This documents an MDD TWAS study. The analysis was carried out by Lorenza Dall’Aglio and Oliver Pain, with supervision from Cathryn Lewis.

This study used GWAS summary statistics from the Wray et al Major Depression GWAS, including 23andMe participants. We used SNP-weights derived by FUSION for brain tissues, HPA tissues, HPT tissues, and blood.

We also included SNP-weights derived by the PsychENCODE team for the DLPFC. The PsychENCODE SNP-weights were derived using all HRC imputed variants within the PsychENCODE dataset. The standard FUSION LD reference is restricted to HapMap3 variants, so when using the PsychENCODE SNP-weight we used an unrestricted version of the 1KG Phase 3 reference to improve SNP overlap.


1 Estimating transcriptome-wide significance threshold

First we need to estimate our transcriptome-wide significance threshold to account for the number of features tested. We will use a permutation-based approach to determine the appropriate transcriptome-wide significance threshold. It works by performing TWAS using the relevent SNP-weight panels.

Show a list of SNP-weight panels in the TWAS

SNP-weight panels used in MDD TWAS
Panel
Adrenal_Gland
Brain_Amygdala
Brain_Anterior_cingulate_cortex_BA24
Brain_Caudate_basal_ganglia
Brain_Cerebellar_Hemisphere
Brain_Cerebellum
Brain_Cortex
Brain_Frontal_Cortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleus_accumbens_basal_ganglia
Brain_Putamen_basal_ganglia
Brain_Substantia_nigra
CMC.BRAIN.RNASEQ
CMC.BRAIN.RNASEQ_SPLICING
NTR.BLOOD.RNAARR
Pituitary
Thyroid
Whole_Blood
YFS.BLOOD.RNAARR
PsychENCODE

Generate null distribution

mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig

for batch in $(seq 1 20); do
sbatch -p brc,shared -n 1 --mem=10G /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASPermuThr.R \
--nperm 50 \
--ncore 1 \
--weights /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/Batch${batch}
done

Estimate transcriptome-wide significance threshold

library(data.table)
library(MKmisc)

# Create list of files containing minimum p values
batches<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',pattern='Batch*')

# Combine all the minimum p-values
min_P_all<-NULL
for(batch in batches){      
    min_P_all<-c(min_P_all,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',batch), header=F)$V1)
}

# Calculate the 5th percentile of the minimum p-values
TWalpha<-MKmisc::quantileCI(x=min_P_all, prob=0.05, method="exact",conf.level=0.95)
TWalpha # 1.368572e-06

# Calculate the 0.1th percentile which will be needed for the high-confidence associations section
TWalpha_001<-MKmisc::quantileCI(x=min_P_all, prob=0.001, method="exact",conf.level=0.99)
TWalpha_001 # 3.685926e-08

# Save the R object for future reference
saveRDS(TWalpha,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_05.RDS')
saveRDS(TWalpha_001,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_001.RDS')

2 TWAS


2.1 FUSION SNP-weights


2.1.1 GWAS summary statistics preparation

GWAS summary statistics were munged using the LDSC munge_sumstats.py.

Show munge log file

## *********************************************************************
## * LD Score Regression (LDSC)
## * Version 1.0.0
## * (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane
## * Broad Institute of MIT and Harvard / MIT Department of Mathematics
## * GNU General Public License v3
## *********************************************************************
## Call: 
## ./munge_sumstats.py \
## --N-con-col Ncon \
## --out /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01 \
## --merge-alleles /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist \
## --N-cas-col Ncas \
## --N-col N \
## --info-min 0.6 \
## --sumstats /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz 
## 
## Interpreting column names as follows:
## INFO:    INFO score (imputation quality; higher --> better imputation)
## SNP: Variant ID (e.g., rs number)
## N:   Sample size
## A1:  Allele 1, interpreted as ref allele for signed sumstat.
## P:   p-Value
## A2:  Allele 2, interpreted as non-ref allele for signed sumstat.
## Ncon:    Number of controls
## Ncas:    Number of cases
## FREQ:    Allele frequency
## OR:  Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
## 
## Reading list of SNPs for allele merge from /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist
## Read 1217311 SNPs for allele merge.
## Reading sumstats from /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz into memory 5000000.0 SNPs at a time.
## Read 10155339 SNPs from --sumstats file.
## Removed 8953119 SNPs not in --merge-alleles.
## Removed 0 SNPs with missing values.
## Removed 0 SNPs with INFO <= 0.6.
## Removed 17182 SNPs with MAF <= 0.01.
## Removed 0 SNPs with out-of-bounds p-values.
## Removed 0 variants that were not SNPs or were strand-ambiguous.
## 1185038 SNPs remain.
## Removed 0 SNPs with duplicated rs numbers (1185038 SNPs remain).
## Removed 0 SNPs with N < 307422.666667 (1185038 SNPs remain).
## Median value of OR was 1.0, which seems sensible.
## Removed 0 SNPs whose alleles did not match --merge-alleles (1185038 SNPs remain).
## Writing summary statistics for 1217311 SNPs (1185038 with nonmissing beta) to /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz.
## 
## Metadata:
## Mean chi^2 = 1.534
## Lambda GC = 1.419
## Max chi^2 = 79.045
## 597 Genome-wide significant SNPs (some may have been removed by filtering).
## 
## Conversion finished at Wed Feb 14 12:45:03 2018
## Total time elapsed: 2.0m:19.39s

Then, I modified the sumstats to remove the rows with missing values.

