	Colon_Sigmoid	0.01	0.071 (0.004)
BMI	Colon_Sigmoid	0.05	0.082 (0.004)
BMI	Colon_Sigmoid	0.1	0.087 (0.004)
BMI	Colon_Sigmoid	0.5	0.095 (0.004)
BMI	Colon_Sigmoid	1	0.095 (0.004)
BMI	Colon_Sigmoid	All	0.095 (0.004)
BMI	Colon_Transverse	1e.06	0.036 (0.004)
BMI	Colon_Transverse	1e.05	0.04 (0.004)
BMI	Colon_Transverse	1e.04	0.048 (0.004)
BMI	Colon_Transverse	0.001	0.058 (0.004)
BMI	Colon_Transverse	0.01	0.071 (0.004)
BMI	Colon_Transverse	0.05	0.08 (0.004)
BMI	Colon_Transverse	0.1	0.089 (0.004)
BMI	Colon_Transverse	0.5	0.1 (0.004)
BMI	Colon_Transverse	1	0.1 (0.004)
BMI	Colon_Transverse	All	0.102 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	1e.06	0.034 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	1e.05	0.036 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	1e.04	0.044 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	0.001	0.056 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	0.01	0.072 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	0.05	0.084 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	0.1	0.09 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	0.5	0.098 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	1	0.099 (0.004)
BMI	Esophagus_Gastroesophageal_Junction	All	0.102 (0.004)
BMI	Esophagus_Mucosa	1e.06	0.042 (0.004)
BMI	Esophagus_Mucosa	1e.05	0.049 (0.004)
BMI	Esophagus_Mucosa	1e.04	0.059 (0.004)
BMI	Esophagus_Mucosa	0.001	0.071 (0.004)
BMI	Esophagus_Mucosa	0.01	0.086 (0.004)
BMI	Esophagus_Mucosa	0.05	0.102 (0.004)
BMI	Esophagus_Mucosa	0.1	0.11 (0.004)
BMI	Esophagus_Mucosa	0.5	0.119 (0.004)
BMI	Esophagus_Mucosa	1	0.119 (0.004)
BMI	Esophagus_Mucosa	All	0.119 (0.004)
BMI	Esophagus_Muscularis	1e.06	0.041 (0.004)
BMI	Esophagus_Muscularis	1e.05	0.05 (0.004)
BMI	Esophagus_Muscularis	1e.04	0.057 (0.004)
BMI	Esophagus_Muscularis	0.001	0.069 (0.004)
BMI	Esophagus_Muscularis	0.01	0.087 (0.004)
BMI	Esophagus_Muscularis	0.05	0.101 (0.004)
BMI	Esophagus_Muscularis	0.1	0.107 (0.004)
BMI	Esophagus_Muscularis	0.5	0.119 (0.004)
BMI	Esophagus_Muscularis	1	0.12 (0.004)
BMI	Esophagus_Muscularis	All	0.12 (0.004)
BMI	Heart_Atrial_Appendage	1e.06	0.045 (0.004)
BMI	Heart_Atrial_Appendage	1e.05	0.049 (0.004)
BMI	Heart_Atrial_Appendage	1e.04	0.054 (0.004)
BMI	Heart_Atrial_Appendage	0.001	0.069 (0.004)
BMI	Heart_Atrial_Appendage	0.01	0.086 (0.004)
BMI	Heart_Atrial_Appendage	0.05	0.101 (0.004)
BMI	Heart_Atrial_Appendage	0.1	0.108 (0.004)
BMI	Heart_Atrial_Appendage	0.5	0.115 (0.004)
BMI	Heart_Atrial_Appendage	1	0.115 (0.004)
BMI	Heart_Atrial_Appendage	All	0.115 (0.004)
BMI	Heart_Left_Ventricle	1e.06	0.047 (0.004)
BMI	Heart_Left_Ventricle	1e.05	0.051 (0.004)
BMI	Heart_Left_Ventricle	1e.04	0.057 (0.004)
BMI	Heart_Left_Ventricle	0.001	0.067 (0.004)
BMI	Heart_Left_Ventricle	0.01	0.082 (0.004)
BMI	Heart_Left_Ventricle	0.05	0.096 (0.004)
BMI	Heart_Left_Ventricle	0.1	0.102 (0.004)
BMI	Heart_Left_Ventricle	0.5	0.112 (0.004)
BMI	Heart_Left_Ventricle	1	0.112 (0.004)
BMI	Heart_Left_Ventricle	All	0.112 (0.004)
BMI	Liver	1e.06	0.037 (0.004)
BMI	Liver	1e.05	0.041 (0.004)
BMI	Liver	1e.04	0.05 (0.004)
BMI	Liver	0.001	0.055 (0.004)
BMI	Liver	0.01	0.065 (0.004)
BMI	Liver	0.05	0.078 (0.004)
BMI	Liver	0.1	0.083 (0.004)
BMI	Liver	0.5	0.091 (0.004)
BMI	Liver	1	0.09 (0.004)
BMI	Liver	All	0.09 (0.004)
BMI	Lung	1e.06	0.047 (0.004)
BMI	Lung	1e.05	0.052 (0.004)
BMI	Lung	1e.04	0.057 (0.004)
BMI	Lung	0.001	0.065 (0.004)
BMI	Lung	0.01	0.082 (0.004)
BMI	Lung	0.05	0.101 (0.004)
BMI	Lung	0.1	0.107 (0.004)
BMI	Lung	0.5	0.117 (0.004)
BMI	Lung	1	0.118 (0.004)
BMI	Lung	All	0.12 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	1e.06	0.041 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	1e.05	0.052 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	1e.04	0.058 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	0.001	0.064 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	0.01	0.079 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	0.05	0.09 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	0.1	0.099 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	0.5	0.107 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	1	0.108 (0.004)
BMI	METSIM.ADIPOSE.RNASEQ	All	0.109 (0.004)
BMI	Minor_Salivary_Gland	1e.06	0.031 (0.004)
BMI	Minor_Salivary_Gland	1e.05	0.034 (0.004)
BMI	Minor_Salivary_Gland	1e.04	0.042 (0.004)
BMI	Minor_Salivary_Gland	0.001	0.054 (0.004)
BMI	Minor_Salivary_Gland	0.01	0.065 (0.004)
BMI	Minor_Salivary_Gland	0.05	0.075 (0.004)
BMI	Minor_Salivary_Gland	0.1	0.079 (0.004)
BMI	Minor_Salivary_Gland	0.5	0.086 (0.004)
BMI	Minor_Salivary_Gland	1	0.086 (0.004)
BMI	Minor_Salivary_Gland	All	0.085 (0.004)
BMI	Muscle_Skeletal	1e.06	0.043 (0.004)
BMI	Muscle_Skeletal	1e.05	0.047 (0.004)
BMI	Muscle_Skeletal	1e.04	0.059 (0.004)
BMI	Muscle_Skeletal	0.001	0.072 (0.004)
BMI	Muscle_Skeletal	0.01	0.088 (0.004)
BMI	Muscle_Skeletal	0.05	0.104 (0.004)
BMI	Muscle_Skeletal	0.1	0.109 (0.004)
BMI	Muscle_Skeletal	0.5	0.118 (0.004)
BMI	Muscle_Skeletal	1	0.12 (0.004)
BMI	Muscle_Skeletal	All	0.12 (0.004)
BMI	Nerve_Tibial	1e.06	0.048 (0.004)
BMI	Nerve_Tibial	1e.05	0.057 (0.004)
BMI	Nerve_Tibial	1e.04	0.065 (0.004)
BMI	Nerve_Tibial	0.001	0.077 (0.004)
BMI	Nerve_Tibial	0.01	0.096 (0.004)
BMI	Nerve_Tibial	0.05	0.111 (0.004)
BMI	Nerve_Tibial	0.1	0.117 (0.004)
BMI	Nerve_Tibial	0.5	0.125 (0.004)
BMI	Nerve_Tibial	1	0.126 (0.004)
BMI	Nerve_Tibial	All	0.127 (0.004)
BMI	NTR.BLOOD.RNAARR	1e.06	0.039 (0.004)
BMI	NTR.BLOOD.RNAARR	1e.05	0.044 (0.004)
BMI	NTR.BLOOD.RNAARR	1e.04	0.052 (0.004)
BMI	NTR.BLOOD.RNAARR	0.001	0.06 (0.004)
BMI	NTR.BLOOD.RNAARR	0.01	0.071 (0.004)
BMI	NTR.BLOOD.RNAARR	0.05	0.082 (0.004)
BMI	NTR.BLOOD.RNAARR	0.1	0.085 (0.004)
BMI	NTR.BLOOD.RNAARR	0.5	0.091 (0.004)
BMI	NTR.BLOOD.RNAARR	1	0.092 (0.004)
BMI	NTR.BLOOD.RNAARR	All	0.094 (0.004)
BMI	Ovary	1e.06	0.036 (0.004)
BMI	Ovary	1e.05	0.038 (0.004)
BMI	Ovary	1e.04	0.042 (0.004)
BMI	Ovary	0.001	0.049 (0.004)
BMI	Ovary	0.01	0.057 (0.004)
BMI	Ovary	0.05	0.065 (0.004)
BMI	Ovary	0.1	0.071 (0.004)
BMI	Ovary	0.5	0.08 (0.004)
BMI	Ovary	1	0.08 (0.004)
BMI	Ovary	All	0.084 (0.004)
BMI	Pancreas	1e.06	0.046 (0.004)
BMI	Pancreas	1e.05	0.052 (0.004)
BMI	Pancreas	1e.04	0.059 (0.004)
BMI	Pancreas	0.001	0.07 (0.004)
BMI	Pancreas	0.01	0.088 (0.004)
BMI	Pancreas	0.05	0.101 (0.004)
BMI	Pancreas	0.1	0.107 (0.004)
BMI	Pancreas	0.5	0.115 (0.004)
BMI	Pancreas	1	0.115 (0.004)
BMI	Pancreas	All	0.116 (0.004)
BMI	Pituitary	1e.06	0.031 (0.004)
BMI	Pituitary	1e.05	0.034 (0.004)
BMI	Pituitary	1e.04	0.043 (0.004)
BMI	Pituitary	0.001	0.053 (0.004)
BMI	Pituitary	0.01	0.069 (0.004)
BMI	Pituitary	0.05	0.082 (0.004)
BMI	Pituitary	0.1	0.088 (0.004)
BMI	Pituitary	0.5	0.095 (0.004)
BMI	Pituitary	1	0.095 (0.004)
BMI	Pituitary	All	0.099 (0.004)
BMI	Prostate	1e.06	0.028 (0.004)
BMI	Prostate	1e.05	0.031 (0.004)
BMI	Prostate	1e.04	0.041 (0.004)
BMI	Prostate	0.001	0.054 (0.004)
BMI	Prostate	0.01	0.067 (0.004)
BMI	Prostate	0.05	0.078 (0.004)
BMI	Prostate	0.1	0.083 (0.004)
BMI	Prostate	0.5	0.089 (0.004)
BMI	Prostate	1	0.09 (0.004)
BMI	Prostate	All	0.093 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.045 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.049 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.059 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.072 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.086 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.099 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.106 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.117 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	1	0.118 (0.004)
BMI	Skin_Not_Sun_Exposed_Suprapubic	All	0.118 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	1e.06	0.044 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	1e.05	0.053 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	1e.04	0.06 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	0.001	0.073 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	0.01	0.088 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	0.05	0.1 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	0.1	0.106 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	0.5	0.118 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	1	0.119 (0.004)
BMI	Skin_Sun_Exposed_Lower_leg	All	0.12 (0.004)
BMI	Small_Intestine_Terminal_Ileum	1e.06	0.036 (0.004)
BMI	Small_Intestine_Terminal_Ileum	1e.05	0.039 (0.004)
BMI	Small_Intestine_Terminal_Ileum	1e.04	0.049 (0.004)
BMI	Small_Intestine_Terminal_Ileum	0.001	0.059 (0.004)
BMI	Small_Intestine_Terminal_Ileum	0.01	0.072 (0.004)
BMI	Small_Intestine_Terminal_Ileum	0.05	0.083 (0.004)
BMI	Small_Intestine_Terminal_Ileum	0.1	0.086 (0.004)
BMI	Small_Intestine_Terminal_Ileum	0.5	0.094 (0.004)
BMI	Small_Intestine_Terminal_Ileum	1	0.095 (0.004)
BMI	Small_Intestine_Terminal_Ileum	All	0.095 (0.004)
BMI	Spleen	1e.06	0.035 (0.004)
BMI	Spleen	1e.05	0.038 (0.004)
BMI	Spleen	1e.04	0.048 (0.004)
BMI	Spleen	0.001	0.064 (0.004)
BMI	Spleen	0.01	0.079 (0.004)
BMI	Spleen	0.05	0.088 (0.004)
BMI	Spleen	0.1	0.096 (0.004)
BMI	Spleen	0.5	0.105 (0.004)
BMI	Spleen	1	0.104 (0.004)
BMI	Spleen	All	0.108 (0.004)
BMI	Stomach	1e.06	0.032 (0.004)
BMI	Stomach	1e.05	0.038 (0.004)
BMI	Stomach	1e.04	0.045 (0.004)
BMI	Stomach	0.001	0.055 (0.004)
BMI	Stomach	0.01	0.074 (0.004)
BMI	Stomach	0.05	0.089 (0.004)
BMI	Stomach	0.1	0.095 (0.004)
BMI	Stomach	0.5	0.106 (0.004)
BMI	Stomach	1	0.106 (0.004)
BMI	Stomach	All	0.111 (0.004)
BMI	Testis	1e.06	0.035 (0.004)
BMI	Testis	1e.05	0.038 (0.004)
BMI	Testis	1e.04	0.047 (0.004)
BMI	Testis	0.001	0.059 (0.004)
BMI	Testis	0.01	0.078 (0.004)
BMI	Testis	0.05	0.099 (0.004)
BMI	Testis	0.1	0.108 (0.004)
BMI	Testis	0.5	0.118 (0.004)
BMI	Testis	1	0.119 (0.004)
BMI	Testis	All	0.125 (0.004)
BMI	Thyroid	1e.06	0.039 (0.004)
BMI	Thyroid	1e.05	0.047 (0.004)
BMI	Thyroid	1e.04	0.056 (0.004)
BMI	Thyroid	0.001	0.07 (0.004)
BMI	Thyroid	0.01	0.089 (0.004)
BMI	Thyroid	0.05	0.107 (0.004)
BMI	Thyroid	0.1	0.114 (0.004)
BMI	Thyroid	0.5	0.125 (0.004)
BMI	Thyroid	1	0.126 (0.004)
BMI	Thyroid	All	0.128 (0.004)
BMI	Uterus	1e.06	0.03 (0.004)
BMI	Uterus	1e.05	0.036 (0.004)
BMI	Uterus	1e.04	0.038 (0.004)
BMI	Uterus	0.001	0.046 (0.004)
BMI	Uterus	0.01	0.056 (0.004)
BMI	Uterus	0.05	0.068 (0.004)
BMI	Uterus	0.1	0.074 (0.004)
BMI	Uterus	0.5	0.081 (0.004)
BMI	Uterus	1	0.081 (0.004)
BMI	Uterus	All	0.083 (0.004)
BMI	Vagina	1e.06	0.035 (0.004)
BMI	Vagina	1e.05	0.037 (0.004)
BMI	Vagina	1e.04	0.04 (0.004)
BMI	Vagina	0.001	0.054 (0.004)
BMI	Vagina	0.01	0.065 (0.004)
BMI	Vagina	0.05	0.075 (0.004)
BMI	Vagina	0.1	0.079 (0.004)
BMI	Vagina	0.5	0.086 (0.004)
BMI	Vagina	1	0.087 (0.004)
BMI	Vagina	All	0.088 (0.004)
BMI	Whole_Blood	1e.06	0.038 (0.004)
BMI	Whole_Blood	1e.05	0.048 (0.004)
BMI	Whole_Blood	1e.04	0.053 (0.004)
BMI	Whole_Blood	0.001	0.065 (0.004)
BMI	Whole_Blood	0.01	0.08 (0.004)
BMI	Whole_Blood	0.05	0.091 (0.004)
BMI	Whole_Blood	0.1	0.096 (0.004)
BMI	Whole_Blood	0.5	0.106 (0.004)
BMI	Whole_Blood	1	0.108 (0.004)
BMI	Whole_Blood	All	0.11 (0.004)
BMI	YFS.BLOOD.RNAARR	1e.06	0.045 (0.004)
BMI	YFS.BLOOD.RNAARR	1e.05	0.05 (0.004)
BMI	YFS.BLOOD.RNAARR	1e.04	0.057 (0.004)
BMI	YFS.BLOOD.RNAARR	0.001	0.067 (0.004)
BMI	YFS.BLOOD.RNAARR	0.01	0.079 (0.004)
BMI	YFS.BLOOD.RNAARR	0.05	0.092 (0.004)
BMI	YFS.BLOOD.RNAARR	0.1	0.098 (0.004)
BMI	YFS.BLOOD.RNAARR	0.5	0.107 (0.004)
BMI	YFS.BLOOD.RNAARR	1	0.108 (0.004)
BMI	YFS.BLOOD.RNAARR	All	0.107 (0.004)
Height	Adipose_Subcutaneous	1e.06	0.143 (0.004)
Height	Adipose_Subcutaneous	1e.05	0.149 (0.004)
Height	Adipose_Subcutaneous	1e.04	0.155 (0.004)
Height	Adipose_Subcutaneous	0.001	0.16 (0.004)
Height	Adipose_Subcutaneous	0.01	0.169 (0.004)
Height	Adipose_Subcutaneous	0.05	0.174 (0.004)
Height	Adipose_Subcutaneous	0.1	0.176 (0.004)
Height	Adipose_Subcutaneous	0.5	0.178 (0.004)
Height	Adipose_Subcutaneous	1	0.178 (0.004)
Height	Adipose_Subcutaneous	All	0.179 (0.004)
Height	Adipose_Visceral_Omentum	1e.06	0.128 (0.004)
Height	Adipose_Visceral_Omentum	1e.05	0.135 (0.004)
Height	Adipose_Visceral_Omentum	1e.04	0.141 (0.004)
Height	Adipose_Visceral_Omentum	0.001	0.148 (0.004)
Height	Adipose_Visceral_Omentum	0.01	0.156 (0.004)
Height	Adipose_Visceral_Omentum	0.05	0.16 (0.004)
Height	Adipose_Visceral_Omentum	0.1	0.161 (0.004)
Height	Adipose_Visceral_Omentum	0.5	0.164 (0.004)
Height	Adipose_Visceral_Omentum	1	0.164 (0.004)
Height	Adipose_Visceral_Omentum	All	0.164 (0.004)
Height	Adrenal_Gland	1e.06	0.115 (0.004)
Height	Adrenal_Gland	1e.05	0.12 (0.004)
Height	Adrenal_Gland	1e.04	0.128 (0.004)
Height	Adrenal_Gland	0.001	0.134 (0.004)
Height	Adrenal_Gland	0.01	0.14 (0.004)
Height	Adrenal_Gland	0.05	0.144 (0.004)
Height	Adrenal_Gland	0.1	0.146 (0.004)
Height	Adrenal_Gland	0.5	0.148 (0.004)
Height	Adrenal_Gland	1	0.147 (0.004)
Height	Adrenal_Gland	All	0.148 (0.004)
Height	Artery_Aorta	1e.06	0.125 (0.004)
Height	Artery_Aorta	1e.05	0.132 (0.004)
Height	Artery_Aorta	1e.04	0.137 (0.004)
Height	Artery_Aorta	0.001	0.143 (0.004)
Height	Artery_Aorta	0.01	0.152 (0.004)
Height	Artery_Aorta	0.05	0.156 (0.004)
Height	Artery_Aorta	0.1	0.158 (0.004)
Height	Artery_Aorta	0.5	0.16 (0.004)
Height	Artery_Aorta	1	0.161 (0.004)
Height	Artery_Aorta	All	0.16 (0.004)
Height	Artery_Coronary	1e.06	0.105 (0.004)
Height	Artery_Coronary	1e.05	0.114 (0.004)
Height	Artery_Coronary	1e.04	0.118 (0.004)
Height	Artery_Coronary	0.001	0.123 (0.004)
Height	Artery_Coronary	0.01	0.128 (0.004)
Height	Artery_Coronary	0.05	0.131 (0.004)
Height	Artery_Coronary	0.1	0.133 (0.004)
Height	Artery_Coronary	0.5	0.135 (0.004)
Height	Artery_Coronary	1	0.135 (0.004)
Height	Artery_Coronary	All	0.136 (0.004)
Height	Artery_Tibial	1e.06	0.142 (0.004)
Height	Artery_Tibial	1e.05	0.149 (0.004)
Height	Artery_Tibial	1e.04	0.156 (0.004)
Height	Artery_Tibial	0.001	0.162 (0.004)
Height	Artery_Tibial	0.01	0.17 (0.004)
Height	Artery_Tibial	0.05	0.175 (0.004)
Height	Artery_Tibial	0.1	0.178 (0.004)
Height	Artery_Tibial	0.5	0.18 (0.004)
Height	Artery_Tibial	1	0.18 (0.004)
Height	Artery_Tibial	All	0.18 (0.004)
Height	Brain_Amygdala	1e.06	0.095 (0.004)
Height	Brain_Amygdala	1e.05	0.101 (0.004)
Height	Brain_Amygdala	1e.04	0.102 (0.004)
Height	Brain_Amygdala	0.001	0.105 (0.004)
Height	Brain_Amygdala	0.01	0.107 (0.004)
Height	Brain_Amygdala	0.05	0.112 (0.004)
Height	Brain_Amygdala	0.1	0.113 (0.004)
Height	Brain_Amygdala	0.5	0.113 (0.004)
Height	Brain_Amygdala	1	0.113 (0.004)
Height	Brain_Amygdala	All	0.114 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.101 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.105 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.109 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	0.001	0.112 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	0.01	0.118 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	0.05	0.122 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	0.1	0.124 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	0.5	0.126 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	1	0.126 (0.004)
Height	Brain_Anterior_cingulate_cortex_BA24	All	0.126 (0.004)
Height	Brain_Caudate_basal_ganglia	1e.06	0.102 (0.004)
Height	Brain_Caudate_basal_ganglia	1e.05	0.109 (0.004)
Height	Brain_Caudate_basal_ganglia	1e.04	0.113 (0.004)
Height	Brain_Caudate_basal_ganglia	0.001	0.116 (0.004)
Height	Brain_Caudate_basal_ganglia	0.01	0.125 (0.004)
Height	Brain_Caudate_basal_ganglia	0.05	0.129 (0.004)
Height	Brain_Caudate_basal_ganglia	0.1	0.131 (0.004)
Height	Brain_Caudate_basal_ganglia	0.5	0.132 (0.004)
Height	Brain_Caudate_basal_ganglia	1	0.132 (0.004)
Height	Brain_Caudate_basal_ganglia	All	0.132 (0.004)
Height	Brain_Cerebellar_Hemisphere	1e.06	0.108 (0.004)
Height	Brain_Cerebellar_Hemisphere	1e.05	0.116 (0.004)
Height	Brain_Cerebellar_Hemisphere	1e.04	0.117 (0.004)
Height	Brain_Cerebellar_Hemisphere	0.001	0.124 (0.004)
Height	Brain_Cerebellar_Hemisphere	0.01	0.13 (0.004)
Height	Brain_Cerebellar_Hemisphere	0.05	0.132 (0.004)
Height	Brain_Cerebellar_Hemisphere	0.1	0.134 (0.004)
Height	Brain_Cerebellar_Hemisphere	0.5	0.134 (0.004)
Height	Brain_Cerebellar_Hemisphere	1	0.134 (0.004)
Height	Brain_Cerebellar_Hemisphere	All	0.137 (0.004)
Height	Brain_Cerebellum	1e.06	0.112 (0.004)
Height	Brain_Cerebellum	1e.05	0.12 (0.004)
Height	Brain_Cerebellum	1e.04	0.124 (0.004)
Height	Brain_Cerebellum	0.001	0.128 (0.004)
Height	Brain_Cerebellum	0.01	0.134 (0.004)
Height	Brain_Cerebellum	0.05	0.14 (0.004)
Height	Brain_Cerebellum	0.1	0.142 (0.004)
Height	Brain_Cerebellum	0.5	0.145 (0.004)
Height	Brain_Cerebellum	1	0.145 (0.004)
Height	Brain_Cerebellum	All	0.146 (0.004)
Height	Brain_Cortex	1e.06	0.109 (0.004)
Height	Brain_Cortex	1e.05	0.115 (0.004)
Height	Brain_Cortex	1e.04	0.119 (0.004)
Height	Brain_Cortex	0.001	0.125 (0.004)
Height	Brain_Cortex	0.01	0.13 (0.004)
Height	Brain_Cortex	0.05	0.134 (0.004)
Height	Brain_Cortex	0.1	0.136 (0.004)
Height	Brain_Cortex	0.5	0.138 (0.004)
Height	Brain_Cortex	1	0.138 (0.004)
Height	Brain_Cortex	All	0.138 (0.004)
Height	Brain_Frontal_Cortex_BA9	1e.06	0.106 (0.004)
Height	Brain_Frontal_Cortex_BA9	1e.05	0.114 (0.004)
Height	Brain_Frontal_Cortex_BA9	1e.04	0.117 (0.004)
Height	Brain_Frontal_Cortex_BA9	0.001	0.12 (0.004)
Height	Brain_Frontal_Cortex_BA9	0.01	0.127 (0.004)
Height	Brain_Frontal_Cortex_BA9	0.05	0.131 (0.004)
Height	Brain_Frontal_Cortex_BA9	0.1	0.133 (0.004)
Height	Brain_Frontal_Cortex_BA9	0.5	0.134 (0.004)
Height	Brain_Frontal_Cortex_BA9	1	0.134 (0.004)
Height	Brain_Frontal_Cortex_BA9	All	0.135 (0.004)
Height	Brain_Hippocampus	1e.06	0.083 (0.004)
Height	Brain_Hippocampus	1e.05	0.089 (0.004)
Height	Brain_Hippocampus	1e.04	0.097 (0.004)
Height	Brain_Hippocampus	0.001	0.104 (0.004)
Height	Brain_Hippocampus	0.01	0.108 (0.004)
Height	Brain_Hippocampus	0.05	0.109 (0.004)
Height	Brain_Hippocampus	0.1	0.111 (0.004)
Height	Brain_Hippocampus	0.5	0.113 (0.004)
Height	Brain_Hippocampus	1	0.113 (0.004)
Height	Brain_Hippocampus	All	0.112 (0.004)
Height	Brain_Hypothalamus	1e.06	0.085 (0.004)
Height	Brain_Hypothalamus	1e.05	0.093 (0.004)
Height	Brain_Hypothalamus	1e.04	0.095 (0.004)
Height	Brain_Hypothalamus	0.001	0.1 (0.004)
Height	Brain_Hypothalamus	0.01	0.105 (0.004)
Height	Brain_Hypothalamus	0.05	0.11 (0.004)
Height	Brain_Hypothalamus	0.1	0.112 (0.004)
Height	Brain_Hypothalamus	0.5	0.113 (0.004)
Height	Brain_Hypothalamus	1	0.113 (0.004)
Height	Brain_Hypothalamus	All	0.113 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.1 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.105 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.105 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.111 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.114 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.118 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.12 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.122 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	1	0.122 (0.004)
Height	Brain_Nucleus_accumbens_basal_ganglia	All	0.125 (0.004)
Height	Brain_Putamen_basal_ganglia	1e.06	0.096 (0.004)
Height	Brain_Putamen_basal_ganglia	1e.05	0.101 (0.004)
Height	Brain_Putamen_basal_ganglia	1e.04	0.106 (0.004)
Height	Brain_Putamen_basal_ganglia	0.001	0.11 (0.004)
Height	Brain_Putamen_basal_ganglia	0.01	0.116 (0.004)
Height	Brain_Putamen_basal_ganglia	0.05	0.12 (0.004)
Height	Brain_Putamen_basal_ganglia	0.1	0.121 (0.004)
Height	Brain_Putamen_basal_ganglia	0.5	0.123 (0.004)
Height	Brain_Putamen_basal_ganglia	1	0.123 (0.004)
Height	Brain_Putamen_basal_ganglia	All	0.123 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	1e.06	0.091 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	1e.05	0.093 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	1e.04	0.098 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	0.001	0.103 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	0.01	0.106 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	0.05	0.109 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	0.1	0.111 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	0.5	0.112 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	1	0.111 (0.004)
Height	Brain_Spinal_cord_cervical_c-1	All	0.113 (0.004)
Height	Brain_Substantia_nigra	1e.06	0.083 (0.004)
Height	Brain_Substantia_nigra	1e.05	0.087 (0.004)
Height	Brain_Substantia_nigra	1e.04	0.092 (0.004)
Height	Brain_Substantia_nigra	0.001	0.096 (0.004)
Height	Brain_Substantia_nigra	0.01	0.097 (0.004)
Height	Brain_Substantia_nigra	0.05	0.099 (0.004)
Height	Brain_Substantia_nigra	0.1	0.099 (0.004)
Height	Brain_Substantia_nigra	0.5	0.1 (0.004)
Height	Brain_Substantia_nigra	1	0.1 (0.004)
Height	Brain_Substantia_nigra	All	0.101 (0.004)
Height	Breast_Mammary_Tissue	1e.06	0.123 (0.004)
Height	Breast_Mammary_Tissue	1e.05	0.129 (0.004)
Height	Breast_Mammary_Tissue	1e.04	0.135 (0.004)
Height	Breast_Mammary_Tissue	0.001	0.138 (0.004)
Height	Breast_Mammary_Tissue	0.01	0.145 (0.004)
Height	Breast_Mammary_Tissue	0.05	0.149 (0.004)
Height	Breast_Mammary_Tissue	0.1	0.15 (0.004)
Height	Breast_Mammary_Tissue	0.5	0.152 (0.004)
Height	Breast_Mammary_Tissue	1	0.152 (0.004)
Height	Breast_Mammary_Tissue	All	0.152 (0.004)
Height	Cells_EBV-transformed_lymphocytes	1e.06	0.101 (0.004)
Height	Cells_EBV-transformed_lymphocytes	1e.05	0.106 (0.004)
Height	Cells_EBV-transformed_lymphocytes	1e.04	0.111 (0.004)
Height	Cells_EBV-transformed_lymphocytes	0.001	0.113 (0.004)
Height	Cells_EBV-transformed_lymphocytes	0.01	0.119 (0.004)
Height	Cells_EBV-transformed_lymphocytes	0.05	0.125 (0.004)
Height	Cells_EBV-transformed_lymphocytes	0.1	0.127 (0.004)
Height	Cells_EBV-transformed_lymphocytes	0.5	0.13 (0.004)
Height	Cells_EBV-transformed_lymphocytes	1	0.13 (0.004)
Height	Cells_EBV-transformed_lymphocytes	All	0.13 (0.004)
Height	Cells_Transformed_fibroblasts	1e.06	0.126 (0.004)
Height	Cells_Transformed_fibroblasts	1e.05	0.136 (0.004)
Height	Cells_Transformed_fibroblasts	1e.04	0.144 (0.004)
Height	Cells_Transformed_fibroblasts	0.001	0.15 (0.004)
Height	Cells_Transformed_fibroblasts	0.01	0.16 (0.004)
Height	Cells_Transformed_fibroblasts	0.05	0.165 (0.004)
Height	Cells_Transformed_fibroblasts	0.1	0.167 (0.004)
Height	Cells_Transformed_fibroblasts	0.5	0.17 (0.004)
Height	Cells_Transformed_fibroblasts	1	0.17 (0.004)
Height	Cells_Transformed_fibroblasts	All	0.17 (0.004)
Height	CMC.BRAIN.RNASEQ	1e.06	0.125 (0.004)
Height	CMC.BRAIN.RNASEQ	1e.05	0.131 (0.004)
Height	CMC.BRAIN.RNASEQ	1e.04	0.139 (0.004)
Height	CMC.BRAIN.RNASEQ	0.001	0.147 (0.004)
Height	CMC.BRAIN.RNASEQ	0.01	0.155 (0.004)
Height	CMC.BRAIN.RNASEQ	0.05	0.162 (0.004)
Height	CMC.BRAIN.RNASEQ	0.1	0.164 (0.004)
Height	CMC.BRAIN.RNASEQ	0.5	0.165 (0.004)
Height	CMC.BRAIN.RNASEQ	1	0.165 (0.004)
Height	CMC.BRAIN.RNASEQ	All	0.165 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.123 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.126 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.131 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.135 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.141 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.144 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.145 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.145 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	1	0.144 (0.004)
Height	CMC.BRAIN.RNASEQ_SPLICING	All	0.147 (0.004)
Height	Colon_Sigmoid	1e.06	0.109 (0.004)
Height	Colon_Sigmoid	1e.05	0.112 (0.004)
Height	Colon_Sigmoid	1e.04	0.121 (0.004)
Height	Colon_Sigmoid	0.001	0.126 (0.004)
Height	Colon_Sigmoid	0.01	0.133 (0.004)
Height	Colon_Sigmoid	0.05	0.137 (0.004)
Height	Colon_Sigmoid	0.1	0.138 (0.004)
Height	Colon_Sigmoid	0.5	0.141 (0.004)
Height	Colon_Sigmoid	1	0.142 (0.004)
Height	Colon_Sigmoid	All	0.142 (0.004)
Height	Colon_Transverse	1e.06	0.113 (0.004)
Height	Colon_Transverse	1e.05	0.122 (0.004)
Height	Colon_Transverse	1e.04	0.129 (0.004)
Height	Colon_Transverse	0.001	0.135 (0.004)
Height	Colon_Transverse	0.01	0.142 (0.004)
Height	Colon_Transverse	0.05	0.147 (0.004)
Height	Colon_Transverse	0.1	0.149 (0.004)
Height	Colon_Transverse	0.5	0.149 (0.004)
Height	Colon_Transverse	1	0.149 (0.004)
Height	Colon_Transverse	All	0.149 (0.004)
Height	Esophagus_Gastroesophageal_Junction	1e.06	0.115 (0.004)
Height	Esophagus_Gastroesophageal_Junction	1e.05	0.122 (0.004)
Height	Esophagus_Gastroesophageal_Junction	1e.04	0.13 (0.004)
Height	Esophagus_Gastroesophageal_Junction	0.001	0.133 (0.004)
Height	Esophagus_Gastroesophageal_Junction	0.01	0.141 (0.004)
Height	Esophagus_Gastroesophageal_Junction	0.05	0.146 (0.004)
Height	Esophagus_Gastroesophageal_Junction	0.1	0.147 (0.004)
Height	Esophagus_Gastroesophageal_Junction	0.5	0.149 (0.004)
Height	Esophagus_Gastroesophageal_Junction	1	0.15 (0.004)
Height	Esophagus_Gastroesophageal_Junction	All	0.149 (0.004)
Height	Esophagus_Mucosa	1e.06	0.132 (0.004)
Height	Esophagus_Mucosa	1e.05	0.141 (0.004)
Height	Esophagus_Mucosa	1e.04	0.149 (0.004)
Height	Esophagus_Mucosa	0.001	0.158 (0.004)
Height	Esophagus_Mucosa	0.01	0.167 (0.004)
Height	Esophagus_Mucosa	0.05	0.172 (0.004)
Height	Esophagus_Mucosa	0.1	0.174 (0.004)
Height	Esophagus_Mucosa	0.5	0.176 (0.004)
Height	Esophagus_Mucosa	1	0.176 (0.004)
Height	Esophagus_Mucosa	All	0.176 (0.004)
Height	Esophagus_Muscularis	1e.06	0.129 (0.004)
Height	Esophagus_Muscularis	1e.05	0.139 (0.004)
Height	Esophagus_Muscularis	1e.04	0.146 (0.004)
Height	Esophagus_Muscularis	0.001	0.152 (0.004)
Height	Esophagus_Muscularis	0.01	0.16 (0.004)
Height	Esophagus_Muscularis	0.05	0.165 (0.004)
Height	Esophagus_Muscularis	0.1	0.168 (0.004)
Height	Esophagus_Muscularis	0.5	0.17 (0.004)
Height	Esophagus_Muscularis	1	0.169 (0.004)
Height	Esophagus_Muscularis	All	0.169 (0.004)
Height	Heart_Atrial_Appendage	1e.06	0.115 (0.004)
Height	Heart_Atrial_Appendage	1e.05	0.124 (0.004)
Height	Heart_Atrial_Appendage	1e.04	0.132 (0.004)
Height	Heart_Atrial_Appendage	0.001	0.138 (0.004)
Height	Heart_Atrial_Appendage	0.01	0.145 (0.004)
Height	Heart_Atrial_Appendage	0.05	0.153 (0.004)
Height	Heart_Atrial_Appendage	0.1	0.155 (0.004)
Height	Heart_Atrial_Appendage	0.5	0.156 (0.004)
Height	Heart_Atrial_Appendage	1	0.156 (0.004)
Height	Heart_Atrial_Appendage	All	0.156 (0.004)
Height	Heart_Left_Ventricle	1e.06	0.109 (0.004)
Height	Heart_Left_Ventricle	1e.05	0.116 (0.004)
Height	Heart_Left_Ventricle	1e.04	0.124 (0.004)
Height	Heart_Left_Ventricle	0.001	0.13 (0.004)
Height	Heart_Left_Ventricle	0.01	0.136 (0.004)
Height	Heart_Left_Ventricle	0.05	0.142 (0.004)
Height	Heart_Left_Ventricle	0.1	0.145 (0.004)
Height	Heart_Left_Ventricle	0.5	0.148 (0.004)
Height	Heart_Left_Ventricle	1	0.148 (0.004)
Height	Heart_Left_Ventricle	All	0.148 (0.004)
Height	Liver	1e.06	0.104 (0.004)
Height	Liver	1e.05	0.109 (0.004)
Height	Liver	1e.04	0.119 (0.004)
Height	Liver	0.001	0.121 (0.004)
Height	Liver	0.01	0.126 (0.004)
Height	Liver	0.05	0.129 (0.004)
Height	Liver	0.1	0.13 (0.004)
Height	Liver	0.5	0.132 (0.004)
Height	Liver	1	0.132 (0.004)
Height	Liver	All	0.134 (0.004)
Height	Lung	1e.06	0.13 (0.004)
Height	Lung	1e.05	0.136 (0.004)
Height	Lung	1e.04	0.144 (0.004)
Height	Lung	0.001	0.15 (0.004)
Height	Lung	0.01	0.159 (0.004)
Height	Lung	0.05	0.165 (0.004)
Height	Lung	0.1	0.167 (0.004)
Height	Lung	0.5	0.169 (0.004)
Height	Lung	1	0.169 (0.004)
Height	Lung	All	0.169 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	1e.06	0.118 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	1e.05	0.125 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	1e.04	0.132 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	0.001	0.137 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	0.01	0.144 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	0.05	0.149 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	0.1	0.15 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	0.5	0.152 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	1	0.152 (0.004)
Height	METSIM.ADIPOSE.RNASEQ	All	0.152 (0.004)
Height	Minor_Salivary_Gland	1e.06	0.084 (0.004)
Height	Minor_Salivary_Gland	1e.05	0.09 (0.004)
Height	Minor_Salivary_Gland	1e.04	0.093 (0.004)
Height	Minor_Salivary_Gland	0.001	0.098 (0.004)
Height	Minor_Salivary_Gland	0.01	0.102 (0.004)
Height	Minor_Salivary_Gland	0.05	0.108 (0.004)
Height	Minor_Salivary_Gland	0.1	0.107 (0.004)
Height	Minor_Salivary_Gland	0.5	0.109 (0.004)
Height	Minor_Salivary_Gland	1	0.108 (0.004)
Height	Minor_Salivary_Gland	All	0.109 (0.004)
Height	Muscle_Skeletal	1e.06	0.126 (0.004)
Height	Muscle_Skeletal	1e.05	0.135 (0.004)
Height	Muscle_Skeletal	1e.04	0.142 (0.004)
Height	Muscle_Skeletal	0.001	0.149 (0.004)
Height	Muscle_Skeletal	0.01	0.157 (0.004)
Height	Muscle_Skeletal	0.05	0.164 (0.004)
Height	Muscle_Skeletal	0.1	0.166 (0.004)
Height	Muscle_Skeletal	0.5	0.169 (0.004)
Height	Muscle_Skeletal	1	0.168 (0.004)
Height	Muscle_Skeletal	All	0.168 (0.004)
Height	Nerve_Tibial	1e.06	0.144 (0.004)
Height	Nerve_Tibial	1e.05	0.149 (0.004)
Height	Nerve_Tibial	1e.04	0.156 (0.004)
Height	Nerve_Tibial	0.001	0.163 (0.004)
Height	Nerve_Tibial	0.01	0.173 (0.004)
Height	Nerve_Tibial	0.05	0.179 (0.004)
Height	Nerve_Tibial	0.1	0.181 (0.004)
Height	Nerve_Tibial	0.5	0.182 (0.004)
Height	Nerve_Tibial	1	0.182 (0.004)
Height	Nerve_Tibial	All	0.182 (0.004)
Height	NTR.BLOOD.RNAARR	1e.06	0.108 (0.004)
Height	NTR.BLOOD.RNAARR	1e.05	0.113 (0.004)
Height	NTR.BLOOD.RNAARR	1e.04	0.119 (0.004)
Height	NTR.BLOOD.RNAARR	0.001	0.124 (0.004)
Height	NTR.BLOOD.RNAARR	0.01	0.131 (0.004)
Height	NTR.BLOOD.RNAARR	0.05	0.136 (0.004)
Height	NTR.BLOOD.RNAARR	0.1	0.137 (0.004)
Height	NTR.BLOOD.RNAARR	0.5	0.138 (0.004)
Height	NTR.BLOOD.RNAARR	1	0.138 (0.004)
Height	NTR.BLOOD.RNAARR	All	0.139 (0.004)
Height	Ovary	1e.06	0.084 (0.004)
Height	Ovary	1e.05	0.092 (0.004)
Height	Ovary	1e.04	0.096 (0.004)
Height	Ovary	0.001	0.103 (0.004)
Height	Ovary	0.01	0.111 (0.004)
Height	Ovary	0.05	0.116 (0.004)
Height	Ovary	0.1	0.118 (0.004)
Height	Ovary	0.5	0.12 (0.004)
Height	Ovary	1	0.12 (0.004)
Height	Ovary	All	0.12 (0.004)
Height	Pancreas	1e.06	0.117 (0.004)
Height	Pancreas	1e.05	0.125 (0.004)
Height	Pancreas	1e.04	0.131 (0.004)
Height	Pancreas	0.001	0.137 (0.004)
Height	Pancreas	0.01	0.146 (0.004)
Height	Pancreas	0.05	0.153 (0.004)
Height	Pancreas	0.1	0.154 (0.004)
Height	Pancreas	0.5	0.154 (0.004)
Height	Pancreas	1	0.155 (0.004)
Height	Pancreas	All	0.155 (0.004)
Height	Pituitary	1e.06	0.113 (0.004)
Height	Pituitary	1e.05	0.121 (0.004)
Height	Pituitary	1e.04	0.124 (0.004)
Height	Pituitary	0.001	0.131 (0.004)
Height	Pituitary	0.01	0.136 (0.004)
Height	Pituitary	0.05	0.139 (0.004)
Height	Pituitary	0.1	0.141 (0.004)
Height	Pituitary	0.5	0.142 (0.004)
Height	Pituitary	1	0.142 (0.004)
Height	Pituitary	All	0.143 (0.004)
Height	Prostate	1e.06	0.08 (0.004)
Height	Prostate	1e.05	0.087 (0.004)
Height	Prostate	1e.04	0.095 (0.004)
Height	Prostate	0.001	0.099 (0.004)
Height	Prostate	0.01	0.106 (0.004)
Height	Prostate	0.05	0.11 (0.004)
Height	Prostate	0.1	0.111 (0.004)
Height	Prostate	0.5	0.114 (0.004)
Height	Prostate	1	0.113 (0.004)
Height	Prostate	All	0.113 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.128 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.136 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.143 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.149 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.157 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.164 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.165 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.168 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	1	0.168 (0.004)
Height	Skin_Not_Sun_Exposed_Suprapubic	All	0.167 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	1e.06	0.137 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	1e.05	0.145 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	1e.04	0.152 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	0.001	0.156 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	0.01	0.167 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	0.05	0.172 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	0.1	0.173 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	0.5	0.174 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	1	0.175 (0.004)
Height	Skin_Sun_Exposed_Lower_leg	All	0.175 (0.004)
Height	Small_Intestine_Terminal_Ileum	1e.06	0.098 (0.004)
Height	Small_Intestine_Terminal_Ileum	1e.05	0.103 (0.004)
Height	Small_Intestine_Terminal_Ileum	1e.04	0.104 (0.004)
Height	Small_Intestine_Terminal_Ileum	0.001	0.113 (0.004)
Height	Small_Intestine_Terminal_Ileum	0.01	0.116 (0.004)
Height	Small_Intestine_Terminal_Ileum	0.05	0.12 (0.004)
Height	Small_Intestine_Terminal_Ileum	0.1	0.121 (0.004)
Height	Small_Intestine_Terminal_Ileum	0.5	0.125 (0.004)
Height	Small_Intestine_Terminal_Ileum	1	0.125 (0.004)
Height	Small_Intestine_Terminal_Ileum	All	0.126 (0.004)
Height	Spleen	1e.06	0.115 (0.004)
Height	Spleen	1e.05	0.121 (0.004)
Height	Spleen	1e.04	0.126 (0.004)
Height	Spleen	0.001	0.13 (0.004)
Height	Spleen	0.01	0.138 (0.004)
Height	Spleen	0.05	0.141 (0.004)
Height	Spleen	0.1	0.142 (0.004)
Height	Spleen	0.5	0.143 (0.004)
Height	Spleen	1	0.143 (0.004)
Height	Spleen	All	0.145 (0.004)
Height	Stomach	1e.06	0.117 (0.004)
Height	Stomach	1e.05	0.123 (0.004)
Height	Stomach	1e.04	0.132 (0.004)
Height	Stomach	0.001	0.137 (0.004)
Height	Stomach	0.01	0.143 (0.004)
Height	Stomach	0.05	0.147 (0.004)
Height	Stomach	0.1	0.148 (0.004)
Height	Stomach	0.5	0.151 (0.004)
Height	Stomach	1	0.15 (0.004)
Height	Stomach	All	0.151 (0.004)
Height	Testis	1e.06	0.138 (0.004)
Height	Testis	1e.05	0.145 (0.004)
Height	Testis	1e.04	0.152 (0.004)
Height	Testis	0.001	0.159 (0.004)
Height	Testis	0.01	0.165 (0.004)
Height	Testis	0.05	0.169 (0.004)
Height	Testis	0.1	0.171 (0.004)
Height	Testis	0.5	0.174 (0.004)
Height	Testis	1	0.174 (0.004)
Height	Testis	All	0.176 (0.004)
Height	Thyroid	1e.06	0.141 (0.004)
Height	Thyroid	1e.05	0.148 (0.004)
Height	Thyroid	1e.04	0.155 (0.004)
Height	Thyroid	0.001	0.161 (0.004)
Height	Thyroid	0.01	0.17 (0.004)
Height	Thyroid	0.05	0.175 (0.004)
Height	Thyroid	0.1	0.177 (0.004)
Height	Thyroid	0.5	0.181 (0.004)
Height	Thyroid	1	0.181 (0.004)
Height	Thyroid	All	0.181 (0.004)
Height	Uterus	1e.06	0.074 (0.004)
Height	Uterus	1e.05	0.08 (0.004)
Height	Uterus	1e.04	0.084 (0.004)
Height	Uterus	0.001	0.091 (0.004)
Height	Uterus	0.01	0.099 (0.004)
Height	Uterus	0.05	0.102 (0.004)
Height	Uterus	0.1	0.104 (0.004)
Height	Uterus	0.5	0.107 (0.004)
Height	Uterus	1	0.107 (0.004)
Height	Uterus	All	0.107 (0.004)
Height	Vagina	1e.06	0.075 (0.004)
Height	Vagina	1e.05	0.081 (0.004)
Height	Vagina	1e.04	0.09 (0.004)
Height	Vagina	0.001	0.091 (0.004)
Height	Vagina	0.01	0.098 (0.004)
Height	Vagina	0.05	0.104 (0.004)
Height	Vagina	0.1	0.106 (0.004)
Height	Vagina	0.5	0.107 (0.004)
Height	Vagina	1	0.108 (0.004)
Height	Vagina	All	0.109 (0.004)
Height	Whole_Blood	1e.06	0.125 (0.004)
Height	Whole_Blood	1e.05	0.133 (0.004)
Height	Whole_Blood	1e.04	0.139 (0.004)
Height	Whole_Blood	0.001	0.148 (0.004)
Height	Whole_Blood	0.01	0.155 (0.004)
Height	Whole_Blood	0.05	0.159 (0.004)
Height	Whole_Blood	0.1	0.16 (0.004)
Height	Whole_Blood	0.5	0.162 (0.004)
Height	Whole_Blood	1	0.162 (0.004)
Height	Whole_Blood	All	0.162 (0.004)
Height	YFS.BLOOD.RNAARR	1e.06	0.124 (0.004)
Height	YFS.BLOOD.RNAARR	1e.05	0.132 (0.004)
Height	YFS.BLOOD.RNAARR	1e.04	0.138 (0.004)
Height	YFS.BLOOD.RNAARR	0.001	0.146 (0.004)
Height	YFS.BLOOD.RNAARR	0.01	0.152 (0.004)
Height	YFS.BLOOD.RNAARR	0.05	0.16 (0.004)
Height	YFS.BLOOD.RNAARR	0.1	0.162 (0.004)
Height	YFS.BLOOD.RNAARR	0.5	0.163 (0.004)
Height	YFS.BLOOD.RNAARR	1	0.164 (0.004)
Height	YFS.BLOOD.RNAARR	All	0.164 (0.004)
T2D	Adipose_Subcutaneous	1e.06	0.064 (0.004)
T2D	Adipose_Subcutaneous	1e.05	0.069 (0.004)
T2D	Adipose_Subcutaneous	1e.04	0.066 (0.004)
T2D	Adipose_Subcutaneous	0.001	0.069 (0.004)
T2D	Adipose_Subcutaneous	0.01	0.082 (0.004)
T2D	Adipose_Subcutaneous	0.05	0.087 (0.004)
T2D	Adipose_Subcutaneous	0.1	0.092 (0.004)
T2D	Adipose_Subcutaneous	0.5	0.096 (0.004)
T2D	Adipose_Subcutaneous	1	0.096 (0.004)
T2D	Adipose_Subcutaneous	All	0.11 (0.004)
T2D	Adipose_Visceral_Omentum	1e.06	0.039 (0.004)
T2D	Adipose_Visceral_Omentum	1e.05	0.039 (0.004)
T2D	Adipose_Visceral_Omentum	1e.04	0.053 (0.004)
T2D	Adipose_Visceral_Omentum	0.001	0.06 (0.004)
T2D	Adipose_Visceral_Omentum	0.01	0.066 (0.004)
T2D	Adipose_Visceral_Omentum	0.05	0.074 (0.004)
T2D	Adipose_Visceral_Omentum	0.1	0.074 (0.004)
T2D	Adipose_Visceral_Omentum	0.5	0.081 (0.004)
T2D	Adipose_Visceral_Omentum	1	0.081 (0.004)
T2D	Adipose_Visceral_Omentum	All	0.088 (0.004)
T2D	Adrenal_Gland	1e.06	0.042 (0.004)
T2D	Adrenal_Gland	1e.05	0.044 (0.004)
T2D	Adrenal_Gland	1e.04	0.049 (0.004)
T2D	Adrenal_Gland	0.001	0.053 (0.004)
T2D	Adrenal_Gland	0.01	0.06 (0.004)
T2D	Adrenal_Gland	0.05	0.07 (0.004)
T2D	Adrenal_Gland	0.1	0.075 (0.004)
T2D	Adrenal_Gland	0.5	0.075 (0.004)
T2D	Adrenal_Gland	1	0.075 (0.004)
T2D	Adrenal_Gland	All	0.082 (0.004)
T2D	Artery_Aorta	1e.06	0.062 (0.004)
T2D	Artery_Aorta	1e.05	0.067 (0.004)
T2D	Artery_Aorta	1e.04	0.068 (0.004)
T2D	Artery_Aorta	0.001	0.068 (0.004)
T2D	Artery_Aorta	0.01	0.08 (0.004)
T2D	Artery_Aorta	0.05	0.084 (0.004)
T2D	Artery_Aorta	0.1	0.084 (0.004)
T2D	Artery_Aorta	0.5	0.086 (0.004)
T2D	Artery_Aorta	1	0.085 (0.004)
T2D	Artery_Aorta	All	0.101 (0.004)
T2D	Artery_Coronary	1e.06	0.01 (0.004)
T2D	Artery_Coronary	1e.05	0.03 (0.004)
T2D	Artery_Coronary	1e.04	0.033 (0.004)
T2D	Artery_Coronary	0.001	0.045 (0.004)
T2D	Artery_Coronary	0.01	0.054 (0.004)
T2D	Artery_Coronary	0.05	0.064 (0.004)
T2D	Artery_Coronary	0.1	0.066 (0.004)
T2D	Artery_Coronary	0.5	0.065 (0.004)
T2D	Artery_Coronary	1	0.064 (0.004)
T2D	Artery_Coronary	All	0.069 (0.004)
T2D	Artery_Tibial	1e.06	0.045 (0.004)
T2D	Artery_Tibial	1e.05	0.054 (0.004)
T2D	Artery_Tibial	1e.04	0.062 (0.004)
T2D	Artery_Tibial	0.001	0.068 (0.004)
T2D	Artery_Tibial	0.01	0.084 (0.004)
T2D	Artery_Tibial	0.05	0.093 (0.004)
T2D	Artery_Tibial	0.1	0.096 (0.004)
T2D	Artery_Tibial	0.5	0.1 (0.004)
T2D	Artery_Tibial	1	0.099 (0.004)
T2D	Artery_Tibial	All	0.107 (0.004)
T2D	Brain_Amygdala	1e.06	0.024 (0.004)
T2D	Brain_Amygdala	1e.05	0.034 (0.004)
T2D	Brain_Amygdala	1e.04	0.037 (0.004)
T2D	Brain_Amygdala	0.001	0.037 (0.004)
T2D	Brain_Amygdala	0.01	0.053 (0.004)
T2D	Brain_Amygdala	0.05	0.058 (0.004)
T2D	Brain_Amygdala	0.1	0.061 (0.004)
T2D	Brain_Amygdala	0.5	0.062 (0.004)
T2D	Brain_Amygdala	1	0.062 (0.004)
T2D	Brain_Amygdala	All	0.067 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.026 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.028 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.034 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	0.001	0.043 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	0.01	0.058 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	0.05	0.066 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	0.1	0.065 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	0.5	0.062 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	1	0.062 (0.004)
T2D	Brain_Anterior_cingulate_cortex_BA24	All	0.069 (0.004)
T2D	Brain_Caudate_basal_ganglia	1e.05	0.022 (0.004)
T2D	Brain_Caudate_basal_ganglia	1e.04	0.035 (0.004)
T2D	Brain_Caudate_basal_ganglia	0.001	0.041 (0.004)
T2D	Brain_Caudate_basal_ganglia	0.01	0.055 (0.004)
T2D	Brain_Caudate_basal_ganglia	0.05	0.062 (0.004)
T2D	Brain_Caudate_basal_ganglia	0.1	0.061 (0.004)
T2D	Brain_Caudate_basal_ganglia	0.5	0.067 (0.004)
T2D	Brain_Caudate_basal_ganglia	1	0.066 (0.004)
T2D	Brain_Caudate_basal_ganglia	All	0.07 (0.004)
T2D	Brain_Cerebellar_Hemisphere	1e.06	0.038 (0.004)
T2D	Brain_Cerebellar_Hemisphere	1e.05	0.046 (0.004)
T2D	Brain_Cerebellar_Hemisphere	1e.04	0.046 (0.004)
T2D	Brain_Cerebellar_Hemisphere	0.001	0.056 (0.004)
T2D	Brain_Cerebellar_Hemisphere	0.01	0.066 (0.004)
T2D	Brain_Cerebellar_Hemisphere	0.05	0.07 (0.004)
T2D	Brain_Cerebellar_Hemisphere	0.1	0.07 (0.004)
T2D	Brain_Cerebellar_Hemisphere	0.5	0.071 (0.004)
T2D	Brain_Cerebellar_Hemisphere	1	0.071 (0.004)
T2D	Brain_Cerebellar_Hemisphere	All	0.081 (0.004)
T2D	Brain_Cerebellum	1e.06	0.03 (0.004)
T2D	Brain_Cerebellum	1e.05	0.039 (0.004)
T2D	Brain_Cerebellum	1e.04	0.048 (0.004)
T2D	Brain_Cerebellum	0.001	0.058 (0.004)
T2D	Brain_Cerebellum	0.01	0.065 (0.004)
T2D	Brain_Cerebellum	0.05	0.074 (0.004)
T2D	Brain_Cerebellum	0.1	0.078 (0.004)
T2D	Brain_Cerebellum	0.5	0.078 (0.004)
T2D	Brain_Cerebellum	1	0.079 (0.004)
T2D	Brain_Cerebellum	All	0.084 (0.004)
T2D	Brain_Cortex	1e.06	0.016 (0.004)
T2D	Brain_Cortex	1e.05	0.024 (0.004)
T2D	Brain_Cortex	1e.04	0.039 (0.004)
T2D	Brain_Cortex	0.001	0.038 (0.004)
T2D	Brain_Cortex	0.01	0.054 (0.004)
T2D	Brain_Cortex	0.05	0.059 (0.004)
T2D	Brain_Cortex	0.1	0.06 (0.004)
T2D	Brain_Cortex	0.5	0.068 (0.004)
T2D	Brain_Cortex	1	0.068 (0.004)
T2D	Brain_Cortex	All	0.07 (0.004)
T2D	Brain_Frontal_Cortex_BA9	1e.06	0.018 (0.004)
T2D	Brain_Frontal_Cortex_BA9	1e.05	0.018 (0.004)
T2D	Brain_Frontal_Cortex_BA9	1e.04	0.034 (0.004)
T2D	Brain_Frontal_Cortex_BA9	0.001	0.038 (0.004)
T2D	Brain_Frontal_Cortex_BA9	0.01	0.054 (0.004)
T2D	Brain_Frontal_Cortex_BA9	0.05	0.059 (0.004)
T2D	Brain_Frontal_Cortex_BA9	0.1	0.064 (0.004)
T2D	Brain_Frontal_Cortex_BA9	0.5	0.068 (0.004)
T2D	Brain_Frontal_Cortex_BA9	1	0.067 (0.004)
T2D	Brain_Frontal_Cortex_BA9	All	0.071 (0.004)
T2D	Brain_Hippocampus	1e.06	0.012 (0.004)
T2D	Brain_Hippocampus	1e.05	0.023 (0.004)
T2D	Brain_Hippocampus	1e.04	0.025 (0.004)
T2D	Brain_Hippocampus	0.001	0.038 (0.004)
T2D	Brain_Hippocampus	0.01	0.045 (0.004)
T2D	Brain_Hippocampus	0.05	0.055 (0.004)
T2D	Brain_Hippocampus	0.1	0.054 (0.004)
T2D	Brain_Hippocampus	0.5	0.058 (0.004)
T2D	Brain_Hippocampus	1	0.057 (0.004)
T2D	Brain_Hippocampus	All	0.062 (0.004)
T2D	Brain_Hypothalamus	1e.06	0.029 (0.004)
T2D	Brain_Hypothalamus	1e.05	0.029 (0.004)
T2D	Brain_Hypothalamus	1e.04	0.029 (0.004)
T2D	Brain_Hypothalamus	0.001	0.039 (0.004)
T2D	Brain_Hypothalamus	0.01	0.048 (0.004)
T2D	Brain_Hypothalamus	0.05	0.05 (0.004)
T2D	Brain_Hypothalamus	0.1	0.055 (0.004)
T2D	Brain_Hypothalamus	0.5	0.057 (0.004)
T2D	Brain_Hypothalamus	1	0.056 (0.004)
T2D	Brain_Hypothalamus	All	0.061 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.025 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.037 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.043 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.06 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.06 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.062 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.068 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	1	0.067 (0.004)
T2D	Brain_Nucleus_accumbens_basal_ganglia	All	0.072 (0.004)
T2D	Brain_Putamen_basal_ganglia	1e.06	0.015 (0.004)
T2D	Brain_Putamen_basal_ganglia	1e.05	0.02 (0.004)
T2D	Brain_Putamen_basal_ganglia	1e.04	0.028 (0.004)
T2D	Brain_Putamen_basal_ganglia	0.001	0.037 (0.004)
T2D	Brain_Putamen_basal_ganglia	0.01	0.049 (0.004)
T2D	Brain_Putamen_basal_ganglia	0.05	0.053 (0.004)
T2D	Brain_Putamen_basal_ganglia	0.1	0.059 (0.004)
T2D	Brain_Putamen_basal_ganglia	0.5	0.065 (0.004)
T2D	Brain_Putamen_basal_ganglia	1	0.065 (0.004)