Show code

module add general/R/3.5.0
R

library(data.table)

# Read in the LDSC munged sumstats using zcat to unzip the file
sumstats<-data.frame(fread('zcat /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz'))

# Remove rows containing NA values
sumstats<-sumstats[complete.cases(sumstats),]

# Save the reformatted sumstats and compress
write.table(sumstats, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA', col.names=T, row.names=F,quote=F)

q()
n

gzip /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA

2.1.2 TWAS analysis

Run TWAS

# Run analysis for each chromomsome and each panel
for chr in $(seq 1 22); do
  for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
    qsub -cwd /mnt/lustre/users/k1894478/scripts/Rscript_correct.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
      --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA.gz \
      --weights /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights}/${weights}.pos \
      --weights_dir /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights} \
      --ref_ld_chr /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/LDREF/1000G.EUR. \
      --out /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr${chr} \
      --chr ${chr} \
      --coloc_P 4.432625e-06 \
      --GWASN 480359
  done
done

# Check each chromosome finished
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
echo $weights
ls /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* | wc -l  #this tells you how many files there are with the name test.X.chrX
done

# To combine per chromosome results file, without duplicating the header
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
head -n 1 /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr1 > /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW      
tail -n +2 -q /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* >> /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW    
done

# Create file containing results for all tissues
awk '
    FNR==1 && NR!=1 { while (/TWAS.P/) getline; }
    1 {print}
' /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.GW >/mnt/lustre/users/k1894478/output_correct/wray_output/AllTissues.GW


# Check the .GW files which were created
ls *.GW

# Delete the per chromosome files
for chr in $(seq 1 22); do
  rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr${chr}
done

rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr6.MHC

2.2 PsychENCODE SNP-weights


2.2.1 GWAS summary statistics preparation

The PsychENCODE SNP-weights are not restricted to HapMap3 variants, so we need to use unrestricted GWAS summary statistics also. To achieve this I use the FOCUS software munge script.

Show code

# Edit BP column name
zcat /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz | sed -e 's/ORIGBP/BP/g' > /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP

# Munge
/users/k1806347/brc_scratch/Software/focus.sh munge /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus

# Delete temporary file
rm /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP

Show munge log file

## ===================================
##               FOCUS v0.6.10            
## ===================================
## focus munge
##  /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP
##  --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus
## 
## Starting log...
## [2019-11-14 10:54:16 - INFO] Interpreting column names as follows:
## [2019-11-14 10:54:16 - INFO] SNP: Variant ID (e.g., rs number)
## [2019-11-14 10:54:16 - INFO] CHR: Chromsome
## [2019-11-14 10:54:16 - INFO] BP: Base position
## [2019-11-14 10:54:16 - INFO] A1: Allele 1, interpreted as ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] A2: Allele 2, interpreted as non-ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] P: p-Value
## [2019-11-14 10:54:16 - INFO] INFO: INFO score (imputation quality; higher --> better imputation)
## [2019-11-14 10:54:16 - INFO] OR: Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
## [2019-11-14 10:54:16 - INFO] N: Sample size
## [2019-11-14 10:54:16 - INFO] Reading sumstats from /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP into memory 5000000 SNPs at a time
## [2019-11-14 10:54:24 - INFO] Reading SNP chunk 1
## [2019-11-14 10:54:44 - INFO] Reading SNP chunk 2
## [2019-11-14 10:54:52 - INFO] Reading SNP chunk 3
## [2019-11-14 10:54:52 - INFO] Done reading SNP chunks
## [2019-11-14 10:54:58 - INFO] Read 10155339 SNPs from --sumstats file
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with missing values
## [2019-11-14 10:54:58 - INFO] Removed 2561651 SNPs with INFO <= 0.9
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with MAF <= 0.01
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with out-of-bounds p-values
## [2019-11-14 10:54:58 - INFO] Removed 1656695 variants that were not SNPs or were strand-ambiguous
## [2019-11-14 10:54:58 - INFO] 5936993 SNPs remain
## [2019-11-14 10:55:04 - INFO] Removed 0 SNPs with duplicated rs numbers (5936993 SNPs remain).
## [2019-11-14 10:55:06 - INFO] Removed 188 SNPs with N < 307422.6666666667 (5936805 SNPs remain)
## [2019-11-14 10:56:30 - INFO] Median value of OR was 1.0, which seems sensible.
## [2019-11-14 10:56:31 - INFO] Writing summary statistics for 5936805 SNPs (5936805 with nonmissing beta) to /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz.
## [2019-11-14 10:57:53 - INFO] METADATA - Mean chi^2 = 1.492
## [2019-11-14 10:57:54 - INFO] METADATA - Lambda GC = 1.383
## [2019-11-14 10:57:54 - INFO] METADATA - Max chi^2 = 79.045
## [2019-11-14 10:57:54 - INFO] METADATA - 2960 Genome-wide significant SNPs (some may have been removed by filtering)
## [2019-11-14 10:57:54 - INFO] Conversion finished

2.2.2 TWAS analysis

Run TWAS

for chr in $(seq 1 22); do
  sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
    --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
    --weights /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos \
    --weights_dir /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights \
    --ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
    --out /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr${chr} \
    --chr ${chr} \
    --coloc_P 4.432625e-06 \
    --GWASN 480359
done

Format to match FUSION panel results

library(data.table)

psychENCODE<-NULL
for(i in 1:22){
    if(i == 6){
        tmp1<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
        tmp2<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i,'.MHC'))
        tmp<-rbind(tmp1,tmp2)
    } else {
        tmp<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
    }
    
    psychENCODE<-rbind(psychENCODE, tmp)
}

col_order<-names(psychENCODE)

psychENCODE$PANEL<-as.character(psychENCODE$PANEL)
psychENCODE$PANEL<-'PsychENCODE'

library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)

psychENCODE<-merge(psychENCODE, Genes, by.x='ID', by.y='ensembl_gene_id')
psychENCODE$ID<-psychENCODE$external_gene_name
psychENCODE$external_gene_name<-NULL

psychENCODE<-psychENCODE[,col_order, with=F]

write.table(psychENCODE, '/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW', col.names=T, row.names=F, quote=F)

2.3 Combine results across FUSION and PsychENCODE panels

Format to match FUSION panel results

library(data.table)

psych<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/AllTissues.GW')

all<-rbind(psych,fusion)

# Write out full results
write.table(all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt', row.names=F, col.names=T, quote=F)

# Write out transcriptome-wide significant results
write.table(all[which(all$TWAS.P < 1.368572e-06),], '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt', row.names=F, col.names=T, quote=F)

dim(all[which(all$TWAS.P < 1.368572e-06),]) # 176 hits
length(unique(all[which(all$TWAS.P < 1.368572e-06),]$ID)) # 94 unique genes

# Check how much PsychENCODE adds
dim(fusion[which(fusion$TWAS.P < 1.368572e-06),]) # 154 hits
length(unique(fusion[which(fusion$TWAS.P < 1.368572e-06),]$ID)) # 84 unique genes

3 Follow-up analyses


3.1 Create Manahattan-style plots

Show code

# Manhattan plot based on permutation significance
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 1.368572e-06 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_Manhattan \
--width 3500 \
--height 2500

#Manhattan plot for high confidence associations
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 3.685926e-08 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_HighConf_Manhattan \
--width 3500 \
--height 2500

Show Manhattan plots

MDD TWAS Manhattan Plot with transcriptome-wide signficance

MDD TWAS Manhattan Plot with transcriptome-wide signficance


MDD TWAS Manhattan Plot with high-confidence threshold

MDD TWAS Manhattan Plot with high-confidence threshold


3.2 Conditional analysis

Run post_process.R script

# Change directory to location of glist-hg19 file
cd /users/k1806347/brc_scratch/Data/Gene_Locations

mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional

for chr in $(seq 1 22); do

  status=$(awk -v var="${chr}" '$4 == var {print "Present";exit;}' /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/post-TWAS/MDD_TWAS_AllTissues_TWSig.txt )
  
  if [ "$status" == "Present" ]; then
    sbatch -p brc,shared --mem 25G -n 1 /users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.post_process.R \
      --input /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt \
      --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
      --report \
      --ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
      --out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/test.cond.chr${chr} \
      --chr ${chr} \
      --plot \
      --plot_legend all \
      --save_loci \
      --locus_win 500000
  fi

done

3.3 Process TWAS results

Clean the TWAS results

###
# Clean file PANEL names 
###

rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt")
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt")

dim(twas)[1] # 97733 features in PANELS
sum(!is.na(twas$TWAS.P)) # 95720 features that could be imputed in GWAS
length(unique(twas$ID)) # 22006 unique genes in PANELS
length(unique(twas$ID[!is.na(twas$TWAS.P)])) # 21507 unique gene that could be imputed in GWAS

twas_sign$BEST.GWAS.P<-2*pnorm(-abs(twas_sign$BEST.GWAS.Z))
sum(twas_sign$BEST.GWAS.P > 5e-8) # 63
sum(twas_sign$BEST.GWAS.P < 5e-8) # 113

str(twas_sign)
str(twas)

#clean the PANEL names of the output df containing results on all tested features
twas$PANEL_clean<-gsub('_',' ',twas$PANEL)
twas$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas$PANEL_clean)
twas$PANEL_clean<-gsub('SPLICING','Splicing',twas$PANEL_clean)
twas$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas$PANEL_clean)
twas$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas$PANEL_clean)
twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)]<-paste0('GTEx ',twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas$PANEL_clean<-gsub('Brain', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas$PANEL_clean)
twas$PANEL_clean <- gsub('basal ganglia', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA9', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA24', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('  ', ' ', twas$PANEL_clean)

# Create a table showing the number of features tested for each SNP-weight set
tab_ob<-table(twas$PANEL_clean)
panel_tab<-data.frame(tab_ob)
names(panel_tab)<-c('PANEL','N_feat')

tab_imp_ob<-table(twas[!is.na(twas$TWAS.P),]$PANEL_clean)
panel_imp_tab<-data.frame(tab_imp_ob)
names(panel_imp_tab)<-c('PANEL','N_feat')

panel_tab_all<-merge(panel_tab, panel_imp_tab, by='PANEL')
names(panel_tab_all)<-c('PANEL','N_feat','N_feat_imp')

write.csv(panel_tab_all, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_Panel_N.csv", row.names = F)

# Shorten panel name to plot easily
twas$PANEL_clean_short<-substr(twas$PANEL_clean, start = 1, stop = 25)  #start the name at the first character and stop at the 25th
twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25]<-paste0(twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25], "...")