T2D	Brain_Putamen_basal_ganglia	All	0.067 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	1e.06	0.011 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	1e.05	0.014 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	1e.04	0.032 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	0.001	0.038 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	0.01	0.05 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	0.05	0.057 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	0.1	0.056 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	0.5	0.056 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	1	0.056 (0.004)
T2D	Brain_Spinal_cord_cervical_c-1	All	0.061 (0.004)
T2D	Brain_Substantia_nigra	1e.06	0.021 (0.004)
T2D	Brain_Substantia_nigra	1e.05	0.022 (0.004)
T2D	Brain_Substantia_nigra	1e.04	0.026 (0.004)
T2D	Brain_Substantia_nigra	0.001	0.03 (0.004)
T2D	Brain_Substantia_nigra	0.01	0.032 (0.004)
T2D	Brain_Substantia_nigra	0.05	0.041 (0.004)
T2D	Brain_Substantia_nigra	0.1	0.04 (0.004)
T2D	Brain_Substantia_nigra	0.5	0.045 (0.004)
T2D	Brain_Substantia_nigra	1	0.047 (0.004)
T2D	Brain_Substantia_nigra	All	0.049 (0.004)
T2D	Breast_Mammary_Tissue	1e.06	0.046 (0.004)
T2D	Breast_Mammary_Tissue	1e.05	0.051 (0.004)
T2D	Breast_Mammary_Tissue	1e.04	0.054 (0.004)
T2D	Breast_Mammary_Tissue	0.001	0.061 (0.004)
T2D	Breast_Mammary_Tissue	0.01	0.074 (0.004)
T2D	Breast_Mammary_Tissue	0.05	0.076 (0.004)
T2D	Breast_Mammary_Tissue	0.1	0.078 (0.004)
T2D	Breast_Mammary_Tissue	0.5	0.082 (0.004)
T2D	Breast_Mammary_Tissue	1	0.08 (0.004)
T2D	Breast_Mammary_Tissue	All	0.088 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	1e.06	0.018 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	1e.05	0.027 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	1e.04	0.032 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	0.001	0.036 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	0.01	0.047 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	0.05	0.058 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	0.1	0.057 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	0.5	0.06 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	1	0.061 (0.004)
T2D	Cells_EBV-transformed_lymphocytes	All	0.063 (0.004)
T2D	Cells_Transformed_fibroblasts	1e.06	0.049 (0.004)
T2D	Cells_Transformed_fibroblasts	1e.05	0.056 (0.004)
T2D	Cells_Transformed_fibroblasts	1e.04	0.065 (0.004)
T2D	Cells_Transformed_fibroblasts	0.001	0.07 (0.004)
T2D	Cells_Transformed_fibroblasts	0.01	0.078 (0.004)
T2D	Cells_Transformed_fibroblasts	0.05	0.086 (0.004)
T2D	Cells_Transformed_fibroblasts	0.1	0.09 (0.004)
T2D	Cells_Transformed_fibroblasts	0.5	0.097 (0.004)
T2D	Cells_Transformed_fibroblasts	1	0.098 (0.004)
T2D	Cells_Transformed_fibroblasts	All	0.104 (0.004)
T2D	CMC.BRAIN.RNASEQ	1e.06	0.045 (0.004)
T2D	CMC.BRAIN.RNASEQ	1e.05	0.056 (0.004)
T2D	CMC.BRAIN.RNASEQ	1e.04	0.059 (0.004)
T2D	CMC.BRAIN.RNASEQ	0.001	0.069 (0.004)
T2D	CMC.BRAIN.RNASEQ	0.01	0.077 (0.004)
T2D	CMC.BRAIN.RNASEQ	0.05	0.086 (0.004)
T2D	CMC.BRAIN.RNASEQ	0.1	0.091 (0.004)
T2D	CMC.BRAIN.RNASEQ	0.5	0.097 (0.004)
T2D	CMC.BRAIN.RNASEQ	1	0.096 (0.004)
T2D	CMC.BRAIN.RNASEQ	All	0.104 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.029 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.044 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.049 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.056 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.062 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.071 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.073 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.076 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	1	0.075 (0.004)
T2D	CMC.BRAIN.RNASEQ_SPLICING	All	0.084 (0.004)
T2D	Colon_Sigmoid	1e.06	0.03 (0.004)
T2D	Colon_Sigmoid	1e.05	0.033 (0.004)
T2D	Colon_Sigmoid	1e.04	0.037 (0.004)
T2D	Colon_Sigmoid	0.001	0.053 (0.004)
T2D	Colon_Sigmoid	0.01	0.059 (0.004)
T2D	Colon_Sigmoid	0.05	0.066 (0.004)
T2D	Colon_Sigmoid	0.1	0.068 (0.004)
T2D	Colon_Sigmoid	0.5	0.072 (0.004)
T2D	Colon_Sigmoid	1	0.071 (0.004)
T2D	Colon_Sigmoid	All	0.076 (0.004)
T2D	Colon_Transverse	1e.06	0.014 (0.004)
T2D	Colon_Transverse	1e.05	0.031 (0.004)
T2D	Colon_Transverse	1e.04	0.035 (0.004)
T2D	Colon_Transverse	0.001	0.04 (0.004)
T2D	Colon_Transverse	0.01	0.057 (0.004)
T2D	Colon_Transverse	0.05	0.072 (0.004)
T2D	Colon_Transverse	0.1	0.077 (0.004)
T2D	Colon_Transverse	0.5	0.08 (0.004)
T2D	Colon_Transverse	1	0.079 (0.004)
T2D	Colon_Transverse	All	0.083 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	1e.06	0.04 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	1e.05	0.042 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	1e.04	0.041 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	0.001	0.051 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	0.01	0.063 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	0.05	0.071 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	0.1	0.077 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	0.5	0.078 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	1	0.078 (0.004)
T2D	Esophagus_Gastroesophageal_Junction	All	0.085 (0.004)
T2D	Esophagus_Mucosa	1e.06	0.022 (0.004)
T2D	Esophagus_Mucosa	1e.05	0.032 (0.004)
T2D	Esophagus_Mucosa	1e.04	0.042 (0.004)
T2D	Esophagus_Mucosa	0.001	0.057 (0.004)
T2D	Esophagus_Mucosa	0.01	0.066 (0.004)
T2D	Esophagus_Mucosa	0.05	0.073 (0.004)
T2D	Esophagus_Mucosa	0.1	0.076 (0.004)
T2D	Esophagus_Mucosa	0.5	0.081 (0.004)
T2D	Esophagus_Mucosa	1	0.081 (0.004)
T2D	Esophagus_Mucosa	All	0.085 (0.004)
T2D	Esophagus_Muscularis	1e.06	0.043 (0.004)
T2D	Esophagus_Muscularis	1e.05	0.047 (0.004)
T2D	Esophagus_Muscularis	1e.04	0.056 (0.004)
T2D	Esophagus_Muscularis	0.001	0.065 (0.004)
T2D	Esophagus_Muscularis	0.01	0.077 (0.004)
T2D	Esophagus_Muscularis	0.05	0.083 (0.004)
T2D	Esophagus_Muscularis	0.1	0.084 (0.004)
T2D	Esophagus_Muscularis	0.5	0.091 (0.004)
T2D	Esophagus_Muscularis	1	0.09 (0.004)
T2D	Esophagus_Muscularis	All	0.097 (0.004)
T2D	Heart_Atrial_Appendage	1e.06	0.044 (0.004)
T2D	Heart_Atrial_Appendage	1e.05	0.053 (0.004)
T2D	Heart_Atrial_Appendage	1e.04	0.059 (0.004)
T2D	Heart_Atrial_Appendage	0.001	0.066 (0.004)
T2D	Heart_Atrial_Appendage	0.01	0.074 (0.004)
T2D	Heart_Atrial_Appendage	0.05	0.079 (0.004)
T2D	Heart_Atrial_Appendage	0.1	0.081 (0.004)
T2D	Heart_Atrial_Appendage	0.5	0.081 (0.004)
T2D	Heart_Atrial_Appendage	1	0.081 (0.004)
T2D	Heart_Atrial_Appendage	All	0.093 (0.004)
T2D	Heart_Left_Ventricle	1e.06	0.035 (0.004)
T2D	Heart_Left_Ventricle	1e.05	0.038 (0.004)
T2D	Heart_Left_Ventricle	1e.04	0.047 (0.004)
T2D	Heart_Left_Ventricle	0.001	0.061 (0.004)
T2D	Heart_Left_Ventricle	0.01	0.066 (0.004)
T2D	Heart_Left_Ventricle	0.05	0.075 (0.004)
T2D	Heart_Left_Ventricle	0.1	0.08 (0.004)
T2D	Heart_Left_Ventricle	0.5	0.079 (0.004)
T2D	Heart_Left_Ventricle	1	0.079 (0.004)
T2D	Heart_Left_Ventricle	All	0.087 (0.004)
T2D	Liver	1e.06	0.033 (0.004)
T2D	Liver	1e.05	0.042 (0.004)
T2D	Liver	1e.04	0.039 (0.004)
T2D	Liver	0.001	0.049 (0.004)
T2D	Liver	0.01	0.052 (0.004)
T2D	Liver	0.05	0.063 (0.004)
T2D	Liver	0.1	0.067 (0.004)
T2D	Liver	0.5	0.07 (0.004)
T2D	Liver	1	0.069 (0.004)
T2D	Liver	All	0.077 (0.004)
T2D	Lung	1e.06	0.042 (0.004)
T2D	Lung	1e.05	0.052 (0.004)
T2D	Lung	1e.04	0.058 (0.004)
T2D	Lung	0.001	0.063 (0.004)
T2D	Lung	0.01	0.078 (0.004)
T2D	Lung	0.05	0.082 (0.004)
T2D	Lung	0.1	0.084 (0.004)
T2D	Lung	0.5	0.086 (0.004)
T2D	Lung	1	0.085 (0.004)
T2D	Lung	All	0.093 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	1e.06	0.029 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	1e.05	0.036 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	1e.04	0.039 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	0.001	0.053 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	0.01	0.06 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	0.05	0.072 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	0.1	0.078 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	0.5	0.085 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	1	0.085 (0.004)
T2D	METSIM.ADIPOSE.RNASEQ	All	0.087 (0.004)
T2D	Minor_Salivary_Gland	1e.06	0.012 (0.004)
T2D	Minor_Salivary_Gland	1e.05	0.012 (0.004)
T2D	Minor_Salivary_Gland	1e.04	0.02 (0.004)
T2D	Minor_Salivary_Gland	0.001	0.024 (0.004)
T2D	Minor_Salivary_Gland	0.01	0.036 (0.004)
T2D	Minor_Salivary_Gland	0.05	0.048 (0.004)
T2D	Minor_Salivary_Gland	0.1	0.054 (0.004)
T2D	Minor_Salivary_Gland	0.5	0.054 (0.004)
T2D	Minor_Salivary_Gland	1	0.053 (0.004)
T2D	Minor_Salivary_Gland	All	0.055 (0.004)
T2D	Muscle_Skeletal	1e.06	0.045 (0.004)
T2D	Muscle_Skeletal	1e.05	0.053 (0.004)
T2D	Muscle_Skeletal	1e.04	0.065 (0.004)
T2D	Muscle_Skeletal	0.001	0.072 (0.004)
T2D	Muscle_Skeletal	0.01	0.083 (0.004)
T2D	Muscle_Skeletal	0.05	0.092 (0.004)
T2D	Muscle_Skeletal	0.1	0.094 (0.004)
T2D	Muscle_Skeletal	0.5	0.097 (0.004)
T2D	Muscle_Skeletal	1	0.096 (0.004)
T2D	Muscle_Skeletal	All	0.104 (0.004)
T2D	Nerve_Tibial	1e.06	0.045 (0.004)
T2D	Nerve_Tibial	1e.05	0.052 (0.004)
T2D	Nerve_Tibial	1e.04	0.055 (0.004)
T2D	Nerve_Tibial	0.001	0.08 (0.004)
T2D	Nerve_Tibial	0.01	0.09 (0.004)
T2D	Nerve_Tibial	0.05	0.098 (0.004)
T2D	Nerve_Tibial	0.1	0.101 (0.004)
T2D	Nerve_Tibial	0.5	0.098 (0.004)
T2D	Nerve_Tibial	1	0.096 (0.004)
T2D	Nerve_Tibial	All	0.109 (0.004)
T2D	NTR.BLOOD.RNAARR	1e.06	0.055 (0.004)
T2D	NTR.BLOOD.RNAARR	1e.05	0.06 (0.004)
T2D	NTR.BLOOD.RNAARR	1e.04	0.06 (0.004)
T2D	NTR.BLOOD.RNAARR	0.001	0.062 (0.004)
T2D	NTR.BLOOD.RNAARR	0.01	0.061 (0.004)
T2D	NTR.BLOOD.RNAARR	0.05	0.065 (0.004)
T2D	NTR.BLOOD.RNAARR	0.1	0.064 (0.004)
T2D	NTR.BLOOD.RNAARR	0.5	0.069 (0.004)
T2D	NTR.BLOOD.RNAARR	1	0.068 (0.004)
T2D	NTR.BLOOD.RNAARR	All	0.082 (0.004)
T2D	Ovary	1e.06	0.015 (0.004)
T2D	Ovary	1e.05	0.02 (0.004)
T2D	Ovary	1e.04	0.03 (0.004)
T2D	Ovary	0.001	0.038 (0.004)
T2D	Ovary	0.01	0.053 (0.004)
T2D	Ovary	0.05	0.063 (0.004)
T2D	Ovary	0.1	0.067 (0.004)
T2D	Ovary	0.5	0.064 (0.004)
T2D	Ovary	1	0.063 (0.004)
T2D	Ovary	All	0.069 (0.004)
T2D	Pancreas	1e.06	0.036 (0.004)
T2D	Pancreas	1e.05	0.041 (0.004)
T2D	Pancreas	1e.04	0.048 (0.004)
T2D	Pancreas	0.001	0.057 (0.004)
T2D	Pancreas	0.01	0.061 (0.004)
T2D	Pancreas	0.05	0.069 (0.004)
T2D	Pancreas	0.1	0.074 (0.004)
T2D	Pancreas	0.5	0.081 (0.004)
T2D	Pancreas	1	0.081 (0.004)
T2D	Pancreas	All	0.086 (0.004)
T2D	Pituitary	1e.06	0.019 (0.004)
T2D	Pituitary	1e.05	0.03 (0.004)
T2D	Pituitary	1e.04	0.037 (0.004)
T2D	Pituitary	0.001	0.053 (0.004)
T2D	Pituitary	0.01	0.06 (0.004)
T2D	Pituitary	0.05	0.064 (0.004)
T2D	Pituitary	0.1	0.066 (0.004)
T2D	Pituitary	0.5	0.071 (0.004)
T2D	Pituitary	1	0.071 (0.004)
T2D	Pituitary	All	0.076 (0.004)
T2D	Prostate	1e.06	0.014 (0.004)
T2D	Prostate	1e.05	0.02 (0.004)
T2D	Prostate	1e.04	0.022 (0.004)
T2D	Prostate	0.001	0.031 (0.004)
T2D	Prostate	0.01	0.043 (0.004)
T2D	Prostate	0.05	0.055 (0.004)
T2D	Prostate	0.1	0.055 (0.004)
T2D	Prostate	0.5	0.057 (0.004)
T2D	Prostate	1	0.057 (0.004)
T2D	Prostate	All	0.059 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.042 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.049 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.059 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.058 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.073 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.079 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.084 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.087 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	1	0.087 (0.004)
T2D	Skin_Not_Sun_Exposed_Suprapubic	All	0.094 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	1e.06	0.036 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	1e.05	0.045 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	1e.04	0.054 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	0.001	0.068 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	0.01	0.076 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	0.05	0.084 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	0.1	0.086 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	0.5	0.091 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	1	0.089 (0.004)
T2D	Skin_Sun_Exposed_Lower_leg	All	0.098 (0.004)
T2D	Small_Intestine_Terminal_Ileum	1e.06	0.04 (0.004)
T2D	Small_Intestine_Terminal_Ileum	1e.05	0.044 (0.004)
T2D	Small_Intestine_Terminal_Ileum	1e.04	0.042 (0.004)
T2D	Small_Intestine_Terminal_Ileum	0.001	0.052 (0.004)
T2D	Small_Intestine_Terminal_Ileum	0.01	0.062 (0.004)
T2D	Small_Intestine_Terminal_Ileum	0.05	0.067 (0.004)
T2D	Small_Intestine_Terminal_Ileum	0.1	0.072 (0.004)
T2D	Small_Intestine_Terminal_Ileum	0.5	0.069 (0.004)
T2D	Small_Intestine_Terminal_Ileum	1	0.068 (0.004)
T2D	Small_Intestine_Terminal_Ileum	All	0.076 (0.004)
T2D	Spleen	1e.06	0.036 (0.004)
T2D	Spleen	1e.05	0.035 (0.004)
T2D	Spleen	1e.04	0.04 (0.004)
T2D	Spleen	0.001	0.06 (0.004)
T2D	Spleen	0.01	0.064 (0.004)
T2D	Spleen	0.05	0.072 (0.004)
T2D	Spleen	0.1	0.078 (0.004)
T2D	Spleen	0.5	0.078 (0.004)
T2D	Spleen	1	0.078 (0.004)
T2D	Spleen	All	0.083 (0.004)
T2D	Stomach	1e.06	0.022 (0.004)
T2D	Stomach	1e.05	0.023 (0.004)
T2D	Stomach	1e.04	0.027 (0.004)
T2D	Stomach	0.001	0.046 (0.004)
T2D	Stomach	0.01	0.062 (0.004)
T2D	Stomach	0.05	0.071 (0.004)
T2D	Stomach	0.1	0.074 (0.004)
T2D	Stomach	0.5	0.079 (0.004)
T2D	Stomach	1	0.079 (0.004)
T2D	Stomach	All	0.081 (0.004)
T2D	Testis	1e.06	0.044 (0.004)
T2D	Testis	1e.05	0.048 (0.004)
T2D	Testis	1e.04	0.055 (0.004)
T2D	Testis	0.001	0.066 (0.004)
T2D	Testis	0.01	0.08 (0.004)
T2D	Testis	0.05	0.088 (0.004)
T2D	Testis	0.1	0.088 (0.004)
T2D	Testis	0.5	0.09 (0.004)
T2D	Testis	1	0.092 (0.004)
T2D	Testis	All	0.098 (0.004)
T2D	Thyroid	1e.06	0.056 (0.004)
T2D	Thyroid	1e.05	0.058 (0.004)
T2D	Thyroid	1e.04	0.063 (0.004)
T2D	Thyroid	0.001	0.066 (0.004)
T2D	Thyroid	0.01	0.082 (0.004)
T2D	Thyroid	0.05	0.09 (0.004)
T2D	Thyroid	0.1	0.093 (0.004)
T2D	Thyroid	0.5	0.099 (0.004)
T2D	Thyroid	1	0.098 (0.004)
T2D	Thyroid	All	0.106 (0.004)
T2D	Uterus	1e.06	0.012 (0.004)
T2D	Uterus	1e.05	0.02 (0.004)
T2D	Uterus	1e.04	0.02 (0.004)
T2D	Uterus	0.001	0.025 (0.004)
T2D	Uterus	0.01	0.043 (0.004)
T2D	Uterus	0.05	0.052 (0.004)
T2D	Uterus	0.1	0.056 (0.004)
T2D	Uterus	0.5	0.054 (0.004)
T2D	Uterus	1	0.055 (0.004)
T2D	Uterus	All	0.057 (0.004)
T2D	Vagina	1e.05	0.005 (0.004)
T2D	Vagina	1e.04	0.023 (0.004)
T2D	Vagina	0.001	0.034 (0.004)
T2D	Vagina	0.01	0.047 (0.004)
T2D	Vagina	0.05	0.05 (0.004)
T2D	Vagina	0.1	0.053 (0.004)
T2D	Vagina	0.5	0.052 (0.004)
T2D	Vagina	1	0.053 (0.004)
T2D	Vagina	All	0.056 (0.004)
T2D	Whole_Blood	1e.06	0.035 (0.004)
T2D	Whole_Blood	1e.05	0.038 (0.004)
T2D	Whole_Blood	1e.04	0.042 (0.004)
T2D	Whole_Blood	0.001	0.05 (0.004)
T2D	Whole_Blood	0.01	0.06 (0.004)
T2D	Whole_Blood	0.05	0.074 (0.004)
T2D	Whole_Blood	0.1	0.078 (0.004)
T2D	Whole_Blood	0.5	0.083 (0.004)
T2D	Whole_Blood	1	0.082 (0.004)
T2D	Whole_Blood	All	0.087 (0.004)
T2D	YFS.BLOOD.RNAARR	1e.06	0.034 (0.004)
T2D	YFS.BLOOD.RNAARR	1e.05	0.036 (0.004)
T2D	YFS.BLOOD.RNAARR	1e.04	0.044 (0.004)
T2D	YFS.BLOOD.RNAARR	0.001	0.053 (0.004)
T2D	YFS.BLOOD.RNAARR	0.01	0.065 (0.004)
T2D	YFS.BLOOD.RNAARR	0.05	0.075 (0.004)
T2D	YFS.BLOOD.RNAARR	0.1	0.078 (0.004)
T2D	YFS.BLOOD.RNAARR	0.5	0.084 (0.004)
T2D	YFS.BLOOD.RNAARR	1	0.084 (0.004)
T2D	YFS.BLOOD.RNAARR	All	0.086 (0.004)
CAD	Adipose_Subcutaneous	1e.06	0.034 (0.004)
CAD	Adipose_Subcutaneous	1e.05	0.036 (0.004)
CAD	Adipose_Subcutaneous	1e.04	0.048 (0.004)
CAD	Adipose_Subcutaneous	0.001	0.05 (0.004)
CAD	Adipose_Subcutaneous	0.01	0.055 (0.004)
CAD	Adipose_Subcutaneous	0.05	0.064 (0.004)
CAD	Adipose_Subcutaneous	0.1	0.068 (0.004)
CAD	Adipose_Subcutaneous	0.5	0.071 (0.004)
CAD	Adipose_Subcutaneous	1	0.072 (0.004)
CAD	Adipose_Subcutaneous	All	0.076 (0.004)
CAD	Adipose_Visceral_Omentum	1e.06	0.04 (0.004)
CAD	Adipose_Visceral_Omentum	1e.05	0.042 (0.004)
CAD	Adipose_Visceral_Omentum	1e.04	0.043 (0.004)
CAD	Adipose_Visceral_Omentum	0.001	0.038 (0.004)
CAD	Adipose_Visceral_Omentum	0.01	0.05 (0.004)
CAD	Adipose_Visceral_Omentum	0.05	0.056 (0.004)
CAD	Adipose_Visceral_Omentum	0.1	0.061 (0.004)
CAD	Adipose_Visceral_Omentum	0.5	0.064 (0.004)
CAD	Adipose_Visceral_Omentum	1	0.064 (0.004)
CAD	Adipose_Visceral_Omentum	All	0.07 (0.004)
CAD	Adrenal_Gland	1e.06	0.02 (0.004)
CAD	Adrenal_Gland	1e.05	0.027 (0.004)
CAD	Adrenal_Gland	1e.04	0.032 (0.004)
CAD	Adrenal_Gland	0.001	0.032 (0.004)
CAD	Adrenal_Gland	0.01	0.044 (0.004)
CAD	Adrenal_Gland	0.05	0.051 (0.004)
CAD	Adrenal_Gland	0.1	0.055 (0.004)
CAD	Adrenal_Gland	0.5	0.058 (0.004)
CAD	Adrenal_Gland	1	0.058 (0.004)
CAD	Adrenal_Gland	All	0.058 (0.004)
CAD	Artery_Aorta	1e.06	0.051 (0.004)
CAD	Artery_Aorta	1e.05	0.053 (0.004)
CAD	Artery_Aorta	1e.04	0.064 (0.004)
CAD	Artery_Aorta	0.001	0.062 (0.004)
CAD	Artery_Aorta	0.01	0.064 (0.004)
CAD	Artery_Aorta	0.05	0.077 (0.004)
CAD	Artery_Aorta	0.1	0.074 (0.004)
CAD	Artery_Aorta	0.5	0.078 (0.004)
CAD	Artery_Aorta	1	0.078 (0.004)
CAD	Artery_Aorta	All	0.089 (0.004)
CAD	Artery_Coronary	1e.06	0.033 (0.004)
CAD	Artery_Coronary	1e.05	0.035 (0.004)
CAD	Artery_Coronary	1e.04	0.04 (0.004)
CAD	Artery_Coronary	0.001	0.042 (0.004)
CAD	Artery_Coronary	0.01	0.049 (0.004)
CAD	Artery_Coronary	0.05	0.057 (0.004)
CAD	Artery_Coronary	0.1	0.057 (0.004)
CAD	Artery_Coronary	0.5	0.059 (0.004)
CAD	Artery_Coronary	1	0.059 (0.004)
CAD	Artery_Coronary	All	0.064 (0.004)
CAD	Artery_Tibial	1e.06	0.041 (0.004)
CAD	Artery_Tibial	1e.05	0.048 (0.004)
CAD	Artery_Tibial	1e.04	0.055 (0.004)
CAD	Artery_Tibial	0.001	0.06 (0.004)
CAD	Artery_Tibial	0.01	0.069 (0.004)
CAD	Artery_Tibial	0.05	0.081 (0.004)
CAD	Artery_Tibial	0.1	0.081 (0.004)
CAD	Artery_Tibial	0.5	0.086 (0.004)
CAD	Artery_Tibial	1	0.084 (0.004)
CAD	Artery_Tibial	All	0.089 (0.004)
CAD	Brain_Amygdala	1e.06	0.011 (0.004)
CAD	Brain_Amygdala	1e.05	0.011 (0.004)
CAD	Brain_Amygdala	1e.04	0.014 (0.004)
CAD	Brain_Amygdala	0.001	0.013 (0.004)
CAD	Brain_Amygdala	0.01	0.014 (0.004)
CAD	Brain_Amygdala	0.05	0.029 (0.004)
CAD	Brain_Amygdala	0.1	0.029 (0.004)
CAD	Brain_Amygdala	0.5	0.036 (0.004)
CAD	Brain_Amygdala	1	0.036 (0.004)
CAD	Brain_Amygdala	All	0.034 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.012 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.015 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.022 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	0.001	0.021 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	0.01	0.022 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	0.05	0.036 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	0.1	0.038 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	0.5	0.039 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	1	0.04 (0.004)
CAD	Brain_Anterior_cingulate_cortex_BA24	All	0.042 (0.004)
CAD	Brain_Caudate_basal_ganglia	1e.06	0.02 (0.004)
CAD	Brain_Caudate_basal_ganglia	1e.05	0.031 (0.004)
CAD	Brain_Caudate_basal_ganglia	1e.04	0.034 (0.004)
CAD	Brain_Caudate_basal_ganglia	0.001	0.037 (0.004)
CAD	Brain_Caudate_basal_ganglia	0.01	0.038 (0.004)
CAD	Brain_Caudate_basal_ganglia	0.05	0.049 (0.004)
CAD	Brain_Caudate_basal_ganglia	0.1	0.051 (0.004)
CAD	Brain_Caudate_basal_ganglia	0.5	0.053 (0.004)
CAD	Brain_Caudate_basal_ganglia	1	0.054 (0.004)
CAD	Brain_Caudate_basal_ganglia	All	0.058 (0.004)
CAD	Brain_Cerebellar_Hemisphere	1e.06	0.024 (0.004)
CAD	Brain_Cerebellar_Hemisphere	1e.05	0.03 (0.004)
CAD	Brain_Cerebellar_Hemisphere	1e.04	0.025 (0.004)
CAD	Brain_Cerebellar_Hemisphere	0.001	0.026 (0.004)
CAD	Brain_Cerebellar_Hemisphere	0.01	0.03 (0.004)
CAD	Brain_Cerebellar_Hemisphere	0.05	0.044 (0.004)
CAD	Brain_Cerebellar_Hemisphere	0.1	0.048 (0.004)
CAD	Brain_Cerebellar_Hemisphere	0.5	0.054 (0.004)
CAD	Brain_Cerebellar_Hemisphere	1	0.055 (0.004)
CAD	Brain_Cerebellar_Hemisphere	All	0.057 (0.004)
CAD	Brain_Cerebellum	1e.06	0.023 (0.004)
CAD	Brain_Cerebellum	1e.05	0.022 (0.004)
CAD	Brain_Cerebellum	1e.04	0.025 (0.004)
CAD	Brain_Cerebellum	0.001	0.025 (0.004)
CAD	Brain_Cerebellum	0.01	0.029 (0.004)
CAD	Brain_Cerebellum	0.05	0.044 (0.004)
CAD	Brain_Cerebellum	0.1	0.047 (0.004)
CAD	Brain_Cerebellum	0.5	0.049 (0.004)
CAD	Brain_Cerebellum	1	0.05 (0.004)
CAD	Brain_Cerebellum	All	0.052 (0.004)
CAD	Brain_Cortex	1e.06	0.027 (0.004)
CAD	Brain_Cortex	1e.05	0.035 (0.004)
CAD	Brain_Cortex	1e.04	0.035 (0.004)
CAD	Brain_Cortex	0.001	0.038 (0.004)
CAD	Brain_Cortex	0.01	0.048 (0.004)
CAD	Brain_Cortex	0.05	0.053 (0.004)
CAD	Brain_Cortex	0.1	0.056 (0.004)
CAD	Brain_Cortex	0.5	0.057 (0.004)
CAD	Brain_Cortex	1	0.057 (0.004)
CAD	Brain_Cortex	All	0.061 (0.004)
CAD	Brain_Frontal_Cortex_BA9	1e.06	0.022 (0.004)
CAD	Brain_Frontal_Cortex_BA9	1e.05	0.031 (0.004)
CAD	Brain_Frontal_Cortex_BA9	1e.04	0.031 (0.004)
CAD	Brain_Frontal_Cortex_BA9	0.001	0.023 (0.004)
CAD	Brain_Frontal_Cortex_BA9	0.01	0.028 (0.004)
CAD	Brain_Frontal_Cortex_BA9	0.05	0.038 (0.004)
CAD	Brain_Frontal_Cortex_BA9	0.1	0.038 (0.004)
CAD	Brain_Frontal_Cortex_BA9	0.5	0.039 (0.004)
CAD	Brain_Frontal_Cortex_BA9	1	0.04 (0.004)
CAD	Brain_Frontal_Cortex_BA9	All	0.047 (0.004)
CAD	Brain_Hippocampus	1e.06	0.017 (0.004)
CAD	Brain_Hippocampus	1e.05	0.023 (0.004)
CAD	Brain_Hippocampus	1e.04	0.026 (0.004)
CAD	Brain_Hippocampus	0.001	0.027 (0.004)
CAD	Brain_Hippocampus	0.01	0.026 (0.004)
CAD	Brain_Hippocampus	0.05	0.036 (0.004)
CAD	Brain_Hippocampus	0.1	0.038 (0.004)
CAD	Brain_Hippocampus	0.5	0.039 (0.004)
CAD	Brain_Hippocampus	1	0.039 (0.004)
CAD	Brain_Hippocampus	All	0.042 (0.004)
CAD	Brain_Hypothalamus	1e.06	0.015 (0.004)
CAD	Brain_Hypothalamus	1e.05	0.016 (0.004)
CAD	Brain_Hypothalamus	1e.04	0.024 (0.004)
CAD	Brain_Hypothalamus	0.001	0.029 (0.004)
CAD	Brain_Hypothalamus	0.01	0.03 (0.004)
CAD	Brain_Hypothalamus	0.05	0.035 (0.004)
CAD	Brain_Hypothalamus	0.1	0.034 (0.004)
CAD	Brain_Hypothalamus	0.5	0.037 (0.004)
CAD	Brain_Hypothalamus	1	0.037 (0.004)
CAD	Brain_Hypothalamus	All	0.038 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.023 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.026 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.023 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.03 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.036 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.043 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.043 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.046 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	1	0.047 (0.004)
CAD	Brain_Nucleus_accumbens_basal_ganglia	All	0.048 (0.004)
CAD	Brain_Putamen_basal_ganglia	1e.06	0.013 (0.004)
CAD	Brain_Putamen_basal_ganglia	1e.05	0.017 (0.004)
CAD	Brain_Putamen_basal_ganglia	1e.04	0.019 (0.004)
CAD	Brain_Putamen_basal_ganglia	0.001	0.029 (0.004)
CAD	Brain_Putamen_basal_ganglia	0.01	0.034 (0.004)
CAD	Brain_Putamen_basal_ganglia	0.05	0.041 (0.004)
CAD	Brain_Putamen_basal_ganglia	0.1	0.041 (0.004)
CAD	Brain_Putamen_basal_ganglia	0.5	0.043 (0.004)
CAD	Brain_Putamen_basal_ganglia	1	0.043 (0.004)
CAD	Brain_Putamen_basal_ganglia	All	0.043 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	1e.06	0.039 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	1e.05	0.041 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	1e.04	0.041 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	0.001	0.029 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	0.01	0.029 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	0.05	0.039 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	0.1	0.041 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	0.5	0.04 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	1	0.042 (0.004)
CAD	Brain_Spinal_cord_cervical_c-1	All	0.053 (0.004)
CAD	Brain_Substantia_nigra	1e.06	0.015 (0.004)
CAD	Brain_Substantia_nigra	1e.05	0.015 (0.004)
CAD	Brain_Substantia_nigra	1e.04	0.015 (0.004)
CAD	Brain_Substantia_nigra	0.001	0.02 (0.004)
CAD	Brain_Substantia_nigra	0.01	0.022 (0.004)
CAD	Brain_Substantia_nigra	0.05	0.028 (0.004)
CAD	Brain_Substantia_nigra	0.1	0.034 (0.004)
CAD	Brain_Substantia_nigra	0.5	0.041 (0.004)
CAD	Brain_Substantia_nigra	1	0.041 (0.004)
CAD	Brain_Substantia_nigra	All	0.041 (0.004)
CAD	Breast_Mammary_Tissue	1e.06	0.023 (0.004)
CAD	Breast_Mammary_Tissue	1e.05	0.031 (0.004)
CAD	Breast_Mammary_Tissue	1e.04	0.035 (0.004)
CAD	Breast_Mammary_Tissue	0.001	0.037 (0.004)
CAD	Breast_Mammary_Tissue	0.01	0.041 (0.004)
CAD	Breast_Mammary_Tissue	0.05	0.051 (0.004)
CAD	Breast_Mammary_Tissue	0.1	0.055 (0.004)
CAD	Breast_Mammary_Tissue	0.5	0.058 (0.004)
CAD	Breast_Mammary_Tissue	1	0.057 (0.004)
CAD	Breast_Mammary_Tissue	All	0.063 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	1e.06	0.018 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	1e.05	0.027 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	1e.04	0.03 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	0.001	0.026 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	0.01	0.034 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	0.05	0.039 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	0.1	0.04 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	0.5	0.048 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	1	0.049 (0.004)
CAD	Cells_EBV-transformed_lymphocytes	All	0.051 (0.004)
CAD	Cells_Transformed_fibroblasts	1e.06	0.037 (0.004)
CAD	Cells_Transformed_fibroblasts	1e.05	0.042 (0.004)
CAD	Cells_Transformed_fibroblasts	1e.04	0.05 (0.004)
CAD	Cells_Transformed_fibroblasts	0.001	0.051 (0.004)
CAD	Cells_Transformed_fibroblasts	0.01	0.063 (0.004)
CAD	Cells_Transformed_fibroblasts	0.05	0.07 (0.004)
CAD	Cells_Transformed_fibroblasts	0.1	0.075 (0.004)
CAD	Cells_Transformed_fibroblasts	0.5	0.082 (0.004)
CAD	Cells_Transformed_fibroblasts	1	0.082 (0.004)
CAD	Cells_Transformed_fibroblasts	All	0.084 (0.004)
CAD	CMC.BRAIN.RNASEQ	1e.06	0.047 (0.004)
CAD	CMC.BRAIN.RNASEQ	1e.05	0.05 (0.004)
CAD	CMC.BRAIN.RNASEQ	1e.04	0.052 (0.004)
CAD	CMC.BRAIN.RNASEQ	0.001	0.061 (0.004)
CAD	CMC.BRAIN.RNASEQ	0.01	0.067 (0.004)
CAD	CMC.BRAIN.RNASEQ	0.05	0.072 (0.004)
CAD	CMC.BRAIN.RNASEQ	0.1	0.073 (0.004)
CAD	CMC.BRAIN.RNASEQ	0.5	0.071 (0.004)
CAD	CMC.BRAIN.RNASEQ	1	0.072 (0.004)
CAD	CMC.BRAIN.RNASEQ	All	0.081 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.025 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.02 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.024 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.033 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.04 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.047 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.047 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.053 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	1	0.054 (0.004)
CAD	CMC.BRAIN.RNASEQ_SPLICING	All	0.054 (0.004)
CAD	Colon_Sigmoid	1e.06	0.025 (0.004)
CAD	Colon_Sigmoid	1e.05	0.032 (0.004)
CAD	Colon_Sigmoid	1e.04	0.032 (0.004)
CAD	Colon_Sigmoid	0.001	0.037 (0.004)
CAD	Colon_Sigmoid	0.01	0.048 (0.004)
CAD	Colon_Sigmoid	0.05	0.054 (0.004)
CAD	Colon_Sigmoid	0.1	0.054 (0.004)
CAD	Colon_Sigmoid	0.5	0.06 (0.004)
CAD	Colon_Sigmoid	1	0.059 (0.004)
CAD	Colon_Sigmoid	All	0.061 (0.004)
CAD	Colon_Transverse	1e.06	0.029 (0.004)
CAD	Colon_Transverse	1e.05	0.035 (0.004)
CAD	Colon_Transverse	1e.04	0.04 (0.004)
CAD	Colon_Transverse	0.001	0.034 (0.004)
CAD	Colon_Transverse	0.01	0.048 (0.004)
CAD	Colon_Transverse	0.05	0.053 (0.004)
CAD	Colon_Transverse	0.1	0.057 (0.004)
CAD	Colon_Transverse	0.5	0.06 (0.004)
CAD	Colon_Transverse	1	0.062 (0.004)
CAD	Colon_Transverse	All	0.064 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	1e.06	0.027 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	1e.05	0.029 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	1e.04	0.03 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	0.001	0.029 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	0.01	0.033 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	0.05	0.047 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	0.1	0.046 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	0.5	0.054 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	1	0.055 (0.004)
CAD	Esophagus_Gastroesophageal_Junction	All	0.058 (0.004)
CAD	Esophagus_Mucosa	1e.06	0.039 (0.004)
CAD	Esophagus_Mucosa	1e.05	0.043 (0.004)
CAD	Esophagus_Mucosa	1e.04	0.047 (0.004)
CAD	Esophagus_Mucosa	0.001	0.052 (0.004)
CAD	Esophagus_Mucosa	0.01	0.051 (0.004)
CAD	Esophagus_Mucosa	0.05	0.061 (0.004)
CAD	Esophagus_Mucosa	0.1	0.06 (0.004)
CAD	Esophagus_Mucosa	0.5	0.067 (0.004)
CAD	Esophagus_Mucosa	1	0.069 (0.004)
CAD	Esophagus_Mucosa	All	0.072 (0.004)
CAD	Esophagus_Muscularis	1e.06	0.033 (0.004)
CAD	Esophagus_Muscularis	1e.05	0.035 (0.004)
CAD	Esophagus_Muscularis	1e.04	0.042 (0.004)
CAD	Esophagus_Muscularis	0.001	0.041 (0.004)
CAD	Esophagus_Muscularis	0.01	0.049 (0.004)
CAD	Esophagus_Muscularis	0.05	0.057 (0.004)
CAD	Esophagus_Muscularis	0.1	0.059 (0.004)
CAD	Esophagus_Muscularis	0.5	0.066 (0.004)
CAD	Esophagus_Muscularis	1	0.067 (0.004)
CAD	Esophagus_Muscularis	All	0.069 (0.004)
CAD	Heart_Atrial_Appendage	1e.06	0.021 (0.004)
CAD	Heart_Atrial_Appendage	1e.05	0.022 (0.004)
CAD	Heart_Atrial_Appendage	1e.04	0.032 (0.004)
CAD	Heart_Atrial_Appendage	0.001	0.037 (0.004)
CAD	Heart_Atrial_Appendage	0.01	0.047 (0.004)
CAD	Heart_Atrial_Appendage	0.05	0.054 (0.004)
CAD	Heart_Atrial_Appendage	0.1	0.059 (0.004)
CAD	Heart_Atrial_Appendage	0.5	0.062 (0.004)
CAD	Heart_Atrial_Appendage	1	0.063 (0.004)
CAD	Heart_Atrial_Appendage	All	0.065 (0.004)
CAD	Heart_Left_Ventricle	1e.06	0.03 (0.004)
CAD	Heart_Left_Ventricle	1e.05	0.038 (0.004)
CAD	Heart_Left_Ventricle	1e.04	0.04 (0.004)
CAD	Heart_Left_Ventricle	0.001	0.041 (0.004)
CAD	Heart_Left_Ventricle	0.01	0.048 (0.004)
CAD	Heart_Left_Ventricle	0.05	0.056 (0.004)
CAD	Heart_Left_Ventricle	0.1	0.055 (0.004)
CAD	Heart_Left_Ventricle	0.5	0.059 (0.004)
CAD	Heart_Left_Ventricle	1	0.06 (0.004)
CAD	Heart_Left_Ventricle	All	0.066 (0.004)
CAD	Liver	1e.06	0.028 (0.004)
CAD	Liver	1e.05	0.03 (0.004)
CAD	Liver	1e.04	0.03 (0.004)
CAD	Liver	0.001	0.034 (0.004)
CAD	Liver	0.01	0.033 (0.004)
CAD	Liver	0.05	0.044 (0.004)
CAD	Liver	0.1	0.044 (0.004)
CAD	Liver	0.5	0.048 (0.004)
CAD	Liver	1	0.048 (0.004)
CAD	Liver	All	0.051 (0.004)
CAD	Lung	1e.06	0.031 (0.004)
CAD	Lung	1e.05	0.029 (0.004)
CAD	Lung	1e.04	0.046 (0.004)
CAD	Lung	0.001	0.045 (0.004)
CAD	Lung	0.01	0.055 (0.004)
CAD	Lung	0.05	0.063 (0.004)
CAD	Lung	0.1	0.068 (0.004)
CAD	Lung	0.5	0.071 (0.004)
CAD	Lung	1	0.072 (0.004)
CAD	Lung	All	0.076 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	1e.06	0.023 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	1e.05	0.032 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	1e.04	0.042 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	0.001	0.049 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	0.01	0.056 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	0.05	0.064 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	0.1	0.069 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	0.5	0.063 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	1	0.063 (0.004)
CAD	METSIM.ADIPOSE.RNASEQ	All	0.07 (0.004)
CAD	Minor_Salivary_Gland	1e.06	0.005 (0.004)
CAD	Minor_Salivary_Gland	1e.05	0.013 (0.004)
CAD	Minor_Salivary_Gland	1e.04	0.021 (0.004)
CAD	Minor_Salivary_Gland	0.001	0.017 (0.004)
CAD	Minor_Salivary_Gland	0.01	0.03 (0.004)
CAD	Minor_Salivary_Gland	0.05	0.038 (0.004)
CAD	Minor_Salivary_Gland	0.1	0.04 (0.004)
CAD	Minor_Salivary_Gland	0.5	0.042 (0.004)
CAD	Minor_Salivary_Gland	1	0.041 (0.004)
CAD	Minor_Salivary_Gland	All	0.043 (0.004)
CAD	Muscle_Skeletal	1e.06	0.054 (0.004)
CAD	Muscle_Skeletal	1e.05	0.054 (0.004)
CAD	Muscle_Skeletal	1e.04	0.053 (0.004)
CAD	Muscle_Skeletal	0.001	0.058 (0.004)
CAD	Muscle_Skeletal	0.01	0.06 (0.004)
CAD	Muscle_Skeletal	0.05	0.067 (0.004)
CAD	Muscle_Skeletal	0.1	0.072 (0.004)
CAD	Muscle_Skeletal	0.5	0.075 (0.004)
CAD	Muscle_Skeletal	1	0.075 (0.004)
CAD	Muscle_Skeletal	All	0.085 (0.004)
CAD	Nerve_Tibial	1e.06	0.039 (0.004)
CAD	Nerve_Tibial	1e.05	0.05 (0.004)
CAD	Nerve_Tibial	1e.04	0.052 (0.004)
CAD	Nerve_Tibial	0.001	0.05 (0.004)
CAD	Nerve_Tibial	0.01	0.058 (0.004)
CAD	Nerve_Tibial	0.05	0.072 (0.004)
CAD	Nerve_Tibial	0.1	0.076 (0.004)
CAD	Nerve_Tibial	0.5	0.08 (0.004)
CAD	Nerve_Tibial	1	0.08 (0.004)
CAD	Nerve_Tibial	All	0.086 (0.004)
CAD	NTR.BLOOD.RNAARR	1e.06	0.03 (0.004)
CAD	NTR.BLOOD.RNAARR	1e.05	0.032 (0.004)
CAD	NTR.BLOOD.RNAARR	1e.04	0.037 (0.004)
CAD	NTR.BLOOD.RNAARR	0.001	0.042 (0.004)
CAD	NTR.BLOOD.RNAARR	0.01	0.05 (0.004)
CAD	NTR.BLOOD.RNAARR	0.05	0.054 (0.004)
CAD	NTR.BLOOD.RNAARR	0.1	0.057 (0.004)
CAD	NTR.BLOOD.RNAARR	0.5	0.059 (0.004)
CAD	NTR.BLOOD.RNAARR	1	0.059 (0.004)
CAD	NTR.BLOOD.RNAARR	All	0.063 (0.004)
CAD	Ovary	1e.06	0.015 (0.004)
CAD	Ovary	1e.05	0.015 (0.004)
CAD	Ovary	1e.04	0.023 (0.004)
CAD	Ovary	0.001	0.032 (0.004)
CAD	Ovary	0.01	0.044 (0.004)
CAD	Ovary	0.05	0.044 (0.004)
CAD	Ovary	0.1	0.046 (0.004)
CAD	Ovary	0.5	0.05 (0.004)
CAD	Ovary	1	0.051 (0.004)
CAD	Ovary	All	0.054 (0.004)
CAD	Pancreas	1e.06	0.033 (0.004)
CAD	Pancreas	1e.05	0.037 (0.004)
CAD	Pancreas	1e.04	0.039 (0.004)
CAD	Pancreas	0.001	0.039 (0.004)
CAD	Pancreas	0.01	0.047 (0.004)
CAD	Pancreas	0.05	0.056 (0.004)
CAD	Pancreas	0.1	0.061 (0.004)
CAD	Pancreas	0.5	0.065 (0.004)
CAD	Pancreas	1	0.065 (0.004)
CAD	Pancreas	All	0.068 (0.004)
CAD	Pituitary	1e.06	0.021 (0.004)
CAD	Pituitary	1e.05	0.027 (0.004)
CAD	Pituitary	1e.04	0.027 (0.004)
CAD	Pituitary	0.001	0.031 (0.004)
CAD	Pituitary	0.01	0.041 (0.004)
CAD	Pituitary	0.05	0.049 (0.004)
CAD	Pituitary	0.1	0.052 (0.004)
CAD	Pituitary	0.5	0.055 (0.004)
CAD	Pituitary	1	0.055 (0.004)
CAD	Pituitary	All	0.057 (0.004)
CAD	Prostate	1e.06	0.016 (0.004)
CAD	Prostate	1e.05	0.021 (0.004)
CAD	Prostate	1e.04	0.032 (0.004)
CAD	Prostate	0.001	0.024 (0.004)
CAD	Prostate	0.01	0.026 (0.004)
CAD	Prostate	0.05	0.032 (0.004)
CAD	Prostate	0.1	0.033 (0.004)
CAD	Prostate	0.5	0.04 (0.004)
CAD	Prostate	1	0.04 (0.004)
CAD	Prostate	All	0.046 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.036 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.045 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.045 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.043 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.043 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.054 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.06 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.065 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	1	0.066 (0.004)
CAD	Skin_Not_Sun_Exposed_Suprapubic	All	0.072 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	1e.06	0.033 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	1e.05	0.041 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	1e.04	0.047 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	0.001	0.045 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	0.01	0.05 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	0.05	0.062 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	0.1	0.068 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	0.5	0.073 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	1	0.074 (0.004)
CAD	Skin_Sun_Exposed_Lower_leg	All	0.077 (0.004)
CAD	Small_Intestine_Terminal_Ileum	1e.06	0.024 (0.004)
CAD	Small_Intestine_Terminal_Ileum	1e.05	0.021 (0.004)
CAD	Small_Intestine_Terminal_Ileum	1e.04	0.024 (0.004)
CAD	Small_Intestine_Terminal_Ileum	0.001	0.016 (0.004)
CAD	Small_Intestine_Terminal_Ileum	0.01	0.025 (0.004)
CAD	Small_Intestine_Terminal_Ileum	0.05	0.035 (0.004)
CAD	Small_Intestine_Terminal_Ileum	0.1	0.038 (0.004)
CAD	Small_Intestine_Terminal_Ileum	0.5	0.042 (0.004)
CAD	Small_Intestine_Terminal_Ileum	1	0.043 (0.004)
CAD	Small_Intestine_Terminal_Ileum	All	0.048 (0.004)
CAD	Spleen	1e.06	0.028 (0.004)
CAD	Spleen	1e.05	0.03 (0.004)
CAD	Spleen	1e.04	0.045 (0.004)
CAD	Spleen	0.001	0.046 (0.004)
CAD	Spleen	0.01	0.048 (0.004)
CAD	Spleen	0.05	0.054 (0.004)
CAD	Spleen	0.1	0.055 (0.004)
CAD	Spleen	0.5	0.058 (0.004)
CAD	Spleen	1	0.059 (0.004)
CAD	Spleen	All	0.064 (0.004)
CAD	Stomach	1e.06	0.02 (0.004)
CAD	Stomach	1e.05	0.019 (0.004)
CAD	Stomach	1e.04	0.023 (0.004)
CAD	Stomach	0.001	0.03 (0.004)
CAD	Stomach	0.01	0.04 (0.004)
CAD	Stomach	0.05	0.046 (0.004)
CAD	Stomach	0.1	0.052 (0.004)
CAD	Stomach	0.5	0.059 (0.004)
CAD	Stomach	1	0.059 (0.004)
CAD	Stomach	All	0.058 (0.004)
CAD	Testis	1e.06	0.033 (0.004)
CAD	Testis	1e.05	0.04 (0.004)
CAD	Testis	1e.04	0.048 (0.004)
CAD	Testis	0.001	0.041 (0.004)
CAD	Testis	0.01	0.046 (0.004)
CAD	Testis	0.05	0.056 (0.004)
CAD	Testis	0.1	0.059 (0.004)
CAD	Testis	0.5	0.067 (0.004)
CAD	Testis	1	0.067 (0.004)
CAD	Testis	All	0.073 (0.004)
CAD	Thyroid	1e.06	0.032 (0.004)
CAD	Thyroid	1e.05	0.038 (0.004)
CAD	Thyroid	1e.04	0.046 (0.004)
CAD	Thyroid	0.001	0.045 (0.004)
CAD	Thyroid	0.01	0.057 (0.004)
CAD	Thyroid	0.05	0.065 (0.004)
CAD	Thyroid	0.1	0.066 (0.004)
CAD	Thyroid	0.5	0.077 (0.004)
CAD	Thyroid	1	0.077 (0.004)
CAD	Thyroid	All	0.08 (0.004)
CAD	Uterus	1e.06	0.013 (0.004)
CAD	Uterus	1e.05	0.018 (0.004)
CAD	Uterus	1e.04	0.025 (0.004)
CAD	Uterus	0.001	0.025 (0.004)
CAD	Uterus	0.01	0.023 (0.004)
CAD	Uterus	0.05	0.032 (0.004)
CAD	Uterus	0.1	0.037 (0.004)
CAD	Uterus	0.5	0.043 (0.004)
CAD	Uterus	1	0.044 (0.004)
CAD	Uterus	All	0.045 (0.004)
CAD	Vagina	1e.06	0.016 (0.004)
CAD	Vagina	1e.05	0.016 (0.004)
CAD	Vagina	1e.04	0.018 (0.004)
CAD	Vagina	0.001	0.025 (0.004)
CAD	Vagina	0.01	0.026 (0.004)
CAD	Vagina	0.05	0.032 (0.004)
CAD	Vagina	0.1	0.034 (0.004)
CAD	Vagina	0.5	0.039 (0.004)
CAD	Vagina	1	0.039 (0.004)
CAD	Vagina	All	0.039 (0.004)
CAD	Whole_Blood	1e.06	0.03 (0.004)
CAD	Whole_Blood	1e.05	0.034 (0.004)
CAD	Whole_Blood	1e.04	0.044 (0.004)
CAD	Whole_Blood	0.001	0.048 (0.004)
CAD	Whole_Blood	0.01	0.054 (0.004)
CAD	Whole_Blood	0.05	0.065 (0.004)
CAD	Whole_Blood	0.1	0.069 (0.004)
CAD	Whole_Blood	0.5	0.07 (0.004)
CAD	Whole_Blood	1	0.07 (0.004)
CAD	Whole_Blood	All	0.074 (0.004)
CAD	YFS.BLOOD.RNAARR	1e.06	0.03 (0.004)
CAD	YFS.BLOOD.RNAARR	1e.05	0.04 (0.004)
CAD	YFS.BLOOD.RNAARR	1e.04	0.047 (0.004)
CAD	YFS.BLOOD.RNAARR	0.001	0.051 (0.004)
CAD	YFS.BLOOD.RNAARR	0.01	0.061 (0.004)
CAD	YFS.BLOOD.RNAARR	0.05	0.062 (0.004)
CAD	YFS.BLOOD.RNAARR	0.1	0.063 (0.004)
CAD	YFS.BLOOD.RNAARR	0.5	0.063 (0.004)
CAD	YFS.BLOOD.RNAARR	1	0.063 (0.004)
CAD	YFS.BLOOD.RNAARR	All	0.071 (0.004)
IBD	Adipose_Subcutaneous	1e.06	0.045 (0.004)
IBD	Adipose_Subcutaneous	1e.05	0.047 (0.004)
IBD	Adipose_Subcutaneous	1e.04	0.05 (0.004)
IBD	Adipose_Subcutaneous	0.001	0.054 (0.004)
IBD	Adipose_Subcutaneous	0.01	0.058 (0.004)
IBD	Adipose_Subcutaneous	0.05	0.062 (0.004)
IBD	Adipose_Subcutaneous	0.1	0.06 (0.004)
IBD	Adipose_Subcutaneous	0.5	0.06 (0.004)
IBD	Adipose_Subcutaneous	1	0.06 (0.004)
IBD	Adipose_Subcutaneous	All	0.064 (0.004)
IBD	Adipose_Visceral_Omentum	1e.06	0.036 (0.004)
IBD	Adipose_Visceral_Omentum	1e.05	0.04 (0.004)
IBD	Adipose_Visceral_Omentum	1e.04	0.042 (0.004)
IBD	Adipose_Visceral_Omentum	0.001	0.049 (0.004)
IBD	Adipose_Visceral_Omentum	0.01	0.051 (0.004)
IBD	Adipose_Visceral_Omentum	0.05	0.056 (0.004)
IBD	Adipose_Visceral_Omentum	0.1	0.057 (0.004)
IBD	Adipose_Visceral_Omentum	0.5	0.058 (0.004)
IBD	Adipose_Visceral_Omentum	1	0.058 (0.004)
IBD	Adipose_Visceral_Omentum	All	0.06 (0.004)
IBD	Adrenal_Gland	1e.06	0.023 (0.004)
IBD	Adrenal_Gland	1e.05	0.028 (0.004)
IBD	Adrenal_Gland	1e.04	0.035 (0.004)
IBD	Adrenal_Gland	0.001	0.039 (0.004)
IBD	Adrenal_Gland	0.01	0.043 (0.004)
IBD	Adrenal_Gland	0.05	0.048 (0.004)
IBD	Adrenal_Gland	0.1	0.048 (0.004)
IBD	Adrenal_Gland	0.5	0.05 (0.004)
IBD	Adrenal_Gland	1	0.05 (0.004)
IBD	Adrenal_Gland	All	0.05 (0.004)
IBD	Artery_Aorta	1e.06	0.035 (0.004)
IBD	Artery_Aorta	1e.05	0.038 (0.004)
IBD	Artery_Aorta	1e.04	0.038 (0.004)
IBD	Artery_Aorta	0.001	0.043 (0.004)
IBD	Artery_Aorta	0.01	0.051 (0.004)
IBD	Artery_Aorta	0.05	0.052 (0.004)
IBD	Artery_Aorta	0.1	0.054 (0.004)
IBD	Artery_Aorta	0.5	0.051 (0.004)
IBD	Artery_Aorta	1	0.051 (0.004)
IBD	Artery_Aorta	All	0.055 (0.004)
IBD	Artery_Coronary	1e.06	0.036 (0.004)
IBD	Artery_Coronary	1e.05	0.036 (0.004)
IBD	Artery_Coronary	1e.04	0.038 (0.004)
IBD	Artery_Coronary	0.001	0.04 (0.004)
IBD	Artery_Coronary	0.01	0.043 (0.004)
IBD	Artery_Coronary	0.05	0.046 (0.004)
IBD	Artery_Coronary	0.1	0.046 (0.004)
IBD	Artery_Coronary	0.5	0.043 (0.004)
IBD	Artery_Coronary	1	0.043 (0.004)
IBD	Artery_Coronary	All	0.047 (0.004)
IBD	Artery_Tibial	1e.06	0.038 (0.004)
IBD	Artery_Tibial	1e.05	0.041 (0.004)
IBD	Artery_Tibial	1e.04	0.042 (0.004)
IBD	Artery_Tibial	0.001	0.047 (0.004)
IBD	Artery_Tibial	0.01	0.052 (0.004)
IBD	Artery_Tibial	0.05	0.059 (0.004)
IBD	Artery_Tibial	0.1	0.058 (0.004)
IBD	Artery_Tibial	0.5	0.058 (0.004)
IBD	Artery_Tibial	1	0.058 (0.004)
IBD	Artery_Tibial	All	0.06 (0.004)
IBD	Brain_Amygdala	1e.06	0.03 (0.004)
IBD	Brain_Amygdala	1e.05	0.031 (0.004)
IBD	Brain_Amygdala	1e.04	0.032 (0.004)
IBD	Brain_Amygdala	0.001	0.028 (0.004)
IBD	Brain_Amygdala	0.01	0.029 (0.004)
IBD	Brain_Amygdala	0.05	0.033 (0.004)
IBD	Brain_Amygdala	0.1	0.033 (0.004)
IBD	Brain_Amygdala	0.5	0.034 (0.004)
IBD	Brain_Amygdala	1	0.034 (0.004)
IBD	Brain_Amygdala	All	0.036 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.033 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.035 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.037 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	0.001	0.036 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	0.01	0.039 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	0.05	0.038 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	0.1	0.04 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	0.5	0.041 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	1	0.041 (0.004)
IBD	Brain_Anterior_cingulate_cortex_BA24	All	0.044 (0.004)
IBD	Brain_Caudate_basal_ganglia	1e.06	0.024 (0.004)
IBD	Brain_Caudate_basal_ganglia	1e.05	0.025 (0.004)
IBD	Brain_Caudate_basal_ganglia	1e.04	0.03 (0.004)
IBD	Brain_Caudate_basal_ganglia	0.001	0.032 (0.004)
IBD	Brain_Caudate_basal_ganglia	0.01	0.04 (0.004)
IBD	Brain_Caudate_basal_ganglia	0.05	0.042 (0.004)
IBD	Brain_Caudate_basal_ganglia	0.1	0.041 (0.004)
IBD	Brain_Caudate_basal_ganglia	0.5	0.043 (0.004)
IBD	Brain_Caudate_basal_ganglia	1	0.043 (0.004)
IBD	Brain_Caudate_basal_ganglia	All	0.045 (0.004)
IBD	Brain_Cerebellar_Hemisphere	1e.06	0.041 (0.004)
IBD	Brain_Cerebellar_Hemisphere	1e.05	0.042 (0.004)
IBD	Brain_Cerebellar_Hemisphere	1e.04	0.043 (0.004)
IBD	Brain_Cerebellar_Hemisphere	0.001	0.043 (0.004)
IBD	Brain_Cerebellar_Hemisphere	0.01	0.046 (0.004)
IBD	Brain_Cerebellar_Hemisphere	0.05	0.045 (0.004)
IBD	Brain_Cerebellar_Hemisphere	0.1	0.043 (0.004)
IBD	Brain_Cerebellar_Hemisphere	0.5	0.048 (0.004)
IBD	Brain_Cerebellar_Hemisphere	1	0.046 (0.004)
IBD	Brain_Cerebellar_Hemisphere	All	0.052 (0.004)
IBD	Brain_Cerebellum	1e.06	0.042 (0.004)
IBD	Brain_Cerebellum	1e.05	0.045 (0.004)
IBD	Brain_Cerebellum	1e.04	0.045 (0.004)
IBD	Brain_Cerebellum	0.001	0.046 (0.004)
IBD	Brain_Cerebellum	0.01	0.048 (0.004)
IBD	Brain_Cerebellum	0.05	0.048 (0.004)
IBD	Brain_Cerebellum	0.1	0.049 (0.004)
IBD	Brain_Cerebellum	0.5	0.054 (0.004)
IBD	Brain_Cerebellum	1	0.053 (0.004)
IBD	Brain_Cerebellum	All	0.057 (0.004)
IBD	Brain_Cortex	1e.06	0.038 (0.004)
IBD	Brain_Cortex	1e.05	0.039 (0.004)
IBD	Brain_Cortex	1e.04	0.041 (0.004)
IBD	Brain_Cortex	0.001	0.037 (0.004)
IBD	Brain_Cortex	0.01	0.039 (0.004)
IBD	Brain_Cortex	0.05	0.045 (0.004)
IBD	Brain_Cortex	0.1	0.047 (0.004)
IBD	Brain_Cortex	0.5	0.049 (0.004)
IBD	Brain_Cortex	1	0.048 (0.004)
IBD	Brain_Cortex	All	0.051 (0.004)
IBD	Brain_Frontal_Cortex_BA9	1e.06	0.024 (0.004)
IBD	Brain_Frontal_Cortex_BA9	1e.05	0.026 (0.004)
IBD	Brain_Frontal_Cortex_BA9	1e.04	0.029 (0.004)
IBD	Brain_Frontal_Cortex_BA9	0.001	0.032 (0.004)
IBD	Brain_Frontal_Cortex_BA9	0.01	0.037 (0.004)
IBD	Brain_Frontal_Cortex_BA9	0.05	0.037 (0.004)
IBD	Brain_Frontal_Cortex_BA9	0.1	0.037 (0.004)
IBD	Brain_Frontal_Cortex_BA9	0.5	0.038 (0.004)
IBD	Brain_Frontal_Cortex_BA9	1	0.037 (0.004)
IBD	Brain_Frontal_Cortex_BA9	All	0.04 (0.004)
IBD	Brain_Hippocampus	1e.06	0.03 (0.004)
IBD	Brain_Hippocampus	1e.05	0.036 (0.004)
IBD	Brain_Hippocampus	1e.04	0.036 (0.004)
IBD	Brain_Hippocampus	0.001	0.036 (0.004)
IBD	Brain_Hippocampus	0.01	0.034 (0.004)
IBD	Brain_Hippocampus	0.05	0.034 (0.004)
IBD	Brain_Hippocampus	0.1	0.033 (0.004)
IBD	Brain_Hippocampus	0.5	0.037 (0.004)
IBD	Brain_Hippocampus	1	0.036 (0.004)
IBD	Brain_Hippocampus	All	0.042 (0.004)
IBD	Brain_Hypothalamus	1e.06	0.025 (0.004)
IBD	Brain_Hypothalamus	1e.05	0.026 (0.004)
IBD	Brain_Hypothalamus	1e.04	0.028 (0.004)
IBD	Brain_Hypothalamus	0.001	0.029 (0.004)
IBD	Brain_Hypothalamus	0.01	0.031 (0.004)
IBD	Brain_Hypothalamus	0.05	0.035 (0.004)
IBD	Brain_Hypothalamus	0.1	0.036 (0.004)
IBD	Brain_Hypothalamus	0.5	0.038 (0.004)
IBD	Brain_Hypothalamus	1	0.037 (0.004)
IBD	Brain_Hypothalamus	All	0.037 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.038 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.038 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.04 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.04 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.044 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.044 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.043 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.044 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	1	0.044 (0.004)
IBD	Brain_Nucleus_accumbens_basal_ganglia	All	0.048 (0.004)
IBD	Brain_Putamen_basal_ganglia	1e.06	0.03 (0.004)
IBD	Brain_Putamen_basal_ganglia	1e.05	0.037 (0.004)
IBD	Brain_Putamen_basal_ganglia	1e.04	0.038 (0.004)
IBD	Brain_Putamen_basal_ganglia	0.001	0.032 (0.004)
IBD	Brain_Putamen_basal_ganglia	0.01	0.032 (0.004)
IBD	Brain_Putamen_basal_ganglia	0.05	0.033 (0.004)
IBD	Brain_Putamen_basal_ganglia	0.1	0.036 (0.004)
IBD	Brain_Putamen_basal_ganglia	0.5	0.033 (0.004)
IBD	Brain_Putamen_basal_ganglia	1	0.033 (0.004)
IBD	Brain_Putamen_basal_ganglia	All	0.042 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	1e.06	0.026 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	1e.05	0.027 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	1e.04	0.027 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	0.001	0.028 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	0.01	0.026 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	0.05	0.023 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	0.1	0.025 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	0.5	0.026 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	1	0.025 (0.004)
IBD	Brain_Spinal_cord_cervical_c-1	All	0.032 (0.004)
IBD	Brain_Substantia_nigra	1e.06	0.022 (0.004)
IBD	Brain_Substantia_nigra	1e.05	0.022 (0.004)
IBD	Brain_Substantia_nigra	1e.04	0.028 (0.004)
IBD	Brain_Substantia_nigra	0.001	0.015 (0.004)
IBD	Brain_Substantia_nigra	0.01	0.023 (0.004)
IBD	Brain_Substantia_nigra	0.05	0.023 (0.004)
IBD	Brain_Substantia_nigra	0.1	0.022 (0.004)
IBD	Brain_Substantia_nigra	0.5	0.026 (0.004)
IBD	Brain_Substantia_nigra	1	0.026 (0.004)
IBD	Brain_Substantia_nigra	All	0.034 (0.004)
IBD	Breast_Mammary_Tissue	1e.06	0.035 (0.004)
IBD	Breast_Mammary_Tissue	1e.05	0.038 (0.004)
IBD	Breast_Mammary_Tissue	1e.04	0.04 (0.004)
IBD	Breast_Mammary_Tissue	0.001	0.04 (0.004)
IBD	Breast_Mammary_Tissue	0.01	0.044 (0.004)
IBD	Breast_Mammary_Tissue	0.05	0.047 (0.004)
IBD	Breast_Mammary_Tissue	0.1	0.05 (0.004)
IBD	Breast_Mammary_Tissue	0.5	0.052 (0.004)
IBD	Breast_Mammary_Tissue	1	0.052 (0.004)
IBD	Breast_Mammary_Tissue	All	0.054 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	1e.06	0.041 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	1e.05	0.041 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	1e.04	0.043 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	0.001	0.042 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	0.01	0.042 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	0.05	0.042 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	0.1	0.038 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	0.5	0.038 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	1	0.038 (0.004)
IBD	Cells_EBV-transformed_lymphocytes	All	0.047 (0.004)
IBD	Cells_Transformed_fibroblasts	1e.06	0.047 (0.004)
IBD	Cells_Transformed_fibroblasts	1e.05	0.044 (0.004)
IBD	Cells_Transformed_fibroblasts	1e.04	0.046 (0.004)
IBD	Cells_Transformed_fibroblasts	0.001	0.049 (0.004)
IBD	Cells_Transformed_fibroblasts	0.01	0.05 (0.004)
IBD	Cells_Transformed_fibroblasts	0.05	0.057 (0.004)
IBD	Cells_Transformed_fibroblasts	0.1	0.059 (0.004)
IBD	Cells_Transformed_fibroblasts	0.5	0.057 (0.004)
IBD	Cells_Transformed_fibroblasts	1	0.058 (0.004)
IBD	Cells_Transformed_fibroblasts	All	0.061 (0.004)
IBD	CMC.BRAIN.RNASEQ	1e.06	0.04 (0.004)
IBD	CMC.BRAIN.RNASEQ	1e.05	0.041 (0.004)
IBD	CMC.BRAIN.RNASEQ	1e.04	0.048 (0.004)
IBD	CMC.BRAIN.RNASEQ	0.001	0.049 (0.004)
IBD	CMC.BRAIN.RNASEQ	0.01	0.051 (0.004)
IBD	CMC.BRAIN.RNASEQ	0.05	0.053 (0.004)
IBD	CMC.BRAIN.RNASEQ	0.1	0.055 (0.004)
IBD	CMC.BRAIN.RNASEQ	0.5	0.051 (0.004)
IBD	CMC.BRAIN.RNASEQ	1	0.052 (0.004)
IBD	CMC.BRAIN.RNASEQ	All	0.058 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.04 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.037 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.038 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.039 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.041 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.045 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.047 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.044 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	1	0.043 (0.004)
IBD	CMC.BRAIN.RNASEQ_SPLICING	All	0.047 (0.004)
IBD	Colon_Sigmoid	1e.06	0.04 (0.004)
IBD	Colon_Sigmoid	1e.05	0.042 (0.004)
IBD	Colon_Sigmoid	1e.04	0.044 (0.004)
IBD	Colon_Sigmoid	0.001	0.043 (0.004)
IBD	Colon_Sigmoid	0.01	0.045 (0.004)
IBD	Colon_Sigmoid	0.05	0.049 (0.004)
IBD	Colon_Sigmoid	0.1	0.05 (0.004)
IBD	Colon_Sigmoid	0.5	0.051 (0.004)
IBD	Colon_Sigmoid	1	0.05 (0.004)
IBD	Colon_Sigmoid	All	0.054 (0.004)
IBD	Colon_Transverse	1e.06	0.042 (0.004)
IBD	Colon_Transverse	1e.05	0.044 (0.004)
IBD	Colon_Transverse	1e.04	0.042 (0.004)
IBD	Colon_Transverse	0.001	0.047 (0.004)
IBD	Colon_Transverse	0.01	0.052 (0.004)
IBD	Colon_Transverse	0.05	0.05 (0.004)
IBD	Colon_Transverse	0.1	0.051 (0.004)
IBD	Colon_Transverse	0.5	0.051 (0.004)
IBD	Colon_Transverse	1	0.05 (0.004)
IBD	Colon_Transverse	All	0.055 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	1e.06	0.031 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	1e.05	0.035 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	1e.04	0.038 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	0.001	0.041 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	0.01	0.044 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	0.05	0.047 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	0.1	0.05 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	0.5	0.053 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	1	0.051 (0.004)
IBD	Esophagus_Gastroesophageal_Junction	All	0.053 (0.004)
IBD	Esophagus_Mucosa	1e.06	0.046 (0.004)
IBD	Esophagus_Mucosa	1e.05	0.049 (0.004)
IBD	Esophagus_Mucosa	1e.04	0.052 (0.004)
IBD	Esophagus_Mucosa	0.001	0.051 (0.004)
IBD	Esophagus_Mucosa	0.01	0.053 (0.004)
IBD	Esophagus_Mucosa	0.05	0.055 (0.004)
IBD	Esophagus_Mucosa	0.1	0.057 (0.004)
IBD	Esophagus_Mucosa	0.5	0.059 (0.004)
IBD	Esophagus_Mucosa	1	0.059 (0.004)
IBD	Esophagus_Mucosa	All	0.063 (0.004)
IBD	Esophagus_Muscularis	1e.06	0.04 (0.004)
IBD	Esophagus_Muscularis	1e.05	0.045 (0.004)
IBD	Esophagus_Muscularis	1e.04	0.045 (0.004)
IBD	Esophagus_Muscularis	0.001	0.048 (0.004)
IBD	Esophagus_Muscularis	0.01	0.054 (0.004)
IBD	Esophagus_Muscularis	0.05	0.057 (0.004)
IBD	Esophagus_Muscularis	0.1	0.058 (0.004)
IBD	Esophagus_Muscularis	0.5	0.062 (0.004)
IBD	Esophagus_Muscularis	1	0.062 (0.004)
IBD	Esophagus_Muscularis	All	0.063 (0.004)
IBD	Heart_Atrial_Appendage	1e.06	0.029 (0.004)
IBD	Heart_Atrial_Appendage	1e.05	0.034 (0.004)
IBD	Heart_Atrial_Appendage	1e.04	0.039 (0.004)
IBD	Heart_Atrial_Appendage	0.001	0.042 (0.004)
IBD	Heart_Atrial_Appendage	0.01	0.043 (0.004)
IBD	Heart_Atrial_Appendage	0.05	0.047 (0.004)
IBD	Heart_Atrial_Appendage	0.1	0.048 (0.004)
IBD	Heart_Atrial_Appendage	0.5	0.05 (0.004)
IBD	Heart_Atrial_Appendage	1	0.05 (0.004)
IBD	Heart_Atrial_Appendage	All	0.053 (0.004)
IBD	Heart_Left_Ventricle	1e.06	0.041 (0.004)
IBD	Heart_Left_Ventricle	1e.05	0.04 (0.004)
IBD	Heart_Left_Ventricle	1e.04	0.041 (0.004)
IBD	Heart_Left_Ventricle	0.001	0.046 (0.004)
IBD	Heart_Left_Ventricle	0.01	0.051 (0.004)
IBD	Heart_Left_Ventricle	0.05	0.051 (0.004)
IBD	Heart_Left_Ventricle	0.1	0.054 (0.004)
IBD	Heart_Left_Ventricle	0.5	0.055 (0.004)
IBD	Heart_Left_Ventricle	1	0.055 (0.004)
IBD	Heart_Left_Ventricle	All	0.058 (0.004)
IBD	Liver	1e.06	0.03 (0.004)
IBD	Liver	1e.05	0.033 (0.004)
IBD	Liver	1e.04	0.035 (0.004)
IBD	Liver	0.001	0.041 (0.004)
IBD	Liver	0.01	0.042 (0.004)
IBD	Liver	0.05	0.043 (0.004)
IBD	Liver	0.1	0.044 (0.004)
IBD	Liver	0.5	0.044 (0.004)
IBD	Liver	1	0.043 (0.004)
IBD	Liver	All	0.047 (0.004)
IBD	Lung	1e.06	0.04 (0.004)
IBD	Lung	1e.05	0.047 (0.004)
IBD	Lung	1e.04	0.047 (0.004)
IBD	Lung	0.001	0.05 (0.004)
IBD	Lung	0.01	0.053 (0.004)
IBD	Lung	0.05	0.057 (0.004)
IBD	Lung	0.1	0.059 (0.004)
IBD	Lung	0.5	0.061 (0.004)
IBD	Lung	1	0.061 (0.004)
IBD	Lung	All	0.063 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	1e.06	0.042 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	1e.05	0.048 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	1e.04	0.051 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	0.001	0.051 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	0.01	0.056 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	0.05	0.057 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	0.1	0.058 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	0.5	0.058 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	1	0.058 (0.004)
IBD	METSIM.ADIPOSE.RNASEQ	All	0.062 (0.004)
IBD	Minor_Salivary_Gland	1e.06	0.036 (0.004)
IBD	Minor_Salivary_Gland	1e.05	0.038 (0.004)
IBD	Minor_Salivary_Gland	1e.04	0.042 (0.004)
IBD	Minor_Salivary_Gland	0.001	0.036 (0.004)
IBD	Minor_Salivary_Gland	0.01	0.037 (0.004)
IBD	Minor_Salivary_Gland	0.05	0.034 (0.004)
IBD	Minor_Salivary_Gland	0.1	0.036 (0.004)
IBD	Minor_Salivary_Gland	0.5	0.034 (0.004)
IBD	Minor_Salivary_Gland	1	0.034 (0.004)
IBD	Minor_Salivary_Gland	All	0.043 (0.004)
IBD	Muscle_Skeletal	1e.06	0.047 (0.004)
IBD	Muscle_Skeletal	1e.05	0.049 (0.004)
IBD	Muscle_Skeletal	1e.04	0.05 (0.004)
IBD	Muscle_Skeletal	0.001	0.051 (0.004)
IBD	Muscle_Skeletal	0.01	0.057 (0.004)
IBD	Muscle_Skeletal	0.05	0.063 (0.004)
IBD	Muscle_Skeletal	0.1	0.064 (0.004)
IBD	Muscle_Skeletal	0.5	0.062 (0.004)
IBD	Muscle_Skeletal	1	0.061 (0.004)
IBD	Muscle_Skeletal	All	0.066 (0.004)
IBD	Nerve_Tibial	1e.06	0.042 (0.004)
IBD	Nerve_Tibial	1e.05	0.046 (0.004)
IBD	Nerve_Tibial	1e.04	0.048 (0.004)
IBD	Nerve_Tibial	0.001	0.051 (0.004)
IBD	Nerve_Tibial	0.01	0.058 (0.004)
IBD	Nerve_Tibial	0.05	0.059 (0.004)
IBD	Nerve_Tibial	0.1	0.064 (0.004)
IBD	Nerve_Tibial	0.5	0.061 (0.004)
IBD	Nerve_Tibial	1	0.06 (0.004)
IBD	Nerve_Tibial	All	0.065 (0.004)
IBD	NTR.BLOOD.RNAARR	1e.06	0.044 (0.004)
IBD	NTR.BLOOD.RNAARR	1e.05	0.046 (0.004)
IBD	NTR.BLOOD.RNAARR	1e.04	0.047 (0.004)
IBD	NTR.BLOOD.RNAARR	0.001	0.045 (0.004)
IBD	NTR.BLOOD.RNAARR	0.01	0.05 (0.004)
IBD	NTR.BLOOD.RNAARR	0.05	0.049 (0.004)
IBD	NTR.BLOOD.RNAARR	0.1	0.05 (0.004)
IBD	NTR.BLOOD.RNAARR	0.5	0.051 (0.004)
IBD	NTR.BLOOD.RNAARR	1	0.051 (0.004)
IBD	NTR.BLOOD.RNAARR	All	0.055 (0.004)
IBD	Ovary	1e.06	0.03 (0.004)
IBD	Ovary	1e.05	0.031 (0.004)
IBD	Ovary	1e.04	0.035 (0.004)
IBD	Ovary	0.001	0.038 (0.004)
IBD	Ovary	0.01	0.043 (0.004)
IBD	Ovary	0.05	0.047 (0.004)
IBD	Ovary	0.1	0.049 (0.004)
IBD	Ovary	0.5	0.048 (0.004)
IBD	Ovary	1	0.047 (0.004)
IBD	Ovary	All	0.049 (0.004)
IBD	Pancreas	1e.06	0.038 (0.004)
IBD	Pancreas	1e.05	0.041 (0.004)
IBD	Pancreas	1e.04	0.045 (0.004)
IBD	Pancreas	0.001	0.046 (0.004)
IBD	Pancreas	0.01	0.052 (0.004)
IBD	Pancreas	0.05	0.051 (0.004)
IBD	Pancreas	0.1	0.049 (0.004)
IBD	Pancreas	0.5	0.051 (0.004)
IBD	Pancreas	1	0.051 (0.004)
IBD	Pancreas	All	0.055 (0.004)
IBD	Pituitary	1e.06	0.03 (0.004)
IBD	Pituitary	1e.05	0.033 (0.004)
IBD	Pituitary	1e.04	0.034 (0.004)
IBD	Pituitary	0.001	0.038 (0.004)
IBD	Pituitary	0.01	0.042 (0.004)
IBD	Pituitary	0.05	0.043 (0.004)
IBD	Pituitary	0.1	0.043 (0.004)
IBD	Pituitary	0.5	0.044 (0.004)
IBD	Pituitary	1	0.043 (0.004)
IBD	Pituitary	All	0.046 (0.004)
IBD	Prostate	1e.06	0.031 (0.004)
IBD	Prostate	1e.05	0.035 (0.004)
IBD	Prostate	1e.04	0.036 (0.004)
IBD	Prostate	0.001	0.036 (0.004)
IBD	Prostate	0.01	0.042 (0.004)
IBD	Prostate	0.05	0.042 (0.004)
IBD	Prostate	0.1	0.043 (0.004)
IBD	Prostate	0.5	0.044 (0.004)
IBD	Prostate	1	0.045 (0.004)
IBD	Prostate	All	0.047 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.041 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.044 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.045 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.049 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.054 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.054 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.056 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.058 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	1	0.058 (0.004)
IBD	Skin_Not_Sun_Exposed_Suprapubic	All	0.06 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	1e.06	0.041 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	1e.05	0.046 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	1e.04	0.048 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	0.001	0.055 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	0.01	0.058 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	0.05	0.062 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	0.1	0.066 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	0.5	0.067 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	1	0.067 (0.004)
IBD	Skin_Sun_Exposed_Lower_leg	All	0.068 (0.004)
IBD	Small_Intestine_Terminal_Ileum	1e.06	0.043 (0.004)
IBD	Small_Intestine_Terminal_Ileum	1e.05	0.042 (0.004)
IBD	Small_Intestine_Terminal_Ileum	1e.04	0.044 (0.004)
IBD	Small_Intestine_Terminal_Ileum	0.001	0.035 (0.004)
IBD	Small_Intestine_Terminal_Ileum	0.01	0.037 (0.004)
IBD	Small_Intestine_Terminal_Ileum	0.05	0.038 (0.004)
IBD	Small_Intestine_Terminal_Ileum	0.1	0.041 (0.004)
IBD	Small_Intestine_Terminal_Ileum	0.5	0.043 (0.004)
IBD	Small_Intestine_Terminal_Ileum	1	0.042 (0.004)
IBD	Small_Intestine_Terminal_Ileum	All	0.05 (0.004)
IBD	Spleen	1e.06	0.04 (0.004)
IBD	Spleen	1e.05	0.043 (0.004)
IBD	Spleen	1e.04	0.045 (0.004)
IBD	Spleen	0.001	0.047 (0.004)
IBD	Spleen	0.01	0.049 (0.004)
IBD	Spleen	0.05	0.049 (0.004)
IBD	Spleen	0.1	0.05 (0.004)
IBD	Spleen	0.5	0.051 (0.004)
IBD	Spleen	1	0.052 (0.004)
IBD	Spleen	All	0.055 (0.004)
IBD	Stomach	1e.06	0.042 (0.004)
IBD	Stomach	1e.05	0.04 (0.004)
IBD	Stomach	1e.04	0.043 (0.004)
IBD	Stomach	0.001	0.044 (0.004)
IBD	Stomach	0.01	0.045 (0.004)
IBD	Stomach	0.05	0.049 (0.004)
IBD	Stomach	0.1	0.05 (0.004)
IBD	Stomach	0.5	0.051 (0.004)
IBD	Stomach	1	0.052 (0.004)
IBD	Stomach	All	0.054 (0.004)
IBD	Testis	1e.06	0.041 (0.004)
IBD	Testis	1e.05	0.047 (0.004)
IBD	Testis	1e.04	0.049 (0.004)
IBD	Testis	0.001	0.047 (0.004)
IBD	Testis	0.01	0.051 (0.004)
IBD	Testis	0.05	0.059 (0.004)
IBD	Testis	0.1	0.06 (0.004)
IBD	Testis	0.5	0.061 (0.004)
IBD	Testis	1	0.061 (0.004)
IBD	Testis	All	0.065 (0.004)
IBD	Thyroid	1e.06	0.044 (0.004)
IBD	Thyroid	1e.05	0.047 (0.004)
IBD	Thyroid	1e.04	0.048 (0.004)
IBD	Thyroid	0.001	0.053 (0.004)
IBD	Thyroid	0.01	0.056 (0.004)
IBD	Thyroid	0.05	0.061 (0.004)
IBD	Thyroid	0.1	0.062 (0.004)
IBD	Thyroid	0.5	0.06 (0.004)
IBD	Thyroid	1	0.059 (0.004)
IBD	Thyroid	All	0.064 (0.004)
IBD	Uterus	1e.06	0.026 (0.004)
IBD	Uterus	1e.05	0.025 (0.004)
IBD	Uterus	1e.04	0.026 (0.004)
IBD	Uterus	0.001	0.021 (0.004)
IBD	Uterus	0.01	0.028 (0.004)
IBD	Uterus	0.05	0.028 (0.004)
IBD	Uterus	0.1	0.033 (0.004)
IBD	Uterus	0.5	0.031 (0.004)
IBD	Uterus	1	0.031 (0.004)
IBD	Uterus	All	0.04 (0.004)
IBD	Vagina	1e.06	0.023 (0.004)
IBD	Vagina	1e.05	0.026 (0.004)
IBD	Vagina	1e.04	0.026 (0.004)
IBD	Vagina	0.001	0.028 (0.004)
IBD	Vagina	0.01	0.029 (0.004)
IBD	Vagina	0.05	0.03 (0.004)
IBD	Vagina	0.1	0.029 (0.004)
IBD	Vagina	0.5	0.027 (0.004)
IBD	Vagina	1	0.027 (0.004)
IBD	Vagina	All	0.03 (0.004)
IBD	Whole_Blood	1e.06	0.045 (0.004)
IBD	Whole_Blood	1e.05	0.054 (0.004)
IBD	Whole_Blood	1e.04	0.058 (0.004)
IBD	Whole_Blood	0.001	0.055 (0.004)
IBD	Whole_Blood	0.01	0.064 (0.004)
IBD	Whole_Blood	0.05	0.067 (0.004)
IBD	Whole_Blood	0.1	0.067 (0.004)
IBD	Whole_Blood	0.5	0.065 (0.004)
IBD	Whole_Blood	1	0.064 (0.004)
IBD	Whole_Blood	All	0.07 (0.004)
IBD	YFS.BLOOD.RNAARR	1e.06	0.045 (0.004)
IBD	YFS.BLOOD.RNAARR	1e.05	0.044 (0.004)
IBD	YFS.BLOOD.RNAARR	1e.04	0.049 (0.004)
IBD	YFS.BLOOD.RNAARR	0.001	0.049 (0.004)
IBD	YFS.BLOOD.RNAARR	0.01	0.053 (0.004)
IBD	YFS.BLOOD.RNAARR	0.05	0.058 (0.004)
IBD	YFS.BLOOD.RNAARR	0.1	0.057 (0.004)
IBD	YFS.BLOOD.RNAARR	0.5	0.058 (0.004)
IBD	YFS.BLOOD.RNAARR	1	0.059 (0.004)
IBD	YFS.BLOOD.RNAARR	All	0.062 (0.004)
RheuArth	Adipose_Subcutaneous	1e.06	0.042 (0.004)
RheuArth	Adipose_Subcutaneous	1e.05	0.041 (0.004)
RheuArth	Adipose_Subcutaneous	1e.04	0.047 (0.004)
RheuArth	Adipose_Subcutaneous	0.001	0.052 (0.004)
RheuArth	Adipose_Subcutaneous	0.01	0.061 (0.004)
RheuArth	Adipose_Subcutaneous	0.05	0.063 (0.004)
RheuArth	Adipose_Subcutaneous	0.1	0.067 (0.004)
RheuArth	Adipose_Subcutaneous	0.5	0.064 (0.004)
RheuArth	Adipose_Subcutaneous	1	0.065 (0.004)
RheuArth	Adipose_Subcutaneous	All	0.07 (0.004)
RheuArth	Adipose_Visceral_Omentum	1e.06	0.063 (0.004)
RheuArth	Adipose_Visceral_Omentum	1e.05	0.061 (0.004)
RheuArth	Adipose_Visceral_Omentum	1e.04	0.065 (0.004)
RheuArth	Adipose_Visceral_Omentum	0.001	0.066 (0.004)
RheuArth	Adipose_Visceral_Omentum	0.01	0.07 (0.004)
RheuArth	Adipose_Visceral_Omentum	0.05	0.068 (0.004)
RheuArth	Adipose_Visceral_Omentum	0.1	0.068 (0.004)
RheuArth	Adipose_Visceral_Omentum	0.5	0.069 (0.004)
RheuArth	Adipose_Visceral_Omentum	1	0.07 (0.004)
RheuArth	Adipose_Visceral_Omentum	All	0.082 (0.004)
RheuArth	Adrenal_Gland	1e.06	0.063 (0.004)
RheuArth	Adrenal_Gland	1e.05	0.064 (0.004)
RheuArth	Adrenal_Gland	1e.04	0.064 (0.004)
RheuArth	Adrenal_Gland	0.001	0.069 (0.004)
RheuArth	Adrenal_Gland	0.01	0.067 (0.004)
RheuArth	Adrenal_Gland	0.05	0.068 (0.004)
RheuArth	Adrenal_Gland	0.1	0.064 (0.004)
RheuArth	Adrenal_Gland	0.5	0.06 (0.004)
RheuArth	Adrenal_Gland	1	0.059 (0.004)
RheuArth	Adrenal_Gland	All	0.075 (0.004)
RheuArth	Artery_Aorta	1e.06	0.062 (0.004)
RheuArth	Artery_Aorta	1e.05	0.065 (0.004)
RheuArth	Artery_Aorta	1e.04	0.066 (0.004)
RheuArth	Artery_Aorta	0.001	0.068 (0.004)
RheuArth	Artery_Aorta	0.01	0.067 (0.004)
RheuArth	Artery_Aorta	0.05	0.061 (0.004)
RheuArth	Artery_Aorta	0.1	0.063 (0.004)
RheuArth	Artery_Aorta	0.5	0.064 (0.004)
RheuArth	Artery_Aorta	1	0.064 (0.004)
RheuArth	Artery_Aorta	All	0.079 (0.004)
RheuArth	Artery_Coronary	1e.06	0.07 (0.004)
RheuArth	Artery_Coronary	1e.05	0.072 (0.004)
RheuArth	Artery_Coronary	1e.04	0.071 (0.004)
RheuArth	Artery_Coronary	0.001	0.072 (0.004)
RheuArth	Artery_Coronary	0.01	0.064 (0.004)
RheuArth	Artery_Coronary	0.05	0.057 (0.004)
RheuArth	Artery_Coronary	0.1	0.055 (0.004)
RheuArth	Artery_Coronary	0.5	0.057 (0.004)
RheuArth	Artery_Coronary	1	0.057 (0.004)
RheuArth	Artery_Coronary	All	0.079 (0.004)
RheuArth	Artery_Tibial	1e.06	0.06 (0.004)
RheuArth	Artery_Tibial	1e.05	0.061 (0.004)
RheuArth	Artery_Tibial	1e.04	0.065 (0.004)
RheuArth	Artery_Tibial	0.001	0.069 (0.004)
RheuArth	Artery_Tibial	0.01	0.073 (0.004)
RheuArth	Artery_Tibial	0.05	0.074 (0.004)
RheuArth	Artery_Tibial	0.1	0.072 (0.004)
RheuArth	Artery_Tibial	0.5	0.072 (0.004)
RheuArth	Artery_Tibial	1	0.072 (0.004)
RheuArth	Artery_Tibial	All	0.081 (0.004)
RheuArth	Brain_Amygdala	1e.06	0.077 (0.004)
RheuArth	Brain_Amygdala	1e.05	0.077 (0.004)
RheuArth	Brain_Amygdala	1e.04	0.077 (0.004)
RheuArth	Brain_Amygdala	0.001	0.073 (0.004)
RheuArth	Brain_Amygdala	0.01	0.07 (0.004)
RheuArth	Brain_Amygdala	0.05	0.068 (0.004)
RheuArth	Brain_Amygdala	0.1	0.067 (0.004)
RheuArth	Brain_Amygdala	0.5	0.065 (0.004)
RheuArth	Brain_Amygdala	1	0.064 (0.004)
RheuArth	Brain_Amygdala	All	0.083 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.026 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.029 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.031 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	0.001	0.037 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	0.01	0.038 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	0.05	0.042 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	0.1	0.042 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	0.5	0.044 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	1	0.044 (0.004)
RheuArth	Brain_Anterior_cingulate_cortex_BA24	All	0.047 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	1e.06	0.066 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	1e.05	0.064 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	1e.04	0.064 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	0.001	0.063 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	0.01	0.064 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	0.05	0.064 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	0.1	0.064 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	0.5	0.062 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	1	0.06 (0.004)
RheuArth	Brain_Caudate_basal_ganglia	All	0.077 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	1e.06	0.051 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	1e.05	0.051 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	1e.04	0.052 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	0.001	0.058 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	0.01	0.063 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	0.05	0.061 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	0.1	0.062 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	0.5	0.067 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	1	0.065 (0.004)
RheuArth	Brain_Cerebellar_Hemisphere	All	0.071 (0.004)
RheuArth	Brain_Cerebellum	1e.06	0.035 (0.004)
RheuArth	Brain_Cerebellum	1e.05	0.036 (0.004)
RheuArth	Brain_Cerebellum	1e.04	0.04 (0.004)
RheuArth	Brain_Cerebellum	0.001	0.046 (0.004)
RheuArth	Brain_Cerebellum	0.01	0.056 (0.004)
RheuArth	Brain_Cerebellum	0.05	0.06 (0.004)
RheuArth	Brain_Cerebellum	0.1	0.058 (0.004)
RheuArth	Brain_Cerebellum	0.5	0.063 (0.004)
RheuArth	Brain_Cerebellum	1	0.062 (0.004)
RheuArth	Brain_Cerebellum	All	0.066 (0.004)
RheuArth	Brain_Cortex	1e.06	0.084 (0.004)
RheuArth	Brain_Cortex	1e.05	0.085 (0.004)
RheuArth	Brain_Cortex	1e.04	0.082 (0.004)
RheuArth	Brain_Cortex	0.001	0.08 (0.004)
RheuArth	Brain_Cortex	0.01	0.078 (0.004)
RheuArth	Brain_Cortex	0.05	0.074 (0.004)
RheuArth	Brain_Cortex	0.1	0.075 (0.004)
RheuArth	Brain_Cortex	0.5	0.072 (0.004)
RheuArth	Brain_Cortex	1	0.072 (0.004)
RheuArth	Brain_Cortex	All	0.097 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	1e.06	0.039 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	1e.05	0.041 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	1e.04	0.043 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	0.001	0.047 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	0.01	0.051 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	0.05	0.053 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	0.1	0.052 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	0.5	0.052 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	1	0.053 (0.004)
RheuArth	Brain_Frontal_Cortex_BA9	All	0.058 (0.004)
RheuArth	Brain_Hippocampus	1e.06	0.031 (0.004)
RheuArth	Brain_Hippocampus	1e.05	0.031 (0.004)
RheuArth	Brain_Hippocampus	1e.04	0.033 (0.004)
RheuArth	Brain_Hippocampus	0.001	0.04 (0.004)
RheuArth	Brain_Hippocampus	0.01	0.043 (0.004)
RheuArth	Brain_Hippocampus	0.05	0.046 (0.004)
RheuArth	Brain_Hippocampus	0.1	0.05 (0.004)
RheuArth	Brain_Hippocampus	0.5	0.049 (0.004)
RheuArth	Brain_Hippocampus	1	0.048 (0.004)
RheuArth	Brain_Hippocampus	All	0.053 (0.004)
RheuArth	Brain_Hypothalamus	1e.06	0.053 (0.004)
RheuArth	Brain_Hypothalamus	1e.05	0.052 (0.004)
RheuArth	Brain_Hypothalamus	1e.04	0.053 (0.004)
RheuArth	Brain_Hypothalamus	0.001	0.053 (0.004)
RheuArth	Brain_Hypothalamus	0.01	0.054 (0.004)
RheuArth	Brain_Hypothalamus	0.05	0.051 (0.004)
RheuArth	Brain_Hypothalamus	0.1	0.052 (0.004)
RheuArth	Brain_Hypothalamus	0.5	0.048 (0.004)
RheuArth	Brain_Hypothalamus	1	0.047 (0.004)
RheuArth	Brain_Hypothalamus	All	0.06 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.077 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.075 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.074 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.074 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.072 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.065 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.065 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.063 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	1	0.063 (0.004)
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	All	0.085 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	1e.06	0.034 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	1e.05	0.033 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	1e.04	0.035 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	0.001	0.041 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	0.01	0.046 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	0.05	0.049 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	0.1	0.05 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	0.5	0.051 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	1	0.05 (0.004)
RheuArth	Brain_Putamen_basal_ganglia	All	0.052 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	1e.06	0.029 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	1e.05	0.029 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	1e.04	0.029 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	0.001	0.033 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	0.01	0.037 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	0.05	0.043 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	0.1	0.041 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	0.5	0.039 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	1	0.038 (0.004)
RheuArth	Brain_Spinal_cord_cervical_c-1	All	0.042 (0.004)
RheuArth	Brain_Substantia_nigra	1e.06	0.076 (0.004)
RheuArth	Brain_Substantia_nigra	1e.05	0.076 (0.004)
RheuArth	Brain_Substantia_nigra	1e.04	0.074 (0.004)
RheuArth	Brain_Substantia_nigra	0.001	0.074 (0.004)
RheuArth	Brain_Substantia_nigra	0.01	0.074 (0.004)
RheuArth	Brain_Substantia_nigra	0.05	0.075 (0.004)
RheuArth	Brain_Substantia_nigra	0.1	0.073 (0.004)
RheuArth	Brain_Substantia_nigra	0.5	0.068 (0.004)
RheuArth	Brain_Substantia_nigra	1	0.068 (0.004)
RheuArth	Brain_Substantia_nigra	All	0.084 (0.004)
RheuArth	Breast_Mammary_Tissue	1e.06	0.058 (0.004)
RheuArth	Breast_Mammary_Tissue	1e.05	0.06 (0.004)
RheuArth	Breast_Mammary_Tissue	1e.04	0.064 (0.004)
RheuArth	Breast_Mammary_Tissue	0.001	0.068 (0.004)
RheuArth	Breast_Mammary_Tissue	0.01	0.07 (0.004)
RheuArth	Breast_Mammary_Tissue	0.05	0.067 (0.004)
RheuArth	Breast_Mammary_Tissue	0.1	0.064 (0.004)
RheuArth	Breast_Mammary_Tissue	0.5	0.065 (0.004)
RheuArth	Breast_Mammary_Tissue	1	0.065 (0.004)
RheuArth	Breast_Mammary_Tissue	All	0.078 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	1e.06	0.059 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	1e.05	0.059 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	1e.04	0.063 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	0.001	0.065 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	0.01	0.066 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	0.05	0.064 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	0.1	0.064 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	0.5	0.06 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	1	0.059 (0.004)
RheuArth	Cells_EBV-transformed_lymphocytes	All	0.071 (0.004)
RheuArth	Cells_Transformed_fibroblasts	1e.06	0.071 (0.004)
RheuArth	Cells_Transformed_fibroblasts	1e.05	0.072 (0.004)
RheuArth	Cells_Transformed_fibroblasts	1e.04	0.073 (0.004)
RheuArth	Cells_Transformed_fibroblasts	0.001	0.076 (0.004)
RheuArth	Cells_Transformed_fibroblasts	0.01	0.076 (0.004)
RheuArth	Cells_Transformed_fibroblasts	0.05	0.074 (0.004)
RheuArth	Cells_Transformed_fibroblasts	0.1	0.072 (0.004)
RheuArth	Cells_Transformed_fibroblasts	0.5	0.07 (0.004)
RheuArth	Cells_Transformed_fibroblasts	1	0.069 (0.004)
RheuArth	Cells_Transformed_fibroblasts	All	0.086 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	1e.06	0.043 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	1e.05	0.046 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	1e.04	0.05 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	0.001	0.054 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	0.01	0.059 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	0.05	0.064 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	0.1	0.065 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	0.5	0.069 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	1	0.069 (0.004)
RheuArth	CMC.BRAIN.RNASEQ	All	0.073 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.037 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.037 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.04 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.043 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.043 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.044 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.047 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.047 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	1	0.046 (0.004)
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	All	0.056 (0.004)
RheuArth	Colon_Sigmoid	1e.06	0.064 (0.004)
RheuArth	Colon_Sigmoid	1e.05	0.065 (0.004)
RheuArth	Colon_Sigmoid	1e.04	0.069 (0.004)
RheuArth	Colon_Sigmoid	0.001	0.07 (0.004)
RheuArth	Colon_Sigmoid	0.01	0.071 (0.004)
RheuArth	Colon_Sigmoid	0.05	0.069 (0.004)
RheuArth	Colon_Sigmoid	0.1	0.066 (0.004)
RheuArth	Colon_Sigmoid	0.5	0.066 (0.004)
RheuArth	Colon_Sigmoid	1	0.066 (0.004)
RheuArth	Colon_Sigmoid	All	0.081 (0.004)
RheuArth	Colon_Transverse	1e.06	0.057 (0.004)
RheuArth	Colon_Transverse	1e.05	0.059 (0.004)
RheuArth	Colon_Transverse	1e.04	0.059 (0.004)
RheuArth	Colon_Transverse	0.001	0.062 (0.004)
RheuArth	Colon_Transverse	0.01	0.063 (0.004)
RheuArth	Colon_Transverse	0.05	0.065 (0.004)
RheuArth	Colon_Transverse	0.1	0.063 (0.004)
RheuArth	Colon_Transverse	0.5	0.064 (0.004)
RheuArth	Colon_Transverse	1	0.063 (0.004)
RheuArth	Colon_Transverse	All	0.074 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	1e.06	0.057 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	1e.05	0.057 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	1e.04	0.059 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	0.001	0.063 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	0.01	0.059 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	0.05	0.059 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	0.1	0.06 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	0.5	0.058 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	1	0.059 (0.004)
RheuArth	Esophagus_Gastroesophageal_Junction	All	0.071 (0.004)
RheuArth	Esophagus_Mucosa	1e.06	0.064 (0.004)
RheuArth	Esophagus_Mucosa	1e.05	0.062 (0.004)
RheuArth	Esophagus_Mucosa	1e.04	0.063 (0.004)
RheuArth	Esophagus_Mucosa	0.001	0.068 (0.004)
RheuArth	Esophagus_Mucosa	0.01	0.069 (0.004)
RheuArth	Esophagus_Mucosa	0.05	0.069 (0.004)
RheuArth	Esophagus_Mucosa	0.1	0.07 (0.004)
RheuArth	Esophagus_Mucosa	0.5	0.07 (0.004)
RheuArth	Esophagus_Mucosa	1	0.07 (0.004)
RheuArth	Esophagus_Mucosa	All	0.081 (0.004)
RheuArth	Esophagus_Muscularis	1e.06	0.07 (0.004)
RheuArth	Esophagus_Muscularis	1e.05	0.072 (0.004)
RheuArth	Esophagus_Muscularis	1e.04	0.077 (0.004)
RheuArth	Esophagus_Muscularis	0.001	0.078 (0.004)
RheuArth	Esophagus_Muscularis	0.01	0.075 (0.004)
RheuArth	Esophagus_Muscularis	0.05	0.072 (0.004)
RheuArth	Esophagus_Muscularis	0.1	0.071 (0.004)
RheuArth	Esophagus_Muscularis	0.5	0.07 (0.004)
RheuArth	Esophagus_Muscularis	1	0.069 (0.004)
RheuArth	Esophagus_Muscularis	All	0.089 (0.004)
RheuArth	Heart_Atrial_Appendage	1e.06	0.076 (0.004)
RheuArth	Heart_Atrial_Appendage	1e.05	0.076 (0.004)
RheuArth	Heart_Atrial_Appendage	1e.04	0.074 (0.004)
RheuArth	Heart_Atrial_Appendage	0.001	0.073 (0.004)
RheuArth	Heart_Atrial_Appendage	0.01	0.07 (0.004)
RheuArth	Heart_Atrial_Appendage	0.05	0.073 (0.004)
RheuArth	Heart_Atrial_Appendage	0.1	0.073 (0.004)
RheuArth	Heart_Atrial_Appendage	0.5	0.069 (0.004)
RheuArth	Heart_Atrial_Appendage	1	0.07 (0.004)
RheuArth	Heart_Atrial_Appendage	All	0.089 (0.004)
RheuArth	Heart_Left_Ventricle	1e.06	0.075 (0.004)
RheuArth	Heart_Left_Ventricle	1e.05	0.076 (0.004)
RheuArth	Heart_Left_Ventricle	1e.04	0.079 (0.004)
RheuArth	Heart_Left_Ventricle	0.001	0.079 (0.004)
RheuArth	Heart_Left_Ventricle	0.01	0.078 (0.004)
RheuArth	Heart_Left_Ventricle	0.05	0.075 (0.004)
RheuArth	Heart_Left_Ventricle	0.1	0.072 (0.004)
RheuArth	Heart_Left_Ventricle	0.5	0.07 (0.004)
RheuArth	Heart_Left_Ventricle	1	0.071 (0.004)
RheuArth	Heart_Left_Ventricle	All	0.089 (0.004)
RheuArth	Liver	1e.06	0.071 (0.004)
RheuArth	Liver	1e.05	0.073 (0.004)
RheuArth	Liver	1e.04	0.07 (0.004)
RheuArth	Liver	0.001	0.072 (0.004)
RheuArth	Liver	0.01	0.072 (0.004)
RheuArth	Liver	0.05	0.073 (0.004)
RheuArth	Liver	0.1	0.071 (0.004)
RheuArth	Liver	0.5	0.066 (0.004)
RheuArth	Liver	1	0.066 (0.004)
RheuArth	Liver	All	0.081 (0.004)
RheuArth	Lung	1e.06	0.074 (0.004)
RheuArth	Lung	1e.05	0.073 (0.004)
RheuArth	Lung	1e.04	0.073 (0.004)
RheuArth	Lung	0.001	0.076 (0.004)
RheuArth	Lung	0.01	0.072 (0.004)
RheuArth	Lung	0.05	0.067 (0.004)
RheuArth	Lung	0.1	0.066 (0.004)
RheuArth	Lung	0.5	0.069 (0.004)
RheuArth	Lung	1	0.069 (0.004)
RheuArth	Lung	All	0.089 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	1e.06	0.083 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	1e.05	0.081 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	1e.04	0.081 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	0.001	0.083 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	0.01	0.083 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	0.05	0.08 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	0.1	0.081 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	0.5	0.08 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	1	0.079 (0.004)
RheuArth	METSIM.ADIPOSE.RNASEQ	All	0.095 (0.004)
RheuArth	Minor_Salivary_Gland	1e.06	0.069 (0.004)
RheuArth	Minor_Salivary_Gland	1e.05	0.07 (0.004)
RheuArth	Minor_Salivary_Gland	1e.04	0.071 (0.004)
RheuArth	Minor_Salivary_Gland	0.001	0.071 (0.004)
RheuArth	Minor_Salivary_Gland	0.01	0.069 (0.004)
RheuArth	Minor_Salivary_Gland	0.05	0.066 (0.004)
RheuArth	Minor_Salivary_Gland	0.1	0.063 (0.004)
RheuArth	Minor_Salivary_Gland	0.5	0.06 (0.004)
RheuArth	Minor_Salivary_Gland	1	0.059 (0.004)
RheuArth	Minor_Salivary_Gland	All	0.076 (0.004)
RheuArth	Muscle_Skeletal	1e.06	0.063 (0.004)
RheuArth	Muscle_Skeletal	1e.05	0.065 (0.004)
RheuArth	Muscle_Skeletal	1e.04	0.063 (0.004)
RheuArth	Muscle_Skeletal	0.001	0.068 (0.004)
RheuArth	Muscle_Skeletal	0.01	0.07 (0.004)
RheuArth	Muscle_Skeletal	0.05	0.071 (0.004)
RheuArth	Muscle_Skeletal	0.1	0.07 (0.004)
RheuArth	Muscle_Skeletal	0.5	0.072 (0.004)
RheuArth	Muscle_Skeletal	1	0.072 (0.004)
RheuArth	Muscle_Skeletal	All	0.082 (0.004)
RheuArth	Nerve_Tibial	1e.06	0.062 (0.004)
RheuArth	Nerve_Tibial	1e.05	0.063 (0.004)
RheuArth	Nerve_Tibial	1e.04	0.065 (0.004)
RheuArth	Nerve_Tibial	0.001	0.069 (0.004)
RheuArth	Nerve_Tibial	0.01	0.068 (0.004)
RheuArth	Nerve_Tibial	0.05	0.071 (0.004)
RheuArth	Nerve_Tibial	0.1	0.069 (0.004)
RheuArth	Nerve_Tibial	0.5	0.067 (0.004)
RheuArth	Nerve_Tibial	1	0.067 (0.004)
RheuArth	Nerve_Tibial	All	0.083 (0.004)
RheuArth	NTR.BLOOD.RNAARR	1e.06	0.042 (0.004)
RheuArth	NTR.BLOOD.RNAARR	1e.05	0.049 (0.004)
RheuArth	NTR.BLOOD.RNAARR	1e.04	0.055 (0.004)
RheuArth	NTR.BLOOD.RNAARR	0.001	0.058 (0.004)
RheuArth	NTR.BLOOD.RNAARR	0.01	0.061 (0.004)
RheuArth	NTR.BLOOD.RNAARR	0.05	0.063 (0.004)
RheuArth	NTR.BLOOD.RNAARR	0.1	0.064 (0.004)
RheuArth	NTR.BLOOD.RNAARR	0.5	0.069 (0.004)
RheuArth	NTR.BLOOD.RNAARR	1	0.07 (0.004)
RheuArth	NTR.BLOOD.RNAARR	All	0.073 (0.004)
RheuArth	Ovary	1e.06	0.066 (0.004)
RheuArth	Ovary	1e.05	0.066 (0.004)
RheuArth	Ovary	1e.04	0.067 (0.004)
RheuArth	Ovary	0.001	0.069 (0.004)
RheuArth	Ovary	0.01	0.068 (0.004)
RheuArth	Ovary	0.05	0.068 (0.004)
RheuArth	Ovary	0.1	0.064 (0.004)
RheuArth	Ovary	0.5	0.062 (0.004)
RheuArth	Ovary	1	0.061 (0.004)
RheuArth	Ovary	All	0.077 (0.004)
RheuArth	Pancreas	1e.06	0.061 (0.004)
RheuArth	Pancreas	1e.05	0.059 (0.004)
RheuArth	Pancreas	1e.04	0.064 (0.004)
RheuArth	Pancreas	0.001	0.066 (0.004)
RheuArth	Pancreas	0.01	0.068 (0.004)
RheuArth	Pancreas	0.05	0.072 (0.004)
RheuArth	Pancreas	0.1	0.07 (0.004)
RheuArth	Pancreas	0.5	0.069 (0.004)
RheuArth	Pancreas	1	0.07 (0.004)
RheuArth	Pancreas	All	0.078 (0.004)
RheuArth	Pituitary	1e.06	0.061 (0.004)
RheuArth	Pituitary	1e.05	0.063 (0.004)
RheuArth	Pituitary	1e.04	0.061 (0.004)
RheuArth	Pituitary	0.001	0.065 (0.004)
RheuArth	Pituitary	0.01	0.065 (0.004)
RheuArth	Pituitary	0.05	0.064 (0.004)
RheuArth	Pituitary	0.1	0.066 (0.004)
RheuArth	Pituitary	0.5	0.065 (0.004)
RheuArth	Pituitary	1	0.064 (0.004)
RheuArth	Pituitary	All	0.077 (0.004)
RheuArth	Prostate	1e.06	0.03 (0.004)
RheuArth	Prostate	1e.05	0.032 (0.004)
RheuArth	Prostate	1e.04	0.034 (0.004)
RheuArth	Prostate	0.001	0.04 (0.004)
RheuArth	Prostate	0.01	0.042 (0.004)
RheuArth	Prostate	0.05	0.043 (0.004)
RheuArth	Prostate	0.1	0.044 (0.004)
RheuArth	Prostate	0.5	0.049 (0.004)
RheuArth	Prostate	1	0.047 (0.004)
RheuArth	Prostate	All	0.05 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.04 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.041 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.044 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.049 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.055 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.055 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.055 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.056 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	1	0.056 (0.004)
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	All	0.062 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	1e.06	0.048 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	1e.05	0.049 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	1e.04	0.05 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	0.001	0.054 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	0.01	0.059 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	0.05	0.06 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	0.1	0.058 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	0.5	0.059 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	1	0.058 (0.004)
RheuArth	Skin_Sun_Exposed_Lower_leg	All	0.067 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	1e.06	0.067 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	1e.05	0.066 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	1e.04	0.066 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	0.001	0.069 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	0.01	0.066 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	0.05	0.063 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	0.1	0.061 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	0.5	0.059 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	1	0.058 (0.004)
RheuArth	Small_Intestine_Terminal_Ileum	All	0.074 (0.004)
RheuArth	Spleen	1e.06	0.068 (0.004)
RheuArth	Spleen	1e.05	0.07 (0.004)
RheuArth	Spleen	1e.04	0.071 (0.004)
RheuArth	Spleen	0.001	0.073 (0.004)
RheuArth	Spleen	0.01	0.071 (0.004)
RheuArth	Spleen	0.05	0.074 (0.004)
RheuArth	Spleen	0.1	0.074 (0.004)
RheuArth	Spleen	0.5	0.068 (0.004)
RheuArth	Spleen	1	0.068 (0.004)
RheuArth	Spleen	All	0.082 (0.004)
RheuArth	Stomach	1e.06	0.076 (0.004)
RheuArth	Stomach	1e.05	0.078 (0.004)
RheuArth	Stomach	1e.04	0.075 (0.004)
RheuArth	Stomach	0.001	0.077 (0.004)
RheuArth	Stomach	0.01	0.073 (0.004)
RheuArth	Stomach	0.05	0.071 (0.004)
RheuArth	Stomach	0.1	0.071 (0.004)
RheuArth	Stomach	0.5	0.065 (0.004)
RheuArth	Stomach	1	0.065 (0.004)
RheuArth	Stomach	All	0.087 (0.004)
RheuArth	Testis	1e.06	0.059 (0.004)
RheuArth	Testis	1e.05	0.06 (0.004)
RheuArth	Testis	1e.04	0.06 (0.004)
RheuArth	Testis	0.001	0.065 (0.004)
RheuArth	Testis	0.01	0.069 (0.004)
RheuArth	Testis	0.05	0.067 (0.004)
RheuArth	Testis	0.1	0.067 (0.004)
RheuArth	Testis	0.5	0.067 (0.004)
RheuArth	Testis	1	0.066 (0.004)
RheuArth	Testis	All	0.076 (0.004)
RheuArth	Thyroid	1e.06	0.068 (0.004)
RheuArth	Thyroid	1e.05	0.07 (0.004)
RheuArth	Thyroid	1e.04	0.071 (0.004)
RheuArth	Thyroid	0.001	0.074 (0.004)
RheuArth	Thyroid	0.01	0.069 (0.004)
RheuArth	Thyroid	0.05	0.07 (0.004)
RheuArth	Thyroid	0.1	0.07 (0.004)
RheuArth	Thyroid	0.5	0.066 (0.004)
RheuArth	Thyroid	1	0.067 (0.004)
RheuArth	Thyroid	All	0.086 (0.004)
RheuArth	Uterus	1e.06	0.064 (0.004)
RheuArth	Uterus	1e.05	0.066 (0.004)
RheuArth	Uterus	1e.04	0.067 (0.004)
RheuArth	Uterus	0.001	0.066 (0.004)
RheuArth	Uterus	0.01	0.067 (0.004)
RheuArth	Uterus	0.05	0.065 (0.004)
RheuArth	Uterus	0.1	0.062 (0.004)
RheuArth	Uterus	0.5	0.062 (0.004)
RheuArth	Uterus	1	0.06 (0.004)
RheuArth	Uterus	All	0.076 (0.004)
RheuArth	Vagina	1e.06	0.074 (0.004)
RheuArth	Vagina	1e.05	0.074 (0.004)
RheuArth	Vagina	1e.04	0.076 (0.004)
RheuArth	Vagina	0.001	0.075 (0.004)
RheuArth	Vagina	0.01	0.068 (0.004)
RheuArth	Vagina	0.05	0.067 (0.004)
RheuArth	Vagina	0.1	0.065 (0.004)
RheuArth	Vagina	0.5	0.062 (0.004)
RheuArth	Vagina	1	0.061 (0.004)
RheuArth	Vagina	All	0.08 (0.004)
RheuArth	Whole_Blood	1e.06	0.06 (0.004)
RheuArth	Whole_Blood	1e.05	0.059 (0.004)
RheuArth	Whole_Blood	1e.04	0.064 (0.004)
RheuArth	Whole_Blood	0.001	0.068 (0.004)
RheuArth	Whole_Blood	0.01	0.068 (0.004)
RheuArth	Whole_Blood	0.05	0.068 (0.004)
RheuArth	Whole_Blood	0.1	0.067 (0.004)
RheuArth	Whole_Blood	0.5	0.071 (0.004)
RheuArth	Whole_Blood	1	0.07 (0.004)
RheuArth	Whole_Blood	All	0.081 (0.004)
RheuArth	YFS.BLOOD.RNAARR	1e.06	0.074 (0.004)
RheuArth	YFS.BLOOD.RNAARR	1e.05	0.075 (0.004)
RheuArth	YFS.BLOOD.RNAARR	1e.04	0.079 (0.004)
RheuArth	YFS.BLOOD.RNAARR	0.001	0.076 (0.004)
RheuArth	YFS.BLOOD.RNAARR	0.01	0.079 (0.004)
RheuArth	YFS.BLOOD.RNAARR	0.05	0.079 (0.004)
RheuArth	YFS.BLOOD.RNAARR	0.1	0.074 (0.004)
RheuArth	YFS.BLOOD.RNAARR	0.5	0.073 (0.004)
RheuArth	YFS.BLOOD.RNAARR	1	0.074 (0.004)
RheuArth	YFS.BLOOD.RNAARR	All	0.091 (0.004)