#do the same for the output file with sign features only
twas_sign$PANEL_clean<-gsub('_',' ',twas_sign$PANEL)
twas_sign$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('SPLICING','Splicing',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)]<-paste0('GTEx ',twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas_sign$PANEL_clean<-gsub('Brain', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('basal ganglia', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA9', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA24', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('  ', ' ', twas_sign$PANEL_clean)

# Shorten panel name to plot easily
twas_sign$PANEL_clean_short<-substr(twas_sign$PANEL_clean, start = 1, stop = 25)  #start the name at the first character and stop at the 25th
twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25]<-paste0(twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25], "...")

#check the variables
str(twas)
str(twas_sign)

###
# Deal with missingness and subset for the relevant cols only
###

##TWAS df
#exclude missings
twas<-twas[!is.na(twas$TWAS.Z),]
twas<-twas[!is.na(twas$TWAS.P),]

#subset columns needed 
twas_sub <- twas[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sub)

##TWAS sign df
#exclude missings
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.Z),]
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.P),]

#subset columns needed 
twas_sign_sub <- twas_sign[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sign_sub)

###
# Update positions
###

# Rationale: the positions in the output files created by FUSION are rounded, thus not completely accurate. 
# Therefore, we need to update the positions (P0 and P1) based on the pos files in Rosalind. This needs to be done on Putty though. A new file will be saved and reopened here. 

setwd('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/')

weights<-c('Adrenal_Gland', 'Brain_Amygdala', 'Brain_Anterior_cingulate_cortex_BA24', 'Brain_Caudate_basal_ganglia', 'Brain_Cerebellar_Hemisphere', 'Brain_Cerebellum', 'Brain_Cortex', 'Brain_Frontal_Cortex_BA9', 'Brain_Hippocampus', 'Brain_Hypothalamus', 'Brain_Nucleus_accumbens_basal_ganglia', 'Brain_Putamen_basal_ganglia', 'Brain_Substantia_nigra', 'CMC.BRAIN.RNASEQ', 'CMC.BRAIN.RNASEQ_SPLICING', 'NTR.BLOOD.RNAARR', 'Pituitary', 'Thyroid', 'Whole_Blood', 'YFS.BLOOD.RNAARR')

#Get all pos files within the SNP-weight sets and bind them 
FUSION_pos<-NULL
for(i in weights){
FUSION_pos_temp<-read.table(paste(i, '/',i, '.pos',sep=''), header=T, stringsAsFactors=F)   #repeating i twice with / in the middle is to get one folder further 
FUSION_pos<-rbind(FUSION_pos, FUSION_pos_temp)
}

PsychENCODE_pos<-read.table('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos', header=T, stringsAsFactors=F)
PsychENCODE_pos$PANEL<-'PsychENCODE'

# Combine pos files
FUSION_pos<-rbind(FUSION_pos, PsychENCODE_pos)

write.table(FUSION_pos,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos', col.names=T, row.names=F, quote=F)

str(FUSION_pos)   #97733 observations of 7 variables (PANEL, WGT, ID, CHR, P0, P1, N)

###
# Merge the pos file with the twas_sub and twas_sign_sub df
###

#the pos file and the output file do not have the same columns with the same information. We therefore need to slightly modify the TWAS columns 
twas_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sub$FILE)
twas_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sub$tmp)
#to delete the full pathway of the file and just keep the important information 
twas_sub$PANEL<-sub('/.*','', twas_sub$tmp)
twas_sub$Feature<-gsub('.*/','',twas_sub$tmp)
twas_sub$WGT<-paste0(twas_sub$PANEL, '/', twas_sub$Feature)
twas_sub$PANEL<-NULL
twas_sub$tmp<-NULL
twas_sub$Feature<-NULL

twas_sub[order(twas_sub$WGT), ]
FUSION_pos[order(FUSION_pos$WGT), ]

#merge
twas_sub_correct <- merge(twas_sub, FUSION_pos, by="WGT")

#check
head(twas_sub_correct)

#clean
twas_sub_correct$ID.y<-NULL
colnames(twas_sub_correct)
names(twas_sub_correct)[3]<-'ID'   #to change the name  of IDx to ID
head(twas_sub_correct)
  
#repeat everything for the twas_sign_sub file
twas_sign_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sign_sub$FILE)
twas_sign_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sign_sub$tmp)

#to delete the full pathway of the file and just keep the important information 
twas_sign_sub$PANEL<-sub('/.*','', twas_sign_sub$tmp)
twas_sign_sub$Feature<-gsub('.*/','',twas_sign_sub$tmp)
twas_sign_sub$WGT<-paste0(twas_sign_sub$PANEL, '/', twas_sign_sub$Feature)
twas_sign_sub$PANEL<-NULL
twas_sign_sub$tmp<-NULL
twas_sign_sub$Feature<-NULL

# twas_sign_sub[order(twas_sign_sub$WGT), ]

#merge
twas_sign_sub_correct <- merge(twas_sign_sub, FUSION_pos, by="WGT")

#check
head(twas_sign_sub_correct)

#clean
twas_sign_sub_correct$ID.y<-NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[3]<-'ID'   #to change the name  of IDx to ID
 
head(twas_sign_sub_correct)
dim(twas_sign_sub_correct)
dim(twas_sub_correct)