Correlation between GeRS model predictions and observed values in UKBB

Phenotype	Weight	Model	R (SE)	P
Depression	Adipose_Subcutaneous	All	0.05 (0.004)	0e+00
Depression	Adipose_Visceral_Omentum	All	0.043 (0.004)	0e+00
Depression	Adrenal_Gland	All	0.049 (0.004)	0e+00
Depression	Artery_Aorta	All	0.051 (0.004)	0e+00
Depression	Artery_Coronary	1	0.036 (0.004)	0e+00
Depression	Artery_Tibial	All	0.05 (0.004)	0e+00
Depression	Brain_Amygdala	0.05	0.034 (0.004)	0e+00
Depression	Brain_Anterior_cingulate_cortex_BA24	All	0.04 (0.004)	0e+00
Depression	Brain_Caudate_basal_ganglia	All	0.041 (0.004)	0e+00
Depression	Brain_Cerebellar_Hemisphere	All	0.047 (0.004)	0e+00
Depression	Brain_Cerebellum	All	0.044 (0.004)	0e+00
Depression	Brain_Cortex	All	0.048 (0.004)	0e+00
Depression	Brain_Frontal_Cortex_BA9	All	0.038 (0.004)	0e+00
Depression	Brain_Hippocampus	All	0.037 (0.004)	0e+00
Depression	Brain_Hypothalamus	All	0.037 (0.004)	0e+00
Depression	Brain_Nucleus_accumbens_basal_ganglia	All	0.039 (0.004)	0e+00
Depression	Brain_Putamen_basal_ganglia	All	0.04 (0.004)	0e+00
Depression	Brain_Spinal_cord_cervical_c-1	All	0.036 (0.004)	0e+00
Depression	Brain_Substantia_nigra	All	0.033 (0.004)	0e+00
Depression	Breast_Mammary_Tissue	All	0.043 (0.004)	0e+00
Depression	Cells_EBV-transformed_lymphocytes	0.5	0.041 (0.004)	0e+00
Depression	Cells_Transformed_fibroblasts	1	0.047 (0.004)	0e+00
Depression	CMC.BRAIN.RNASEQ	0.5	0.052 (0.004)	0e+00
Depression	CMC.BRAIN.RNASEQ_SPLICING	All	0.048 (0.004)	0e+00
Depression	Colon_Sigmoid	All	0.044 (0.004)	0e+00
Depression	Colon_Transverse	All	0.05 (0.004)	0e+00
Depression	Esophagus_Gastroesophageal_Junction	All	0.043 (0.004)	0e+00
Depression	Esophagus_Mucosa	All	0.05 (0.004)	0e+00
Depression	Esophagus_Muscularis	All	0.053 (0.004)	0e+00
Depression	Heart_Atrial_Appendage	All	0.045 (0.004)	0e+00
Depression	Heart_Left_Ventricle	0.5	0.043 (0.004)	0e+00
Depression	Liver	1	0.034 (0.004)	0e+00
Depression	Lung	All	0.052 (0.004)	0e+00
Depression	METSIM.ADIPOSE.RNASEQ	All	0.043 (0.004)	0e+00
Depression	Minor_Salivary_Gland	All	0.03 (0.004)	0e+00
Depression	Muscle_Skeletal	1	0.041 (0.004)	0e+00
Depression	Nerve_Tibial	All	0.052 (0.004)	0e+00
Depression	NTR.BLOOD.RNAARR	1	0.037 (0.004)	0e+00
Depression	Ovary	All	0.04 (0.004)	0e+00
Depression	Pancreas	All	0.048 (0.004)	0e+00
Depression	Pituitary	1	0.037 (0.004)	0e+00
Depression	Prostate	All	0.031 (0.004)	0e+00
Depression	Skin_Not_Sun_Exposed_Suprapubic	All	0.051 (0.004)	0e+00
Depression	Skin_Sun_Exposed_Lower_leg	All	0.049 (0.004)	0e+00
Depression	Small_Intestine_Terminal_Ileum	1	0.04 (0.004)	0e+00
Depression	Spleen	All	0.043 (0.004)	0e+00
Depression	Stomach	All	0.044 (0.004)	0e+00
Depression	Testis	All	0.054 (0.004)	0e+00
Depression	Thyroid	All	0.058 (0.004)	0e+00
Depression	Uterus	All	0.032 (0.004)	0e+00
Depression	Vagina	All	0.033 (0.004)	0e+00
Depression	Whole_Blood	All	0.049 (0.004)	0e+00
Depression	YFS.BLOOD.RNAARR	1	0.043 (0.004)	0e+00
Intelligence	Adipose_Subcutaneous	All	0.056 (0.004)	0e+00
Intelligence	Adipose_Visceral_Omentum	All	0.048 (0.004)	0e+00
Intelligence	Adrenal_Gland	All	0.046 (0.004)	0e+00
Intelligence	Artery_Aorta	All	0.045 (0.004)	0e+00
Intelligence	Artery_Coronary	All	0.041 (0.004)	0e+00
Intelligence	Artery_Tibial	All	0.055 (0.004)	0e+00
Intelligence	Brain_Amygdala	All	0.04 (0.004)	0e+00
Intelligence	Brain_Anterior_cingulate_cortex_BA24	All	0.041 (0.004)	0e+00
Intelligence	Brain_Caudate_basal_ganglia	All	0.044 (0.004)	0e+00
Intelligence	Brain_Cerebellar_Hemisphere	All	0.045 (0.004)	0e+00
Intelligence	Brain_Cerebellum	All	0.047 (0.004)	0e+00
Intelligence	Brain_Cortex	All	0.041 (0.004)	0e+00
Intelligence	Brain_Frontal_Cortex_BA9	All	0.038 (0.004)	0e+00
Intelligence	Brain_Hippocampus	All	0.037 (0.004)	0e+00
Intelligence	Brain_Hypothalamus	All	0.037 (0.004)	0e+00
Intelligence	Brain_Nucleus_accumbens_basal_ganglia	All	0.04 (0.004)	0e+00
Intelligence	Brain_Putamen_basal_ganglia	All	0.043 (0.004)	0e+00
Intelligence	Brain_Spinal_cord_cervical_c-1	All	0.034 (0.004)	0e+00
Intelligence	Brain_Substantia_nigra	All	0.033 (0.004)	0e+00
Intelligence	Breast_Mammary_Tissue	All	0.045 (0.004)	0e+00
Intelligence	Cells_EBV-transformed_lymphocytes	0.05	0.036 (0.004)	0e+00
Intelligence	Cells_Transformed_fibroblasts	All	0.049 (0.004)	0e+00
Intelligence	CMC.BRAIN.RNASEQ	All	0.048 (0.004)	0e+00
Intelligence	CMC.BRAIN.RNASEQ_SPLICING	All	0.046 (0.004)	0e+00
Intelligence	Colon_Sigmoid	All	0.045 (0.004)	0e+00
Intelligence	Colon_Transverse	All	0.046 (0.004)	0e+00
Intelligence	Esophagus_Gastroesophageal_Junction	All	0.046 (0.004)	0e+00
Intelligence	Esophagus_Mucosa	All	0.044 (0.004)	0e+00
Intelligence	Esophagus_Muscularis	All	0.053 (0.004)	0e+00
Intelligence	Heart_Atrial_Appendage	All	0.051 (0.004)	0e+00
Intelligence	Heart_Left_Ventricle	0.5	0.044 (0.004)	0e+00
Intelligence	Liver	All	0.04 (0.004)	0e+00
Intelligence	Lung	All	0.05 (0.004)	0e+00
Intelligence	METSIM.ADIPOSE.RNASEQ	All	0.049 (0.004)	0e+00
Intelligence	Minor_Salivary_Gland	All	0.035 (0.004)	0e+00
Intelligence	Muscle_Skeletal	0.5	0.054 (0.004)	0e+00
Intelligence	Nerve_Tibial	All	0.052 (0.004)	0e+00
Intelligence	NTR.BLOOD.RNAARR	All	0.038 (0.004)	0e+00
Intelligence	Ovary	0.5	0.039 (0.004)	0e+00
Intelligence	Pancreas	All	0.049 (0.004)	0e+00
Intelligence	Pituitary	All	0.043 (0.004)	0e+00
Intelligence	Prostate	All	0.042 (0.004)	0e+00
Intelligence	Skin_Not_Sun_Exposed_Suprapubic	All	0.051 (0.004)	0e+00
Intelligence	Skin_Sun_Exposed_Lower_leg	All	0.046 (0.004)	0e+00
Intelligence	Small_Intestine_Terminal_Ileum	All	0.035 (0.004)	0e+00
Intelligence	Spleen	All	0.047 (0.004)	0e+00
Intelligence	Stomach	All	0.048 (0.004)	0e+00
Intelligence	Testis	All	0.051 (0.004)	0e+00
Intelligence	Thyroid	All	0.052 (0.004)	0e+00
Intelligence	Uterus	All	0.04 (0.004)	0e+00
Intelligence	Vagina	All	0.039 (0.004)	0e+00
Intelligence	Whole_Blood	All	0.043 (0.004)	0e+00
Intelligence	YFS.BLOOD.RNAARR	All	0.049 (0.004)	0e+00
BMI	Adipose_Subcutaneous	All	0.128 (0.004)	0e+00
BMI	Adipose_Visceral_Omentum	All	0.119 (0.004)	0e+00
BMI	Adrenal_Gland	All	0.103 (0.004)	0e+00
BMI	Artery_Aorta	All	0.116 (0.004)	0e+00
BMI	Artery_Coronary	All	0.103 (0.004)	0e+00
BMI	Artery_Tibial	All	0.13 (0.004)	0e+00
BMI	Brain_Amygdala	All	0.083 (0.004)	0e+00
BMI	Brain_Anterior_cingulate_cortex_BA24	All	0.1 (0.004)	0e+00
BMI	Brain_Caudate_basal_ganglia	All	0.102 (0.004)	0e+00
BMI	Brain_Cerebellar_Hemisphere	All	0.105 (0.004)	0e+00
BMI	Brain_Cerebellum	All	0.11 (0.004)	0e+00
BMI	Brain_Cortex	All	0.102 (0.004)	0e+00
BMI	Brain_Frontal_Cortex_BA9	All	0.098 (0.004)	0e+00
BMI	Brain_Hippocampus	All	0.084 (0.004)	0e+00
BMI	Brain_Hypothalamus	All	0.082 (0.004)	0e+00
BMI	Brain_Nucleus_accumbens_basal_ganglia	All	0.095 (0.004)	0e+00
BMI	Brain_Putamen_basal_ganglia	All	0.096 (0.004)	0e+00
BMI	Brain_Spinal_cord_cervical_c-1	1	0.09 (0.004)	0e+00
BMI	Brain_Substantia_nigra	All	0.083 (0.004)	0e+00
BMI	Breast_Mammary_Tissue	All	0.111 (0.004)	0e+00
BMI	Cells_EBV-transformed_lymphocytes	1	0.097 (0.004)	0e+00
BMI	Cells_Transformed_fibroblasts	All	0.124 (0.004)	0e+00
BMI	CMC.BRAIN.RNASEQ	All	0.126 (0.004)	0e+00
BMI	CMC.BRAIN.RNASEQ_SPLICING	All	0.109 (0.004)	0e+00
BMI	Colon_Sigmoid	All	0.095 (0.004)	0e+00
BMI	Colon_Transverse	All	0.102 (0.004)	0e+00
BMI	Esophagus_Gastroesophageal_Junction	All	0.102 (0.004)	0e+00
BMI	Esophagus_Mucosa	1	0.119 (0.004)	0e+00
BMI	Esophagus_Muscularis	All	0.12 (0.004)	0e+00
BMI	Heart_Atrial_Appendage	All	0.115 (0.004)	0e+00
BMI	Heart_Left_Ventricle	1	0.112 (0.004)	0e+00
BMI	Liver	0.5	0.091 (0.004)	0e+00
BMI	Lung	All	0.12 (0.004)	0e+00
BMI	METSIM.ADIPOSE.RNASEQ	All	0.109 (0.004)	0e+00
BMI	Minor_Salivary_Gland	1	0.086 (0.004)	0e+00
BMI	Muscle_Skeletal	All	0.12 (0.004)	0e+00
BMI	Nerve_Tibial	All	0.127 (0.004)	0e+00
BMI	NTR.BLOOD.RNAARR	All	0.094 (0.004)	0e+00
BMI	Ovary	All	0.084 (0.004)	0e+00
BMI	Pancreas	All	0.116 (0.004)	0e+00
BMI	Pituitary	All	0.099 (0.004)	0e+00
BMI	Prostate	All	0.093 (0.004)	0e+00
BMI	Skin_Not_Sun_Exposed_Suprapubic	All	0.118 (0.004)	0e+00
BMI	Skin_Sun_Exposed_Lower_leg	All	0.12 (0.004)	0e+00
BMI	Small_Intestine_Terminal_Ileum	All	0.095 (0.004)	0e+00
BMI	Spleen	All	0.108 (0.004)	0e+00
BMI	Stomach	All	0.111 (0.004)	0e+00
BMI	Testis	All	0.125 (0.004)	0e+00
BMI	Thyroid	All	0.128 (0.004)	0e+00
BMI	Uterus	All	0.083 (0.004)	0e+00
BMI	Vagina	All	0.088 (0.004)	0e+00
BMI	Whole_Blood	All	0.11 (0.004)	0e+00
BMI	YFS.BLOOD.RNAARR	1	0.108 (0.004)	0e+00
Height	Adipose_Subcutaneous	All	0.179 (0.004)	0e+00
Height	Adipose_Visceral_Omentum	All	0.164 (0.004)	0e+00
Height	Adrenal_Gland	All	0.148 (0.004)	0e+00
Height	Artery_Aorta	1	0.161 (0.004)	0e+00
Height	Artery_Coronary	All	0.136 (0.004)	0e+00
Height	Artery_Tibial	All	0.18 (0.004)	0e+00
Height	Brain_Amygdala	All	0.114 (0.004)	0e+00
Height	Brain_Anterior_cingulate_cortex_BA24	All	0.126 (0.004)	0e+00
Height	Brain_Caudate_basal_ganglia	All	0.132 (0.004)	0e+00
Height	Brain_Cerebellar_Hemisphere	All	0.137 (0.004)	0e+00
Height	Brain_Cerebellum	All	0.146 (0.004)	0e+00
Height	Brain_Cortex	All	0.138 (0.004)	0e+00
Height	Brain_Frontal_Cortex_BA9	All	0.135 (0.004)	0e+00
Height	Brain_Hippocampus	1	0.113 (0.004)	0e+00
Height	Brain_Hypothalamus	0.5	0.113 (0.004)	0e+00
Height	Brain_Nucleus_accumbens_basal_ganglia	All	0.125 (0.004)	0e+00
Height	Brain_Putamen_basal_ganglia	1	0.123 (0.004)	0e+00
Height	Brain_Spinal_cord_cervical_c-1	All	0.113 (0.004)	0e+00
Height	Brain_Substantia_nigra	All	0.101 (0.004)	0e+00
Height	Breast_Mammary_Tissue	All	0.152 (0.004)	0e+00
Height	Cells_EBV-transformed_lymphocytes	0.5	0.13 (0.004)	0e+00
Height	Cells_Transformed_fibroblasts	1	0.17 (0.004)	0e+00
Height	CMC.BRAIN.RNASEQ	0.5	0.165 (0.004)	0e+00
Height	CMC.BRAIN.RNASEQ_SPLICING	All	0.147 (0.004)	0e+00
Height	Colon_Sigmoid	All	0.142 (0.004)	0e+00
Height	Colon_Transverse	All	0.149 (0.004)	0e+00
Height	Esophagus_Gastroesophageal_Junction	1	0.15 (0.004)	0e+00
Height	Esophagus_Mucosa	0.5	0.176 (0.004)	0e+00
Height	Esophagus_Muscularis	0.5	0.17 (0.004)	0e+00
Height	Heart_Atrial_Appendage	0.5	0.156 (0.004)	0e+00
Height	Heart_Left_Ventricle	0.5	0.148 (0.004)	0e+00
Height	Liver	All	0.134 (0.004)	0e+00
Height	Lung	All	0.169 (0.004)	0e+00
Height	METSIM.ADIPOSE.RNASEQ	0.5	0.152 (0.004)	0e+00
Height	Minor_Salivary_Gland	All	0.109 (0.004)	0e+00
Height	Muscle_Skeletal	0.5	0.169 (0.004)	0e+00
Height	Nerve_Tibial	All	0.182 (0.004)	0e+00
Height	NTR.BLOOD.RNAARR	All	0.139 (0.004)	0e+00
Height	Ovary	All	0.12 (0.004)	0e+00
Height	Pancreas	All	0.155 (0.004)	0e+00
Height	Pituitary	All	0.143 (0.004)	0e+00
Height	Prostate	0.5	0.114 (0.004)	0e+00
Height	Skin_Not_Sun_Exposed_Suprapubic	1	0.168 (0.004)	0e+00
Height	Skin_Sun_Exposed_Lower_leg	All	0.175 (0.004)	0e+00
Height	Small_Intestine_Terminal_Ileum	All	0.126 (0.004)	0e+00
Height	Spleen	All	0.145 (0.004)	0e+00
Height	Stomach	All	0.151 (0.004)	0e+00
Height	Testis	All	0.176 (0.004)	0e+00
Height	Thyroid	0.5	0.181 (0.004)	0e+00
Height	Uterus	1	0.107 (0.004)	0e+00
Height	Vagina	All	0.109 (0.004)	0e+00
Height	Whole_Blood	All	0.162 (0.004)	0e+00
Height	YFS.BLOOD.RNAARR	All	0.164 (0.004)	0e+00
T2D	Adipose_Subcutaneous	All	0.11 (0.004)	0e+00
T2D	Adipose_Visceral_Omentum	All	0.088 (0.004)	0e+00
T2D	Adrenal_Gland	All	0.082 (0.004)	0e+00
T2D	Artery_Aorta	All	0.101 (0.004)	0e+00
T2D	Artery_Coronary	All	0.069 (0.004)	0e+00
T2D	Artery_Tibial	All	0.107 (0.004)	0e+00
T2D	Brain_Amygdala	All	0.067 (0.004)	0e+00
T2D	Brain_Anterior_cingulate_cortex_BA24	All	0.069 (0.004)	0e+00
T2D	Brain_Caudate_basal_ganglia	All	0.07 (0.004)	0e+00
T2D	Brain_Cerebellar_Hemisphere	All	0.081 (0.004)	0e+00
T2D	Brain_Cerebellum	All	0.084 (0.004)	0e+00
T2D	Brain_Cortex	All	0.07 (0.004)	0e+00
T2D	Brain_Frontal_Cortex_BA9	All	0.071 (0.004)	0e+00
T2D	Brain_Hippocampus	All	0.062 (0.004)	0e+00
T2D	Brain_Hypothalamus	All	0.061 (0.004)	0e+00
T2D	Brain_Nucleus_accumbens_basal_ganglia	All	0.072 (0.004)	0e+00
T2D	Brain_Putamen_basal_ganglia	All	0.067 (0.004)	0e+00
T2D	Brain_Spinal_cord_cervical_c-1	All	0.061 (0.004)	0e+00
T2D	Brain_Substantia_nigra	All	0.049 (0.004)	0e+00
T2D	Breast_Mammary_Tissue	All	0.088 (0.004)	0e+00
T2D	Cells_EBV-transformed_lymphocytes	All	0.063 (0.004)	0e+00
T2D	Cells_Transformed_fibroblasts	All	0.104 (0.004)	0e+00
T2D	CMC.BRAIN.RNASEQ	All	0.104 (0.004)	0e+00
T2D	CMC.BRAIN.RNASEQ_SPLICING	All	0.084 (0.004)	0e+00
T2D	Colon_Sigmoid	All	0.076 (0.004)	0e+00
T2D	Colon_Transverse	All	0.083 (0.004)	0e+00
T2D	Esophagus_Gastroesophageal_Junction	All	0.085 (0.004)	0e+00
T2D	Esophagus_Mucosa	All	0.085 (0.004)	0e+00
T2D	Esophagus_Muscularis	All	0.097 (0.004)	0e+00
T2D	Heart_Atrial_Appendage	All	0.093 (0.004)	0e+00
T2D	Heart_Left_Ventricle	All	0.087 (0.004)	0e+00
T2D	Liver	All	0.077 (0.004)	0e+00
T2D	Lung	All	0.093 (0.004)	0e+00
T2D	METSIM.ADIPOSE.RNASEQ	All	0.087 (0.004)	0e+00
T2D	Minor_Salivary_Gland	All	0.055 (0.004)	0e+00
T2D	Muscle_Skeletal	All	0.104 (0.004)	0e+00
T2D	Nerve_Tibial	All	0.109 (0.004)	0e+00
T2D	NTR.BLOOD.RNAARR	All	0.082 (0.004)	0e+00
T2D	Ovary	All	0.069 (0.004)	0e+00
T2D	Pancreas	All	0.086 (0.004)	0e+00
T2D	Pituitary	All	0.076 (0.004)	0e+00
T2D	Prostate	All	0.059 (0.004)	0e+00
T2D	Skin_Not_Sun_Exposed_Suprapubic	All	0.094 (0.004)	0e+00
T2D	Skin_Sun_Exposed_Lower_leg	All	0.098 (0.004)	0e+00
T2D	Small_Intestine_Terminal_Ileum	All	0.076 (0.004)	0e+00
T2D	Spleen	All	0.083 (0.004)	0e+00
T2D	Stomach	All	0.081 (0.004)	0e+00
T2D	Testis	All	0.098 (0.004)	0e+00
T2D	Thyroid	All	0.106 (0.004)	0e+00
T2D	Uterus	All	0.057 (0.004)	0e+00
T2D	Vagina	All	0.056 (0.004)	0e+00
T2D	Whole_Blood	All	0.087 (0.004)	0e+00
T2D	YFS.BLOOD.RNAARR	All	0.086 (0.004)	0e+00
CAD	Adipose_Subcutaneous	All	0.076 (0.004)	0e+00
CAD	Adipose_Visceral_Omentum	All	0.07 (0.004)	0e+00
CAD	Adrenal_Gland	All	0.058 (0.004)	0e+00
CAD	Artery_Aorta	All	0.089 (0.004)	0e+00
CAD	Artery_Coronary	All	0.064 (0.004)	0e+00
CAD	Artery_Tibial	All	0.089 (0.004)	0e+00
CAD	Brain_Amygdala	1	0.036 (0.004)	0e+00
CAD	Brain_Anterior_cingulate_cortex_BA24	All	0.042 (0.004)	0e+00
CAD	Brain_Caudate_basal_ganglia	All	0.058 (0.004)	0e+00
CAD	Brain_Cerebellar_Hemisphere	All	0.057 (0.004)	0e+00
CAD	Brain_Cerebellum	All	0.052 (0.004)	0e+00
CAD	Brain_Cortex	All	0.061 (0.004)	0e+00
CAD	Brain_Frontal_Cortex_BA9	All	0.047 (0.004)	0e+00
CAD	Brain_Hippocampus	All	0.042 (0.004)	0e+00
CAD	Brain_Hypothalamus	All	0.038 (0.004)	0e+00
CAD	Brain_Nucleus_accumbens_basal_ganglia	All	0.048 (0.004)	0e+00
CAD	Brain_Putamen_basal_ganglia	All	0.043 (0.004)	0e+00
CAD	Brain_Spinal_cord_cervical_c-1	All	0.053 (0.004)	0e+00
CAD	Brain_Substantia_nigra	0.5	0.041 (0.004)	0e+00
CAD	Breast_Mammary_Tissue	All	0.063 (0.004)	0e+00
CAD	Cells_EBV-transformed_lymphocytes	All	0.051 (0.004)	0e+00
CAD	Cells_Transformed_fibroblasts	All	0.084 (0.004)	0e+00
CAD	CMC.BRAIN.RNASEQ	All	0.081 (0.004)	0e+00
CAD	CMC.BRAIN.RNASEQ_SPLICING	All	0.054 (0.004)	0e+00
CAD	Colon_Sigmoid	All	0.061 (0.004)	0e+00
CAD	Colon_Transverse	All	0.064 (0.004)	0e+00
CAD	Esophagus_Gastroesophageal_Junction	All	0.058 (0.004)	0e+00
CAD	Esophagus_Mucosa	All	0.072 (0.004)	0e+00
CAD	Esophagus_Muscularis	All	0.069 (0.004)	0e+00
CAD	Heart_Atrial_Appendage	All	0.065 (0.004)	0e+00
CAD	Heart_Left_Ventricle	All	0.066 (0.004)	0e+00
CAD	Liver	All	0.051 (0.004)	0e+00
CAD	Lung	All	0.076 (0.004)	0e+00
CAD	METSIM.ADIPOSE.RNASEQ	All	0.07 (0.004)	0e+00
CAD	Minor_Salivary_Gland	All	0.043 (0.004)	0e+00
CAD	Muscle_Skeletal	All	0.085 (0.004)	0e+00
CAD	Nerve_Tibial	All	0.086 (0.004)	0e+00
CAD	NTR.BLOOD.RNAARR	All	0.063 (0.004)	0e+00
CAD	Ovary	All	0.054 (0.004)	0e+00
CAD	Pancreas	All	0.068 (0.004)	0e+00
CAD	Pituitary	All	0.057 (0.004)	0e+00
CAD	Prostate	All	0.046 (0.004)	0e+00
CAD	Skin_Not_Sun_Exposed_Suprapubic	All	0.072 (0.004)	0e+00
CAD	Skin_Sun_Exposed_Lower_leg	All	0.077 (0.004)	0e+00
CAD	Small_Intestine_Terminal_Ileum	All	0.048 (0.004)	0e+00
CAD	Spleen	All	0.064 (0.004)	0e+00
CAD	Stomach	1	0.059 (0.004)	0e+00
CAD	Testis	All	0.073 (0.004)	0e+00
CAD	Thyroid	All	0.08 (0.004)	0e+00
CAD	Uterus	All	0.045 (0.004)	0e+00
CAD	Vagina	All	0.039 (0.004)	0e+00
CAD	Whole_Blood	All	0.074 (0.004)	0e+00
CAD	YFS.BLOOD.RNAARR	All	0.071 (0.004)	0e+00
IBD	Adipose_Subcutaneous	All	0.064 (0.004)	0e+00
IBD	Adipose_Visceral_Omentum	All	0.06 (0.004)	0e+00
IBD	Adrenal_Gland	All	0.05 (0.004)	0e+00
IBD	Artery_Aorta	All	0.055 (0.004)	0e+00
IBD	Artery_Coronary	All	0.047 (0.004)	0e+00
IBD	Artery_Tibial	All	0.06 (0.004)	0e+00
IBD	Brain_Amygdala	All	0.036 (0.004)	0e+00
IBD	Brain_Anterior_cingulate_cortex_BA24	All	0.044 (0.004)	0e+00
IBD	Brain_Caudate_basal_ganglia	All	0.045 (0.004)	0e+00
IBD	Brain_Cerebellar_Hemisphere	All	0.052 (0.004)	0e+00
IBD	Brain_Cerebellum	All	0.057 (0.004)	0e+00
IBD	Brain_Cortex	All	0.051 (0.004)	0e+00
IBD	Brain_Frontal_Cortex_BA9	All	0.04 (0.004)	0e+00
IBD	Brain_Hippocampus	All	0.042 (0.004)	0e+00
IBD	Brain_Hypothalamus	0.5	0.038 (0.004)	0e+00
IBD	Brain_Nucleus_accumbens_basal_ganglia	All	0.048 (0.004)	0e+00
IBD	Brain_Putamen_basal_ganglia	All	0.042 (0.004)	0e+00
IBD	Brain_Spinal_cord_cervical_c-1	All	0.032 (0.004)	0e+00
IBD	Brain_Substantia_nigra	All	0.034 (0.004)	0e+00
IBD	Breast_Mammary_Tissue	All	0.054 (0.004)	0e+00
IBD	Cells_EBV-transformed_lymphocytes	All	0.047 (0.004)	0e+00
IBD	Cells_Transformed_fibroblasts	All	0.061 (0.004)	0e+00
IBD	CMC.BRAIN.RNASEQ	All	0.058 (0.004)	0e+00
IBD	CMC.BRAIN.RNASEQ_SPLICING	All	0.047 (0.004)	0e+00
IBD	Colon_Sigmoid	All	0.054 (0.004)	0e+00
IBD	Colon_Transverse	All	0.055 (0.004)	0e+00
IBD	Esophagus_Gastroesophageal_Junction	0.5	0.053 (0.004)	0e+00
IBD	Esophagus_Mucosa	All	0.063 (0.004)	0e+00
IBD	Esophagus_Muscularis	All	0.063 (0.004)	0e+00
IBD	Heart_Atrial_Appendage	All	0.053 (0.004)	0e+00
IBD	Heart_Left_Ventricle	All	0.058 (0.004)	0e+00
IBD	Liver	All	0.047 (0.004)	0e+00
IBD	Lung	All	0.063 (0.004)	0e+00
IBD	METSIM.ADIPOSE.RNASEQ	All	0.062 (0.004)	0e+00
IBD	Minor_Salivary_Gland	All	0.043 (0.004)	0e+00
IBD	Muscle_Skeletal	All	0.066 (0.004)	0e+00
IBD	Nerve_Tibial	All	0.065 (0.004)	0e+00
IBD	NTR.BLOOD.RNAARR	All	0.055 (0.004)	0e+00
IBD	Ovary	All	0.049 (0.004)	0e+00
IBD	Pancreas	All	0.055 (0.004)	0e+00
IBD	Pituitary	All	0.046 (0.004)	0e+00
IBD	Prostate	All	0.047 (0.004)	0e+00
IBD	Skin_Not_Sun_Exposed_Suprapubic	All	0.06 (0.004)	0e+00
IBD	Skin_Sun_Exposed_Lower_leg	All	0.068 (0.004)	0e+00
IBD	Small_Intestine_Terminal_Ileum	All	0.05 (0.004)	0e+00
IBD	Spleen	All	0.055 (0.004)	0e+00
IBD	Stomach	All	0.054 (0.004)	0e+00
IBD	Testis	All	0.065 (0.004)	0e+00
IBD	Thyroid	All	0.064 (0.004)	0e+00
IBD	Uterus	All	0.04 (0.004)	0e+00
IBD	Vagina	All	0.03 (0.004)	0e+00
IBD	Whole_Blood	All	0.07 (0.004)	0e+00
IBD	YFS.BLOOD.RNAARR	All	0.062 (0.004)	0e+00
RheuArth	Adipose_Subcutaneous	All	0.07 (0.004)	0e+00
RheuArth	Adipose_Visceral_Omentum	All	0.082 (0.004)	0e+00
RheuArth	Adrenal_Gland	All	0.075 (0.004)	0e+00
RheuArth	Artery_Aorta	All	0.079 (0.004)	0e+00
RheuArth	Artery_Coronary	All	0.079 (0.004)	0e+00
RheuArth	Artery_Tibial	All	0.081 (0.004)	0e+00
RheuArth	Brain_Amygdala	All	0.083 (0.004)	0e+00
RheuArth	Brain_Anterior_cingulate_cortex_BA24	All	0.047 (0.004)	0e+00
RheuArth	Brain_Caudate_basal_ganglia	All	0.077 (0.004)	0e+00
RheuArth	Brain_Cerebellar_Hemisphere	All	0.071 (0.004)	0e+00
RheuArth	Brain_Cerebellum	All	0.066 (0.004)	0e+00
RheuArth	Brain_Cortex	All	0.097 (0.004)	0e+00
RheuArth	Brain_Frontal_Cortex_BA9	All	0.058 (0.004)	0e+00
RheuArth	Brain_Hippocampus	All	0.053 (0.004)	0e+00
RheuArth	Brain_Hypothalamus	All	0.06 (0.004)	0e+00
RheuArth	Brain_Nucleus_accumbens_basal_ganglia	All	0.085 (0.004)	0e+00
RheuArth	Brain_Putamen_basal_ganglia	All	0.052 (0.004)	0e+00
RheuArth	Brain_Spinal_cord_cervical_c-1	0.05	0.043 (0.004)	0e+00
RheuArth	Brain_Substantia_nigra	All	0.084 (0.004)	0e+00
RheuArth	Breast_Mammary_Tissue	All	0.078 (0.004)	0e+00
RheuArth	Cells_EBV-transformed_lymphocytes	All	0.071 (0.004)	0e+00
RheuArth	Cells_Transformed_fibroblasts	All	0.086 (0.004)	0e+00
RheuArth	CMC.BRAIN.RNASEQ	All	0.073 (0.004)	0e+00
RheuArth	CMC.BRAIN.RNASEQ_SPLICING	All	0.056 (0.004)	0e+00
RheuArth	Colon_Sigmoid	All	0.081 (0.004)	0e+00
RheuArth	Colon_Transverse	All	0.074 (0.004)	0e+00
RheuArth	Esophagus_Gastroesophageal_Junction	All	0.071 (0.004)	0e+00
RheuArth	Esophagus_Mucosa	All	0.081 (0.004)	0e+00
RheuArth	Esophagus_Muscularis	All	0.089 (0.004)	0e+00
RheuArth	Heart_Atrial_Appendage	All	0.089 (0.004)	0e+00
RheuArth	Heart_Left_Ventricle	All	0.089 (0.004)	0e+00
RheuArth	Liver	All	0.081 (0.004)	0e+00
RheuArth	Lung	All	0.089 (0.004)	0e+00
RheuArth	METSIM.ADIPOSE.RNASEQ	All	0.095 (0.004)	0e+00
RheuArth	Minor_Salivary_Gland	All	0.076 (0.004)	0e+00
RheuArth	Muscle_Skeletal	All	0.082 (0.004)	0e+00
RheuArth	Nerve_Tibial	All	0.083 (0.004)	0e+00
RheuArth	NTR.BLOOD.RNAARR	All	0.073 (0.004)	0e+00
RheuArth	Ovary	All	0.077 (0.004)	0e+00
RheuArth	Pancreas	All	0.078 (0.004)	0e+00
RheuArth	Pituitary	All	0.077 (0.004)	0e+00
RheuArth	Prostate	All	0.05 (0.004)	0e+00
RheuArth	Skin_Not_Sun_Exposed_Suprapubic	All	0.062 (0.004)	0e+00
RheuArth	Skin_Sun_Exposed_Lower_leg	All	0.067 (0.004)	0e+00
RheuArth	Small_Intestine_Terminal_Ileum	All	0.074 (0.004)	0e+00
RheuArth	Spleen	All	0.082 (0.004)	0e+00
RheuArth	Stomach	All	0.087 (0.004)	0e+00
RheuArth	Testis	All	0.076 (0.004)	0e+00
RheuArth	Thyroid	All	0.086 (0.004)	0e+00
RheuArth	Uterus	All	0.076 (0.004)	0e+00
RheuArth	Vagina	All	0.08 (0.004)	0e+00
RheuArth	Whole_Blood	All	0.081 (0.004)	0e+00
RheuArth	YFS.BLOOD.RNAARR	All	0.091 (0.004)	0e+00

Show cis-regulated expression-based heritability

Proportion of heritability explained

Show summary of GeRS tests

Predictive utility

Correlation between GeRS model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R perc diff	R diff pval
1	Depression	GeRS_multi_pT	All	1	0.064	0.064	0.001	1.1%	7.93e-01
6	Intelligence	GeRS_multi_pT	All	0.5	0.067	0.061	0.005	7.9%	6.95e-03
11	BMI	GeRS_multi_pT	All	1	0.168	0.159	0.009	5.3%	1.87e-07
16	Height	GeRS_multi_pT	All	0.5	0.219	0.216	0.003	1.3%	2.23e-03
21	T2D	GeRS_multi_pT	All	0.5	0.158	0.121	0.037	23.6%	2.21e-28
26	CAD	GeRS_multi_pT	All	0.5	0.118	0.106	0.012	10.2%	1.56e-05
31	IBD	GeRS_multi_pT	All	0.1	0.080	0.078	0.002	2.3%	6.21e-01
36	RheuArth	GeRS_multi_pT	All	1e.06	0.136	0.118	0.018	13.4%	2.80e-10
2	Depression	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.063	0.059	0.004	5.8%	3.10e-01
7	Intelligence	GeRS_multi_tissue	All	Adipose.Subcutaneous	0.067	0.056	0.010	15.5%	1.89e-04
12	BMI	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.168	0.138	0.030	17.8%	1.69e-25
17	Height	GeRS_multi_tissue	All	Nerve.Tibial	0.219	0.182	0.037	16.9%	1.36e-44
22	T2D	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.159	0.114	0.045	28.3%	1.18e-33
27	CAD	GeRS_multi_tissue	All	Artery.Tibial	0.117	0.089	0.028	23.6%	1.13e-13
32	IBD	GeRS_multi_tissue	All	Whole.Blood	0.080	0.070	0.010	13%	1.61e-02
37	RheuArth	GeRS_multi_tissue	All	Brain.Cortex	0.136	0.097	0.040	29%	3.46e-27
3	Depression	PRS_and_GeRS	All	PRS	0.131	0.131	0.000	-0.1%	9.66e-01
8	Intelligence	PRS_and_GeRS	All	PRS	0.095	0.090	0.006	5.9%	8.24e-04
13	BMI	PRS_and_GeRS	All	PRS	0.284	0.281	0.003	1%	4.36e-05
18	Height	PRS_and_GeRS	All	PRS	0.325	0.322	0.004	1.2%	6.98e-08
23	T2D	PRS_and_GeRS	All	PRS	0.227	0.218	0.009	4.1%	1.89e-06
28	CAD	PRS_and_GeRS	All	PRS	0.173	0.170	0.003	1.6%	2.95e-02
33	IBD	PRS_and_GeRS	All	PRS	0.116	0.127	-0.011	-9.6%	6.01e-04
38	RheuArth	PRS_and_GeRS	All	PRS	0.168	0.133	0.035	20.8%	1.69e-31
5	Depression	PRScs_and_GeRS	All	PRS	0.141	0.142	-0.002	-1.2%	1.62e-01
10	Intelligence	PRScs_and_GeRS	All	PRS	0.102	0.099	0.002	2.5%	2.82e-02
15	BMI	PRScs_and_GeRS	All	PRS	0.303	0.302	0.001	0.4%	2.82e-02
20	Height	PRScs_and_GeRS	All	PRS	0.352	0.351	0.001	0.3%	2.64e-02
25	T2D	PRScs_and_GeRS	All	PRS	0.238	0.237	0.001	0.5%	3.65e-01
30	CAD	PRScs_and_GeRS	All	PRS	0.187	0.187	0.000	0.2%	4.62e-01
35	IBD	PRScs_and_GeRS	All	PRS	0.121	0.134	-0.012	-10.2%	1.36e-05
40	RheuArth	PRScs_and_GeRS	All	PRS	0.163	0.157	0.007	4%	5.89e-08
4	Depression	Strat_PRS	All	strat.PRS	0.107	0.109	-0.002	-1.4%	4.17e-01
9	Intelligence	Strat_PRS	All	strat.PRS	0.086	0.079	0.007	7.8%	9.51e-04
14	BMI	Strat_PRS	All	strat.PRS	0.251	0.246	0.004	1.7%	7.12e-06
19	Height	Strat_PRS	All	strat.PRS	0.309	0.306	0.003	1.1%	3.19e-06
24	T2D	Strat_PRS	All	strat.PRS	0.214	0.206	0.009	4.1%	7.92e-06
29	CAD	Strat_PRS	All	strat.PRS	0.162	0.160	0.001	0.9%	2.28e-01
34	IBD	Strat_PRS	All	strat.PRS	0.098	0.108	-0.010	-10.7%	3.48e-03
39	RheuArth	Strat_PRS	All	strat.PRS	0.158	0.112	0.045	28.9%	4.06e-34

Sensitivity analysis for Rheumatoid Arthritis

Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R perc diff	R diff pval
RheuArth	PRS_and_GeRS	All	PRS	0.168	0.133	0.035	20.8%	1.69e-31
RheuArth	PRS_noMHCClump_and_GeRS	All	PRS	0.160	0.147	0.013	8.4%	6.40e-11
RheuArth	PRScs_and_GeRS	All	PRS	0.163	0.157	0.007	4%	5.89e-08

Show summary of GeRS PP4 tests

Predictive utility

Correlation between GeRS PP4 model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R diff pval
1	Depression	GeRS_multi_tissue	All	Thyroid	0.043	0.036	0.008	9.16e-02
3	Intelligence	GeRS_multi_tissue	All	Stomach	0.045	0.039	0.006	8.54e-02
5	BMI	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.101	0.075	0.025	1.82e-12
7	Height	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.198	0.140	0.059	6.97e-59
9	T2D	GeRS_multi_tissue	All	Adipose.Subcutaneous	0.111	0.068	0.043	1.50e-24
11	CAD	GeRS_multi_tissue	All	Nerve.Tibial	0.090	0.060	0.030	1.33e-13
13	IBD	GeRS_multi_tissue	All	Whole.Blood	0.082	0.057	0.025	6.46e-12
15	RheuArth	GeRS_multi_tissue	All	Brain.Cortex	0.117	0.083	0.033	8.28e-23
2	Depression	PRS_and_GeRS	All	PRS	0.126	0.131	-0.004	8.88e-03
4	Intelligence	PRS_and_GeRS	All	PRS	0.095	0.090	0.005	1.56e-02
6	BMI	PRS_and_GeRS	All	PRS	0.282	0.281	0.001	1.10e-01
8	Height	PRS_and_GeRS	All	PRS	0.327	0.322	0.006	5.31e-07
10	T2D	PRS_and_GeRS	All	PRS	0.220	0.218	0.002	2.02e-01
12	CAD	PRS_and_GeRS	All	PRS	0.170	0.170	0.000	6.58e-01
14	IBD	PRS_and_GeRS	All	PRS	0.127	0.127	0.000	9.24e-01
16	RheuArth	PRS_and_GeRS	All	PRS	0.161	0.133	0.028	2.70e-29

Show summary of GeRS TissueSpecific tests

Predictive utility

Correlation between GeRS TissueSpecific model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R diff pval
1	Depression	GeRS_multi_tissue	All	Thyroid	0.067	0.053	0.015	1.97e-05
3	Intelligence	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.058	0.050	0.008	1.49e-02
5	BMI	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.165	0.113	0.052	1.12e-44
7	Height	GeRS_multi_tissue	All	Nerve.Tibial	0.210	0.154	0.056	9.83e-64
9	T2D	GeRS_multi_tissue	All	Artery.Tibial	0.155	0.095	0.059	4.68e-47
11	CAD	GeRS_multi_tissue	All	Artery.Tibial	0.109	0.075	0.034	1.70e-16
13	IBD	GeRS_multi_tissue	All	Whole.Blood	0.099	0.066	0.033	8.13e-16
15	RheuArth	GeRS_multi_tissue	All	YFS.BLOOD.RNAARR	0.121	0.080	0.042	1.40e-19
2	Depression	PRS_and_GeRS	All	PRS	0.129	0.131	-0.001	2.64e-01
4	Intelligence	PRS_and_GeRS	All	PRS	0.093	0.090	0.003	5.59e-02
6	BMI	PRS_and_GeRS	All	PRS	0.284	0.281	0.002	1.24e-03
8	Height	PRS_and_GeRS	All	PRS	0.325	0.322	0.004	4.41e-06
10	T2D	PRS_and_GeRS	All	PRS	0.226	0.218	0.009	2.15e-08
12	CAD	PRS_and_GeRS	All	PRS	0.169	0.170	-0.001	2.26e-01
14	IBD	PRS_and_GeRS	All	PRS	0.132	0.127	0.005	1.04e-01
16	RheuArth	PRS_and_GeRS	All	PRS	0.166	0.133	0.033	6.75e-25

Show GeRS, PRS, stratified-PRS comparison

Predictive utility

Proportion of PRS explained by GeRS

Phenotype	Prop_GE	Prop_GE_coloc
Depression	0.479	0.300
Intelligence	0.743	0.361
BMI	0.596	0.320
Height	0.681	0.554
T2D	0.729	0.475
CAD	0.687	0.488
IBD	0.630	0.571
RheuArth	1.026	0.964

Show GeRS coloc and tissue specific comparison

Predictive utility

Comparison of GeRS

Phenotype	Method	R	SE
Depression	GeRS (best)	0.059	0.004
Depression	GeRS (all)	0.063	0.004
Depression	GeRS coloc (best)	0.029	0.004
Depression	GeRS coloc (all)	0.039	0.004
Depression	GeRS TS (best)	0.053	0.004
Depression	GeRS TS (all)	0.067	0.004
Intelligence	GeRS (best)	0.056	0.004
Intelligence	GeRS (all)	0.067	0.004
Intelligence	GeRS coloc (best)	0.033	0.004
Intelligence	GeRS coloc (all)	0.032	0.004
Intelligence	GeRS TS (best)	0.050	0.004
Intelligence	GeRS TS (all)	0.058	0.004
BMI	GeRS (best)	0.138	0.004
BMI	GeRS (all)	0.168	0.004
BMI	GeRS coloc (best)	0.069	0.004
BMI	GeRS coloc (all)	0.090	0.004
BMI	GeRS TS (best)	0.113	0.004
BMI	GeRS TS (all)	0.165	0.004
Height	GeRS (best)	0.182	0.004
Height	GeRS (all)	0.219	0.004
Height	GeRS coloc (best)	0.132	0.004
Height	GeRS coloc (all)	0.178	0.004
Height	GeRS TS (best)	0.154	0.004
Height	GeRS TS (all)	0.210	0.004
T2D	GeRS (best)	0.114	0.004
T2D	GeRS (all)	0.159	0.004
T2D	GeRS coloc (best)	0.066	0.004
T2D	GeRS coloc (all)	0.103	0.004
T2D	GeRS TS (best)	0.095	0.004
T2D	GeRS TS (all)	0.155	0.004
CAD	GeRS (best)	0.089	0.004
CAD	GeRS (all)	0.117	0.004
CAD	GeRS coloc (best)	0.059	0.004
CAD	GeRS coloc (all)	0.083	0.004
CAD	GeRS TS (best)	0.075	0.004
CAD	GeRS TS (all)	0.109	0.004
IBD	GeRS (best)	0.070	0.004
IBD	GeRS (all)	0.080	0.004
IBD	GeRS coloc (best)	0.044	0.004
IBD	GeRS coloc (all)	0.073	0.004
IBD	GeRS TS (best)	0.066	0.004
IBD	GeRS TS (all)	0.099	0.004
RheuArth	GeRS (best)	0.097	0.004
RheuArth	GeRS (all)	0.136	0.004
RheuArth	GeRS coloc (best)	0.083	0.004
RheuArth	GeRS coloc (all)	0.128	0.004
RheuArth	GeRS TS (best)	0.080	0.004
RheuArth	GeRS TS (all)	0.121	0.004

Show association with GeRS for each SNP-weight set

Predictive utility

Show association with GeRS PP4 for each SNP-weight set

Predictive utility

Show association with GeRS TissueSpecific for each SNP-weight set

Predictive utility

Show association with GeRS for each SNP-weight set after accounting for number of features

Predictive utility

Show effect of number of features in the SNP-weight set

Number of feature effect

4.2 TEDS

Plot per pT GeRS results

#####
# Compare results across pTs for each phenotype
#####
pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
weights=read.table('/users/k1806347/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', header=F)$V1

weights_clean<-gsub('_',' ',weights)
weights_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',weights_clean)
weights_clean<-gsub('SPLICING','Splicing',weights_clean)
weights_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',weights_clean)
weights_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',weights_clean)
weights_clean<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',weights_clean)
weights_clean[!grepl('CMC|NTR|YFS|METSIM', weights)]<-paste0('GTEx ',weights_clean[!grepl('CMC|NTR|YFS|METSIM', weights)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
weights_clean<-gsub('Brain', '', weights_clean)
weights_clean <- gsub('Anterior cingulate cortex', 'ACC', weights_clean)
weights_clean <- gsub('basal ganglia', '', weights_clean)
weights_clean <- gsub('BA9', '', weights_clean)
weights_clean <- gsub('BA24', '', weights_clean)
weights_clean <- gsub('  ', ' ', weights_clean)
weights_clean_short<-substr(weights_clean, start = 1, stop = 15)  #start the name at the first character and stop at the 25th
weights_clean_short[nchar(weights_clean) > 15]<-paste0(weights_clean_short[nchar(weights_clean) > 15], "...")

res<-NULL
res_best<-NULL
for(i in 1:length(gwas)){
  for(weight in 1:length(weights)){
    res_i<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.',weights[weight],'.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

    res_i$Phenotype<-pheno[i]
    res_i$Weight<-weights[weight]
    
    if(sum(grepl('R2l',names(res_i)))>0){
        res_i<-res_i[,c('Phenotype','Weight','Model','R','SE','P','R2l')]
        names(res_i)<-c('Phenotype','Weight','Model','R','SE','P','R2')
        res_i$Binary<-T
    } else {
        res_i<-res_i[,c('Phenotype','Weight','Model','R','SE','P','R2o')]
        names(res_i)<-c('Phenotype','Weight','Model','R','SE','P','R2')
        res_i$Binary<-F
    }
    
    res_i$Model<-gsub('_group','',gsub(paste0(gwas[i],'.'),'',res_i$Model))
    res_i$Model<-factor(res_i$Model, levels=res_i$Model)
    
    res_i_best<-res_i[res_i$R == max(res_i$R),]
  
    res<-rbind(res, res_i)
    res_best<-rbind(res_best, res_i_best)
  }
}

res_brief<-res[,c('Phenotype','Weight','Model','R','SE','P')]
res_best_brief<-res_best[,c('Phenotype','Weight','Model','R','SE','P')]

write.csv(res_brief, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_per_pT.csv', row.names=F, quote=F)
write.csv(res_best_brief, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_best_pT.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)

res$Model<-gsub('e.0','*x*10^-', res$Model)
res$Model<-factor(res$Model, levels=unique(res$Model))
res$P<-format(res$P, scientific = TRUE, digits = 1)
res$P<-gsub('e-','*x*10^-',res$P)

res_plot<-list()
for(i in 1:length(gwas)){
  # Extract result for 5 most predictive tissue
  tmp<-res_best[res_best$Phenotype == pheno[i],]
  tmp<-tmp[order(-tmp$R2),]
  tmp<-tmp[1:3,]
  best_weights<-tmp$Weight
  
  res_tmp<-res[res$Phenotype == pheno[i] & (res$Weight %in% best_weights),]
  ylim_max<-max(res_tmp$R2)
  ylim_max<-ylim_max+ylim_max/1.5
  if(res[res$Phenotype == pheno[i],]$Binary[1] == T){
    ylab<-'Liability R-squared'
  } else {
    ylab<-'R-squared'
  }
  
  res_plot_tmp<-list()
  for(weight in best_weights){
    weights_index<-which(weights == weight)
    print(weight)
  res_plot_tmp[[as.character(weights[weights_index])]]<-ggplot(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R2)) +
                                    geom_bar(stat="identity", position=position_dodge(), fill='#3399FF') +
                                    labs(y=ylab, x='pT', title=paste0('\n\n',weights_clean_short[weights_index])) +
                                    theme_half_open() +
                                    ylim(0,ylim_max) +
                                    geom_text(data=res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R2, label=P), vjust=0.5, hjust= -0.15, angle=90, size=4, parse=T) +
                                    theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust=1), plot.title = element_text(hjust = 0.5, size=12)) +
                                    background_grid(major = 'y', minor = 'y') +
                                    scale_x_discrete(labels = parse(text = as.character(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],]$Model))) +
                                    coord_cartesian(clip='off')

  }
  res_plot[[pheno[i]]]<-plot_grid(plotlist=res_plot_tmp, nrow = 1)
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_per_pT_TEDS.png', units='px', res=300, width=3000, height=3750)
  plot_grid(plotlist=res_plot, ncol = 1, labels = paste0(pheno_label))
dev.off()

#######
# Recreate plots using R on the y axis and full SNP-weight set names
#######

res_plot<-list()
for(i in 1:length(gwas)){
  # Extract result for 5 most predictive tissue
  tmp<-res_best[res_best$Phenotype == pheno[i],]
  tmp<-tmp[order(-tmp$R),]
  tmp<-tmp[1:3,]
  best_weights<-tmp$Weight
  
  res_tmp<-res[res$Phenotype == pheno[i] & (res$Weight %in% best_weights),]
  ylim_max<-max(res_tmp$R)
  ylim_max<-ylim_max+ylim_max*1.5
  if(min(res_tmp$R) < 0){
    ylim_min<-min(res_tmp$R)
    ylim_min<-ylim_min-max(res_tmp$SE)
  } else {
    ylim_min<-NA
  }

  res_plot_tmp<-list()
  for(weight in best_weights){
    weights_index<-which(weights == weight)
    print(weight)
  res_plot_tmp[[as.character(weights[weights_index])]]<-ggplot(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R)) +
                                    geom_bar(stat="identity", position=position_dodge(), fill='#3399FF') +
                                    geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2, position=position_dodge(.9)) +
                                    labs(y='Correlation', x='pT', title=paste0('\n\n',weights_clean[weights_index])) +
                                    theme_half_open() +
                                    ylim(ylim_min,ylim_max) +
                                    geom_text(data=res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],], aes(x=Model, y=R+SE, label=P), vjust=0.3, hjust= -0.15, angle=90, size=4, parse=T) +
                                    theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust=1), plot.title = element_text(hjust = 0.4, size=12)) +
                                    background_grid(major = 'y', minor = 'y') +
                                    scale_x_discrete(labels = parse(text = as.character(res[res$Phenotype == pheno[i] & res$Weight == weights[weights_index],]$Model))) +
                                    coord_cartesian(clip='off')

  }
  res_plot[[pheno[i]]]<-plot_grid(plotlist=res_plot_tmp, nrow = 1)
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_per_pT_TEDS_R.png', units='px', res=300, width=3000, height=3750)
  plot_grid(plotlist=res_plot, ncol = 1, labels = paste0(pheno_label))
dev.off()

Plot comparison results

#####
# Compare results from each approach
#####
pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

res<-list()
for(i in 1:length(gwas)){
res_1<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.per_PT.pred_comp.txt'), header=T, stringsAsFactors=F)
res_1<-res_1[res_1$Model_1 == 'All',]
res_1<-res_1[res_1$Model_2 != 'All',]
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)
res_4<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/TEDS.w_hm3.',gwas[i],'.EUR-PRSs-TWAS_gene_stratified.pred_comp.txt'), header=T, stringsAsFactors=F)
res_5<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.EUR-PRSs.PRScs.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_pT','GeRS_multi_tissue','PRS_and_GeRS','Strat_PRS','PRScs_and_GeRS'),        
                        do.call(rbind,list( res_1[res_1$Model_2_R == max(res_1$Model_2_R),],
                                                    res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,],
                                                    res_4[8,],
                                                    res_5[8,])))
}

res_table<-do.call(rbind, res)

# Calculate percentage difference
res_table$R_diff_perc<-res_table$R_diff/res_table$Model_1_R*100

res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
  tmp_res<-res[[pheno[i]]]

  tmp_res$R_diff_pval_num<-tmp_res$R_diff_pval
  
  tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
  tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)
  
  tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
  names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval','R_diff_pval_num')
  tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
  names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval','R_diff_pval_num')
  tmp_res_Model_2$R_diff<-NA
  tmp_res_Model_2$R_diff_pval<-NA
  
  tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
  tmp_res_plot$Phenotype<-pheno_label[i]
  
  res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_pT']<-'GeRS Best pT   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_pT']<-'GeRS Multi pT'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS Best Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS Multi Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'Strat_PRS']<-'Strat_PRS only'
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'Strat_PRS']<-'Strat_PRS + GeRS'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRScs_and_GeRS']<-'PRScs only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRScs_and_GeRS']<-'PRScs + GeRS'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS Best pT   ","GeRS Multi pT", "GeRS Best Tissue   ","GeRS Multi Tissue","PRS only   ","PRS + GeRS", "Strat_PRS only", "Strat_PRS + GeRS","PRScs only   ","PRScs + GeRS"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_1<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_4<-ggplot(All_res_plot[All_res_plot$Test == 'Strat_PRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_5<-ggplot(All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_tests_summary_TEDS.png', units='px', res=300, width=3500, height=3000)
  plot_grid(Plot_1,Plot_2,Plot_3, Plot_5, labels = "AUTO")
dev.off()

###
# Recreate figure higlighting significant results
###

All_res_plot$Sig<-'NS'
All_res_plot$Sig[All_res_plot$R_diff_pval_num < 0.05 & All_res_plot$R_diff > 0]<-'Pos'
All_res_plot$Sig[All_res_plot$R_diff_pval_num < 0.05 & All_res_plot$R_diff < 0]<-'Neg'
All_res_plot$Sig<-factor(All_res_plot$Sig, levels=c('NS','Pos','Neg'))

scale_colour_op <- function(...){
    ggplot2:::manual_scale(
        'colour', 
        values = setNames(c("#000000", "#009933","#FF0000"), c('NS', 'Pos', 'Neg')), 
        ...
    )
}

Plot_1<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_pT',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
              ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_4<-ggplot(All_res_plot[All_res_plot$Test == 'Strat_PRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'Strat_PRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_5<-ggplot(All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0,show.legend = FALSE) +
          geom_text(aes(y=R+0.04, colour=Sig), label=All_res_plot[All_res_plot$Test == 'PRScs_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0,show.legend = FALSE) +
          scale_colour_op() +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_tests_summary_TEDS.png', units='px', res=300, width=3500, height=3000)
  plot_grid(Plot_1,Plot_2,Plot_3, Plot_5, labels = "AUTO")
dev.off()

Plot comparison results (PP4+clump)

#####
# Compare results from each approach
#####

pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
weight=c('YFS.BLOOD.RNAARR')

res<-list()
for(i in 1:length(gwas)){
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_tissue','PRS_and_GeRS'),     
                        do.call(rbind,list( res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,])))
}

res_table<-do.call(rbind, res)
res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_PP4_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
tmp_res<-res[[pheno[i]]]

tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)

tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2$R_diff<-NA
tmp_res_Model_2$R_diff_pval<-NA

tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
tmp_res_plot$Phenotype<-pheno[i]

res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS PP4 Best Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS PP4 Multi Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS PP4'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS PP4 Best Tissue   ","GeRS PP4 Multi Tissue","PRS only   ","PRS + GeRS PP4"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_PP4_tests_summary_TEDS.png', units='px', res=300, width=3500, height=1500)
  plot_grid(Plot_2,Plot_3, labels = "AUTO")
dev.off()

Plot comparison results (Tissue Specific)

#####
# Compare results from each approach
#####

pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
weight=c('YFS.BLOOD.RNAARR')

res<-list()
for(i in 1:length(gwas)){
res_2<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_comp.txt'), header=T, stringsAsFactors=F)
res_2<-res_2[res_2$Model_1 == 'All',]
res_2<-res_2[res_2$Model_2 != 'All',]
res_3<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRS_and_GeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.EUR-PRSs.pt_clump.pred_comp.txt'), header=T, stringsAsFactors=F)

res[[pheno[i]]]<-data.frame(Test=c('GeRS_multi_tissue','PRS_and_GeRS'),     
                        do.call(rbind,list( res_2[res_2$Model_2_R == max(res_2$Model_2_R),],
                                                    res_3[8,])))
}

res_table<-do.call(rbind, res)
res_table$Phenotype<-gsub('\\..*','',rownames(res_table))
res_table<-res_table[,c('Phenotype',names(res_table)[-length(names(res_table))])]
write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_TissueSpecific_tests_summary.csv', row.names=F, quote=F)

####
# Plot the R2 when using PRS only, and using PRS + multi-tissue GeRS
####

# Organise the results
res_plot<-list()
for(i in 1:length(gwas)){
tmp_res<-res[[pheno[i]]]

tmp_res$R_diff_pval<-format(tmp_res$R_diff_pval, scientific = TRUE, digits = 2)
tmp_res$R_diff_pval<-gsub('e-','*x*10^-',tmp_res$R_diff_pval)

tmp_res_Model_1<-tmp_res[,grepl('Test|Model_1|R_diff',names(tmp_res))]
names(tmp_res_Model_1)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2<-tmp_res[,grepl('Test|Model_2|R_diff',names(tmp_res))]
names(tmp_res_Model_2)<-c('Test','Model','R','R_diff','R_diff_pval')
tmp_res_Model_2$R_diff<-NA
tmp_res_Model_2$R_diff_pval<-NA

tmp_res_plot<-rbind(tmp_res_Model_1,tmp_res_Model_2)
tmp_res_plot$Phenotype<-pheno[i]

res_plot[[pheno[i]]]<-tmp_res_plot
}

# Combine results for each phenotype and prepare for plotting
All_res_plot<-do.call(rbind, res_plot)

All_res_plot$Test<-factor(All_res_plot$Test, levels=res[[1]]$Test)
All_res_plot$Phenotype<-factor(All_res_plot$Phenotype, level=unique(All_res_plot$Phenotype))
All_res_plot<-All_res_plot[order(All_res_plot$Phenotype,All_res_plot$Test),]

All_res_plot$Val_Label_1<-NA
All_res_plot$Val_Label_1[!is.na(All_res_plot$R_diff)]<-paste0('Diff == ',round(All_res_plot$R_diff[!is.na(All_res_plot$R_diff)],3))

All_res_plot$Val_Label_2<-NA
All_res_plot$Val_Label_2[!is.na(All_res_plot$R_diff)]<-paste0('italic(p) == ',All_res_plot$R_diff_pval[!is.na(All_res_plot$R_diff)])

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS TissueSpecific\nBest Tissue   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'GeRS_multi_tissue']<-'GeRS TissueSpecific\nMulti Tissue'

All_res_plot$Model[!All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS only   '
All_res_plot$Model[All_res_plot$Model == 'All' & All_res_plot$Test == 'PRS_and_GeRS']<-'PRS + GeRS TissueSpecific'

All_res_plot$Model<-factor(All_res_plot$Model, levels=c("GeRS TissueSpecific\nBest Tissue   ","GeRS TissueSpecific\nMulti Tissue","PRS only   ","PRS + GeRS TissueSpecific"))

library(ggplot2)
library(cowplot)

# Plot results
Plot_2<-ggplot(All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'GeRS_multi_tissue',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

Plot_3<-ggplot(All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',], aes(x=Phenotype, y=R, fill=Model)) +
          geom_bar(stat="identity", position=position_dodge()) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_1, parse=T, vjust=-0.5, hjust=0) +
          geom_text(aes(y=R+0.04), label=All_res_plot[All_res_plot$Test == 'PRS_and_GeRS',]$Val_Label_2, parse=T, vjust=1, hjust=0) +
          labs(y='Correlation', x='') +
          ylim(NA,0.65) +
          theme_half_open() +
          theme(legend.title=element_blank(),legend.position="top") +
          background_grid(major = 'x', minor = 'x') +
          coord_flip()

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_TissueSpecific_tests_summary_TEDS.png', units='px', res=300, width=3500, height=1500)
  plot_grid(Plot_2,Plot_3, labels = "AUTO")
dev.off()

Compare stratified PRS to multi-tissue GeRS

# Plot the results of the stratified PRS against Multi-tissue GeRS
# And look at the variance exaplained by each tissue
pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

res<-list()
crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS<-res_GeRS[dim(res_GeRS)[1],]

res_GeRS_coloc<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_coloc<-res_GeRS_coloc[dim(res_GeRS_coloc)[1],]

res_stratPRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/TEDS.w_hm3.',gwas[i],'.EUR-PRSs-TWAS_gene_stratified.pred_eval.txt'), header=T, stringsAsFactors=F)

res_stratPRS<-res_stratPRS[2,]

res_GWPRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withPRSs/TEDS.w_hm3.',gwas[i],'.EUR-PRSs.pred_eval.txt'), header=T, stringsAsFactors=F)
res_GWPRS<-res_GWPRS[dim(res_GWPRS)[1],]

res_all<-do.call(rbind, list(res_GeRS,res_GeRS_coloc, res_stratPRS, res_GWPRS))
res_all$Method<-c('GeRS',"GeRS (coloc)",'PRS (Gene)','PRS')
res_all$Phenotype<-pheno_label[i]

res_all<-res_all[,c('Model','R','SE','P','N','Method','Phenotype')]

res[[pheno[i]]]<-res_all
}

res_table<-do.call(rbind, res)
res_table<-res_table[,c('Phenotype','Method','R','SE')]

write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/StratPRS_comp_summary.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)
# Plot comparison across PRS, stratified PRS and GeRS
res_table$Phenotype<-factor(res_table$Phenotype, level=unique(res_table$Phenotype))

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/StratPRS_comp_TEDS.png', units='px', res=300, width=1500, height=1000)

ggplot(res_table, aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(NA,0.41) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

Compare GeRS to PP4 and TissueSpecific GeRS

# Plot the results of the stratified PRS against Multi-tissue GeRS
# And look at the variance exaplained by each tissue
pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

res<-list()
crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_all<-res_GeRS[dim(res_GeRS)[1],]
res_GeRS<-res_GeRS[-dim(res_GeRS)[1],]
res_GeRS_best<-res_GeRS[which(res_GeRS$R == max(res_GeRS$R)),]

res_GeRS_coloc<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_pT_withColoc.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_coloc_all<-res_GeRS_coloc[dim(res_GeRS_coloc)[1],]
res_GeRS_coloc<-res_GeRS_coloc[-dim(res_GeRS_coloc)[1],]
res_GeRS_coloc_best<-res_GeRS_coloc[which(res_GeRS_coloc$R == max(res_GeRS_coloc$R)),]

res_GeRS_TS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

res_GeRS_TS_all<-res_GeRS_TS[dim(res_GeRS_TS)[1],]
res_GeRS_TS<-res_GeRS_TS[-dim(res_GeRS_TS)[1],]
res_GeRS_TS_best<-res_GeRS_TS[which(res_GeRS_TS$R == max(res_GeRS_TS$R)),]

res_all<-do.call(rbind, list(res_GeRS_best,res_GeRS_all,res_GeRS_coloc_best,res_GeRS_coloc_all,res_GeRS_TS_best, res_GeRS_TS_all))
res_all$Method<-c("GeRS (best)","GeRS (all)","GeRS coloc (best)","GeRS coloc (all)","GeRS TS (best)","GeRS TS (all)")

res_all$Phenotype<-pheno_label[i]

res_all<-res_all[,c('Model','R','SE','P','N','Method','Phenotype')]

res[[pheno[i]]]<-res_all
}

res_table<-do.call(rbind, res)
res_table<-res_table[,c('Phenotype','Method','R','SE')]

write.csv(res_table, '/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_coloc_TissueSpecific_comp_summary.csv', row.names=F, quote=F)

library(ggplot2)
library(cowplot)
# Plot comparison across GeRS
res_table$Phenotype<-factor(res_table$Phenotype, level=unique(res_table$Phenotype))

res_table$Method<-factor(res_table$Method, levels=c("GeRS (best)","GeRS (all)","GeRS coloc (best)","GeRS coloc (all)","GeRS TS (best)","GeRS TS (all)"))
  
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_coloc_TissueSpecific_comp_TEDS.png', units='px', res=300, width=2000, height=1000)

ggplot(res_table, aes(x=Phenotype, y=R, fill=Method)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='') +
              ylim(0,0.3) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="top", legend.justification = c(0.5, 0), legend.title=element_blank()) +
          guides(fill=guide_legend(title.hjust =0.5)) +
          background_grid(major = 'y', minor = 'y')

dev.off()

Plot GeRS across tissues

pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)


crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno_label[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)
crossTissue_i$Panel<-crossTissue_i$Model

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue_i$R_scaled<-scale(crossTissue_i$R)

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE','Panel','R_scaled')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno_label[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_Tissue_comp_TEDS.png', units='px', res=300, width=3000, height=4000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Estimate the correlation between SNP-weight set sample size, number of features and predictive utility
weight_info<-fread('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv')
weight_info$Set<-gsub('_','.',weight_info$Set)
weight_info$Set<-gsub('-','.',weight_info$Set)

crossTissue_table<-merge(crossTissue_table, weight_info, by.x='Panel', by.y='Set')

# Check correlation across 
cor(crossTissue_table$R, crossTissue_table$N_indiv) # 0.1516963
feat_cor<-cor(crossTissue_table$R, crossTissue_table$N_feat) # 0.2879782
cor(crossTissue_table$N_indiv, crossTissue_table$N_feat) # 0.3263912

summary(lm(R ~ N_feat + N_indiv, data=crossTissue_table)) # R2 = 0.08666
# N_indiv effect is non significant when moddeling N_feat
crossTissue_table$R_resid<-resid(lm(R ~ N_feat, data=crossTissue_table))

plot_list<-list()
for(i in 1:length(gwas)){
  crossTissue_table$R_resid[crossTissue_table$Phenotype == pheno_label[i]]<-scale(crossTissue_table$R_resid[crossTissue_table$Phenotype == pheno_label[i]])
  tmp<-crossTissue_table[crossTissue_table$Phenotype == pheno_label[i],]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R_resid))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R_resid, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          labs(y="Residual Correlation", x='', title=pheno_label[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_Tissue_comp_resid_TEDS.png', units='px', res=300, width=3000, height=4000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Plot relationship between N_feat and R2 scaled for each phenotype
png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_Tissue_comp_Nfeat_TEDS.png', units='px', res=300, width=1500, height=1000)
ggplot(crossTissue_table, aes(x=N_feat, R_scaled)) +
  labs(y="Relative prediction", x='Number of features') +
  geom_smooth(method='lm') +
  annotate("text", x=7500, y=-2, label = paste0("italic('r') == ",round(feat_cor,2)), parse=T) +
  geom_point(data=crossTissue_table, aes(x=N_feat, R_scaled, colour=Phenotype)) +
  theme_half_open()
dev.off()

Plot GeRS (PP4+clump) across tissues

pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.',gwas[i],'.EUR-GeRSs_PP4.pred_eval.txt'), header=T, stringsAsFactors=F)

crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno_label[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)
crossTissue_i$Panel<-crossTissue_i$Model

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE','Panel')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno_label[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_PP4_Tissue_comp_TEDS.png', units='px', res=300, width=3000, height=4000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

# Estimate the correlation between SNP-weight set sample size, number of features and predictive utility
weight_info<-fread('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv')
weight_info$Set<-gsub('_','.',weight_info$Set)
weight_info$Set<-gsub('-','.',weight_info$Set)

crossTissue_table<-merge(crossTissue_table, weight_info, by.x='Panel', by.y='Set')

# Check correlation across 
cor(crossTissue_table$R, crossTissue_table$N_indiv) # 0.1086192
cor(crossTissue_table$R, crossTissue_table$N_feat) # 0.2683005
cor(crossTissue_table$N_indiv, crossTissue_table$N_feat) # 0.3263912

summary(lm(R ~ N_feat + N_indiv, data=crossTissue_table)) # R2 = 0.07248
crossTissue_table$R_resid<-resid(lm(R ~ N_feat + N_indiv, data=crossTissue_table))

Plot GeRS (TissueSpecific) across tissues

pheno<-c('Height21', 'BMI21', 'GCSE', 'ADHD')
pheno_label<-c('Height', 'BMI', 'GCSE', 'ADHD')
gwas<-c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')

crossTissue<-list()

for(i in 1:length(gwas)){
res_GeRS<-read.table(paste0('/users/k1806347/brc_scratch/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/',pheno[i],'/Association_withGeRSs/TEDS.w_hm3.AllTissue.TissueSpecific.',gwas[i],'.EUR-GeRSs.pred_eval.txt'), header=T, stringsAsFactors=F)

crossTissue_i<-res_GeRS

crossTissue_i$Phenotype<-pheno_label[i]

crossTissue_i<-crossTissue_i[,c('Phenotype','Model','R','SE','P')]

crossTissue_i$Model<-gsub('_group','',crossTissue_i$Model)

crossTissue_i$Model<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',crossTissue_i$Model)
crossTissue_i$Model<-gsub('SPLICING','Splicing',crossTissue_i$Model)
crossTissue_i$Model<-gsub('NTR.BLOOD.RNAARR','NTR Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('YFS.BLOOD.RNAARR','YFS Blood',crossTissue_i$Model)
crossTissue_i$Model<-gsub('METSIM.ADIPOSE.RNASEQ','METSIM Adipose',crossTissue_i$Model)
crossTissue_i$Model<-gsub('\\.',' ',crossTissue_i$Model)
crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)]<-paste0('GTEx ',crossTissue_i$Model[!grepl('CMC|NTR|YFS|METSIM|All', crossTissue_i$Model)])
crossTissue_i$Model<-gsub('Brain', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('Anterior cingulate cortex', 'ACC', crossTissue_i$Model)
crossTissue_i$Model <- gsub('basal ganglia', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA9', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('BA24', '', crossTissue_i$Model)
crossTissue_i$Model <- gsub('  ', ' ', crossTissue_i$Model)
crossTissue_i$Model_short<-substr(crossTissue_i$Model, start = 1, stop = 18)  #start the name at the first character and stop at the 25th
crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18]<-paste0(crossTissue_i$Model_short[nchar(crossTissue_i$Model) > 18], "...")

crossTissue[[pheno[i]]]<-crossTissue_i

}

crossTissue_table<-do.call(rbind, crossTissue)
crossTissue_table<-crossTissue_table[,c('Phenotype','Model','Model_short','R','SE')]

library(ggplot2)
library(cowplot)

plot_list<-list()
for(i in 1:length(gwas)){
  tmp<-crossTissue[[pheno[i]]]
  tmp$Model_short<-factor(tmp$Model_short, level=tmp$Model_short[rev(order(tmp$R))])
  tmp$Colour<-ifelse(tmp$Model_short == 'All', 'All', 'Single')

plot_list[[pheno[i]]]<-ggplot(tmp, aes(x=Model_short, y=R, fill=Colour)) +
          geom_bar(stat="identity", position=position_dodge(0.9)) +
          geom_errorbar(aes(ymin=R-SE, ymax=R+SE), width=.2,
                 position=position_dodge(0.9)) +
          labs(y="Correlation (SE)", x='', title=pheno_label[i]) +
          theme_half_open() +
          theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5, size=10), legend.position = "none") +
          background_grid(major = 'y', minor = 'y')
}

png('/mnt/lustre/users/k1806347/Analyses/GeRS_comparison/TEDS_outcomes_for_prediction/GeRS_TissueSpecific_Tissue_comp_TEDS.png', units='px', res=300, width=3000, height=4000)
  plot_grid(plotlist=plot_list, ncol=1)
dev.off()