###
# Clean output files for future scripts
###

#clean both output files to have clean outputs to use in future scripts

#twas sign sub correct df
colnames(twas_sign_sub_correct)

names(twas_sign_sub_correct)[6] <- "CHR"
twas_sign_sub_correct$CHR.y <- NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[17] <- "P0" #turn POy into P0 - nb P0y is the one withh the more accurate positions
names(twas_sign_sub_correct)[18] <- "P1" 

twas_sign_sub_correct$P0.x <- NULL
twas_sign_sub_correct$P1.x <- NULL


colnames(twas_sign_sub_correct)
twas_sign_sub_correct$N <- NULL
str(twas_sign_sub_correct)

#change the variable types for those which are wrong 

#turn PO and P1 into numerical variables
twas_sign_sub_correct$P0 <- as.numeric(as.character(twas_sign_sub_correct$P0))
twas_sign_sub_correct$P1 <- as.numeric(as.character(twas_sign_sub_correct$P1))

str(twas_sign_sub_correct)

#twas_sub correct df
colnames(twas_sub_correct)

names(twas_sub_correct)[6]<- "CHR"
names(twas_sub_correct)[18]<- "P0"
names(twas_sub_correct)[19]<- "P1"
twas_sub_correct$CHR.y <- NULL
twas_sub_correct$P0.x <- NULL
twas_sub_correct$P1.x <- NULL

colnames(twas_sub_correct)
twas_sub_correct$N <- NULL

str(twas_sub_correct)

#change variable type for PO and P1
twas_sub_correct$P0 <- as.numeric(as.character(twas_sub_correct$P0))
twas_sub_correct$P1 <- as.numeric(as.character(twas_sub_correct$P1))

#save 
write.table(twas_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", sep = " ", col.names = T, row.names = F)
write.table(twas_sign_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt", sep = " ", col.names = T, row.names=F)

q()
n

Create a table with the transcriptome-wide significant findings

rm(list=ls())
library(data.table)

twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

str(twas_sign)
twas_sign$CHR <- as.numeric(as.character(twas_sign$CHR))
twas_sign$P0 <- as.numeric(as.character(twas_sign$P0))
twas_sign$P1 <- as.numeric(as.character(twas_sign$P1))

twas_sign <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]

twas_sign$Location <- paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1) 

colnames(twas_sign)

library(dplyr)
library(tibble)
twas_sign <- as_data_frame(twas_sign)

col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P")

twas_sign_final <- twas_sign[, col_order]

write.csv(twas_sign_final, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.brief.csv", row.names=F)

twas_sign_final

Show transcriptome-wide significant table

Transcriptome-wide significant associations with Major Depression
Location ID PANEL_clean_short TWAS.Z TWAS.P
chr1:8412457-8877702 RERE GTEx Whole Blood -5.095707 3.47e-07
chr1:8412457-8877702 RERE YFS Blood -5.310078 1.10e-07
chr1:8484705-8494898 RP5-1115A15.1 GTEx Thyroid -5.175240 2.28e-07
chr1:8484705-8494898 RP5-1115A15.1 GTEx Whole Blood -4.866386 1.14e-06
chr1:36884051-36884179 SNORA63 GTEx Nucleus accumbens 4.848870 1.24e-06
chr1:71861623-72748417 NEGR1 GTEx Caudate 5.780100 7.47e-09
chr1:71861623-72748417 NEGR1 GTEx Putamen 5.548510 2.88e-08
chr1:71861623-72748417 NEGR1 GTEx Whole Blood 8.760622 1.94e-18
chr1:72767155-72767512 RPL31P12 GTEx Cerebellar Hemispher… -7.785520 6.94e-15
chr1:72767155-72767512 RPL31P12 GTEx Cerebellum -7.708820 1.27e-14
chr1:72767155-72767512 RPL31P12 PsychENCODE -7.742756 9.73e-15
chr1:175873898-175889649 RP11-318C24.2 GTEx Thyroid -5.027510 4.97e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -4.958690 7.10e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing 5.039850 4.66e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -5.005960 5.56e-07
chr1:181452685-181775921 CACNA1E CMC DLPFC Splicing -4.989390 6.06e-07
chr1:197473878-197744623 DENND1B CMC DLPFC 4.848374 1.24e-06
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing -5.421950 5.90e-08
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing 5.018050 5.22e-07
chr2:58386377-58468515 FANCL CMC DLPFC -5.183180 2.18e-07
chr2:58386377-58468515 FANCL CMC DLPFC Splicing 4.897476 9.71e-07
chr2:197831741-198175897 ANKRD44 YFS Blood -5.690140 1.27e-08
chr2:198254508-198299815 SF3B1 GTEx Hypothalamus 5.214900 1.84e-07
chr3:44481261-44561226 ZNF445 CMC DLPFC -5.103280 3.34e-07
chr4:41937137-41962589 TMEM33 PsychENCODE 4.837418 1.32e-06
chr4:41983713-41988476 DCAF4L1 GTEx Thyroid -5.128000 2.93e-07
chr4:41990758-41991254 RP11-814H16.2 GTEx Cerebellar Hemispher… 5.009600 5.45e-07
chr4:41992489-42092474 SLC30A9 GTEx Amygdala -5.253400 1.49e-07
chr4:41992489-42092474 SLC30A9 GTEx ACC -5.001690 5.68e-07
chr4:41992489-42092474 SLC30A9 GTEx Caudate -4.854800 1.21e-06
chr4:41992489-42092474 SLC30A9 GTEx Cortex -5.774530 7.72e-09
chr4:41992489-42092474 SLC30A9 GTEx Hypothalamus -5.085140 3.67e-07
chr4:41992489-42092474 SLC30A9 GTEx Nucleus accumbens -5.602700 2.11e-08
chr4:41992489-42092474 SLC30A9 PsychENCODE -5.259200 1.45e-07
chr5:87564712-87732502 TMEM161B-AS1 PsychENCODE 6.091010 1.12e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Adrenal Gland 5.360090 8.32e-08
chr5:87564888-87732502 TMEM161B-AS1 GTEx Amygdala 6.118500 9.45e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx ACC 6.445500 1.15e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Caudate 6.282167 3.34e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellar Hemispher… 6.011700 1.84e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellum 6.053050 1.42e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cortex 6.021420 1.73e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Frontal Cortex 6.720000 1.82e-11
chr5:87564888-87732502 TMEM161B-AS1 GTEx Hypothalamus 5.875800 4.21e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Nucleus accumbens 6.010490 1.85e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Putamen 6.372050 1.87e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Substantia nigra 6.057270 1.38e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Pituitary 6.048500 1.46e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Thyroid 5.889760 3.87e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Whole Blood 5.526440 3.27e-08
chr5:87729709-87794514 CTC-498M16.4 GTEx Substantia nigra 5.403610 6.53e-08
chr5:87988462-87989789 CTC-467M3.3 GTEx ACC -5.813700 6.11e-09
chr5:87988462-87989789 CTC-467M3.3 GTEx Cerebellar Hemispher… -5.861000 4.60e-09
chr5:87988462-87989789 CTC-467M3.3 GTEx Cortex -6.510990 7.47e-11
chr5:87988462-87989789 CTC-467M3.3 GTEx Frontal Cortex -7.091600 1.33e-12
chr5:87988462-87989789 CTC-467M3.3 PsychENCODE -6.097890 1.07e-09
chr5:140024947-140027370 NDUFA2 CMC DLPFC 5.190020 2.10e-07
chr5:140201222-140203811 PCDHA5 GTEx Thyroid -5.402970 6.55e-08
chr5:140220907-140223351 PCDHA8 GTEx Cerebellar Hemispher… -4.980100 6.36e-07
chr6:26188921-26189323 HIST1H4D NTR Blood -4.987600 6.11e-07
chr6:26365386-26378540 BTN3A2 NTR Blood 5.326600 1.00e-07
chr6:26365387-26378546 BTN3A2 GTEx Cerebellar Hemispher… 5.188200 2.12e-07
chr6:26365387-26378546 BTN3A2 GTEx Hippocampus 4.963000 6.96e-07
chr6:26365387-26378546 BTN3A2 GTEx Pituitary 5.898930 3.66e-09
chr6:26365387-26378546 BTN3A2 GTEx Thyroid 5.481600 4.22e-08
chr6:26365387-26378546 BTN3A2 GTEx Whole Blood 5.086960 3.64e-07
chr6:26538633-26546482 HMGN4 GTEx Cerebellum 5.395400 6.84e-08
chr6:27215480-27224250 PRSS16 GTEx Cerebellar Hemispher… -4.891200 1.00e-06
chr6:27215480-27224250 PRSS16 GTEx Cerebellum -4.947900 7.50e-07
chr6:27215480-27224250 PRSS16 GTEx Frontal Cortex -5.045000 4.54e-07
chr6:27215480-27224250 PRSS16 GTEx Pituitary -5.916080 3.30e-09
chr6:27215480-27224250 PRSS16 GTEx Whole Blood -5.335920 9.51e-08
chr6:27325604-27339304 ZNF204P GTEx Adrenal Gland -5.032700 4.84e-07
chr6:27371789-27374743 RP1-153G14.4 GTEx Hippocampus 5.354000 8.60e-08
chr6:27418522-27440897 ZNF184 GTEx Caudate -6.325200 2.53e-10
chr6:27418522-27440897 ZNF184 GTEx Hypothalamus -4.952200 7.34e-07
chr6:27840926-27841289 HIST1H4L NTR Blood 4.870800 1.11e-06
chr6:28058932-28061442 ZSCAN12P1 PsychENCODE 6.268010 3.66e-10
chr6:28058932-28061442 ZSCAN12P1 GTEx Whole Blood -4.936930 7.94e-07
chr6:28083406-28084329 RP1-265C24.5 GTEx Hippocampus 5.532000 3.16e-08
chr6:28092338-28097860 ZSCAN16 YFS Blood -6.109000 1.00e-09
chr6:28192664-28201260 ZSCAN9 GTEx Cerebellum -5.307800 1.11e-07
chr6:28192664-28201260 ZSCAN9 GTEx Hippocampus -6.017000 1.77e-09
chr6:28192664-28201260 ZSCAN9 GTEx Pituitary -6.159020 7.32e-10