Show GeRS prediction across p-value thresholds

Predictive utility: R2

Predictive utility:R

Correlation between GeRS model predictions and observed values in TEDS

Phenotype	Weight	Model	R (SE)
Height21	Adipose_Subcutaneous	1e.06	0.152 (0.013)
Height21	Adipose_Subcutaneous	1e.05	0.16 (0.013)
Height21	Adipose_Subcutaneous	1e.04	0.171 (0.013)
Height21	Adipose_Subcutaneous	0.001	0.186 (0.013)
Height21	Adipose_Subcutaneous	0.01	0.198 (0.013)
Height21	Adipose_Subcutaneous	0.05	0.205 (0.013)
Height21	Adipose_Subcutaneous	0.1	0.207 (0.013)
Height21	Adipose_Subcutaneous	0.5	0.211 (0.013)
Height21	Adipose_Subcutaneous	1	0.211 (0.013)
Height21	Adipose_Subcutaneous	All	0.209 (0.013)
Height21	Adipose_Visceral_Omentum	1e.06	0.145 (0.013)
Height21	Adipose_Visceral_Omentum	1e.05	0.151 (0.013)
Height21	Adipose_Visceral_Omentum	1e.04	0.159 (0.013)
Height21	Adipose_Visceral_Omentum	0.001	0.176 (0.013)
Height21	Adipose_Visceral_Omentum	0.01	0.187 (0.013)
Height21	Adipose_Visceral_Omentum	0.05	0.193 (0.013)
Height21	Adipose_Visceral_Omentum	0.1	0.196 (0.013)
Height21	Adipose_Visceral_Omentum	0.5	0.192 (0.013)
Height21	Adipose_Visceral_Omentum	1	0.192 (0.013)
Height21	Adipose_Visceral_Omentum	All	0.193 (0.013)
Height21	Adrenal_Gland	1e.06	0.125 (0.013)
Height21	Adrenal_Gland	1e.05	0.135 (0.013)
Height21	Adrenal_Gland	1e.04	0.145 (0.013)
Height21	Adrenal_Gland	0.001	0.15 (0.013)
Height21	Adrenal_Gland	0.01	0.149 (0.013)
Height21	Adrenal_Gland	0.05	0.16 (0.013)
Height21	Adrenal_Gland	0.1	0.161 (0.013)
Height21	Adrenal_Gland	0.5	0.159 (0.013)
Height21	Adrenal_Gland	1	0.16 (0.013)
Height21	Adrenal_Gland	All	0.161 (0.013)
Height21	Artery_Aorta	1e.06	0.134 (0.013)
Height21	Artery_Aorta	1e.05	0.143 (0.013)
Height21	Artery_Aorta	1e.04	0.153 (0.013)
Height21	Artery_Aorta	0.001	0.165 (0.013)
Height21	Artery_Aorta	0.01	0.179 (0.013)
Height21	Artery_Aorta	0.05	0.188 (0.013)
Height21	Artery_Aorta	0.1	0.191 (0.013)
Height21	Artery_Aorta	0.5	0.19 (0.013)
Height21	Artery_Aorta	1	0.191 (0.013)
Height21	Artery_Aorta	All	0.193 (0.013)
Height21	Artery_Coronary	1e.06	0.104 (0.013)
Height21	Artery_Coronary	1e.05	0.115 (0.013)
Height21	Artery_Coronary	1e.04	0.123 (0.013)
Height21	Artery_Coronary	0.001	0.132 (0.013)
Height21	Artery_Coronary	0.01	0.141 (0.013)
Height21	Artery_Coronary	0.05	0.147 (0.013)
Height21	Artery_Coronary	0.1	0.147 (0.013)
Height21	Artery_Coronary	0.5	0.149 (0.013)
Height21	Artery_Coronary	1	0.149 (0.013)
Height21	Artery_Coronary	All	0.146 (0.013)
Height21	Artery_Tibial	1e.06	0.155 (0.013)
Height21	Artery_Tibial	1e.05	0.164 (0.013)
Height21	Artery_Tibial	1e.04	0.171 (0.013)
Height21	Artery_Tibial	0.001	0.181 (0.013)
Height21	Artery_Tibial	0.01	0.189 (0.013)
Height21	Artery_Tibial	0.05	0.2 (0.013)
Height21	Artery_Tibial	0.1	0.204 (0.013)
Height21	Artery_Tibial	0.5	0.207 (0.013)
Height21	Artery_Tibial	1	0.207 (0.013)
Height21	Artery_Tibial	All	0.204 (0.013)
Height21	Brain_Amygdala	1e.06	0.094 (0.013)
Height21	Brain_Amygdala	1e.05	0.094 (0.013)
Height21	Brain_Amygdala	1e.04	0.103 (0.013)
Height21	Brain_Amygdala	0.001	0.108 (0.013)
Height21	Brain_Amygdala	0.01	0.117 (0.013)
Height21	Brain_Amygdala	0.05	0.118 (0.013)
Height21	Brain_Amygdala	0.1	0.122 (0.013)
Height21	Brain_Amygdala	0.5	0.126 (0.013)
Height21	Brain_Amygdala	1	0.127 (0.013)
Height21	Brain_Amygdala	All	0.123 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.079 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.079 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.077 (0.014)
Height21	Brain_Anterior_cingulate_cortex_BA24	0.001	0.087 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	0.01	0.096 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	0.05	0.102 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	0.1	0.105 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	0.5	0.108 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	1	0.108 (0.013)
Height21	Brain_Anterior_cingulate_cortex_BA24	All	0.103 (0.013)
Height21	Brain_Caudate_basal_ganglia	1e.06	0.092 (0.013)
Height21	Brain_Caudate_basal_ganglia	1e.05	0.091 (0.013)
Height21	Brain_Caudate_basal_ganglia	1e.04	0.095 (0.013)
Height21	Brain_Caudate_basal_ganglia	0.001	0.099 (0.013)
Height21	Brain_Caudate_basal_ganglia	0.01	0.112 (0.013)
Height21	Brain_Caudate_basal_ganglia	0.05	0.118 (0.013)
Height21	Brain_Caudate_basal_ganglia	0.1	0.119 (0.013)
Height21	Brain_Caudate_basal_ganglia	0.5	0.123 (0.013)
Height21	Brain_Caudate_basal_ganglia	1	0.123 (0.013)
Height21	Brain_Caudate_basal_ganglia	All	0.119 (0.013)
Height21	Brain_Cerebellar_Hemisphere	1e.06	0.116 (0.013)
Height21	Brain_Cerebellar_Hemisphere	1e.05	0.119 (0.013)
Height21	Brain_Cerebellar_Hemisphere	1e.04	0.127 (0.013)
Height21	Brain_Cerebellar_Hemisphere	0.001	0.137 (0.013)
Height21	Brain_Cerebellar_Hemisphere	0.01	0.151 (0.013)
Height21	Brain_Cerebellar_Hemisphere	0.05	0.147 (0.013)
Height21	Brain_Cerebellar_Hemisphere	0.1	0.147 (0.013)
Height21	Brain_Cerebellar_Hemisphere	0.5	0.147 (0.013)
Height21	Brain_Cerebellar_Hemisphere	1	0.148 (0.013)
Height21	Brain_Cerebellar_Hemisphere	All	0.15 (0.013)
Height21	Brain_Cerebellum	1e.06	0.122 (0.013)
Height21	Brain_Cerebellum	1e.05	0.131 (0.013)
Height21	Brain_Cerebellum	1e.04	0.137 (0.013)
Height21	Brain_Cerebellum	0.001	0.147 (0.013)
Height21	Brain_Cerebellum	0.01	0.16 (0.013)
Height21	Brain_Cerebellum	0.05	0.163 (0.013)
Height21	Brain_Cerebellum	0.1	0.163 (0.013)
Height21	Brain_Cerebellum	0.5	0.164 (0.013)
Height21	Brain_Cerebellum	1	0.162 (0.013)
Height21	Brain_Cerebellum	All	0.162 (0.013)
Height21	Brain_Cortex	1e.06	0.111 (0.013)
Height21	Brain_Cortex	1e.05	0.12 (0.013)
Height21	Brain_Cortex	1e.04	0.125 (0.013)
Height21	Brain_Cortex	0.001	0.131 (0.013)
Height21	Brain_Cortex	0.01	0.135 (0.013)
Height21	Brain_Cortex	0.05	0.135 (0.013)
Height21	Brain_Cortex	0.1	0.134 (0.013)
Height21	Brain_Cortex	0.5	0.139 (0.013)
Height21	Brain_Cortex	1	0.14 (0.013)
Height21	Brain_Cortex	All	0.137 (0.013)
Height21	Brain_Frontal_Cortex_BA9	1e.06	0.09 (0.013)
Height21	Brain_Frontal_Cortex_BA9	1e.05	0.099 (0.013)
Height21	Brain_Frontal_Cortex_BA9	1e.04	0.1 (0.013)
Height21	Brain_Frontal_Cortex_BA9	0.001	0.107 (0.013)
Height21	Brain_Frontal_Cortex_BA9	0.01	0.114 (0.013)
Height21	Brain_Frontal_Cortex_BA9	0.05	0.121 (0.013)
Height21	Brain_Frontal_Cortex_BA9	0.1	0.125 (0.013)
Height21	Brain_Frontal_Cortex_BA9	0.5	0.131 (0.013)
Height21	Brain_Frontal_Cortex_BA9	1	0.131 (0.013)
Height21	Brain_Frontal_Cortex_BA9	All	0.126 (0.013)
Height21	Brain_Hippocampus	1e.06	0.065 (0.014)
Height21	Brain_Hippocampus	1e.05	0.069 (0.014)
Height21	Brain_Hippocampus	1e.04	0.077 (0.014)
Height21	Brain_Hippocampus	0.001	0.086 (0.013)
Height21	Brain_Hippocampus	0.01	0.099 (0.013)
Height21	Brain_Hippocampus	0.05	0.101 (0.013)
Height21	Brain_Hippocampus	0.1	0.103 (0.013)
Height21	Brain_Hippocampus	0.5	0.106 (0.013)
Height21	Brain_Hippocampus	1	0.105 (0.013)
Height21	Brain_Hippocampus	All	0.1 (0.013)
Height21	Brain_Hypothalamus	1e.06	0.078 (0.014)
Height21	Brain_Hypothalamus	1e.05	0.087 (0.013)
Height21	Brain_Hypothalamus	1e.04	0.086 (0.013)
Height21	Brain_Hypothalamus	0.001	0.09 (0.013)
Height21	Brain_Hypothalamus	0.01	0.096 (0.013)
Height21	Brain_Hypothalamus	0.05	0.109 (0.013)
Height21	Brain_Hypothalamus	0.1	0.11 (0.013)
Height21	Brain_Hypothalamus	0.5	0.105 (0.013)
Height21	Brain_Hypothalamus	1	0.105 (0.013)
Height21	Brain_Hypothalamus	All	0.106 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.086 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.095 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.095 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.102 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.116 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.119 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.123 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.122 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	1	0.121 (0.013)
Height21	Brain_Nucleus_accumbens_basal_ganglia	All	0.12 (0.013)
Height21	Brain_Putamen_basal_ganglia	1e.06	0.09 (0.013)
Height21	Brain_Putamen_basal_ganglia	1e.05	0.094 (0.013)
Height21	Brain_Putamen_basal_ganglia	1e.04	0.094 (0.013)
Height21	Brain_Putamen_basal_ganglia	0.001	0.105 (0.013)
Height21	Brain_Putamen_basal_ganglia	0.01	0.109 (0.013)
Height21	Brain_Putamen_basal_ganglia	0.05	0.112 (0.013)
Height21	Brain_Putamen_basal_ganglia	0.1	0.118 (0.013)
Height21	Brain_Putamen_basal_ganglia	0.5	0.116 (0.013)
Height21	Brain_Putamen_basal_ganglia	1	0.116 (0.013)
Height21	Brain_Putamen_basal_ganglia	All	0.115 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	1e.06	0.073 (0.014)
Height21	Brain_Spinal_cord_cervical_c-1	1e.05	0.076 (0.014)
Height21	Brain_Spinal_cord_cervical_c-1	1e.04	0.078 (0.014)
Height21	Brain_Spinal_cord_cervical_c-1	0.001	0.086 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	0.01	0.092 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	0.05	0.097 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	0.1	0.099 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	0.5	0.105 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	1	0.106 (0.013)
Height21	Brain_Spinal_cord_cervical_c-1	All	0.104 (0.013)
Height21	Brain_Substantia_nigra	1e.06	0.088 (0.013)
Height21	Brain_Substantia_nigra	1e.05	0.091 (0.013)
Height21	Brain_Substantia_nigra	1e.04	0.091 (0.013)
Height21	Brain_Substantia_nigra	0.001	0.092 (0.013)
Height21	Brain_Substantia_nigra	0.01	0.099 (0.013)
Height21	Brain_Substantia_nigra	0.05	0.103 (0.013)
Height21	Brain_Substantia_nigra	0.1	0.104 (0.013)
Height21	Brain_Substantia_nigra	0.5	0.103 (0.013)
Height21	Brain_Substantia_nigra	1	0.102 (0.013)
Height21	Brain_Substantia_nigra	All	0.102 (0.013)
Height21	Breast_Mammary_Tissue	1e.06	0.15 (0.013)
Height21	Breast_Mammary_Tissue	1e.05	0.155 (0.013)
Height21	Breast_Mammary_Tissue	1e.04	0.164 (0.013)
Height21	Breast_Mammary_Tissue	0.001	0.172 (0.013)
Height21	Breast_Mammary_Tissue	0.01	0.174 (0.013)
Height21	Breast_Mammary_Tissue	0.05	0.178 (0.013)
Height21	Breast_Mammary_Tissue	0.1	0.178 (0.013)
Height21	Breast_Mammary_Tissue	0.5	0.182 (0.013)
Height21	Breast_Mammary_Tissue	1	0.182 (0.013)
Height21	Breast_Mammary_Tissue	All	0.182 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	1e.06	0.109 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	1e.05	0.118 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	1e.04	0.12 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	0.001	0.126 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	0.01	0.14 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	0.05	0.146 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	0.1	0.147 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	0.5	0.148 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	1	0.147 (0.013)
Height21	Cells_EBV-transformed_lymphocytes	All	0.144 (0.013)
Height21	Cells_Transformed_fibroblasts	1e.06	0.129 (0.013)
Height21	Cells_Transformed_fibroblasts	1e.05	0.137 (0.013)
Height21	Cells_Transformed_fibroblasts	1e.04	0.145 (0.013)
Height21	Cells_Transformed_fibroblasts	0.001	0.154 (0.013)
Height21	Cells_Transformed_fibroblasts	0.01	0.171 (0.013)
Height21	Cells_Transformed_fibroblasts	0.05	0.18 (0.013)
Height21	Cells_Transformed_fibroblasts	0.1	0.184 (0.013)
Height21	Cells_Transformed_fibroblasts	0.5	0.189 (0.013)
Height21	Cells_Transformed_fibroblasts	1	0.19 (0.013)
Height21	Cells_Transformed_fibroblasts	All	0.188 (0.013)
Height21	CMC.BRAIN.RNASEQ	1e.06	0.121 (0.013)
Height21	CMC.BRAIN.RNASEQ	1e.05	0.127 (0.013)
Height21	CMC.BRAIN.RNASEQ	1e.04	0.131 (0.013)
Height21	CMC.BRAIN.RNASEQ	0.001	0.142 (0.013)
Height21	CMC.BRAIN.RNASEQ	0.01	0.167 (0.013)
Height21	CMC.BRAIN.RNASEQ	0.05	0.177 (0.013)
Height21	CMC.BRAIN.RNASEQ	0.1	0.18 (0.013)
Height21	CMC.BRAIN.RNASEQ	0.5	0.18 (0.013)
Height21	CMC.BRAIN.RNASEQ	1	0.18 (0.013)
Height21	CMC.BRAIN.RNASEQ	All	0.184 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.115 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.115 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.115 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.123 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.137 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.144 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.145 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.147 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	1	0.147 (0.013)
Height21	CMC.BRAIN.RNASEQ_SPLICING	All	0.147 (0.013)
Height21	Colon_Sigmoid	1e.06	0.103 (0.013)
Height21	Colon_Sigmoid	1e.05	0.108 (0.013)
Height21	Colon_Sigmoid	1e.04	0.117 (0.013)
Height21	Colon_Sigmoid	0.001	0.128 (0.013)
Height21	Colon_Sigmoid	0.01	0.146 (0.013)
Height21	Colon_Sigmoid	0.05	0.152 (0.013)
Height21	Colon_Sigmoid	0.1	0.152 (0.013)
Height21	Colon_Sigmoid	0.5	0.159 (0.013)
Height21	Colon_Sigmoid	1	0.16 (0.013)
Height21	Colon_Sigmoid	All	0.156 (0.013)
Height21	Colon_Transverse	1e.06	0.121 (0.013)
Height21	Colon_Transverse	1e.05	0.131 (0.013)
Height21	Colon_Transverse	1e.04	0.139 (0.013)
Height21	Colon_Transverse	0.001	0.151 (0.013)
Height21	Colon_Transverse	0.01	0.158 (0.013)
Height21	Colon_Transverse	0.05	0.162 (0.013)
Height21	Colon_Transverse	0.1	0.163 (0.013)
Height21	Colon_Transverse	0.5	0.17 (0.013)
Height21	Colon_Transverse	1	0.171 (0.013)
Height21	Colon_Transverse	All	0.17 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	1e.06	0.13 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	1e.05	0.136 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	1e.04	0.141 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	0.001	0.149 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	0.01	0.161 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	0.05	0.173 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	0.1	0.172 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	0.5	0.177 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	1	0.178 (0.013)
Height21	Esophagus_Gastroesophageal_Junction	All	0.175 (0.013)
Height21	Esophagus_Mucosa	1e.06	0.137 (0.013)
Height21	Esophagus_Mucosa	1e.05	0.15 (0.013)
Height21	Esophagus_Mucosa	1e.04	0.157 (0.013)
Height21	Esophagus_Mucosa	0.001	0.17 (0.013)
Height21	Esophagus_Mucosa	0.01	0.185 (0.013)
Height21	Esophagus_Mucosa	0.05	0.192 (0.013)
Height21	Esophagus_Mucosa	0.1	0.19 (0.013)
Height21	Esophagus_Mucosa	0.5	0.194 (0.013)
Height21	Esophagus_Mucosa	1	0.194 (0.013)
Height21	Esophagus_Mucosa	All	0.193 (0.013)
Height21	Esophagus_Muscularis	1e.06	0.136 (0.013)
Height21	Esophagus_Muscularis	1e.05	0.149 (0.013)
Height21	Esophagus_Muscularis	1e.04	0.157 (0.013)
Height21	Esophagus_Muscularis	0.001	0.17 (0.013)
Height21	Esophagus_Muscularis	0.01	0.179 (0.013)
Height21	Esophagus_Muscularis	0.05	0.19 (0.013)
Height21	Esophagus_Muscularis	0.1	0.196 (0.013)
Height21	Esophagus_Muscularis	0.5	0.196 (0.013)
Height21	Esophagus_Muscularis	1	0.196 (0.013)
Height21	Esophagus_Muscularis	All	0.195 (0.013)
Height21	Heart_Atrial_Appendage	1e.06	0.101 (0.013)
Height21	Heart_Atrial_Appendage	1e.05	0.112 (0.013)
Height21	Heart_Atrial_Appendage	1e.04	0.126 (0.013)
Height21	Heart_Atrial_Appendage	0.001	0.138 (0.013)
Height21	Heart_Atrial_Appendage	0.01	0.149 (0.013)
Height21	Heart_Atrial_Appendage	0.05	0.157 (0.013)
Height21	Heart_Atrial_Appendage	0.1	0.158 (0.013)
Height21	Heart_Atrial_Appendage	0.5	0.162 (0.013)
Height21	Heart_Atrial_Appendage	1	0.163 (0.013)
Height21	Heart_Atrial_Appendage	All	0.162 (0.013)
Height21	Heart_Left_Ventricle	1e.06	0.117 (0.013)
Height21	Heart_Left_Ventricle	1e.05	0.123 (0.013)
Height21	Heart_Left_Ventricle	1e.04	0.128 (0.013)
Height21	Heart_Left_Ventricle	0.001	0.138 (0.013)
Height21	Heart_Left_Ventricle	0.01	0.153 (0.013)
Height21	Heart_Left_Ventricle	0.05	0.151 (0.013)
Height21	Heart_Left_Ventricle	0.1	0.153 (0.013)
Height21	Heart_Left_Ventricle	0.5	0.159 (0.013)
Height21	Heart_Left_Ventricle	1	0.159 (0.013)
Height21	Heart_Left_Ventricle	All	0.158 (0.013)
Height21	Liver	1e.06	0.106 (0.013)
Height21	Liver	1e.05	0.112 (0.013)
Height21	Liver	1e.04	0.124 (0.013)
Height21	Liver	0.001	0.128 (0.013)
Height21	Liver	0.01	0.139 (0.013)
Height21	Liver	0.05	0.144 (0.013)
Height21	Liver	0.1	0.142 (0.013)
Height21	Liver	0.5	0.147 (0.013)
Height21	Liver	1	0.146 (0.013)
Height21	Liver	All	0.145 (0.013)
Height21	Lung	1e.06	0.137 (0.013)
Height21	Lung	1e.05	0.146 (0.013)
Height21	Lung	1e.04	0.152 (0.013)
Height21	Lung	0.001	0.164 (0.013)
Height21	Lung	0.01	0.177 (0.013)
Height21	Lung	0.05	0.187 (0.013)
Height21	Lung	0.1	0.185 (0.013)
Height21	Lung	0.5	0.184 (0.013)
Height21	Lung	1	0.184 (0.013)
Height21	Lung	All	0.185 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	1e.06	0.132 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	1e.05	0.136 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	1e.04	0.15 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	0.001	0.164 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	0.01	0.175 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	0.05	0.176 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	0.1	0.181 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	0.5	0.179 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	1	0.179 (0.013)
Height21	METSIM.ADIPOSE.RNASEQ	All	0.178 (0.013)
Height21	Minor_Salivary_Gland	1e.06	0.078 (0.014)
Height21	Minor_Salivary_Gland	1e.05	0.079 (0.013)
Height21	Minor_Salivary_Gland	1e.04	0.078 (0.014)
Height21	Minor_Salivary_Gland	0.001	0.092 (0.013)
Height21	Minor_Salivary_Gland	0.01	0.094 (0.013)
Height21	Minor_Salivary_Gland	0.05	0.101 (0.013)
Height21	Minor_Salivary_Gland	0.1	0.107 (0.013)
Height21	Minor_Salivary_Gland	0.5	0.103 (0.013)
Height21	Minor_Salivary_Gland	1	0.102 (0.013)
Height21	Minor_Salivary_Gland	All	0.106 (0.013)
Height21	Muscle_Skeletal	1e.06	0.137 (0.013)
Height21	Muscle_Skeletal	1e.05	0.149 (0.013)
Height21	Muscle_Skeletal	1e.04	0.156 (0.013)
Height21	Muscle_Skeletal	0.001	0.161 (0.013)
Height21	Muscle_Skeletal	0.01	0.17 (0.013)
Height21	Muscle_Skeletal	0.05	0.178 (0.013)
Height21	Muscle_Skeletal	0.1	0.179 (0.013)
Height21	Muscle_Skeletal	0.5	0.184 (0.013)
Height21	Muscle_Skeletal	1	0.185 (0.013)
Height21	Muscle_Skeletal	All	0.184 (0.013)
Height21	Nerve_Tibial	1e.06	0.154 (0.013)
Height21	Nerve_Tibial	1e.05	0.161 (0.013)
Height21	Nerve_Tibial	1e.04	0.168 (0.013)
Height21	Nerve_Tibial	0.001	0.183 (0.013)
Height21	Nerve_Tibial	0.01	0.201 (0.013)
Height21	Nerve_Tibial	0.05	0.211 (0.013)
Height21	Nerve_Tibial	0.1	0.213 (0.013)
Height21	Nerve_Tibial	0.5	0.22 (0.013)
Height21	Nerve_Tibial	1	0.219 (0.013)
Height21	Nerve_Tibial	All	0.22 (0.013)
Height21	NTR.BLOOD.RNAARR	1e.06	0.11 (0.013)
Height21	NTR.BLOOD.RNAARR	1e.05	0.117 (0.013)
Height21	NTR.BLOOD.RNAARR	1e.04	0.124 (0.013)
Height21	NTR.BLOOD.RNAARR	0.001	0.124 (0.013)
Height21	NTR.BLOOD.RNAARR	0.01	0.132 (0.013)
Height21	NTR.BLOOD.RNAARR	0.05	0.138 (0.013)
Height21	NTR.BLOOD.RNAARR	0.1	0.14 (0.013)
Height21	NTR.BLOOD.RNAARR	0.5	0.154 (0.013)
Height21	NTR.BLOOD.RNAARR	1	0.154 (0.013)
Height21	NTR.BLOOD.RNAARR	All	0.155 (0.013)
Height21	Ovary	1e.06	0.086 (0.013)
Height21	Ovary	1e.05	0.088 (0.013)
Height21	Ovary	1e.04	0.093 (0.013)
Height21	Ovary	0.001	0.102 (0.013)
Height21	Ovary	0.01	0.115 (0.013)
Height21	Ovary	0.05	0.121 (0.013)
Height21	Ovary	0.1	0.119 (0.013)
Height21	Ovary	0.5	0.115 (0.013)
Height21	Ovary	1	0.117 (0.013)
Height21	Ovary	All	0.117 (0.013)
Height21	Pancreas	1e.06	0.126 (0.013)
Height21	Pancreas	1e.05	0.138 (0.013)
Height21	Pancreas	1e.04	0.14 (0.013)
Height21	Pancreas	0.001	0.154 (0.013)
Height21	Pancreas	0.01	0.161 (0.013)
Height21	Pancreas	0.05	0.17 (0.013)
Height21	Pancreas	0.1	0.165 (0.013)
Height21	Pancreas	0.5	0.167 (0.013)
Height21	Pancreas	1	0.166 (0.013)
Height21	Pancreas	All	0.167 (0.013)
Height21	Pituitary	1e.06	0.123 (0.013)
Height21	Pituitary	1e.05	0.138 (0.013)
Height21	Pituitary	1e.04	0.135 (0.013)
Height21	Pituitary	0.001	0.146 (0.013)
Height21	Pituitary	0.01	0.155 (0.013)
Height21	Pituitary	0.05	0.16 (0.013)
Height21	Pituitary	0.1	0.163 (0.013)
Height21	Pituitary	0.5	0.161 (0.013)
Height21	Pituitary	1	0.159 (0.013)
Height21	Pituitary	All	0.166 (0.013)
Height21	Prostate	1e.06	0.077 (0.014)
Height21	Prostate	1e.05	0.086 (0.013)
Height21	Prostate	1e.04	0.092 (0.013)
Height21	Prostate	0.001	0.102 (0.013)
Height21	Prostate	0.01	0.108 (0.013)
Height21	Prostate	0.05	0.115 (0.013)
Height21	Prostate	0.1	0.117 (0.013)
Height21	Prostate	0.5	0.121 (0.013)
Height21	Prostate	1	0.122 (0.013)
Height21	Prostate	All	0.119 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.136 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.145 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.154 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.16 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.171 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.179 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.181 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.183 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	1	0.182 (0.013)
Height21	Skin_Not_Sun_Exposed_Suprapubic	All	0.181 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	1e.06	0.145 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	1e.05	0.153 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	1e.04	0.16 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	0.001	0.18 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	0.01	0.192 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	0.05	0.202 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	0.1	0.206 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	0.5	0.209 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	1	0.208 (0.013)
Height21	Skin_Sun_Exposed_Lower_leg	All	0.207 (0.013)
Height21	Small_Intestine_Terminal_Ileum	1e.06	0.103 (0.013)
Height21	Small_Intestine_Terminal_Ileum	1e.05	0.107 (0.013)
Height21	Small_Intestine_Terminal_Ileum	1e.04	0.108 (0.013)
Height21	Small_Intestine_Terminal_Ileum	0.001	0.116 (0.013)
Height21	Small_Intestine_Terminal_Ileum	0.01	0.122 (0.013)
Height21	Small_Intestine_Terminal_Ileum	0.05	0.125 (0.013)
Height21	Small_Intestine_Terminal_Ileum	0.1	0.127 (0.013)
Height21	Small_Intestine_Terminal_Ileum	0.5	0.13 (0.013)
Height21	Small_Intestine_Terminal_Ileum	1	0.131 (0.013)
Height21	Small_Intestine_Terminal_Ileum	All	0.129 (0.013)
Height21	Spleen	1e.06	0.136 (0.013)
Height21	Spleen	1e.05	0.147 (0.013)
Height21	Spleen	1e.04	0.145 (0.013)
Height21	Spleen	0.001	0.153 (0.013)
Height21	Spleen	0.01	0.165 (0.013)
Height21	Spleen	0.05	0.167 (0.013)
Height21	Spleen	0.1	0.167 (0.013)
Height21	Spleen	0.5	0.166 (0.013)
Height21	Spleen	1	0.166 (0.013)
Height21	Spleen	All	0.171 (0.013)
Height21	Stomach	1e.06	0.132 (0.013)
Height21	Stomach	1e.05	0.139 (0.013)
Height21	Stomach	1e.04	0.147 (0.013)
Height21	Stomach	0.001	0.153 (0.013)
Height21	Stomach	0.01	0.163 (0.013)
Height21	Stomach	0.05	0.171 (0.013)
Height21	Stomach	0.1	0.167 (0.013)
Height21	Stomach	0.5	0.167 (0.013)
Height21	Stomach	1	0.166 (0.013)
Height21	Stomach	All	0.168 (0.013)
Height21	Testis	1e.06	0.144 (0.013)
Height21	Testis	1e.05	0.15 (0.013)
Height21	Testis	1e.04	0.163 (0.013)
Height21	Testis	0.001	0.179 (0.013)
Height21	Testis	0.01	0.185 (0.013)
Height21	Testis	0.05	0.193 (0.013)
Height21	Testis	0.1	0.191 (0.013)
Height21	Testis	0.5	0.191 (0.013)
Height21	Testis	1	0.192 (0.013)
Height21	Testis	All	0.193 (0.013)
Height21	Thyroid	1e.06	0.159 (0.013)
Height21	Thyroid	1e.05	0.171 (0.013)
Height21	Thyroid	1e.04	0.178 (0.013)
Height21	Thyroid	0.001	0.188 (0.013)
Height21	Thyroid	0.01	0.198 (0.013)
Height21	Thyroid	0.05	0.203 (0.013)
Height21	Thyroid	0.1	0.204 (0.013)
Height21	Thyroid	0.5	0.205 (0.013)
Height21	Thyroid	1	0.204 (0.013)
Height21	Thyroid	All	0.204 (0.013)
Height21	Uterus	1e.06	0.073 (0.014)
Height21	Uterus	1e.05	0.083 (0.013)
Height21	Uterus	1e.04	0.079 (0.013)
Height21	Uterus	0.001	0.08 (0.013)
Height21	Uterus	0.01	0.095 (0.013)
Height21	Uterus	0.05	0.099 (0.013)
Height21	Uterus	0.1	0.098 (0.013)
Height21	Uterus	0.5	0.102 (0.013)
Height21	Uterus	1	0.104 (0.013)
Height21	Uterus	All	0.101 (0.013)
Height21	Vagina	1e.06	0.093 (0.013)
Height21	Vagina	1e.05	0.091 (0.013)
Height21	Vagina	1e.04	0.096 (0.013)
Height21	Vagina	0.001	0.107 (0.013)
Height21	Vagina	0.01	0.117 (0.013)
Height21	Vagina	0.05	0.111 (0.013)
Height21	Vagina	0.1	0.112 (0.013)
Height21	Vagina	0.5	0.118 (0.013)
Height21	Vagina	1	0.119 (0.013)
Height21	Vagina	All	0.12 (0.013)
Height21	Whole_Blood	1e.06	0.125 (0.013)
Height21	Whole_Blood	1e.05	0.133 (0.013)
Height21	Whole_Blood	1e.04	0.134 (0.013)
Height21	Whole_Blood	0.001	0.146 (0.013)
Height21	Whole_Blood	0.01	0.16 (0.013)
Height21	Whole_Blood	0.05	0.171 (0.013)
Height21	Whole_Blood	0.1	0.173 (0.013)
Height21	Whole_Blood	0.5	0.172 (0.013)
Height21	Whole_Blood	1	0.173 (0.013)
Height21	Whole_Blood	All	0.174 (0.013)
Height21	YFS.BLOOD.RNAARR	1e.06	0.134 (0.013)
Height21	YFS.BLOOD.RNAARR	1e.05	0.148 (0.013)
Height21	YFS.BLOOD.RNAARR	1e.04	0.155 (0.013)
Height21	YFS.BLOOD.RNAARR	0.001	0.167 (0.013)
Height21	YFS.BLOOD.RNAARR	0.01	0.182 (0.013)
Height21	YFS.BLOOD.RNAARR	0.05	0.186 (0.013)
Height21	YFS.BLOOD.RNAARR	0.1	0.191 (0.013)
Height21	YFS.BLOOD.RNAARR	0.5	0.194 (0.013)
Height21	YFS.BLOOD.RNAARR	1	0.194 (0.013)
Height21	YFS.BLOOD.RNAARR	All	0.192 (0.013)
BMI21	Adipose_Subcutaneous	1e.06	0.06 (0.014)
BMI21	Adipose_Subcutaneous	1e.05	0.063 (0.014)
BMI21	Adipose_Subcutaneous	1e.04	0.073 (0.014)
BMI21	Adipose_Subcutaneous	0.001	0.089 (0.014)
BMI21	Adipose_Subcutaneous	0.01	0.1 (0.014)
BMI21	Adipose_Subcutaneous	0.05	0.11 (0.014)
BMI21	Adipose_Subcutaneous	0.1	0.114 (0.014)
BMI21	Adipose_Subcutaneous	0.5	0.116 (0.014)
BMI21	Adipose_Subcutaneous	1	0.116 (0.014)
BMI21	Adipose_Subcutaneous	All	0.122 (0.014)
BMI21	Adipose_Visceral_Omentum	1e.06	0.053 (0.014)
BMI21	Adipose_Visceral_Omentum	1e.05	0.063 (0.014)
BMI21	Adipose_Visceral_Omentum	1e.04	0.071 (0.014)
BMI21	Adipose_Visceral_Omentum	0.001	0.087 (0.014)
BMI21	Adipose_Visceral_Omentum	0.01	0.092 (0.014)
BMI21	Adipose_Visceral_Omentum	0.05	0.098 (0.014)
BMI21	Adipose_Visceral_Omentum	0.1	0.1 (0.014)
BMI21	Adipose_Visceral_Omentum	0.5	0.102 (0.014)
BMI21	Adipose_Visceral_Omentum	1	0.104 (0.014)
BMI21	Adipose_Visceral_Omentum	All	0.099 (0.014)
BMI21	Adrenal_Gland	1e.06	0.049 (0.014)
BMI21	Adrenal_Gland	1e.05	0.057 (0.014)
BMI21	Adrenal_Gland	1e.04	0.062 (0.014)
BMI21	Adrenal_Gland	0.001	0.069 (0.014)
BMI21	Adrenal_Gland	0.01	0.074 (0.014)
BMI21	Adrenal_Gland	0.05	0.079 (0.014)
BMI21	Adrenal_Gland	0.1	0.082 (0.014)
BMI21	Adrenal_Gland	0.5	0.089 (0.014)
BMI21	Adrenal_Gland	1	0.089 (0.014)
BMI21	Adrenal_Gland	All	0.088 (0.014)
BMI21	Artery_Aorta	1e.06	0.042 (0.014)
BMI21	Artery_Aorta	1e.05	0.047 (0.014)
BMI21	Artery_Aorta	1e.04	0.052 (0.014)
BMI21	Artery_Aorta	0.001	0.064 (0.014)
BMI21	Artery_Aorta	0.01	0.076 (0.014)
BMI21	Artery_Aorta	0.05	0.085 (0.014)
BMI21	Artery_Aorta	0.1	0.086 (0.014)
BMI21	Artery_Aorta	0.5	0.09 (0.014)
BMI21	Artery_Aorta	1	0.089 (0.014)
BMI21	Artery_Aorta	All	0.089 (0.014)
BMI21	Artery_Coronary	1e.06	0.033 (0.014)
BMI21	Artery_Coronary	1e.05	0.039 (0.014)
BMI21	Artery_Coronary	1e.04	0.04 (0.014)
BMI21	Artery_Coronary	0.001	0.054 (0.014)
BMI21	Artery_Coronary	0.01	0.061 (0.014)
BMI21	Artery_Coronary	0.05	0.068 (0.014)
BMI21	Artery_Coronary	0.1	0.071 (0.014)
BMI21	Artery_Coronary	0.5	0.067 (0.014)
BMI21	Artery_Coronary	1	0.066 (0.014)
BMI21	Artery_Coronary	All	0.065 (0.014)
BMI21	Artery_Tibial	1e.06	0.067 (0.014)
BMI21	Artery_Tibial	1e.05	0.072 (0.014)
BMI21	Artery_Tibial	1e.04	0.085 (0.014)
BMI21	Artery_Tibial	0.001	0.099 (0.014)
BMI21	Artery_Tibial	0.01	0.114 (0.014)
BMI21	Artery_Tibial	0.05	0.118 (0.014)
BMI21	Artery_Tibial	0.1	0.119 (0.014)
BMI21	Artery_Tibial	0.5	0.12 (0.014)
BMI21	Artery_Tibial	1	0.12 (0.014)
BMI21	Artery_Tibial	All	0.123 (0.014)
BMI21	Brain_Amygdala	1e.06	0.045 (0.014)
BMI21	Brain_Amygdala	1e.05	0.051 (0.014)
BMI21	Brain_Amygdala	1e.04	0.057 (0.014)
BMI21	Brain_Amygdala	0.001	0.069 (0.014)
BMI21	Brain_Amygdala	0.01	0.073 (0.014)
BMI21	Brain_Amygdala	0.05	0.079 (0.014)
BMI21	Brain_Amygdala	0.1	0.083 (0.014)
BMI21	Brain_Amygdala	0.5	0.08 (0.014)
BMI21	Brain_Amygdala	1	0.08 (0.014)
BMI21	Brain_Amygdala	All	0.079 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.042 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.044 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.05 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	0.001	0.068 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	0.01	0.081 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	0.05	0.086 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	0.1	0.087 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	0.5	0.087 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	1	0.088 (0.014)
BMI21	Brain_Anterior_cingulate_cortex_BA24	All	0.097 (0.014)
BMI21	Brain_Caudate_basal_ganglia	1e.06	0.023 (0.014)
BMI21	Brain_Caudate_basal_ganglia	1e.05	0.027 (0.014)
BMI21	Brain_Caudate_basal_ganglia	1e.04	0.037 (0.014)
BMI21	Brain_Caudate_basal_ganglia	0.001	0.046 (0.014)
BMI21	Brain_Caudate_basal_ganglia	0.01	0.053 (0.014)
BMI21	Brain_Caudate_basal_ganglia	0.05	0.058 (0.014)
BMI21	Brain_Caudate_basal_ganglia	0.1	0.06 (0.014)
BMI21	Brain_Caudate_basal_ganglia	0.5	0.064 (0.014)
BMI21	Brain_Caudate_basal_ganglia	1	0.064 (0.014)
BMI21	Brain_Caudate_basal_ganglia	All	0.077 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	1e.06	0.045 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	1e.05	0.045 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	1e.04	0.051 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	0.001	0.067 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	0.01	0.077 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	0.05	0.083 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	0.1	0.085 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	0.5	0.084 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	1	0.083 (0.014)
BMI21	Brain_Cerebellar_Hemisphere	All	0.092 (0.014)
BMI21	Brain_Cerebellum	1e.06	0.056 (0.014)
BMI21	Brain_Cerebellum	1e.05	0.057 (0.014)
BMI21	Brain_Cerebellum	1e.04	0.063 (0.014)
BMI21	Brain_Cerebellum	0.001	0.079 (0.014)
BMI21	Brain_Cerebellum	0.01	0.089 (0.014)
BMI21	Brain_Cerebellum	0.05	0.097 (0.014)
BMI21	Brain_Cerebellum	0.1	0.102 (0.014)
BMI21	Brain_Cerebellum	0.5	0.106 (0.014)
BMI21	Brain_Cerebellum	1	0.107 (0.014)
BMI21	Brain_Cerebellum	All	0.116 (0.014)
BMI21	Brain_Cortex	1e.06	0.044 (0.014)
BMI21	Brain_Cortex	1e.05	0.046 (0.014)
BMI21	Brain_Cortex	1e.04	0.047 (0.014)
BMI21	Brain_Cortex	0.001	0.059 (0.014)
BMI21	Brain_Cortex	0.01	0.074 (0.014)
BMI21	Brain_Cortex	0.05	0.077 (0.014)
BMI21	Brain_Cortex	0.1	0.08 (0.014)
BMI21	Brain_Cortex	0.5	0.083 (0.014)
BMI21	Brain_Cortex	1	0.083 (0.014)
BMI21	Brain_Cortex	All	0.092 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	1e.06	0.041 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	1e.05	0.049 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	1e.04	0.053 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	0.001	0.059 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	0.01	0.064 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	0.05	0.07 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	0.1	0.074 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	0.5	0.077 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	1	0.076 (0.014)
BMI21	Brain_Frontal_Cortex_BA9	All	0.073 (0.014)
BMI21	Brain_Hippocampus	1e.06	0.033 (0.014)
BMI21	Brain_Hippocampus	1e.05	0.037 (0.014)
BMI21	Brain_Hippocampus	1e.04	0.044 (0.014)
BMI21	Brain_Hippocampus	0.001	0.05 (0.014)
BMI21	Brain_Hippocampus	0.01	0.059 (0.014)
BMI21	Brain_Hippocampus	0.05	0.067 (0.014)
BMI21	Brain_Hippocampus	0.1	0.069 (0.014)
BMI21	Brain_Hippocampus	0.5	0.071 (0.014)
BMI21	Brain_Hippocampus	1	0.071 (0.014)
BMI21	Brain_Hippocampus	All	0.066 (0.014)
BMI21	Brain_Hypothalamus	1e.06	0.023 (0.014)
BMI21	Brain_Hypothalamus	1e.05	0.022 (0.014)
BMI21	Brain_Hypothalamus	1e.04	0.03 (0.014)
BMI21	Brain_Hypothalamus	0.001	0.039 (0.014)
BMI21	Brain_Hypothalamus	0.01	0.046 (0.014)
BMI21	Brain_Hypothalamus	0.05	0.05 (0.014)
BMI21	Brain_Hypothalamus	0.1	0.049 (0.014)
BMI21	Brain_Hypothalamus	0.5	0.051 (0.014)
BMI21	Brain_Hypothalamus	1	0.051 (0.014)
BMI21	Brain_Hypothalamus	All	0.041 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.009 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.011 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.023 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.03 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.037 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.044 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.047 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.051 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	1	0.05 (0.014)
BMI21	Brain_Nucleus_accumbens_basal_ganglia	All	0.05 (0.014)
BMI21	Brain_Putamen_basal_ganglia	1e.06	0.023 (0.014)
BMI21	Brain_Putamen_basal_ganglia	1e.05	0.024 (0.014)
BMI21	Brain_Putamen_basal_ganglia	1e.04	0.032 (0.014)
BMI21	Brain_Putamen_basal_ganglia	0.001	0.046 (0.014)
BMI21	Brain_Putamen_basal_ganglia	0.01	0.054 (0.014)
BMI21	Brain_Putamen_basal_ganglia	0.05	0.068 (0.014)
BMI21	Brain_Putamen_basal_ganglia	0.1	0.069 (0.014)
BMI21	Brain_Putamen_basal_ganglia	0.5	0.071 (0.014)
BMI21	Brain_Putamen_basal_ganglia	1	0.071 (0.014)
BMI21	Brain_Putamen_basal_ganglia	All	0.077 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	1e.06	0.025 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	1e.05	0.031 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	1e.04	0.035 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	0.001	0.051 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	0.01	0.049 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	0.05	0.052 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	0.1	0.049 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	0.5	0.048 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	1	0.048 (0.014)
BMI21	Brain_Spinal_cord_cervical_c-1	All	0.046 (0.014)
BMI21	Brain_Substantia_nigra	1e.06	0.023 (0.014)
BMI21	Brain_Substantia_nigra	1e.05	0.021 (0.014)
BMI21	Brain_Substantia_nigra	1e.04	0.032 (0.014)
BMI21	Brain_Substantia_nigra	0.001	0.036 (0.014)
BMI21	Brain_Substantia_nigra	0.01	0.036 (0.014)
BMI21	Brain_Substantia_nigra	0.05	0.037 (0.014)
BMI21	Brain_Substantia_nigra	0.1	0.036 (0.014)
BMI21	Brain_Substantia_nigra	0.5	0.04 (0.014)
BMI21	Brain_Substantia_nigra	1	0.039 (0.014)
BMI21	Brain_Substantia_nigra	All	0.032 (0.014)
BMI21	Breast_Mammary_Tissue	1e.06	0.043 (0.014)
BMI21	Breast_Mammary_Tissue	1e.05	0.042 (0.014)
BMI21	Breast_Mammary_Tissue	1e.04	0.048 (0.014)
BMI21	Breast_Mammary_Tissue	0.001	0.052 (0.014)
BMI21	Breast_Mammary_Tissue	0.01	0.059 (0.014)
BMI21	Breast_Mammary_Tissue	0.05	0.071 (0.014)
BMI21	Breast_Mammary_Tissue	0.1	0.076 (0.014)
BMI21	Breast_Mammary_Tissue	0.5	0.077 (0.014)
BMI21	Breast_Mammary_Tissue	1	0.078 (0.014)
BMI21	Breast_Mammary_Tissue	All	0.075 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	1e.06	0.054 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	1e.05	0.057 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	1e.04	0.063 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	0.001	0.073 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	0.01	0.083 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	0.05	0.093 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	0.1	0.093 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	0.5	0.091 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	1	0.091 (0.014)
BMI21	Cells_EBV-transformed_lymphocytes	All	0.093 (0.014)
BMI21	Cells_Transformed_fibroblasts	1e.06	0.056 (0.014)
BMI21	Cells_Transformed_fibroblasts	1e.05	0.063 (0.014)
BMI21	Cells_Transformed_fibroblasts	1e.04	0.068 (0.014)
BMI21	Cells_Transformed_fibroblasts	0.001	0.078 (0.014)
BMI21	Cells_Transformed_fibroblasts	0.01	0.088 (0.014)
BMI21	Cells_Transformed_fibroblasts	0.05	0.095 (0.014)
BMI21	Cells_Transformed_fibroblasts	0.1	0.096 (0.014)
BMI21	Cells_Transformed_fibroblasts	0.5	0.099 (0.014)
BMI21	Cells_Transformed_fibroblasts	1	0.099 (0.014)
BMI21	Cells_Transformed_fibroblasts	All	0.098 (0.014)
BMI21	CMC.BRAIN.RNASEQ	1e.06	0.069 (0.014)
BMI21	CMC.BRAIN.RNASEQ	1e.05	0.072 (0.014)
BMI21	CMC.BRAIN.RNASEQ	1e.04	0.081 (0.014)
BMI21	CMC.BRAIN.RNASEQ	0.001	0.09 (0.014)
BMI21	CMC.BRAIN.RNASEQ	0.01	0.097 (0.014)
BMI21	CMC.BRAIN.RNASEQ	0.05	0.108 (0.014)
BMI21	CMC.BRAIN.RNASEQ	0.1	0.11 (0.014)
BMI21	CMC.BRAIN.RNASEQ	0.5	0.111 (0.014)
BMI21	CMC.BRAIN.RNASEQ	1	0.11 (0.014)
BMI21	CMC.BRAIN.RNASEQ	All	0.106 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.059 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.062 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.07 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.088 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.094 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.096 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.095 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.092 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	1	0.089 (0.014)
BMI21	CMC.BRAIN.RNASEQ_SPLICING	All	0.091 (0.014)
BMI21	Colon_Sigmoid	1e.06	0.026 (0.014)
BMI21	Colon_Sigmoid	1e.05	0.024 (0.014)
BMI21	Colon_Sigmoid	1e.04	0.034 (0.014)
BMI21	Colon_Sigmoid	0.001	0.052 (0.014)
BMI21	Colon_Sigmoid	0.01	0.054 (0.014)
BMI21	Colon_Sigmoid	0.05	0.063 (0.014)
BMI21	Colon_Sigmoid	0.1	0.065 (0.014)
BMI21	Colon_Sigmoid	0.5	0.06 (0.014)
BMI21	Colon_Sigmoid	1	0.061 (0.014)
BMI21	Colon_Sigmoid	All	0.067 (0.014)
BMI21	Colon_Transverse	1e.06	0.038 (0.014)
BMI21	Colon_Transverse	1e.05	0.046 (0.014)
BMI21	Colon_Transverse	1e.04	0.052 (0.014)
BMI21	Colon_Transverse	0.001	0.058 (0.014)
BMI21	Colon_Transverse	0.01	0.063 (0.014)
BMI21	Colon_Transverse	0.05	0.074 (0.014)
BMI21	Colon_Transverse	0.1	0.074 (0.014)
BMI21	Colon_Transverse	0.5	0.076 (0.014)
BMI21	Colon_Transverse	1	0.076 (0.014)
BMI21	Colon_Transverse	All	0.074 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	1e.06	0.028 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	1e.05	0.04 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	1e.04	0.041 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	0.001	0.053 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	0.01	0.063 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	0.05	0.071 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	0.1	0.074 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	0.5	0.074 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	1	0.074 (0.014)
BMI21	Esophagus_Gastroesophageal_Junction	All	0.078 (0.014)
BMI21	Esophagus_Mucosa	1e.06	0.046 (0.014)
BMI21	Esophagus_Mucosa	1e.05	0.049 (0.014)
BMI21	Esophagus_Mucosa	1e.04	0.057 (0.014)
BMI21	Esophagus_Mucosa	0.001	0.069 (0.014)
BMI21	Esophagus_Mucosa	0.01	0.08 (0.014)
BMI21	Esophagus_Mucosa	0.05	0.094 (0.014)
BMI21	Esophagus_Mucosa	0.1	0.096 (0.014)
BMI21	Esophagus_Mucosa	0.5	0.094 (0.014)
BMI21	Esophagus_Mucosa	1	0.094 (0.014)
BMI21	Esophagus_Mucosa	All	0.097 (0.014)
BMI21	Esophagus_Muscularis	1e.06	0.054 (0.014)
BMI21	Esophagus_Muscularis	1e.05	0.058 (0.014)
BMI21	Esophagus_Muscularis	1e.04	0.065 (0.014)
BMI21	Esophagus_Muscularis	0.001	0.079 (0.014)
BMI21	Esophagus_Muscularis	0.01	0.091 (0.014)
BMI21	Esophagus_Muscularis	0.05	0.098 (0.014)
BMI21	Esophagus_Muscularis	0.1	0.102 (0.014)
BMI21	Esophagus_Muscularis	0.5	0.105 (0.014)
BMI21	Esophagus_Muscularis	1	0.105 (0.014)
BMI21	Esophagus_Muscularis	All	0.107 (0.014)
BMI21	Heart_Atrial_Appendage	1e.06	0.034 (0.014)
BMI21	Heart_Atrial_Appendage	1e.05	0.042 (0.014)
BMI21	Heart_Atrial_Appendage	1e.04	0.058 (0.014)
BMI21	Heart_Atrial_Appendage	0.001	0.072 (0.014)
BMI21	Heart_Atrial_Appendage	0.01	0.087 (0.014)
BMI21	Heart_Atrial_Appendage	0.05	0.097 (0.014)
BMI21	Heart_Atrial_Appendage	0.1	0.095 (0.014)
BMI21	Heart_Atrial_Appendage	0.5	0.098 (0.014)
BMI21	Heart_Atrial_Appendage	1	0.096 (0.014)
BMI21	Heart_Atrial_Appendage	All	0.107 (0.014)
BMI21	Heart_Left_Ventricle	1e.06	0.031 (0.014)
BMI21	Heart_Left_Ventricle	1e.05	0.04 (0.014)
BMI21	Heart_Left_Ventricle	1e.04	0.047 (0.014)
BMI21	Heart_Left_Ventricle	0.001	0.053 (0.014)
BMI21	Heart_Left_Ventricle	0.01	0.071 (0.014)
BMI21	Heart_Left_Ventricle	0.05	0.084 (0.014)
BMI21	Heart_Left_Ventricle	0.1	0.084 (0.014)
BMI21	Heart_Left_Ventricle	0.5	0.082 (0.014)
BMI21	Heart_Left_Ventricle	1	0.081 (0.014)
BMI21	Heart_Left_Ventricle	All	0.08 (0.014)
BMI21	Liver	1e.06	0.04 (0.014)
BMI21	Liver	1e.05	0.035 (0.014)
BMI21	Liver	1e.04	0.042 (0.014)
BMI21	Liver	0.001	0.058 (0.014)
BMI21	Liver	0.01	0.066 (0.014)
BMI21	Liver	0.05	0.079 (0.014)
BMI21	Liver	0.1	0.078 (0.014)
BMI21	Liver	0.5	0.074 (0.014)
BMI21	Liver	1	0.072 (0.014)
BMI21	Liver	All	0.08 (0.014)
BMI21	Lung	1e.06	0.059 (0.014)
BMI21	Lung	1e.05	0.065 (0.014)
BMI21	Lung	1e.04	0.069 (0.014)
BMI21	Lung	0.001	0.08 (0.014)
BMI21	Lung	0.01	0.094 (0.014)
BMI21	Lung	0.05	0.1 (0.014)
BMI21	Lung	0.1	0.102 (0.014)
BMI21	Lung	0.5	0.103 (0.014)
BMI21	Lung	1	0.102 (0.014)
BMI21	Lung	All	0.104 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	1e.06	0.045 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	1e.05	0.056 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	1e.04	0.064 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	0.001	0.07 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	0.01	0.083 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	0.05	0.09 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	0.1	0.098 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	0.5	0.099 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	1	0.098 (0.014)
BMI21	METSIM.ADIPOSE.RNASEQ	All	0.099 (0.014)
BMI21	Minor_Salivary_Gland	1e.06	0.005 (0.014)
BMI21	Minor_Salivary_Gland	1e.05	0 (0.014)
BMI21	Minor_Salivary_Gland	1e.04	0.016 (0.014)
BMI21	Minor_Salivary_Gland	0.001	0.022 (0.014)
BMI21	Minor_Salivary_Gland	0.01	0.02 (0.014)
BMI21	Minor_Salivary_Gland	0.05	0.018 (0.014)
BMI21	Minor_Salivary_Gland	0.1	0.013 (0.014)
BMI21	Minor_Salivary_Gland	0.5	0.016 (0.014)
BMI21	Minor_Salivary_Gland	1	0.016 (0.014)
BMI21	Minor_Salivary_Gland	All	0.011 (0.014)
BMI21	Muscle_Skeletal	1e.06	0.059 (0.014)
BMI21	Muscle_Skeletal	1e.05	0.063 (0.014)
BMI21	Muscle_Skeletal	1e.04	0.068 (0.014)
BMI21	Muscle_Skeletal	0.001	0.081 (0.014)
BMI21	Muscle_Skeletal	0.01	0.092 (0.014)
BMI21	Muscle_Skeletal	0.05	0.099 (0.014)
BMI21	Muscle_Skeletal	0.1	0.099 (0.014)
BMI21	Muscle_Skeletal	0.5	0.106 (0.014)
BMI21	Muscle_Skeletal	1	0.105 (0.014)
BMI21	Muscle_Skeletal	All	0.105 (0.014)
BMI21	Nerve_Tibial	1e.06	0.072 (0.014)
BMI21	Nerve_Tibial	1e.05	0.072 (0.014)
BMI21	Nerve_Tibial	1e.04	0.078 (0.014)
BMI21	Nerve_Tibial	0.001	0.09 (0.014)
BMI21	Nerve_Tibial	0.01	0.099 (0.014)
BMI21	Nerve_Tibial	0.05	0.109 (0.014)
BMI21	Nerve_Tibial	0.1	0.111 (0.014)
BMI21	Nerve_Tibial	0.5	0.114 (0.014)
BMI21	Nerve_Tibial	1	0.115 (0.014)
BMI21	Nerve_Tibial	All	0.117 (0.014)
BMI21	NTR.BLOOD.RNAARR	1e.06	0.036 (0.014)
BMI21	NTR.BLOOD.RNAARR	1e.05	0.048 (0.014)
BMI21	NTR.BLOOD.RNAARR	1e.04	0.054 (0.014)
BMI21	NTR.BLOOD.RNAARR	0.001	0.06 (0.014)
BMI21	NTR.BLOOD.RNAARR	0.01	0.063 (0.014)
BMI21	NTR.BLOOD.RNAARR	0.05	0.076 (0.014)
BMI21	NTR.BLOOD.RNAARR	0.1	0.08 (0.014)
BMI21	NTR.BLOOD.RNAARR	0.5	0.073 (0.014)
BMI21	NTR.BLOOD.RNAARR	1	0.074 (0.014)
BMI21	NTR.BLOOD.RNAARR	All	0.075 (0.014)
BMI21	Ovary	1e.06	0.027 (0.014)
BMI21	Ovary	1e.05	0.021 (0.014)
BMI21	Ovary	1e.04	0.033 (0.014)
BMI21	Ovary	0.001	0.044 (0.014)
BMI21	Ovary	0.01	0.049 (0.014)
BMI21	Ovary	0.05	0.049 (0.014)
BMI21	Ovary	0.1	0.049 (0.014)
BMI21	Ovary	0.5	0.055 (0.014)
BMI21	Ovary	1	0.057 (0.014)
BMI21	Ovary	All	0.058 (0.014)
BMI21	Pancreas	1e.06	0.053 (0.014)
BMI21	Pancreas	1e.05	0.059 (0.014)
BMI21	Pancreas	1e.04	0.061 (0.014)
BMI21	Pancreas	0.001	0.074 (0.014)
BMI21	Pancreas	0.01	0.082 (0.014)
BMI21	Pancreas	0.05	0.091 (0.014)
BMI21	Pancreas	0.1	0.092 (0.014)
BMI21	Pancreas	0.5	0.092 (0.014)
BMI21	Pancreas	1	0.093 (0.014)
BMI21	Pancreas	All	0.086 (0.014)
BMI21	Pituitary	1e.06	0.023 (0.014)
BMI21	Pituitary	1e.05	0.028 (0.014)
BMI21	Pituitary	1e.04	0.035 (0.014)
BMI21	Pituitary	0.001	0.042 (0.014)
BMI21	Pituitary	0.01	0.054 (0.014)
BMI21	Pituitary	0.05	0.064 (0.014)
BMI21	Pituitary	0.1	0.064 (0.014)
BMI21	Pituitary	0.5	0.066 (0.014)
BMI21	Pituitary	1	0.066 (0.014)
BMI21	Pituitary	All	0.077 (0.014)
BMI21	Prostate	1e.06	0.019 (0.014)
BMI21	Prostate	1e.05	0.021 (0.014)
BMI21	Prostate	1e.04	0.022 (0.014)
BMI21	Prostate	0.001	0.035 (0.014)
BMI21	Prostate	0.01	0.052 (0.014)
BMI21	Prostate	0.05	0.049 (0.014)
BMI21	Prostate	0.1	0.051 (0.014)
BMI21	Prostate	0.5	0.049 (0.014)
BMI21	Prostate	1	0.048 (0.014)
BMI21	Prostate	All	0.061 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.061 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.065 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.07 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.077 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.089 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.095 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.096 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.094 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	1	0.094 (0.014)
BMI21	Skin_Not_Sun_Exposed_Suprapubic	All	0.092 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	1e.06	0.054 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	1e.05	0.059 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	1e.04	0.066 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	0.001	0.078 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	0.01	0.086 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	0.05	0.095 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	0.1	0.097 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	0.5	0.098 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	1	0.098 (0.014)
BMI21	Skin_Sun_Exposed_Lower_leg	All	0.096 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	1e.06	0.039 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	1e.05	0.044 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	1e.04	0.052 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	0.001	0.052 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	0.01	0.066 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	0.05	0.071 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	0.1	0.075 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	0.5	0.071 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	1	0.072 (0.014)
BMI21	Small_Intestine_Terminal_Ileum	All	0.064 (0.014)
BMI21	Spleen	1e.06	0.045 (0.014)
BMI21	Spleen	1e.05	0.05 (0.014)
BMI21	Spleen	1e.04	0.053 (0.014)
BMI21	Spleen	0.001	0.064 (0.014)
BMI21	Spleen	0.01	0.077 (0.014)
BMI21	Spleen	0.05	0.087 (0.014)
BMI21	Spleen	0.1	0.088 (0.014)
BMI21	Spleen	0.5	0.082 (0.014)
BMI21	Spleen	1	0.081 (0.014)
BMI21	Spleen	All	0.09 (0.014)
BMI21	Stomach	1e.06	0.051 (0.014)
BMI21	Stomach	1e.05	0.06 (0.014)
BMI21	Stomach	1e.04	0.066 (0.014)
BMI21	Stomach	0.001	0.074 (0.014)
BMI21	Stomach	0.01	0.081 (0.014)
BMI21	Stomach	0.05	0.089 (0.014)
BMI21	Stomach	0.1	0.093 (0.014)
BMI21	Stomach	0.5	0.086 (0.014)
BMI21	Stomach	1	0.085 (0.014)
BMI21	Stomach	All	0.085 (0.014)
BMI21	Testis	1e.06	0.059 (0.014)
BMI21	Testis	1e.05	0.069 (0.014)
BMI21	Testis	1e.04	0.078 (0.014)
BMI21	Testis	0.001	0.086 (0.014)
BMI21	Testis	0.01	0.098 (0.014)
BMI21	Testis	0.05	0.106 (0.014)
BMI21	Testis	0.1	0.11 (0.014)
BMI21	Testis	0.5	0.109 (0.014)
BMI21	Testis	1	0.109 (0.014)
BMI21	Testis	All	0.119 (0.014)
BMI21	Thyroid	1e.06	0.048 (0.014)
BMI21	Thyroid	1e.05	0.049 (0.014)
BMI21	Thyroid	1e.04	0.057 (0.014)
BMI21	Thyroid	0.001	0.072 (0.014)
BMI21	Thyroid	0.01	0.082 (0.014)
BMI21	Thyroid	0.05	0.091 (0.014)
BMI21	Thyroid	0.1	0.095 (0.014)
BMI21	Thyroid	0.5	0.095 (0.014)
BMI21	Thyroid	1	0.096 (0.014)
BMI21	Thyroid	All	0.093 (0.014)
BMI21	Uterus	1e.06	0.01 (0.014)
BMI21	Uterus	1e.05	0.012 (0.014)
BMI21	Uterus	1e.04	0.022 (0.014)
BMI21	Uterus	0.001	0.032 (0.014)
BMI21	Uterus	0.01	0.04 (0.014)
BMI21	Uterus	0.05	0.036 (0.014)