chr6:28227098-28228736 NKAPL PsychENCODE 5.002860 5.65e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Adrenal Gland 5.094600 3.49e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Cerebellum 5.062800 4.13e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Hippocampus 5.198000 2.01e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Putamen 5.739000 9.52e-09
chr6:28234788-28245974 RP5-874C20.3 GTEx Thyroid 5.338400 9.38e-08
chr6:28234788-28245974 RP5-874C20.3 GTEx Whole Blood 5.662330 1.49e-08
chr6:28249314-28270326 PGBD1 GTEx Cerebellar Hemispher… -6.313100 2.74e-10
chr6:28292470-28324048 ZSCAN31 GTEx Amygdala -5.084150 3.69e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Amygdala 4.949900 7.43e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Hippocampus 4.951000 7.37e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Thyroid 6.093300 1.11e-09
chr6:28399707-28411279 ZSCAN23 GTEx Hypothalamus -5.777500 7.58e-09
chr6:28399707-28411279 ZSCAN23 GTEx Putamen -4.894000 9.90e-07
chr6:28399707-28411279 ZSCAN23 GTEx Pituitary -4.953290 7.30e-07
chr6:30644166-30655672 PPP1R18 GTEx Adrenal Gland 4.910200 9.10e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 1.16e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 4.04e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 7.95e-07
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 1.16e-07
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 1.49e-07
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 2.74e-08
chr6:30881982-30894236 VARS2 GTEx Cortex 5.922000 3.18e-09
chr6:30881982-30894236 VARS2 GTEx Whole Blood 6.323130 2.56e-10
chr6:31255287-31256741 WASF5P GTEx Pituitary -5.156240 2.52e-07
chr6:31368479-31445283 HCP5 GTEx Thyroid 6.400800 1.55e-10
chr6:31462658-31478901 MICB GTEx Thyroid -5.557000 2.74e-08
chr6:31606805-31620482 BAG6 CMC DLPFC Splicing -5.580000 2.40e-08
chr6:31694815-31698357 DDAH2 GTEx Frontal Cortex 5.409500 6.32e-08
chr6:31694816-31698039 DDAH2 CMC DLPFC 5.344500 9.07e-08
chr6:99817347-99842082 COQ3 CMC DLPFC Splicing 5.146560 2.65e-07
chr6:105404922-105531207 LIN28B CMC DLPFC -5.232050 1.68e-07
chr6:105404923-105531207 LIN28B PsychENCODE -5.105689 3.30e-07
chr6:105584224-105617820 BVES-AS1 GTEx Amygdala -5.578300 2.43e-08
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 3.69e-08
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 7.01e-09
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 3.18e-08
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 7.72e-08
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 1.88e-08
chr8:52232136-52722005 PXDNL CMC DLPFC 5.887460 3.92e-09
chr8:61297147-61429354 RP11-163N6.2 GTEx Thyroid -5.336530 9.47e-08
chr9:126605315-126605965 PIGFP2 PsychENCODE -5.305600 1.12e-07
chr11:57067112-57092426 TNKS1BP1 GTEx Adrenal Gland 4.922610 8.54e-07
chr11:57405497-57420263 AP000662.4 GTEx Thyroid -4.980256 6.35e-07
chr11:57424488-57429340 CLP1 GTEx Whole Blood 5.195860 2.04e-07
chr11:61535973-61560274 TMEM258 PsychENCODE 5.021730 5.12e-07
chr11:113280318-113346111 DRD2 GTEx Frontal Cortex -5.073787 3.90e-07
chr13:53602875-53626196 OLFM4 CMC DLPFC 5.091290 3.56e-07
chr14:42057064-42074059 CTD-2298J14.2 GTEx Thyroid -5.678860 1.36e-08
chr14:42076773-42373752 LRFN5 GTEx Cerebellar Hemispher… 5.423400 5.85e-08
chr14:42076773-42373752 LRFN5 GTEx Cerebellum 5.597540 2.17e-08
chr14:59951161-59971429 JKAMP GTEx Thyroid -5.125100 2.97e-07
chr14:59971779-60043549 CCDC175 GTEx Thyroid -5.478850 4.28e-08
chr14:60062693-60337557 RTN1 CMC DLPFC Splicing -4.874920 1.09e-06
chr14:60062695-60337684 RTN1 GTEx Thyroid -5.348450 8.87e-08
chr14:64319682-64693151 SYNE2 NTR Blood 5.609528 2.03e-08
chr14:64550950-64770377 ESR2 GTEx Pituitary -5.982300 2.20e-09
chr14:64550950-64770377 ESR2 GTEx Whole Blood -5.655371 1.56e-08
chr14:75120140-75179818 AREL1 PsychENCODE -5.015110 5.30e-07
chr14:75319736-75330537 PROX2 GTEx Thyroid -5.758100 8.51e-09
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 6.31e-07
chr14:75348594-75370448 DLST PsychENCODE 5.089700 3.59e-07
chr14:75370656-75389188 RPS6KL1 CMC DLPFC Splicing -5.023810 5.07e-07
chr14:75370657-75390099 RPS6KL1 PsychENCODE -4.952550 7.32e-07
chr14:103878456-103879098 RP11-600F24.2 PsychENCODE 5.185660 2.15e-07
chr14:103985996-103989448 CKB YFS Blood 5.346000 8.99e-08
chr14:103995508-104003410 TRMT61A CMC DLPFC 5.051300 4.39e-07
chr14:103995521-104003410 TRMT61A GTEx Whole Blood 4.977593 6.44e-07
chr14:104019758-104028214 RP11-894P9.2 GTEx Thyroid -5.462560 4.69e-08
chr14:104153913-104154464 RP11-73M18.6 PsychENCODE 5.031320 4.87e-07
chr14:104160897-104161507 RP11-73M18.7 PsychENCODE 4.856130 1.20e-06
chr14:104162690-104163500 RP11-73M18.8 GTEx Amygdala 5.142000 2.72e-07
chr14:104177607-104179149 AL049840.1 GTEx Cerebellum 5.029540 4.92e-07
chr14:104177607-104179149 AL049840.1 GTEx Cortex 5.143620 2.69e-07
chr14:104179904-104180441 RP11-73M18.9 GTEx Cortex 4.977330 6.45e-07
chr14:104179904-104180586 RP11-73M18.9 PsychENCODE 4.830100 1.36e-06
chr16:72146056-72210777 PMFBP1 PsychENCODE -5.160620 2.46e-07
chr17:27400528-27507430 MYO18A GTEx Adrenal Gland -5.128570 2.92e-07
chr17:27401933-27405875 TIAF1 GTEx Adrenal Gland -5.361200 8.27e-08
chr17:65520597-65521538 CTD-2653B5.1 PsychENCODE 5.105730 3.30e-07
chr18:52385091-52562747 RAB27B PsychENCODE 5.012900 5.36e-07
chr18:52495707-52562747 RAB27B CMC DLPFC Splicing 4.843190 1.28e-06
chr20:47835831-47860614 DDX27 CMC DLPFC 4.836260 1.32e-06
chr22:41165634-41215403 SLC25A17 GTEx Nucleus accumbens 5.076990 3.83e-07
chr22:41165634-41215403 SLC25A17 GTEx Thyroid 4.896100 9.78e-07
chr22:41253088-41351450 XPNPEP3 GTEx Frontal Cortex 4.951000 7.38e-07
chr22:41258260-41363888 XPNPEP3 CMC DLPFC 5.110000 3.21e-07
chr22:41487790-41576081 EP300 GTEx Cerebellum 5.493900 3.93e-08
chr22:41487790-41576081 EP300 YFS Blood 5.059100 4.21e-07
chr22:41641614-41682216 RANGAP1 CMC DLPFC Splicing 5.240100 1.61e-07
chr22:41641615-41682255 RANGAP1 PsychENCODE -5.575273 2.47e-08
chr22:41697526-41756151 ZC3H7B GTEx Cerebellum 5.729100 1.01e-08