BMI21	Uterus	0.1	0.037 (0.014)
BMI21	Uterus	0.5	0.042 (0.014)
BMI21	Uterus	1	0.043 (0.014)
BMI21	Uterus	All	0.032 (0.014)
BMI21	Vagina	1e.06	0.03 (0.014)
BMI21	Vagina	1e.05	0.03 (0.014)
BMI21	Vagina	1e.04	0.044 (0.014)
BMI21	Vagina	0.001	0.057 (0.014)
BMI21	Vagina	0.01	0.054 (0.014)
BMI21	Vagina	0.05	0.063 (0.014)
BMI21	Vagina	0.1	0.065 (0.014)
BMI21	Vagina	0.5	0.065 (0.014)
BMI21	Vagina	1	0.066 (0.014)
BMI21	Vagina	All	0.061 (0.014)
BMI21	Whole_Blood	1e.06	0.054 (0.014)
BMI21	Whole_Blood	1e.05	0.056 (0.014)
BMI21	Whole_Blood	1e.04	0.062 (0.014)
BMI21	Whole_Blood	0.001	0.068 (0.014)
BMI21	Whole_Blood	0.01	0.087 (0.014)
BMI21	Whole_Blood	0.05	0.093 (0.014)
BMI21	Whole_Blood	0.1	0.095 (0.014)
BMI21	Whole_Blood	0.5	0.095 (0.014)
BMI21	Whole_Blood	1	0.096 (0.014)
BMI21	Whole_Blood	All	0.097 (0.014)
BMI21	YFS.BLOOD.RNAARR	1e.06	0.032 (0.014)
BMI21	YFS.BLOOD.RNAARR	1e.05	0.039 (0.014)
BMI21	YFS.BLOOD.RNAARR	1e.04	0.054 (0.014)
BMI21	YFS.BLOOD.RNAARR	0.001	0.063 (0.014)
BMI21	YFS.BLOOD.RNAARR	0.01	0.071 (0.014)
BMI21	YFS.BLOOD.RNAARR	0.05	0.077 (0.014)
BMI21	YFS.BLOOD.RNAARR	0.1	0.078 (0.014)
BMI21	YFS.BLOOD.RNAARR	0.5	0.086 (0.014)
BMI21	YFS.BLOOD.RNAARR	1	0.084 (0.014)
BMI21	YFS.BLOOD.RNAARR	All	0.083 (0.014)
GCSE	Adipose_Subcutaneous	1e.06	0.086 (0.012)
GCSE	Adipose_Subcutaneous	1e.05	0.092 (0.012)
GCSE	Adipose_Subcutaneous	1e.04	0.106 (0.012)
GCSE	Adipose_Subcutaneous	0.001	0.126 (0.012)
GCSE	Adipose_Subcutaneous	0.01	0.14 (0.012)
GCSE	Adipose_Subcutaneous	0.05	0.151 (0.012)
GCSE	Adipose_Subcutaneous	0.1	0.156 (0.012)
GCSE	Adipose_Subcutaneous	0.5	0.159 (0.012)
GCSE	Adipose_Subcutaneous	1	0.159 (0.012)
GCSE	Adipose_Subcutaneous	All	0.16 (0.012)
GCSE	Adipose_Visceral_Omentum	1e.06	0.081 (0.012)
GCSE	Adipose_Visceral_Omentum	1e.05	0.1 (0.012)
GCSE	Adipose_Visceral_Omentum	1e.04	0.109 (0.012)
GCSE	Adipose_Visceral_Omentum	0.001	0.131 (0.012)
GCSE	Adipose_Visceral_Omentum	0.01	0.141 (0.012)
GCSE	Adipose_Visceral_Omentum	0.05	0.149 (0.012)
GCSE	Adipose_Visceral_Omentum	0.1	0.155 (0.012)
GCSE	Adipose_Visceral_Omentum	0.5	0.158 (0.012)
GCSE	Adipose_Visceral_Omentum	1	0.157 (0.012)
GCSE	Adipose_Visceral_Omentum	All	0.156 (0.012)
GCSE	Adrenal_Gland	1e.06	0.074 (0.012)
GCSE	Adrenal_Gland	1e.05	0.091 (0.012)
GCSE	Adrenal_Gland	1e.04	0.097 (0.012)
GCSE	Adrenal_Gland	0.001	0.103 (0.012)
GCSE	Adrenal_Gland	0.01	0.115 (0.012)
GCSE	Adrenal_Gland	0.05	0.12 (0.012)
GCSE	Adrenal_Gland	0.1	0.125 (0.012)
GCSE	Adrenal_Gland	0.5	0.122 (0.012)
GCSE	Adrenal_Gland	1	0.123 (0.012)
GCSE	Adrenal_Gland	All	0.122 (0.012)
GCSE	Artery_Aorta	1e.06	0.079 (0.012)
GCSE	Artery_Aorta	1e.05	0.087 (0.012)
GCSE	Artery_Aorta	1e.04	0.097 (0.012)
GCSE	Artery_Aorta	0.001	0.109 (0.012)
GCSE	Artery_Aorta	0.01	0.12 (0.012)
GCSE	Artery_Aorta	0.05	0.128 (0.012)
GCSE	Artery_Aorta	0.1	0.131 (0.012)
GCSE	Artery_Aorta	0.5	0.133 (0.012)
GCSE	Artery_Aorta	1	0.134 (0.012)
GCSE	Artery_Aorta	All	0.135 (0.012)
GCSE	Artery_Coronary	1e.06	0.074 (0.012)
GCSE	Artery_Coronary	1e.05	0.08 (0.012)
GCSE	Artery_Coronary	1e.04	0.087 (0.012)
GCSE	Artery_Coronary	0.001	0.095 (0.012)
GCSE	Artery_Coronary	0.01	0.111 (0.012)
GCSE	Artery_Coronary	0.05	0.121 (0.012)
GCSE	Artery_Coronary	0.1	0.117 (0.012)
GCSE	Artery_Coronary	0.5	0.117 (0.012)
GCSE	Artery_Coronary	1	0.119 (0.012)
GCSE	Artery_Coronary	All	0.122 (0.012)
GCSE	Artery_Tibial	1e.06	0.079 (0.012)
GCSE	Artery_Tibial	1e.05	0.088 (0.012)
GCSE	Artery_Tibial	1e.04	0.104 (0.012)
GCSE	Artery_Tibial	0.001	0.122 (0.012)
GCSE	Artery_Tibial	0.01	0.133 (0.012)
GCSE	Artery_Tibial	0.05	0.149 (0.012)
GCSE	Artery_Tibial	0.1	0.151 (0.012)
GCSE	Artery_Tibial	0.5	0.16 (0.012)
GCSE	Artery_Tibial	1	0.16 (0.012)
GCSE	Artery_Tibial	All	0.165 (0.012)
GCSE	Brain_Amygdala	1e.06	0.063 (0.012)
GCSE	Brain_Amygdala	1e.05	0.069 (0.012)
GCSE	Brain_Amygdala	1e.04	0.071 (0.012)
GCSE	Brain_Amygdala	0.001	0.083 (0.012)
GCSE	Brain_Amygdala	0.01	0.094 (0.012)
GCSE	Brain_Amygdala	0.05	0.097 (0.012)
GCSE	Brain_Amygdala	0.1	0.092 (0.012)
GCSE	Brain_Amygdala	0.5	0.096 (0.012)
GCSE	Brain_Amygdala	1	0.096 (0.012)
GCSE	Brain_Amygdala	All	0.094 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	1e.06	0.082 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.091 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.102 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	0.001	0.113 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	0.01	0.121 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	0.05	0.128 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	0.1	0.125 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	0.5	0.124 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	1	0.124 (0.012)
GCSE	Brain_Anterior_cingulate_cortex_BA24	All	0.129 (0.012)
GCSE	Brain_Caudate_basal_ganglia	1e.06	0.078 (0.012)
GCSE	Brain_Caudate_basal_ganglia	1e.05	0.084 (0.012)
GCSE	Brain_Caudate_basal_ganglia	1e.04	0.095 (0.012)
GCSE	Brain_Caudate_basal_ganglia	0.001	0.101 (0.012)
GCSE	Brain_Caudate_basal_ganglia	0.01	0.115 (0.012)
GCSE	Brain_Caudate_basal_ganglia	0.05	0.12 (0.012)
GCSE	Brain_Caudate_basal_ganglia	0.1	0.126 (0.012)
GCSE	Brain_Caudate_basal_ganglia	0.5	0.123 (0.012)
GCSE	Brain_Caudate_basal_ganglia	1	0.124 (0.012)
GCSE	Brain_Caudate_basal_ganglia	All	0.123 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	1e.06	0.086 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	1e.05	0.098 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	1e.04	0.111 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	0.001	0.12 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	0.01	0.13 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	0.05	0.138 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	0.1	0.139 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	0.5	0.142 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	1	0.142 (0.012)
GCSE	Brain_Cerebellar_Hemisphere	All	0.142 (0.012)
GCSE	Brain_Cerebellum	1e.06	0.088 (0.012)
GCSE	Brain_Cerebellum	1e.05	0.101 (0.012)
GCSE	Brain_Cerebellum	1e.04	0.107 (0.012)
GCSE	Brain_Cerebellum	0.001	0.118 (0.012)
GCSE	Brain_Cerebellum	0.01	0.136 (0.012)
GCSE	Brain_Cerebellum	0.05	0.141 (0.012)
GCSE	Brain_Cerebellum	0.1	0.142 (0.012)
GCSE	Brain_Cerebellum	0.5	0.143 (0.012)
GCSE	Brain_Cerebellum	1	0.144 (0.012)
GCSE	Brain_Cerebellum	All	0.143 (0.012)
GCSE	Brain_Cortex	1e.06	0.079 (0.012)
GCSE	Brain_Cortex	1e.05	0.091 (0.012)
GCSE	Brain_Cortex	1e.04	0.095 (0.012)
GCSE	Brain_Cortex	0.001	0.111 (0.012)
GCSE	Brain_Cortex	0.01	0.124 (0.012)
GCSE	Brain_Cortex	0.05	0.128 (0.012)
GCSE	Brain_Cortex	0.1	0.127 (0.012)
GCSE	Brain_Cortex	0.5	0.132 (0.012)
GCSE	Brain_Cortex	1	0.133 (0.012)
GCSE	Brain_Cortex	All	0.131 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	1e.06	0.078 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	1e.05	0.083 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	1e.04	0.091 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	0.001	0.097 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	0.01	0.108 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	0.05	0.109 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	0.1	0.109 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	0.5	0.115 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	1	0.115 (0.012)
GCSE	Brain_Frontal_Cortex_BA9	All	0.114 (0.012)
GCSE	Brain_Hippocampus	1e.06	0.076 (0.012)
GCSE	Brain_Hippocampus	1e.05	0.084 (0.012)
GCSE	Brain_Hippocampus	1e.04	0.089 (0.012)
GCSE	Brain_Hippocampus	0.001	0.103 (0.012)
GCSE	Brain_Hippocampus	0.01	0.114 (0.012)
GCSE	Brain_Hippocampus	0.05	0.121 (0.012)
GCSE	Brain_Hippocampus	0.1	0.122 (0.012)
GCSE	Brain_Hippocampus	0.5	0.124 (0.012)
GCSE	Brain_Hippocampus	1	0.124 (0.012)
GCSE	Brain_Hippocampus	All	0.123 (0.012)
GCSE	Brain_Hypothalamus	1e.06	0.07 (0.012)
GCSE	Brain_Hypothalamus	1e.05	0.075 (0.012)
GCSE	Brain_Hypothalamus	1e.04	0.081 (0.012)
GCSE	Brain_Hypothalamus	0.001	0.093 (0.012)
GCSE	Brain_Hypothalamus	0.01	0.098 (0.012)
GCSE	Brain_Hypothalamus	0.05	0.1 (0.012)
GCSE	Brain_Hypothalamus	0.1	0.104 (0.012)
GCSE	Brain_Hypothalamus	0.5	0.108 (0.012)
GCSE	Brain_Hypothalamus	1	0.108 (0.012)
GCSE	Brain_Hypothalamus	All	0.103 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	1e.06	0.072 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	1e.05	0.077 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	1e.04	0.081 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.001	0.097 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.01	0.106 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.05	0.112 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.115 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.116 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	1	0.115 (0.012)
GCSE	Brain_Nucleus_accumbens_basal_ganglia	All	0.116 (0.012)
GCSE	Brain_Putamen_basal_ganglia	1e.06	0.083 (0.012)
GCSE	Brain_Putamen_basal_ganglia	1e.05	0.091 (0.012)
GCSE	Brain_Putamen_basal_ganglia	1e.04	0.091 (0.012)
GCSE	Brain_Putamen_basal_ganglia	0.001	0.105 (0.012)
GCSE	Brain_Putamen_basal_ganglia	0.01	0.114 (0.012)
GCSE	Brain_Putamen_basal_ganglia	0.05	0.118 (0.012)
GCSE	Brain_Putamen_basal_ganglia	0.1	0.119 (0.012)
GCSE	Brain_Putamen_basal_ganglia	0.5	0.126 (0.012)
GCSE	Brain_Putamen_basal_ganglia	1	0.126 (0.012)
GCSE	Brain_Putamen_basal_ganglia	All	0.126 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	1e.06	0.069 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	1e.05	0.079 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	1e.04	0.08 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	0.001	0.092 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	0.01	0.107 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	0.05	0.105 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	0.1	0.108 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	0.5	0.104 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	1	0.104 (0.012)
GCSE	Brain_Spinal_cord_cervical_c-1	All	0.109 (0.012)
GCSE	Brain_Substantia_nigra	1e.06	0.073 (0.012)
GCSE	Brain_Substantia_nigra	1e.05	0.082 (0.012)
GCSE	Brain_Substantia_nigra	1e.04	0.089 (0.012)
GCSE	Brain_Substantia_nigra	0.001	0.086 (0.012)
GCSE	Brain_Substantia_nigra	0.01	0.099 (0.012)
GCSE	Brain_Substantia_nigra	0.05	0.104 (0.012)
GCSE	Brain_Substantia_nigra	0.1	0.102 (0.012)
GCSE	Brain_Substantia_nigra	0.5	0.098 (0.012)
GCSE	Brain_Substantia_nigra	1	0.099 (0.012)
GCSE	Brain_Substantia_nigra	All	0.101 (0.012)
GCSE	Breast_Mammary_Tissue	1e.06	0.081 (0.012)
GCSE	Breast_Mammary_Tissue	1e.05	0.099 (0.012)
GCSE	Breast_Mammary_Tissue	1e.04	0.105 (0.012)
GCSE	Breast_Mammary_Tissue	0.001	0.116 (0.012)
GCSE	Breast_Mammary_Tissue	0.01	0.126 (0.012)
GCSE	Breast_Mammary_Tissue	0.05	0.132 (0.012)
GCSE	Breast_Mammary_Tissue	0.1	0.138 (0.012)
GCSE	Breast_Mammary_Tissue	0.5	0.14 (0.012)
GCSE	Breast_Mammary_Tissue	1	0.139 (0.012)
GCSE	Breast_Mammary_Tissue	All	0.137 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	1e.06	0.081 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	1e.05	0.084 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	1e.04	0.09 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	0.001	0.094 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	0.01	0.106 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	0.05	0.119 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	0.1	0.117 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	0.5	0.114 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	1	0.115 (0.012)
GCSE	Cells_EBV-transformed_lymphocytes	All	0.116 (0.012)
GCSE	Cells_Transformed_fibroblasts	1e.06	0.08 (0.012)
GCSE	Cells_Transformed_fibroblasts	1e.05	0.091 (0.012)
GCSE	Cells_Transformed_fibroblasts	1e.04	0.11 (0.012)
GCSE	Cells_Transformed_fibroblasts	0.001	0.121 (0.012)
GCSE	Cells_Transformed_fibroblasts	0.01	0.137 (0.012)
GCSE	Cells_Transformed_fibroblasts	0.05	0.144 (0.012)
GCSE	Cells_Transformed_fibroblasts	0.1	0.154 (0.012)
GCSE	Cells_Transformed_fibroblasts	0.5	0.159 (0.012)
GCSE	Cells_Transformed_fibroblasts	1	0.159 (0.012)
GCSE	Cells_Transformed_fibroblasts	All	0.16 (0.012)
GCSE	CMC.BRAIN.RNASEQ	1e.06	0.111 (0.012)
GCSE	CMC.BRAIN.RNASEQ	1e.05	0.121 (0.012)
GCSE	CMC.BRAIN.RNASEQ	1e.04	0.13 (0.012)
GCSE	CMC.BRAIN.RNASEQ	0.001	0.144 (0.012)
GCSE	CMC.BRAIN.RNASEQ	0.01	0.157 (0.012)
GCSE	CMC.BRAIN.RNASEQ	0.05	0.164 (0.012)
GCSE	CMC.BRAIN.RNASEQ	0.1	0.171 (0.012)
GCSE	CMC.BRAIN.RNASEQ	0.5	0.18 (0.012)
GCSE	CMC.BRAIN.RNASEQ	1	0.181 (0.012)
GCSE	CMC.BRAIN.RNASEQ	All	0.181 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.069 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.081 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.091 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.103 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.125 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.13 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.133 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.134 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	1	0.132 (0.012)
GCSE	CMC.BRAIN.RNASEQ_SPLICING	All	0.132 (0.012)
GCSE	Colon_Sigmoid	1e.06	0.066 (0.012)
GCSE	Colon_Sigmoid	1e.05	0.078 (0.012)
GCSE	Colon_Sigmoid	1e.04	0.089 (0.012)
GCSE	Colon_Sigmoid	0.001	0.096 (0.012)
GCSE	Colon_Sigmoid	0.01	0.112 (0.012)
GCSE	Colon_Sigmoid	0.05	0.117 (0.012)
GCSE	Colon_Sigmoid	0.1	0.122 (0.012)
GCSE	Colon_Sigmoid	0.5	0.129 (0.012)
GCSE	Colon_Sigmoid	1	0.128 (0.012)
GCSE	Colon_Sigmoid	All	0.125 (0.012)
GCSE	Colon_Transverse	1e.06	0.065 (0.012)
GCSE	Colon_Transverse	1e.05	0.076 (0.012)
GCSE	Colon_Transverse	1e.04	0.086 (0.012)
GCSE	Colon_Transverse	0.001	0.1 (0.012)
GCSE	Colon_Transverse	0.01	0.108 (0.012)
GCSE	Colon_Transverse	0.05	0.118 (0.012)
GCSE	Colon_Transverse	0.1	0.126 (0.012)
GCSE	Colon_Transverse	0.5	0.131 (0.012)
GCSE	Colon_Transverse	1	0.132 (0.012)
GCSE	Colon_Transverse	All	0.129 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	1e.06	0.07 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	1e.05	0.08 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	1e.04	0.085 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	0.001	0.102 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	0.01	0.119 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	0.05	0.127 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	0.1	0.129 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	0.5	0.132 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	1	0.131 (0.012)
GCSE	Esophagus_Gastroesophageal_Junction	All	0.133 (0.012)
GCSE	Esophagus_Mucosa	1e.06	0.077 (0.012)
GCSE	Esophagus_Mucosa	1e.05	0.088 (0.012)
GCSE	Esophagus_Mucosa	1e.04	0.104 (0.012)
GCSE	Esophagus_Mucosa	0.001	0.12 (0.012)
GCSE	Esophagus_Mucosa	0.01	0.132 (0.012)
GCSE	Esophagus_Mucosa	0.05	0.142 (0.012)
GCSE	Esophagus_Mucosa	0.1	0.146 (0.012)
GCSE	Esophagus_Mucosa	0.5	0.15 (0.012)
GCSE	Esophagus_Mucosa	1	0.15 (0.012)
GCSE	Esophagus_Mucosa	All	0.152 (0.012)
GCSE	Esophagus_Muscularis	1e.06	0.088 (0.012)
GCSE	Esophagus_Muscularis	1e.05	0.102 (0.012)
GCSE	Esophagus_Muscularis	1e.04	0.116 (0.012)
GCSE	Esophagus_Muscularis	0.001	0.127 (0.012)
GCSE	Esophagus_Muscularis	0.01	0.142 (0.012)
GCSE	Esophagus_Muscularis	0.05	0.149 (0.012)
GCSE	Esophagus_Muscularis	0.1	0.152 (0.012)
GCSE	Esophagus_Muscularis	0.5	0.155 (0.012)
GCSE	Esophagus_Muscularis	1	0.155 (0.012)
GCSE	Esophagus_Muscularis	All	0.156 (0.012)
GCSE	Heart_Atrial_Appendage	1e.06	0.082 (0.012)
GCSE	Heart_Atrial_Appendage	1e.05	0.092 (0.012)
GCSE	Heart_Atrial_Appendage	1e.04	0.101 (0.012)
GCSE	Heart_Atrial_Appendage	0.001	0.115 (0.012)
GCSE	Heart_Atrial_Appendage	0.01	0.131 (0.012)
GCSE	Heart_Atrial_Appendage	0.05	0.137 (0.012)
GCSE	Heart_Atrial_Appendage	0.1	0.141 (0.012)
GCSE	Heart_Atrial_Appendage	0.5	0.142 (0.012)
GCSE	Heart_Atrial_Appendage	1	0.142 (0.012)
GCSE	Heart_Atrial_Appendage	All	0.14 (0.012)
GCSE	Heart_Left_Ventricle	1e.06	0.08 (0.012)
GCSE	Heart_Left_Ventricle	1e.05	0.091 (0.012)
GCSE	Heart_Left_Ventricle	1e.04	0.097 (0.012)
GCSE	Heart_Left_Ventricle	0.001	0.113 (0.012)
GCSE	Heart_Left_Ventricle	0.01	0.132 (0.012)
GCSE	Heart_Left_Ventricle	0.05	0.138 (0.012)
GCSE	Heart_Left_Ventricle	0.1	0.142 (0.012)
GCSE	Heart_Left_Ventricle	0.5	0.147 (0.012)
GCSE	Heart_Left_Ventricle	1	0.148 (0.012)
GCSE	Heart_Left_Ventricle	All	0.148 (0.012)
GCSE	Liver	1e.06	0.066 (0.012)
GCSE	Liver	1e.05	0.07 (0.012)
GCSE	Liver	1e.04	0.073 (0.012)
GCSE	Liver	0.001	0.09 (0.012)
GCSE	Liver	0.01	0.101 (0.012)
GCSE	Liver	0.05	0.102 (0.012)
GCSE	Liver	0.1	0.102 (0.012)
GCSE	Liver	0.5	0.105 (0.012)
GCSE	Liver	1	0.105 (0.012)
GCSE	Liver	All	0.107 (0.012)
GCSE	Lung	1e.06	0.091 (0.012)
GCSE	Lung	1e.05	0.104 (0.012)
GCSE	Lung	1e.04	0.116 (0.012)
GCSE	Lung	0.001	0.133 (0.012)
GCSE	Lung	0.01	0.149 (0.012)
GCSE	Lung	0.05	0.155 (0.012)
GCSE	Lung	0.1	0.158 (0.012)
GCSE	Lung	0.5	0.161 (0.012)
GCSE	Lung	1	0.162 (0.012)
GCSE	Lung	All	0.163 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	1e.06	0.086 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	1e.05	0.102 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	1e.04	0.111 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	0.001	0.128 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	0.01	0.139 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	0.05	0.149 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	0.1	0.154 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	0.5	0.159 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	1	0.16 (0.012)
GCSE	METSIM.ADIPOSE.RNASEQ	All	0.158 (0.012)
GCSE	Minor_Salivary_Gland	1e.06	0.073 (0.012)
GCSE	Minor_Salivary_Gland	1e.05	0.077 (0.012)
GCSE	Minor_Salivary_Gland	1e.04	0.077 (0.012)
GCSE	Minor_Salivary_Gland	0.001	0.089 (0.012)
GCSE	Minor_Salivary_Gland	0.01	0.092 (0.012)
GCSE	Minor_Salivary_Gland	0.05	0.102 (0.012)
GCSE	Minor_Salivary_Gland	0.1	0.111 (0.012)
GCSE	Minor_Salivary_Gland	0.5	0.111 (0.012)
GCSE	Minor_Salivary_Gland	1	0.111 (0.012)
GCSE	Minor_Salivary_Gland	All	0.107 (0.012)
GCSE	Muscle_Skeletal	1e.06	0.09 (0.012)
GCSE	Muscle_Skeletal	1e.05	0.105 (0.012)
GCSE	Muscle_Skeletal	1e.04	0.117 (0.012)
GCSE	Muscle_Skeletal	0.001	0.131 (0.012)
GCSE	Muscle_Skeletal	0.01	0.147 (0.012)
GCSE	Muscle_Skeletal	0.05	0.15 (0.012)
GCSE	Muscle_Skeletal	0.1	0.154 (0.012)
GCSE	Muscle_Skeletal	0.5	0.16 (0.012)
GCSE	Muscle_Skeletal	1	0.16 (0.012)
GCSE	Muscle_Skeletal	All	0.162 (0.012)
GCSE	Nerve_Tibial	1e.06	0.081 (0.012)
GCSE	Nerve_Tibial	1e.05	0.09 (0.012)
GCSE	Nerve_Tibial	1e.04	0.106 (0.012)
GCSE	Nerve_Tibial	0.001	0.122 (0.012)
GCSE	Nerve_Tibial	0.01	0.139 (0.012)
GCSE	Nerve_Tibial	0.05	0.148 (0.012)
GCSE	Nerve_Tibial	0.1	0.151 (0.012)
GCSE	Nerve_Tibial	0.5	0.158 (0.012)
GCSE	Nerve_Tibial	1	0.158 (0.012)
GCSE	Nerve_Tibial	All	0.16 (0.012)
GCSE	NTR.BLOOD.RNAARR	1e.06	0.061 (0.012)
GCSE	NTR.BLOOD.RNAARR	1e.05	0.073 (0.012)
GCSE	NTR.BLOOD.RNAARR	1e.04	0.082 (0.012)
GCSE	NTR.BLOOD.RNAARR	0.001	0.098 (0.012)
GCSE	NTR.BLOOD.RNAARR	0.01	0.105 (0.012)
GCSE	NTR.BLOOD.RNAARR	0.05	0.113 (0.012)
GCSE	NTR.BLOOD.RNAARR	0.1	0.113 (0.012)
GCSE	NTR.BLOOD.RNAARR	0.5	0.113 (0.012)
GCSE	NTR.BLOOD.RNAARR	1	0.114 (0.012)
GCSE	NTR.BLOOD.RNAARR	All	0.112 (0.012)
GCSE	Ovary	1e.06	0.065 (0.012)
GCSE	Ovary	1e.05	0.076 (0.012)
GCSE	Ovary	1e.04	0.084 (0.012)
GCSE	Ovary	0.001	0.091 (0.012)
GCSE	Ovary	0.01	0.1 (0.012)
GCSE	Ovary	0.05	0.109 (0.012)
GCSE	Ovary	0.1	0.113 (0.012)
GCSE	Ovary	0.5	0.111 (0.012)
GCSE	Ovary	1	0.111 (0.012)
GCSE	Ovary	All	0.112 (0.012)
GCSE	Pancreas	1e.06	0.082 (0.012)
GCSE	Pancreas	1e.05	0.086 (0.012)
GCSE	Pancreas	1e.04	0.096 (0.012)
GCSE	Pancreas	0.001	0.11 (0.012)
GCSE	Pancreas	0.01	0.127 (0.012)
GCSE	Pancreas	0.05	0.133 (0.012)
GCSE	Pancreas	0.1	0.134 (0.012)
GCSE	Pancreas	0.5	0.132 (0.012)
GCSE	Pancreas	1	0.132 (0.012)
GCSE	Pancreas	All	0.135 (0.012)
GCSE	Pituitary	1e.06	0.07 (0.012)
GCSE	Pituitary	1e.05	0.079 (0.012)
GCSE	Pituitary	1e.04	0.088 (0.012)
GCSE	Pituitary	0.001	0.096 (0.012)
GCSE	Pituitary	0.01	0.112 (0.012)
GCSE	Pituitary	0.05	0.117 (0.012)
GCSE	Pituitary	0.1	0.117 (0.012)
GCSE	Pituitary	0.5	0.125 (0.012)
GCSE	Pituitary	1	0.126 (0.012)
GCSE	Pituitary	All	0.121 (0.012)
GCSE	Prostate	1e.06	0.069 (0.012)
GCSE	Prostate	1e.05	0.077 (0.012)
GCSE	Prostate	1e.04	0.089 (0.012)
GCSE	Prostate	0.001	0.094 (0.012)
GCSE	Prostate	0.01	0.11 (0.012)
GCSE	Prostate	0.05	0.11 (0.012)
GCSE	Prostate	0.1	0.117 (0.012)
GCSE	Prostate	0.5	0.117 (0.012)
GCSE	Prostate	1	0.117 (0.012)
GCSE	Prostate	All	0.119 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.082 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.093 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.103 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.116 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.134 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.144 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.147 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.151 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	1	0.152 (0.012)
GCSE	Skin_Not_Sun_Exposed_Suprapubic	All	0.153 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	1e.06	0.088 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	1e.05	0.105 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	1e.04	0.112 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	0.001	0.135 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	0.01	0.144 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	0.05	0.154 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	0.1	0.16 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	0.5	0.163 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	1	0.163 (0.012)
GCSE	Skin_Sun_Exposed_Lower_leg	All	0.17 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	1e.06	0.067 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	1e.05	0.074 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	1e.04	0.08 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	0.001	0.09 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	0.01	0.112 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	0.05	0.12 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	0.1	0.12 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	0.5	0.121 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	1	0.121 (0.012)
GCSE	Small_Intestine_Terminal_Ileum	All	0.118 (0.012)
GCSE	Spleen	1e.06	0.075 (0.012)
GCSE	Spleen	1e.05	0.081 (0.012)
GCSE	Spleen	1e.04	0.091 (0.012)
GCSE	Spleen	0.001	0.11 (0.012)
GCSE	Spleen	0.01	0.119 (0.012)
GCSE	Spleen	0.05	0.129 (0.012)
GCSE	Spleen	0.1	0.134 (0.012)
GCSE	Spleen	0.5	0.139 (0.012)
GCSE	Spleen	1	0.139 (0.012)
GCSE	Spleen	All	0.136 (0.012)
GCSE	Stomach	1e.06	0.066 (0.012)
GCSE	Stomach	1e.05	0.074 (0.012)
GCSE	Stomach	1e.04	0.075 (0.012)
GCSE	Stomach	0.001	0.087 (0.012)
GCSE	Stomach	0.01	0.102 (0.012)
GCSE	Stomach	0.05	0.104 (0.012)
GCSE	Stomach	0.1	0.105 (0.012)
GCSE	Stomach	0.5	0.111 (0.012)
GCSE	Stomach	1	0.111 (0.012)
GCSE	Stomach	All	0.111 (0.012)
GCSE	Testis	1e.06	0.092 (0.012)
GCSE	Testis	1e.05	0.103 (0.012)
GCSE	Testis	1e.04	0.113 (0.012)
GCSE	Testis	0.001	0.13 (0.012)
GCSE	Testis	0.01	0.146 (0.012)
GCSE	Testis	0.05	0.152 (0.012)
GCSE	Testis	0.1	0.157 (0.012)
GCSE	Testis	0.5	0.163 (0.012)
GCSE	Testis	1	0.163 (0.012)
GCSE	Testis	All	0.161 (0.012)
GCSE	Thyroid	1e.06	0.084 (0.012)
GCSE	Thyroid	1e.05	0.097 (0.012)
GCSE	Thyroid	1e.04	0.108 (0.012)
GCSE	Thyroid	0.001	0.12 (0.012)
GCSE	Thyroid	0.01	0.144 (0.012)
GCSE	Thyroid	0.05	0.154 (0.012)
GCSE	Thyroid	0.1	0.155 (0.012)
GCSE	Thyroid	0.5	0.157 (0.012)
GCSE	Thyroid	1	0.157 (0.012)
GCSE	Thyroid	All	0.16 (0.012)
GCSE	Uterus	1e.06	0.063 (0.012)
GCSE	Uterus	1e.05	0.067 (0.012)
GCSE	Uterus	1e.04	0.074 (0.012)
GCSE	Uterus	0.001	0.084 (0.012)
GCSE	Uterus	0.01	0.089 (0.012)
GCSE	Uterus	0.05	0.096 (0.012)
GCSE	Uterus	0.1	0.095 (0.012)
GCSE	Uterus	0.5	0.093 (0.012)
GCSE	Uterus	1	0.093 (0.012)
GCSE	Uterus	All	0.093 (0.012)
GCSE	Vagina	1e.06	0.07 (0.012)
GCSE	Vagina	1e.05	0.08 (0.012)
GCSE	Vagina	1e.04	0.081 (0.012)
GCSE	Vagina	0.001	0.083 (0.012)
GCSE	Vagina	0.01	0.091 (0.012)
GCSE	Vagina	0.05	0.094 (0.012)
GCSE	Vagina	0.1	0.094 (0.012)
GCSE	Vagina	0.5	0.093 (0.012)
GCSE	Vagina	1	0.093 (0.012)
GCSE	Vagina	All	0.092 (0.012)
GCSE	Whole_Blood	1e.06	0.096 (0.012)
GCSE	Whole_Blood	1e.05	0.109 (0.012)
GCSE	Whole_Blood	1e.04	0.115 (0.012)
GCSE	Whole_Blood	0.001	0.128 (0.012)
GCSE	Whole_Blood	0.01	0.145 (0.012)
GCSE	Whole_Blood	0.05	0.152 (0.012)
GCSE	Whole_Blood	0.1	0.157 (0.012)
GCSE	Whole_Blood	0.5	0.159 (0.012)
GCSE	Whole_Blood	1	0.159 (0.012)
GCSE	Whole_Blood	All	0.157 (0.012)
GCSE	YFS.BLOOD.RNAARR	1e.06	0.087 (0.012)
GCSE	YFS.BLOOD.RNAARR	1e.05	0.096 (0.012)
GCSE	YFS.BLOOD.RNAARR	1e.04	0.107 (0.012)
GCSE	YFS.BLOOD.RNAARR	0.001	0.123 (0.012)
GCSE	YFS.BLOOD.RNAARR	0.01	0.141 (0.012)
GCSE	YFS.BLOOD.RNAARR	0.05	0.148 (0.012)
GCSE	YFS.BLOOD.RNAARR	0.1	0.147 (0.012)
GCSE	YFS.BLOOD.RNAARR	0.5	0.15 (0.012)
GCSE	YFS.BLOOD.RNAARR	1	0.15 (0.012)
GCSE	YFS.BLOOD.RNAARR	All	0.151 (0.012)
ADHD	Adipose_Subcutaneous	1e.06	0.019 (0.011)
ADHD	Adipose_Subcutaneous	1e.05	0.012 (0.011)
ADHD	Adipose_Subcutaneous	1e.04	0.012 (0.011)
ADHD	Adipose_Subcutaneous	0.001	0.008 (0.011)
ADHD	Adipose_Subcutaneous	0.01	0.02 (0.011)
ADHD	Adipose_Subcutaneous	0.05	0.031 (0.011)
ADHD	Adipose_Subcutaneous	0.1	0.038 (0.011)
ADHD	Adipose_Subcutaneous	0.5	0.053 (0.011)
ADHD	Adipose_Subcutaneous	1	0.056 (0.011)
ADHD	Adipose_Subcutaneous	All	0.054 (0.011)
ADHD	Adipose_Visceral_Omentum	1e.06	-0.014 (0.011)
ADHD	Adipose_Visceral_Omentum	1e.05	-0.02 (0.011)
ADHD	Adipose_Visceral_Omentum	1e.04	-0.014 (0.011)
ADHD	Adipose_Visceral_Omentum	0.001	0.004 (0.011)
ADHD	Adipose_Visceral_Omentum	0.01	0.027 (0.011)
ADHD	Adipose_Visceral_Omentum	0.05	0.034 (0.011)
ADHD	Adipose_Visceral_Omentum	0.1	0.028 (0.011)
ADHD	Adipose_Visceral_Omentum	0.5	0.051 (0.011)
ADHD	Adipose_Visceral_Omentum	1	0.05 (0.011)
ADHD	Adipose_Visceral_Omentum	All	0.044 (0.011)
ADHD	Adrenal_Gland	1e.05	-0.006 (0.011)
ADHD	Adrenal_Gland	1e.04	-0.018 (0.011)
ADHD	Adrenal_Gland	0.001	-0.02 (0.011)
ADHD	Adrenal_Gland	0.01	0.002 (0.011)
ADHD	Adrenal_Gland	0.05	0.021 (0.011)
ADHD	Adrenal_Gland	0.1	0.032 (0.011)
ADHD	Adrenal_Gland	0.5	0.052 (0.011)
ADHD	Adrenal_Gland	1	0.052 (0.011)
ADHD	Adrenal_Gland	All	0.046 (0.011)
ADHD	Artery_Aorta	1e.06	0.015 (0.011)
ADHD	Artery_Aorta	1e.05	0.021 (0.011)
ADHD	Artery_Aorta	1e.04	0.019 (0.011)
ADHD	Artery_Aorta	0.001	0.009 (0.011)
ADHD	Artery_Aorta	0.01	0.013 (0.011)
ADHD	Artery_Aorta	0.05	0.026 (0.011)
ADHD	Artery_Aorta	0.1	0.033 (0.011)
ADHD	Artery_Aorta	0.5	0.058 (0.011)
ADHD	Artery_Aorta	1	0.057 (0.011)
ADHD	Artery_Aorta	All	0.054 (0.011)
ADHD	Artery_Coronary	1e.06	0.02 (0.011)
ADHD	Artery_Coronary	1e.05	0.014 (0.011)
ADHD	Artery_Coronary	1e.04	0.012 (0.011)
ADHD	Artery_Coronary	0.001	0.026 (0.011)
ADHD	Artery_Coronary	0.01	0.007 (0.011)
ADHD	Artery_Coronary	0.05	0.007 (0.011)
ADHD	Artery_Coronary	0.1	0.029 (0.011)
ADHD	Artery_Coronary	0.5	0.029 (0.011)
ADHD	Artery_Coronary	1	0.026 (0.011)
ADHD	Artery_Coronary	All	0.029 (0.011)
ADHD	Artery_Tibial	1e.06	0.026 (0.011)
ADHD	Artery_Tibial	1e.05	0.018 (0.011)
ADHD	Artery_Tibial	1e.04	0.018 (0.011)
ADHD	Artery_Tibial	0.001	0.028 (0.011)
ADHD	Artery_Tibial	0.01	0.023 (0.011)
ADHD	Artery_Tibial	0.05	0.033 (0.011)
ADHD	Artery_Tibial	0.1	0.031 (0.011)
ADHD	Artery_Tibial	0.5	0.053 (0.011)
ADHD	Artery_Tibial	1	0.055 (0.011)
ADHD	Artery_Tibial	All	0.05 (0.011)
ADHD	Brain_Amygdala	1e.05	-0.005 (0.011)
ADHD	Brain_Amygdala	1e.04	-0.012 (0.011)
ADHD	Brain_Amygdala	0.001	-0.007 (0.011)
ADHD	Brain_Amygdala	0.01	0.012 (0.011)
ADHD	Brain_Amygdala	0.05	0.004 (0.011)
ADHD	Brain_Amygdala	0.1	0.009 (0.011)
ADHD	Brain_Amygdala	0.5	0.016 (0.011)
ADHD	Brain_Amygdala	1	0.018 (0.011)
ADHD	Brain_Amygdala	All	0.001 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	1e.05	0.011 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	1e.04	0.005 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	0.001	-0.019 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	0.01	-0.012 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	0.05	0.007 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	0.1	0.02 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	0.5	0.033 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	1	0.039 (0.011)
ADHD	Brain_Anterior_cingulate_cortex_BA24	All	0.03 (0.011)
ADHD	Brain_Caudate_basal_ganglia	1e.06	0.024 (0.011)
ADHD	Brain_Caudate_basal_ganglia	1e.05	0.015 (0.011)
ADHD	Brain_Caudate_basal_ganglia	1e.04	0.015 (0.011)
ADHD	Brain_Caudate_basal_ganglia	0.001	0.006 (0.011)
ADHD	Brain_Caudate_basal_ganglia	0.01	0.011 (0.011)
ADHD	Brain_Caudate_basal_ganglia	0.05	0.024 (0.011)
ADHD	Brain_Caudate_basal_ganglia	0.1	0.027 (0.011)
ADHD	Brain_Caudate_basal_ganglia	0.5	0.041 (0.011)
ADHD	Brain_Caudate_basal_ganglia	1	0.046 (0.011)
ADHD	Brain_Caudate_basal_ganglia	All	0.046 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	1e.06	0.008 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	1e.05	-0.017 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	1e.04	-0.014 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	0.001	-0.017 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	0.01	0.007 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	0.05	0.009 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	0.1	0.024 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	0.5	0.025 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	1	0.029 (0.011)
ADHD	Brain_Cerebellar_Hemisphere	All	0.026 (0.011)
ADHD	Brain_Cerebellum	1e.06	0.024 (0.011)
ADHD	Brain_Cerebellum	1e.05	0.006 (0.011)
ADHD	Brain_Cerebellum	1e.04	0.001 (0.011)
ADHD	Brain_Cerebellum	0.001	-0.019 (0.011)
ADHD	Brain_Cerebellum	0.01	0.001 (0.011)
ADHD	Brain_Cerebellum	0.05	0.008 (0.011)
ADHD	Brain_Cerebellum	0.1	0.015 (0.011)
ADHD	Brain_Cerebellum	0.5	0.025 (0.011)
ADHD	Brain_Cerebellum	1	0.028 (0.011)
ADHD	Brain_Cerebellum	All	0.033 (0.011)
ADHD	Brain_Cortex	1e.05	-0.028 (0.011)
ADHD	Brain_Cortex	1e.04	0.018 (0.011)
ADHD	Brain_Cortex	0.001	0.011 (0.011)
ADHD	Brain_Cortex	0.01	0.007 (0.011)
ADHD	Brain_Cortex	0.05	0.017 (0.011)
ADHD	Brain_Cortex	0.1	0.009 (0.011)
ADHD	Brain_Cortex	0.5	0.033 (0.011)
ADHD	Brain_Cortex	1	0.036 (0.011)
ADHD	Brain_Cortex	All	0.035 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	1e.06	0.008 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	1e.05	0.001 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	1e.04	-0.016 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	0.001	-0.019 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	0.01	0.01 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	0.05	0.001 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	0.1	0.009 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	0.5	0.035 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	1	0.036 (0.011)
ADHD	Brain_Frontal_Cortex_BA9	All	0.026 (0.011)
ADHD	Brain_Hippocampus	1e.06	0.008 (0.011)
ADHD	Brain_Hippocampus	1e.05	0.015 (0.011)
ADHD	Brain_Hippocampus	1e.04	0.019 (0.011)
ADHD	Brain_Hippocampus	0.001	-0.023 (0.011)
ADHD	Brain_Hippocampus	0.01	-0.001 (0.011)
ADHD	Brain_Hippocampus	0.05	0.013 (0.011)
ADHD	Brain_Hippocampus	0.1	0.013 (0.011)
ADHD	Brain_Hippocampus	0.5	0.034 (0.011)
ADHD	Brain_Hippocampus	1	0.032 (0.011)
ADHD	Brain_Hippocampus	All	0.025 (0.011)
ADHD	Brain_Hypothalamus	1e.05	-0.01 (0.011)
ADHD	Brain_Hypothalamus	1e.04	-0.002 (0.011)
ADHD	Brain_Hypothalamus	0.001	0.006 (0.011)
ADHD	Brain_Hypothalamus	0.01	0.002 (0.011)
ADHD	Brain_Hypothalamus	0.05	0.007 (0.011)
ADHD	Brain_Hypothalamus	0.1	0.012 (0.011)
ADHD	Brain_Hypothalamus	0.5	0.024 (0.011)
ADHD	Brain_Hypothalamus	1	0.026 (0.011)
ADHD	Brain_Hypothalamus	All	0.021 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	1e.05	-0.024 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	1e.04	-0.011 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	0.001	-0.012 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	0.01	-0.021 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	0.05	-0.01 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.009 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.017 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	1	0.017 (0.011)
ADHD	Brain_Nucleus_accumbens_basal_ganglia	All	0.001 (0.011)
ADHD	Brain_Putamen_basal_ganglia	1e.06	0.043 (0.011)
ADHD	Brain_Putamen_basal_ganglia	1e.05	0.002 (0.011)
ADHD	Brain_Putamen_basal_ganglia	1e.04	0.008 (0.011)
ADHD	Brain_Putamen_basal_ganglia	0.001	0.01 (0.011)
ADHD	Brain_Putamen_basal_ganglia	0.01	0.012 (0.011)
ADHD	Brain_Putamen_basal_ganglia	0.05	0.008 (0.011)
ADHD	Brain_Putamen_basal_ganglia	0.1	0.021 (0.011)
ADHD	Brain_Putamen_basal_ganglia	0.5	0.029 (0.011)
ADHD	Brain_Putamen_basal_ganglia	1	0.029 (0.011)
ADHD	Brain_Putamen_basal_ganglia	All	0.046 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	1e.06	-0.003 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	1e.05	-0.014 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	1e.04	-0.006 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	0.001	0.017 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	0.01	0.008 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	0.05	0.002 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	0.1	0.002 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	0.5	0.005 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	1	0.012 (0.011)
ADHD	Brain_Spinal_cord_cervical_c-1	All	0.003 (0.011)
ADHD	Brain_Substantia_nigra	1e.06	-0.006 (0.011)
ADHD	Brain_Substantia_nigra	1e.05	-0.006 (0.011)
ADHD	Brain_Substantia_nigra	1e.04	0 (0.011)
ADHD	Brain_Substantia_nigra	0.001	0.013 (0.011)
ADHD	Brain_Substantia_nigra	0.01	-0.009 (0.011)
ADHD	Brain_Substantia_nigra	0.05	-0.008 (0.011)
ADHD	Brain_Substantia_nigra	0.1	-0.007 (0.011)
ADHD	Brain_Substantia_nigra	0.5	0.012 (0.011)
ADHD	Brain_Substantia_nigra	1	0.011 (0.011)
ADHD	Brain_Substantia_nigra	All	-0.018 (0.011)
ADHD	Breast_Mammary_Tissue	1e.06	-0.023 (0.011)
ADHD	Breast_Mammary_Tissue	1e.05	-0.013 (0.011)
ADHD	Breast_Mammary_Tissue	1e.04	0.005 (0.011)
ADHD	Breast_Mammary_Tissue	0.001	-0.018 (0.011)
ADHD	Breast_Mammary_Tissue	0.01	0.018 (0.011)
ADHD	Breast_Mammary_Tissue	0.05	0.023 (0.011)
ADHD	Breast_Mammary_Tissue	0.1	0.018 (0.011)
ADHD	Breast_Mammary_Tissue	0.5	0.039 (0.011)
ADHD	Breast_Mammary_Tissue	1	0.043 (0.011)
ADHD	Breast_Mammary_Tissue	All	0.046 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	1e.06	0.039 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	1e.05	0.032 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	1e.04	0.02 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	0.001	0.022 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	0.01	0.016 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	0.05	0.028 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	0.1	0.029 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	0.5	0.037 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	1	0.037 (0.011)
ADHD	Cells_EBV-transformed_lymphocytes	All	0.045 (0.011)
ADHD	Cells_Transformed_fibroblasts	1e.06	0.019 (0.011)
ADHD	Cells_Transformed_fibroblasts	1e.05	0.005 (0.011)
ADHD	Cells_Transformed_fibroblasts	1e.04	0.017 (0.011)
ADHD	Cells_Transformed_fibroblasts	0.001	0.018 (0.011)
ADHD	Cells_Transformed_fibroblasts	0.01	0.032 (0.011)
ADHD	Cells_Transformed_fibroblasts	0.05	0.03 (0.011)
ADHD	Cells_Transformed_fibroblasts	0.1	0.037 (0.011)
ADHD	Cells_Transformed_fibroblasts	0.5	0.053 (0.011)
ADHD	Cells_Transformed_fibroblasts	1	0.054 (0.011)
ADHD	Cells_Transformed_fibroblasts	All	0.045 (0.011)
ADHD	CMC.BRAIN.RNASEQ	1e.06	0.013 (0.011)
ADHD	CMC.BRAIN.RNASEQ	1e.05	0.011 (0.011)
ADHD	CMC.BRAIN.RNASEQ	1e.04	0.011 (0.011)
ADHD	CMC.BRAIN.RNASEQ	0.001	0.003 (0.011)
ADHD	CMC.BRAIN.RNASEQ	0.01	0.014 (0.011)
ADHD	CMC.BRAIN.RNASEQ	0.05	0.039 (0.011)
ADHD	CMC.BRAIN.RNASEQ	0.1	0.038 (0.011)
ADHD	CMC.BRAIN.RNASEQ	0.5	0.051 (0.011)
ADHD	CMC.BRAIN.RNASEQ	1	0.051 (0.011)
ADHD	CMC.BRAIN.RNASEQ	All	0.049 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	1e.06	0.008 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	1e.05	0.023 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	1e.04	0.024 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	0.001	0.014 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	0.01	0.011 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.025 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	0.1	0.038 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.053 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	1	0.051 (0.011)
ADHD	CMC.BRAIN.RNASEQ_SPLICING	All	0.054 (0.011)
ADHD	Colon_Sigmoid	1e.06	0.022 (0.011)
ADHD	Colon_Sigmoid	1e.05	0.023 (0.011)
ADHD	Colon_Sigmoid	1e.04	0.009 (0.011)
ADHD	Colon_Sigmoid	0.001	0.016 (0.011)
ADHD	Colon_Sigmoid	0.01	0.023 (0.011)
ADHD	Colon_Sigmoid	0.05	0.031 (0.011)
ADHD	Colon_Sigmoid	0.1	0.031 (0.011)
ADHD	Colon_Sigmoid	0.5	0.054 (0.011)
ADHD	Colon_Sigmoid	1	0.053 (0.011)
ADHD	Colon_Sigmoid	All	0.055 (0.011)
ADHD	Colon_Transverse	1e.05	-0.022 (0.011)
ADHD	Colon_Transverse	1e.04	-0.003 (0.011)
ADHD	Colon_Transverse	0.001	-0.016 (0.011)
ADHD	Colon_Transverse	0.01	0.017 (0.011)
ADHD	Colon_Transverse	0.05	0.027 (0.011)
ADHD	Colon_Transverse	0.1	0.044 (0.011)
ADHD	Colon_Transverse	0.5	0.055 (0.011)
ADHD	Colon_Transverse	1	0.057 (0.011)
ADHD	Colon_Transverse	All	0.044 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	1e.06	-0.027 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	1e.05	-0.013 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	1e.04	-0.013 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	0.001	0.007 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	0.01	0.024 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	0.05	0.032 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	0.1	0.03 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	0.5	0.038 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	1	0.039 (0.011)
ADHD	Esophagus_Gastroesophageal_Junction	All	0.035 (0.011)
ADHD	Esophagus_Mucosa	1e.06	-0.001 (0.011)
ADHD	Esophagus_Mucosa	1e.05	-0.005 (0.011)
ADHD	Esophagus_Mucosa	1e.04	0.008 (0.011)
ADHD	Esophagus_Mucosa	0.001	0.021 (0.011)
ADHD	Esophagus_Mucosa	0.01	0.024 (0.011)
ADHD	Esophagus_Mucosa	0.05	0.039 (0.011)
ADHD	Esophagus_Mucosa	0.1	0.045 (0.011)
ADHD	Esophagus_Mucosa	0.5	0.06 (0.011)
ADHD	Esophagus_Mucosa	1	0.059 (0.011)
ADHD	Esophagus_Mucosa	All	0.059 (0.011)
ADHD	Esophagus_Muscularis	1e.06	0.03 (0.011)
ADHD	Esophagus_Muscularis	1e.05	0.034 (0.011)
ADHD	Esophagus_Muscularis	1e.04	0.025 (0.011)
ADHD	Esophagus_Muscularis	0.001	0.012 (0.011)
ADHD	Esophagus_Muscularis	0.01	0.025 (0.011)
ADHD	Esophagus_Muscularis	0.05	0.037 (0.011)
ADHD	Esophagus_Muscularis	0.1	0.035 (0.011)
ADHD	Esophagus_Muscularis	0.5	0.051 (0.011)
ADHD	Esophagus_Muscularis	1	0.054 (0.011)
ADHD	Esophagus_Muscularis	All	0.052 (0.011)
ADHD	Heart_Atrial_Appendage	1e.06	0.028 (0.011)
ADHD	Heart_Atrial_Appendage	1e.05	0.01 (0.011)
ADHD	Heart_Atrial_Appendage	1e.04	0.01 (0.011)
ADHD	Heart_Atrial_Appendage	0.001	0.013 (0.011)
ADHD	Heart_Atrial_Appendage	0.01	0.02 (0.011)
ADHD	Heart_Atrial_Appendage	0.05	0.03 (0.011)
ADHD	Heart_Atrial_Appendage	0.1	0.033 (0.011)
ADHD	Heart_Atrial_Appendage	0.5	0.043 (0.011)
ADHD	Heart_Atrial_Appendage	1	0.046 (0.011)
ADHD	Heart_Atrial_Appendage	All	0.051 (0.011)
ADHD	Heart_Left_Ventricle	1e.06	0.021 (0.011)
ADHD	Heart_Left_Ventricle	1e.05	0.025 (0.011)
ADHD	Heart_Left_Ventricle	1e.04	0.017 (0.011)
ADHD	Heart_Left_Ventricle	0.001	0.026 (0.011)
ADHD	Heart_Left_Ventricle	0.01	0.029 (0.011)
ADHD	Heart_Left_Ventricle	0.05	0.032 (0.011)
ADHD	Heart_Left_Ventricle	0.1	0.027 (0.011)
ADHD	Heart_Left_Ventricle	0.5	0.043 (0.011)
ADHD	Heart_Left_Ventricle	1	0.043 (0.011)
ADHD	Heart_Left_Ventricle	All	0.037 (0.011)
ADHD	Liver	1e.06	0.035 (0.011)
ADHD	Liver	1e.05	0.029 (0.011)
ADHD	Liver	1e.04	0.008 (0.011)
ADHD	Liver	0.001	0.001 (0.011)
ADHD	Liver	0.01	0.005 (0.011)
ADHD	Liver	0.05	0.02 (0.011)
ADHD	Liver	0.1	0.018 (0.011)
ADHD	Liver	0.5	0.03 (0.011)
ADHD	Liver	1	0.026 (0.011)
ADHD	Liver	All	0.039 (0.011)
ADHD	Lung	1e.06	0.033 (0.011)
ADHD	Lung	1e.05	0.021 (0.011)
ADHD	Lung	1e.04	0.018 (0.011)
ADHD	Lung	0.001	0.024 (0.011)
ADHD	Lung	0.01	0.031 (0.011)
ADHD	Lung	0.05	0.043 (0.011)
ADHD	Lung	0.1	0.041 (0.011)
ADHD	Lung	0.5	0.062 (0.011)
ADHD	Lung	1	0.061 (0.011)
ADHD	Lung	All	0.058 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	1e.05	0.016 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	1e.04	-0.003 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	0.001	-0.011 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	0.01	0.028 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	0.05	0.037 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	0.1	0.04 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	0.5	0.057 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	1	0.058 (0.011)
ADHD	METSIM.ADIPOSE.RNASEQ	All	0.046 (0.011)
ADHD	Minor_Salivary_Gland	1e.06	0 (0.011)
ADHD	Minor_Salivary_Gland	1e.05	0.002 (0.011)
ADHD	Minor_Salivary_Gland	1e.04	-0.02 (0.011)
ADHD	Minor_Salivary_Gland	0.001	-0.021 (0.011)
ADHD	Minor_Salivary_Gland	0.01	-0.016 (0.011)
ADHD	Minor_Salivary_Gland	0.05	-0.008 (0.011)
ADHD	Minor_Salivary_Gland	0.1	0.001 (0.011)
ADHD	Minor_Salivary_Gland	0.5	0.014 (0.011)
ADHD	Minor_Salivary_Gland	1	0.015 (0.011)
ADHD	Minor_Salivary_Gland	All	-0.002 (0.011)
ADHD	Muscle_Skeletal	1e.06	0.036 (0.011)
ADHD	Muscle_Skeletal	1e.05	0.034 (0.011)
ADHD	Muscle_Skeletal	1e.04	0.027 (0.011)
ADHD	Muscle_Skeletal	0.001	0.006 (0.011)
ADHD	Muscle_Skeletal	0.01	0.018 (0.011)
ADHD	Muscle_Skeletal	0.05	0.038 (0.011)
ADHD	Muscle_Skeletal	0.1	0.036 (0.011)
ADHD	Muscle_Skeletal	0.5	0.055 (0.011)
ADHD	Muscle_Skeletal	1	0.051 (0.011)
ADHD	Muscle_Skeletal	All	0.063 (0.011)
ADHD	Nerve_Tibial	1e.06	0.005 (0.011)
ADHD	Nerve_Tibial	1e.05	0.005 (0.011)
ADHD	Nerve_Tibial	1e.04	0 (0.011)
ADHD	Nerve_Tibial	0.001	-0.003 (0.011)
ADHD	Nerve_Tibial	0.01	0.016 (0.011)
ADHD	Nerve_Tibial	0.05	0.011 (0.011)
ADHD	Nerve_Tibial	0.1	0.017 (0.011)
ADHD	Nerve_Tibial	0.5	0.034 (0.011)
ADHD	Nerve_Tibial	1	0.036 (0.011)
ADHD	Nerve_Tibial	All	0.03 (0.011)
ADHD	NTR.BLOOD.RNAARR	1e.04	-0.011 (0.011)
ADHD	NTR.BLOOD.RNAARR	0.001	-0.028 (0.011)
ADHD	NTR.BLOOD.RNAARR	0.01	0.009 (0.011)
ADHD	NTR.BLOOD.RNAARR	0.05	0.007 (0.011)
ADHD	NTR.BLOOD.RNAARR	0.1	0.023 (0.011)
ADHD	NTR.BLOOD.RNAARR	0.5	0.027 (0.011)
ADHD	NTR.BLOOD.RNAARR	1	0.034 (0.011)
ADHD	NTR.BLOOD.RNAARR	All	0.027 (0.011)
ADHD	Ovary	1e.05	-0.018 (0.011)
ADHD	Ovary	1e.04	-0.018 (0.011)
ADHD	Ovary	0.001	-0.024 (0.011)
ADHD	Ovary	0.01	0.011 (0.011)
ADHD	Ovary	0.05	0.011 (0.011)
ADHD	Ovary	0.1	0.017 (0.011)
ADHD	Ovary	0.5	0.036 (0.011)
ADHD	Ovary	1	0.037 (0.011)
ADHD	Ovary	All	0.032 (0.011)
ADHD	Pancreas	1e.05	0.004 (0.011)
ADHD	Pancreas	1e.04	-0.015 (0.011)
ADHD	Pancreas	0.001	0.005 (0.011)
ADHD	Pancreas	0.01	0.004 (0.011)
ADHD	Pancreas	0.05	0.016 (0.011)
ADHD	Pancreas	0.1	0.026 (0.011)
ADHD	Pancreas	0.5	0.037 (0.011)
ADHD	Pancreas	1	0.037 (0.011)
ADHD	Pancreas	All	0.034 (0.011)
ADHD	Pituitary	1e.06	-0.002 (0.011)
ADHD	Pituitary	1e.05	-0.004 (0.011)
ADHD	Pituitary	1e.04	-0.02 (0.011)
ADHD	Pituitary	0.001	-0.02 (0.011)
ADHD	Pituitary	0.01	0.014 (0.011)
ADHD	Pituitary	0.05	0.018 (0.011)
ADHD	Pituitary	0.1	0.024 (0.011)
ADHD	Pituitary	0.5	0.041 (0.011)
ADHD	Pituitary	1	0.042 (0.011)
ADHD	Pituitary	All	0.031 (0.011)
ADHD	Prostate	1e.06	-0.003 (0.011)
ADHD	Prostate	1e.05	-0.023 (0.011)
ADHD	Prostate	1e.04	-0.015 (0.011)
ADHD	Prostate	0.001	-0.012 (0.011)
ADHD	Prostate	0.01	0.003 (0.011)
ADHD	Prostate	0.05	0.004 (0.011)
ADHD	Prostate	0.1	0.002 (0.011)
ADHD	Prostate	0.5	0.024 (0.011)
ADHD	Prostate	1	0.024 (0.011)
ADHD	Prostate	All	0.023 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	1e.06	0.018 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	1e.05	0.017 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	1e.04	0.026 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	0.001	0.024 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	0.01	0.034 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	0.05	0.038 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.039 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.054 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	1	0.055 (0.011)
ADHD	Skin_Not_Sun_Exposed_Suprapubic	All	0.053 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	1e.06	0.003 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	1e.05	-0.005 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	1e.04	-0.007 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	0.001	0.004 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	0.01	0.021 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	0.05	0.026 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	0.1	0.03 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	0.5	0.056 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	1	0.056 (0.011)
ADHD	Skin_Sun_Exposed_Lower_leg	All	0.055 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	1e.06	0.023 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	1e.05	0.006 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	1e.04	0.012 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	0.001	0.014 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	0.01	-0.02 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	0.05	0.015 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	0.1	0.022 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	0.5	0.039 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	1	0.04 (0.011)
ADHD	Small_Intestine_Terminal_Ileum	All	0.043 (0.011)
ADHD	Spleen	1e.06	-0.003 (0.011)
ADHD	Spleen	1e.05	-0.024 (0.011)
ADHD	Spleen	1e.04	-0.021 (0.011)
ADHD	Spleen	0.001	0.002 (0.011)
ADHD	Spleen	0.01	0.013 (0.011)
ADHD	Spleen	0.05	0.009 (0.011)
ADHD	Spleen	0.1	0.012 (0.011)
ADHD	Spleen	0.5	0.024 (0.011)
ADHD	Spleen	1	0.027 (0.011)
ADHD	Spleen	All	0.011 (0.011)
ADHD	Stomach	1e.06	0.013 (0.011)
ADHD	Stomach	1e.05	0.014 (0.011)
ADHD	Stomach	1e.04	-0.002 (0.011)
ADHD	Stomach	0.001	-0.001 (0.011)
ADHD	Stomach	0.01	0.019 (0.011)
ADHD	Stomach	0.05	0.03 (0.011)
ADHD	Stomach	0.1	0.028 (0.011)
ADHD	Stomach	0.5	0.052 (0.011)
ADHD	Stomach	1	0.048 (0.011)
ADHD	Stomach	All	0.056 (0.011)
ADHD	Testis	1e.06	-0.006 (0.011)
ADHD	Testis	1e.05	-0.01 (0.011)
ADHD	Testis	1e.04	-0.002 (0.011)
ADHD	Testis	0.001	-0.007 (0.011)
ADHD	Testis	0.01	0.027 (0.011)
ADHD	Testis	0.05	0.031 (0.011)
ADHD	Testis	0.1	0.038 (0.011)
ADHD	Testis	0.5	0.043 (0.011)
ADHD	Testis	1	0.041 (0.011)
ADHD	Testis	All	0.039 (0.011)
ADHD	Thyroid	1e.06	-0.008 (0.011)
ADHD	Thyroid	1e.05	0.005 (0.011)
ADHD	Thyroid	1e.04	0.005 (0.011)
ADHD	Thyroid	0.001	0.018 (0.011)
ADHD	Thyroid	0.01	0.024 (0.011)
ADHD	Thyroid	0.05	0.018 (0.011)
ADHD	Thyroid	0.1	0.029 (0.011)
ADHD	Thyroid	0.5	0.043 (0.011)
ADHD	Thyroid	1	0.044 (0.011)
ADHD	Thyroid	All	0.039 (0.011)
ADHD	Uterus	1e.04	-0.002 (0.011)
ADHD	Uterus	0.001	0.007 (0.011)
ADHD	Uterus	0.01	0.008 (0.011)
ADHD	Uterus	0.05	0.01 (0.011)
ADHD	Uterus	0.1	0.018 (0.011)
ADHD	Uterus	0.5	0.037 (0.011)
ADHD	Uterus	1	0.04 (0.011)
ADHD	Uterus	All	0.044 (0.011)
ADHD	Vagina	1e.05	-0.025 (0.011)
ADHD	Vagina	1e.04	-0.008 (0.011)
ADHD	Vagina	0.001	0.014 (0.011)
ADHD	Vagina	0.01	0.022 (0.011)
ADHD	Vagina	0.05	0.02 (0.011)
ADHD	Vagina	0.1	0.031 (0.011)
ADHD	Vagina	0.5	0.039 (0.011)
ADHD	Vagina	1	0.039 (0.011)
ADHD	Vagina	All	0.036 (0.011)
ADHD	Whole_Blood	1e.06	0.019 (0.011)
ADHD	Whole_Blood	1e.05	0.007 (0.011)
ADHD	Whole_Blood	1e.04	0.004 (0.011)
ADHD	Whole_Blood	0.001	0.023 (0.011)
ADHD	Whole_Blood	0.01	0.022 (0.011)
ADHD	Whole_Blood	0.05	0.03 (0.011)
ADHD	Whole_Blood	0.1	0.045 (0.011)
ADHD	Whole_Blood	0.5	0.052 (0.011)
ADHD	Whole_Blood	1	0.053 (0.011)
ADHD	Whole_Blood	All	0.046 (0.011)
ADHD	YFS.BLOOD.RNAARR	1e.05	-0.018 (0.011)
ADHD	YFS.BLOOD.RNAARR	1e.04	0.008 (0.011)
ADHD	YFS.BLOOD.RNAARR	0.001	0.01 (0.011)
ADHD	YFS.BLOOD.RNAARR	0.01	0.013 (0.011)
ADHD	YFS.BLOOD.RNAARR	0.05	0.019 (0.011)
ADHD	YFS.BLOOD.RNAARR	0.1	0.024 (0.011)
ADHD	YFS.BLOOD.RNAARR	0.5	0.044 (0.011)
ADHD	YFS.BLOOD.RNAARR	1	0.042 (0.011)
ADHD	YFS.BLOOD.RNAARR	All	0.043 (0.011)

Correlation between GeRS model predictions and observed values in TEDS

Phenotype	Weight	Model	R (SE)	P
Height21	Adipose_Subcutaneous	0.5	0.211 (0.013)	0.00e+00
Height21	Adipose_Visceral_Omentum	0.1	0.196 (0.013)	0.00e+00
Height21	Adrenal_Gland	All	0.161 (0.013)	0.00e+00
Height21	Artery_Aorta	All	0.193 (0.013)	0.00e+00
Height21	Artery_Coronary	1	0.149 (0.013)	0.00e+00
Height21	Artery_Tibial	0.5	0.207 (0.013)	0.00e+00
Height21	Brain_Amygdala	1	0.127 (0.013)	0.00e+00
Height21	Brain_Anterior_cingulate_cortex_BA24	1	0.108 (0.013)	0.00e+00
Height21	Brain_Caudate_basal_ganglia	1	0.123 (0.013)	0.00e+00
Height21	Brain_Cerebellar_Hemisphere	0.01	0.151 (0.013)	0.00e+00
Height21	Brain_Cerebellum	0.5	0.164 (0.013)	0.00e+00
Height21	Brain_Cortex	1	0.14 (0.013)	0.00e+00
Height21	Brain_Frontal_Cortex_BA9	0.5	0.131 (0.013)	0.00e+00
Height21	Brain_Hippocampus	0.5	0.106 (0.013)	0.00e+00
Height21	Brain_Hypothalamus	0.1	0.11 (0.013)	0.00e+00
Height21	Brain_Nucleus_accumbens_basal_ganglia	0.1	0.123 (0.013)	0.00e+00
Height21	Brain_Putamen_basal_ganglia	0.1	0.118 (0.013)	0.00e+00
Height21	Brain_Spinal_cord_cervical_c-1	1	0.106 (0.013)	0.00e+00
Height21	Brain_Substantia_nigra	0.1	0.104 (0.013)	0.00e+00
Height21	Breast_Mammary_Tissue	All	0.182 (0.013)	0.00e+00
Height21	Cells_EBV-transformed_lymphocytes	0.5	0.148 (0.013)	0.00e+00
Height21	Cells_Transformed_fibroblasts	1	0.19 (0.013)	0.00e+00
Height21	CMC.BRAIN.RNASEQ	All	0.184 (0.013)	0.00e+00
Height21	CMC.BRAIN.RNASEQ_SPLICING	1	0.147 (0.013)	0.00e+00
Height21	Colon_Sigmoid	1	0.16 (0.013)	0.00e+00
Height21	Colon_Transverse	1	0.171 (0.013)	0.00e+00
Height21	Esophagus_Gastroesophageal_Junction	1	0.178 (0.013)	0.00e+00
Height21	Esophagus_Mucosa	1	0.194 (0.013)	0.00e+00
Height21	Esophagus_Muscularis	0.5	0.196 (0.013)	0.00e+00
Height21	Heart_Atrial_Appendage	1	0.163 (0.013)	0.00e+00
Height21	Heart_Left_Ventricle	0.5	0.159 (0.013)	0.00e+00
Height21	Liver	0.5	0.147 (0.013)	0.00e+00
Height21	Lung	0.05	0.187 (0.013)	0.00e+00
Height21	METSIM.ADIPOSE.RNASEQ	0.1	0.181 (0.013)	0.00e+00
Height21	Minor_Salivary_Gland	0.1	0.107 (0.013)	0.00e+00
Height21	Muscle_Skeletal	1	0.185 (0.013)	0.00e+00
Height21	Nerve_Tibial	All	0.22 (0.013)	0.00e+00
Height21	NTR.BLOOD.RNAARR	All	0.155 (0.013)	0.00e+00
Height21	Ovary	0.05	0.121 (0.013)	0.00e+00
Height21	Pancreas	0.05	0.17 (0.013)	0.00e+00
Height21	Pituitary	All	0.166 (0.013)	0.00e+00
Height21	Prostate	1	0.122 (0.013)	0.00e+00
Height21	Skin_Not_Sun_Exposed_Suprapubic	0.5	0.183 (0.013)	0.00e+00
Height21	Skin_Sun_Exposed_Lower_leg	0.5	0.209 (0.013)	0.00e+00
Height21	Small_Intestine_Terminal_Ileum	1	0.131 (0.013)	0.00e+00
Height21	Spleen	All	0.171 (0.013)	0.00e+00
Height21	Stomach	0.05	0.171 (0.013)	0.00e+00
Height21	Testis	All	0.193 (0.013)	0.00e+00
Height21	Thyroid	0.5	0.205 (0.013)	0.00e+00
Height21	Uterus	1	0.104 (0.013)	0.00e+00
Height21	Vagina	All	0.12 (0.013)	0.00e+00
Height21	Whole_Blood	All	0.174 (0.013)	0.00e+00
Height21	YFS.BLOOD.RNAARR	0.5	0.194 (0.013)	0.00e+00
BMI21	Adipose_Subcutaneous	All	0.122 (0.014)	0.00e+00
BMI21	Adipose_Visceral_Omentum	1	0.104 (0.014)	0.00e+00
BMI21	Adrenal_Gland	1	0.089 (0.014)	0.00e+00
BMI21	Artery_Aorta	0.5	0.09 (0.014)	0.00e+00
BMI21	Artery_Coronary	0.1	0.071 (0.014)	0.00e+00
BMI21	Artery_Tibial	All	0.123 (0.014)	0.00e+00
BMI21	Brain_Amygdala	0.1	0.083 (0.014)	0.00e+00
BMI21	Brain_Anterior_cingulate_cortex_BA24	All	0.097 (0.014)	0.00e+00
BMI21	Brain_Caudate_basal_ganglia	All	0.077 (0.014)	0.00e+00
BMI21	Brain_Cerebellar_Hemisphere	All	0.092 (0.014)	0.00e+00
BMI21	Brain_Cerebellum	All	0.116 (0.014)	0.00e+00
BMI21	Brain_Cortex	All	0.092 (0.014)	0.00e+00
BMI21	Brain_Frontal_Cortex_BA9	0.5	0.077 (0.014)	0.00e+00
BMI21	Brain_Hippocampus	0.5	0.071 (0.014)	0.00e+00
BMI21	Brain_Hypothalamus	1	0.051 (0.014)	0.00e+00
BMI21	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.051 (0.014)	0.00e+00
BMI21	Brain_Putamen_basal_ganglia	All	0.077 (0.014)	0.00e+00
BMI21	Brain_Spinal_cord_cervical_c-1	0.05	0.052 (0.014)	0.00e+00
BMI21	Brain_Substantia_nigra	0.5	0.04 (0.014)	4.00e-03
BMI21	Breast_Mammary_Tissue	1	0.078 (0.014)	0.00e+00
BMI21	Cells_EBV-transformed_lymphocytes	0.05	0.093 (0.014)	0.00e+00
BMI21	Cells_Transformed_fibroblasts	1	0.099 (0.014)	0.00e+00
BMI21	CMC.BRAIN.RNASEQ	0.5	0.111 (0.014)	0.00e+00
BMI21	CMC.BRAIN.RNASEQ_SPLICING	0.05	0.096 (0.014)	0.00e+00
BMI21	Colon_Sigmoid	All	0.067 (0.014)	0.00e+00
BMI21	Colon_Transverse	0.5	0.076 (0.014)	0.00e+00
BMI21	Esophagus_Gastroesophageal_Junction	All	0.078 (0.014)	0.00e+00
BMI21	Esophagus_Mucosa	All	0.097 (0.014)	0.00e+00
BMI21	Esophagus_Muscularis	All	0.107 (0.014)	0.00e+00
BMI21	Heart_Atrial_Appendage	All	0.107 (0.014)	0.00e+00
BMI21	Heart_Left_Ventricle	0.1	0.084 (0.014)	0.00e+00
BMI21	Liver	All	0.08 (0.014)	0.00e+00
BMI21	Lung	All	0.104 (0.014)	0.00e+00
BMI21	METSIM.ADIPOSE.RNASEQ	0.5	0.099 (0.014)	0.00e+00
BMI21	Minor_Salivary_Gland	0.001	0.022 (0.014)	1.05e-01
BMI21	Muscle_Skeletal	0.5	0.106 (0.014)	0.00e+00
BMI21	Nerve_Tibial	All	0.117 (0.014)	0.00e+00
BMI21	NTR.BLOOD.RNAARR	0.1	0.08 (0.014)	0.00e+00
BMI21	Ovary	All	0.058 (0.014)	0.00e+00
BMI21	Pancreas	1	0.093 (0.014)	0.00e+00
BMI21	Pituitary	All	0.077 (0.014)	0.00e+00
BMI21	Prostate	All	0.061 (0.014)	0.00e+00
BMI21	Skin_Not_Sun_Exposed_Suprapubic	0.1	0.096 (0.014)	0.00e+00
BMI21	Skin_Sun_Exposed_Lower_leg	1	0.098 (0.014)	0.00e+00
BMI21	Small_Intestine_Terminal_Ileum	0.1	0.075 (0.014)	0.00e+00
BMI21	Spleen	All	0.09 (0.014)	0.00e+00
BMI21	Stomach	0.1	0.093 (0.014)	0.00e+00
BMI21	Testis	All	0.119 (0.014)	0.00e+00
BMI21	Thyroid	1	0.096 (0.014)	0.00e+00
BMI21	Uterus	1	0.043 (0.014)	2.00e-03
BMI21	Vagina	1	0.066 (0.014)	0.00e+00
BMI21	Whole_Blood	All	0.097 (0.014)	0.00e+00
BMI21	YFS.BLOOD.RNAARR	0.5	0.086 (0.014)	0.00e+00
GCSE	Adipose_Subcutaneous	All	0.16 (0.012)	0.00e+00
GCSE	Adipose_Visceral_Omentum	0.5	0.158 (0.012)	0.00e+00
GCSE	Adrenal_Gland	0.1	0.125 (0.012)	0.00e+00
GCSE	Artery_Aorta	All	0.135 (0.012)	0.00e+00
GCSE	Artery_Coronary	All	0.122 (0.012)	0.00e+00
GCSE	Artery_Tibial	All	0.165 (0.012)	0.00e+00
GCSE	Brain_Amygdala	0.05	0.097 (0.012)	0.00e+00
GCSE	Brain_Anterior_cingulate_cortex_BA24	All	0.129 (0.012)	0.00e+00
GCSE	Brain_Caudate_basal_ganglia	0.1	0.126 (0.012)	0.00e+00
GCSE	Brain_Cerebellar_Hemisphere	0.5	0.142 (0.012)	0.00e+00
GCSE	Brain_Cerebellum	1	0.144 (0.012)	0.00e+00
GCSE	Brain_Cortex	1	0.133 (0.012)	0.00e+00
GCSE	Brain_Frontal_Cortex_BA9	1	0.115 (0.012)	0.00e+00
GCSE	Brain_Hippocampus	1	0.124 (0.012)	0.00e+00
GCSE	Brain_Hypothalamus	1	0.108 (0.012)	0.00e+00
GCSE	Brain_Nucleus_accumbens_basal_ganglia	0.5	0.116 (0.012)	0.00e+00
GCSE	Brain_Putamen_basal_ganglia	1	0.126 (0.012)	0.00e+00
GCSE	Brain_Spinal_cord_cervical_c-1	All	0.109 (0.012)	0.00e+00
GCSE	Brain_Substantia_nigra	0.05	0.104 (0.012)	0.00e+00
GCSE	Breast_Mammary_Tissue	0.5	0.14 (0.012)	0.00e+00
GCSE	Cells_EBV-transformed_lymphocytes	0.05	0.119 (0.012)	0.00e+00
GCSE	Cells_Transformed_fibroblasts	All	0.16 (0.012)	0.00e+00
GCSE	CMC.BRAIN.RNASEQ	All	0.181 (0.012)	0.00e+00
GCSE	CMC.BRAIN.RNASEQ_SPLICING	0.5	0.134 (0.012)	0.00e+00
GCSE	Colon_Sigmoid	0.5	0.129 (0.012)	0.00e+00
GCSE	Colon_Transverse	1	0.132 (0.012)	0.00e+00
GCSE	Esophagus_Gastroesophageal_Junction	All	0.133 (0.012)	0.00e+00
GCSE	Esophagus_Mucosa	All	0.152 (0.012)	0.00e+00
GCSE	Esophagus_Muscularis	All	0.156 (0.012)	0.00e+00
GCSE	Heart_Atrial_Appendage	0.5	0.142 (0.012)	0.00e+00
GCSE	Heart_Left_Ventricle	All	0.148 (0.012)	0.00e+00
GCSE	Liver	All	0.107 (0.012)	0.00e+00
GCSE	Lung	All	0.163 (0.012)	0.00e+00
GCSE	METSIM.ADIPOSE.RNASEQ	1	0.16 (0.012)	0.00e+00
GCSE	Minor_Salivary_Gland	0.5	0.111 (0.012)	0.00e+00
GCSE	Muscle_Skeletal	All	0.162 (0.012)	0.00e+00
GCSE	Nerve_Tibial	All	0.16 (0.012)	0.00e+00
GCSE	NTR.BLOOD.RNAARR	1	0.114 (0.012)	0.00e+00
GCSE	Ovary	0.1	0.113 (0.012)	0.00e+00
GCSE	Pancreas	All	0.135 (0.012)	0.00e+00
GCSE	Pituitary	1	0.126 (0.012)	0.00e+00
GCSE	Prostate	All	0.119 (0.012)	0.00e+00
GCSE	Skin_Not_Sun_Exposed_Suprapubic	All	0.153 (0.012)	0.00e+00
GCSE	Skin_Sun_Exposed_Lower_leg	All	0.17 (0.012)	0.00e+00
GCSE	Small_Intestine_Terminal_Ileum	0.5	0.121 (0.012)	0.00e+00
GCSE	Spleen	0.5	0.139 (0.012)	0.00e+00
GCSE	Stomach	All	0.111 (0.012)	0.00e+00
GCSE	Testis	0.5	0.163 (0.012)	0.00e+00
GCSE	Thyroid	All	0.16 (0.012)	0.00e+00
GCSE	Uterus	0.05	0.096 (0.012)	0.00e+00
GCSE	Vagina	0.05	0.094 (0.012)	0.00e+00
GCSE	Whole_Blood	0.5	0.159 (0.012)	0.00e+00
GCSE	YFS.BLOOD.RNAARR	All	0.151 (0.012)	0.00e+00
ADHD	Adipose_Subcutaneous	1	0.056 (0.011)	0.00e+00
ADHD	Adipose_Visceral_Omentum	0.5	0.051 (0.011)	0.00e+00
ADHD	Adrenal_Gland	1	0.052 (0.011)	0.00e+00
ADHD	Artery_Aorta	0.5	0.058 (0.011)	0.00e+00
ADHD	Artery_Coronary	0.1	0.029 (0.011)	1.00e-02
ADHD	Artery_Tibial	1	0.055 (0.011)	0.00e+00
ADHD	Brain_Amygdala	1	0.018 (0.011)	1.19e-01
ADHD	Brain_Anterior_cingulate_cortex_BA24	1	0.039 (0.011)	1.00e-03
ADHD	Brain_Caudate_basal_ganglia	All	0.046 (0.011)	0.00e+00
ADHD	Brain_Cerebellar_Hemisphere	1	0.029 (0.011)	9.00e-03
ADHD	Brain_Cerebellum	All	0.033 (0.011)	3.00e-03
ADHD	Brain_Cortex	1	0.036 (0.011)	1.00e-03
ADHD	Brain_Frontal_Cortex_BA9	1	0.036 (0.011)	1.00e-03
ADHD	Brain_Hippocampus	0.5	0.034 (0.011)	3.00e-03
ADHD	Brain_Hypothalamus	1	0.026 (0.011)	2.20e-02
ADHD	Brain_Nucleus_accumbens_basal_ganglia	1	0.017 (0.011)	1.39e-01
ADHD	Brain_Putamen_basal_ganglia	All	0.046 (0.011)	0.00e+00
ADHD	Brain_Spinal_cord_cervical_c-1	0.001	0.017 (0.011)	1.32e-01
ADHD	Brain_Substantia_nigra	0.001	0.013 (0.011)	2.44e-01
ADHD	Breast_Mammary_Tissue	All	0.046 (0.011)	0.00e+00
ADHD	Cells_EBV-transformed_lymphocytes	All	0.045 (0.011)	0.00e+00
ADHD	Cells_Transformed_fibroblasts	1	0.054 (0.011)	0.00e+00
ADHD	CMC.BRAIN.RNASEQ	1	0.051 (0.011)	0.00e+00
ADHD	CMC.BRAIN.RNASEQ_SPLICING	All	0.054 (0.011)	0.00e+00
ADHD	Colon_Sigmoid	All	0.055 (0.011)	0.00e+00
ADHD	Colon_Transverse	1	0.057 (0.011)	0.00e+00
ADHD	Esophagus_Gastroesophageal_Junction	1	0.039 (0.011)	1.00e-03
ADHD	Esophagus_Mucosa	0.5	0.06 (0.011)	0.00e+00
ADHD	Esophagus_Muscularis	1	0.054 (0.011)	0.00e+00
ADHD	Heart_Atrial_Appendage	All	0.051 (0.011)	0.00e+00
ADHD	Heart_Left_Ventricle	0.5	0.043 (0.011)	0.00e+00
ADHD	Liver	All	0.039 (0.011)	1.00e-03
ADHD	Lung	0.5	0.062 (0.011)	0.00e+00
ADHD	METSIM.ADIPOSE.RNASEQ	1	0.058 (0.011)	0.00e+00
ADHD	Minor_Salivary_Gland	1	0.015 (0.011)	1.91e-01
ADHD	Muscle_Skeletal	All	0.063 (0.011)	0.00e+00
ADHD	Nerve_Tibial	1	0.036 (0.011)	1.00e-03
ADHD	NTR.BLOOD.RNAARR	1	0.034 (0.011)	2.00e-03
ADHD	Ovary	1	0.037 (0.011)	1.00e-03
ADHD	Pancreas	1	0.037 (0.011)	1.00e-03
ADHD	Pituitary	1	0.042 (0.011)	0.00e+00
ADHD	Prostate	1	0.024 (0.011)	3.20e-02
ADHD	Skin_Not_Sun_Exposed_Suprapubic	1	0.055 (0.011)	0.00e+00
ADHD	Skin_Sun_Exposed_Lower_leg	0.5	0.056 (0.011)	0.00e+00
ADHD	Small_Intestine_Terminal_Ileum	All	0.043 (0.011)	0.00e+00
ADHD	Spleen	1	0.027 (0.011)	1.70e-02
ADHD	Stomach	All	0.056 (0.011)	0.00e+00
ADHD	Testis	0.5	0.043 (0.011)	0.00e+00
ADHD	Thyroid	1	0.044 (0.011)	0.00e+00
ADHD	Uterus	All	0.044 (0.011)	0.00e+00
ADHD	Vagina	1	0.039 (0.011)	0.00e+00
ADHD	Whole_Blood	1	0.053 (0.011)	0.00e+00
ADHD	YFS.BLOOD.RNAARR	0.5	0.044 (0.011)	0.00e+00

Show cis-regulated expression-based heritability

Proportion of heritability explained

Show summary of GeRS tests

Predictive utility

Correlation between GeRS model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R perc diff	R diff pval
1	Height21	GeRS_multi_pT	All	0.5	0.237	0.241	-0.004	-1.8%	3.23e-01
6	BMI21	GeRS_multi_pT	All	0.1	0.142	0.136	0.005	3.8%	3.75e-01
11	GCSE	GeRS_multi_pT	All	0.5	0.197	0.209	-0.012	-6.1%	1.91e-03
16	ADHD	GeRS_multi_pT	All	0.5	0.055	0.057	-0.002	-4.5%	7.46e-01
2	Height21	GeRS_multi_tissue	All	Nerve.Tibial	0.237	0.220	0.017	7.1%	2.45e-02
7	BMI21	GeRS_multi_tissue	All	Artery.Tibial	0.142	0.123	0.019	13.1%	3.91e-02
12	GCSE	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.197	0.188	0.009	4.4%	1.46e-01
17	ADHD	GeRS_multi_tissue	All	Muscle.Skeletal	0.055	0.063	-0.009	-15.6%	4.10e-01
3	Height21	PRS_and_GeRS	All	PRS	0.396	0.397	-0.002	-0.4%	5.37e-01
8	BMI21	PRS_and_GeRS	All	PRS	0.315	0.317	-0.002	-0.5%	4.65e-01
13	GCSE	PRS_and_GeRS	All	PRS	0.370	0.369	0.001	0.2%	6.75e-01
18	ADHD	PRS_and_GeRS	All	PRS	0.112	0.113	-0.001	-0.9%	8.09e-01
5	Height21	PRScs_and_GeRS	All	PRS	0.428	0.433	-0.005	-1.1%	1.13e-02
10	BMI21	PRScs_and_GeRS	All	PRS	0.347	0.349	-0.002	-0.7%	1.80e-01
15	GCSE	PRScs_and_GeRS	All	PRS	0.395	0.399	-0.004	-0.9%	3.46e-03
20	ADHD	PRScs_and_GeRS	All	PRS	0.127	0.132	-0.005	-4.1%	6.51e-02
4	Height21	Strat_PRS	All	strat.PRS	0.380	0.381	-0.001	-0.4%	6.06e-01
9	BMI21	Strat_PRS	All	strat.PRS	0.272	0.271	0.001	0.5%	2.89e-01
14	GCSE	Strat_PRS	All	strat.PRS	0.331	0.333	-0.003	-0.8%	1.70e-01
19	ADHD	Strat_PRS	All	strat.PRS	0.094	0.104	-0.010	-10.7%	4.32e-02

Show summary of GeRS PP4 tests

Predictive utility

Correlation between GeRS PP4 model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R diff pval
1	Height21	GeRS_multi_tissue	All	Thyroid	0.202	0.168	0.034	1.78e-03
3	BMI21	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.086	0.090	-0.004	6.05e-01
5	GCSE	GeRS_multi_tissue	All	Esophagus.Muscularis	0.156	0.125	0.031	1.18e-03
7	ADHD	GeRS_multi_tissue	All	Heart.Left.Ventricle	-0.015	0.011	-0.026	4.56e-02
2	Height21	PRS_and_GeRS	All	PRS	0.398	0.397	0.001	8.19e-01
4	BMI21	PRS_and_GeRS	All	PRS	0.317	0.317	0.000	5.86e-01
6	GCSE	PRS_and_GeRS	All	PRS	0.368	0.369	-0.002	3.02e-01
8	ADHD	PRS_and_GeRS	All	PRS	0.111	0.113	-0.003	3.26e-01

Show summary of GeRS TissueSpecific tests

Predictive utility

Correlation between GeRS TissueSpecific model predictions and observed values in UK Biobank

FALSE	Phenotype	Test	Model 1	Model 2	Model 1 R	Model 2 R	R diff	R diff pval
1	Height21	GeRS_multi_tissue	All	Adipose.Subcutaneous	0.241	0.185	0.056	7.99e-09
3	BMI21	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.123	0.114	0.009	3.50e-01
5	GCSE	GeRS_multi_tissue	All	CMC.BRAIN.RNASEQ	0.196	0.147	0.049	2.32e-08
7	ADHD	GeRS_multi_tissue	All	Brain.Cerebellar.Hemisphere	0.055	0.062	-0.006	6.36e-01
2	Height21	PRS_and_GeRS	All	PRS	0.397	0.397	0.000	9.74e-01
4	BMI21	PRS_and_GeRS	All	PRS	0.319	0.317	0.002	1.24e-01
6	GCSE	PRS_and_GeRS	All	PRS	0.367	0.369	-0.002	2.67e-01
8	ADHD	PRS_and_GeRS	All	PRS	0.111	0.113	-0.003	5.65e-01

Show GeRS, PRS, stratified-PRS comparison

Predictive utility

Proportion of PRS explained by GeRS

Phenotype	Prop_GE	Prop_GE_coloc
Height	0.597	0.462
BMI	0.446	0.253
GCSE	0.533	0.360
ADHD	0.484	-0.028

Show GeRS coloc and tissue specific comparison

Predictive utility

Comparison of GeRS

Phenotype	Method	R	SE
Height	GeRS (best)	0.220	0.013
Height	GeRS (all)	0.237	0.013
Height	GeRS coloc (best)	0.154	0.013
Height	GeRS coloc (all)	0.183	0.013
Height	GeRS TS (best)	0.185	0.013
Height	GeRS TS (all)	0.241	0.013
BMI	GeRS (best)	0.123	0.014
BMI	GeRS (all)	0.142	0.014
BMI	GeRS coloc (best)	0.088	0.014
BMI	GeRS coloc (all)	0.080	0.014
BMI	GeRS TS (best)	0.114	0.014
BMI	GeRS TS (all)	0.123	0.014
GCSE	GeRS (best)	0.188	0.012
GCSE	GeRS (all)	0.197	0.011
GCSE	GeRS coloc (best)	0.104	0.012
GCSE	GeRS coloc (all)	0.133	0.012
GCSE	GeRS TS (best)	0.147	0.012
GCSE	GeRS TS (all)	0.196	0.011
ADHD	GeRS (best)	0.063	0.011
ADHD	GeRS (all)	0.055	0.011
ADHD	GeRS coloc (best)	0.025	0.011
ADHD	GeRS coloc (all)	-0.003	0.011
ADHD	GeRS TS (best)	0.062	0.011
ADHD	GeRS TS (all)	0.055	0.011

Show association with GeRS for each SNP-weight set

Predictive utility

Show association with GeRS PP4 for each SNP-weight set

Predictive utility

Show association with GeRS TissueSpecific for each SNP-weight set

Predictive utility

Show association with GeRS for each SNP-weight set after accounting for number of features

Predictive utility

Show effect of number of features in the SNP-weight set

Number of feature effect

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5 Conclusion

• Gene expression risk scores (GeRS) can explain a significantly non-zero amount of variance in a range of phenotypes
• The amount of variance explained by GeRS is less than explained by a genome-wide pT+clump polygenic score or TWAS SNP-weight stratified polygenic scores, except for Rheumatoid Arthritis.
• Use of elastic net models to combine GeRSs derived using multiple pTs does not significantly improve prediction over the single best pT as idenitified using 10-fold cross validation, though prediction never decreases
• Inclusion of GeRS based on SNP-weights derived using multiple tissues consistently improves prediction over the single best tissues as idenitified using 10-fold cross validation
• Inclusion of GeRSs only improved prediction over genome-wide polygenic scores for rheumatoid arthritis, providing an 1.6 times increase in r2 (0.021 to 0.035). Prediction improvements were non significant for all other phenotypes in UK Biobank and TEDS.
• However, PRScs polygenic scores which does not use LD clumping reduces the benefit of using GeRS for all outcomes, and the increase for RheumArth is no longer significant. This indicates the gain is in the fact that GeRS are based on joint SNP models.
• This findings suggest that GeRS represent a component of risk captured within genome-wide polygenic scores, rather than containing novel information.
• This indicates GeRS may be more useful for stratifying risk rather than improving risk prediction in a linear model.
• TWAS SNP PRS explain a suprisingly large amount of variance considering they only contain a fraction of the genome.

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