Show number of features for each panel

Number of features for each panel
PANEL N_feat N_feat_imp
CMC DLPFC 5419 5382
CMC DLPFC Splicing 7771 7728
GTEx ACC 2709 2674
GTEx Adrenal Gland 4520 4483
GTEx Amygdala 1836 1810
GTEx Caudate 3660 3617
GTEx Cerebellar Hemisphere 4407 4361
GTEx Cerebellum 5854 5803
GTEx Cortex 4011 3966
GTEx Frontal Cortex 3143 3112
GTEx Hippocampus 2294 2269
GTEx Hypothalamus 2314 2290
GTEx Nucleus accumbens 3239 3209
GTEx Pituitary 4401 4353
GTEx Putamen 2817 2784
GTEx Substantia nigra 1603 1582
GTEx Thyroid 9825 9731
GTEx Whole Blood 6006 5948
NTR Blood 2454 2439
PsychENCODE 14750 13522
YFS Blood 4700 4657

3.4 Plot TWAS results

Create QQ-plot and histogram of p-values

####
#QQplot
###

#load file with all hits - not just sign, ones
twas_sub_correct <- read.table("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", header=T, stringsAsFactors = F)

ggd.qqplot = function(pvector, main=NULL, ...) {
  o = -log10(sort(pvector,decreasing=F))
  e = -log10( 1:length(o)/length(o) )
  plot(e,o,pch=19,cex=1, main=main, ...,
       xlab=expression(Expected~~-log[10](italic(p))),
       ylab=expression(Observed~~-log[10](italic(p))),
       xlim=c(0,max(e)), ylim=c(0,max(o)))
  lines(e,e,col="red")
}

pvalues <- twas_sub_correct$TWAS.P

# Add a title
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_QQplot.png",width = 2000, height = 2000, units = "px", res=300)
ggd.qqplot(pvalues, "QQ-plot of TWAS p-values")
dev.off() 

###
# Histogram of p-values
###

library(ggplot2)

## HISTOGRAM OF P-VALUES
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_pValHist.png",width = 2000, height = 2000, units = "px", res=300)
hist(twas_sub_correct$TWAS.P,
     main = "Histogram of TWAS p-values",  
     xlab ="P-values", 
     ylab = "Frequency")
dev.off()

Show plots

MDD TWAS QQ-plot

MDD TWAS QQ-plot

As shown below in the QQ-plot, our p-values were smaller than expected, indicating the presence of multiple significant associations. Inflation is present, but this expected due to the polygenicity of Major Depression and the the correlation between predicted expression of genes.


MDD TWAS P-value histogram

MDD TWAS P-value histogram

As shown in the histogram of p-values, our p-values followed a normal distribution as evidenced in the bottom of the graph where a similar amount of p-values is present. Additionally, a peak in correspondence to very small p-values is present at the top of the graph, indicating the presence of signal for our alternative hypothesis.

Create heatmaps for shared and unique associations

# Given the high number of hits we identified (N=177), from 91 unique genes, a single heatmap representing all of such genes cannot be created. Therefore, we depicted our results in two heatmaps: 1) heatmap of genes differentially expressed across multiple SNP-weight sets & 2) heatmap of genes differentially across a single SNP-weight

rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)

#load data
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")
head(twas)

# Extract only certain columns
twas_sub <- twas[,c('ID','PANEL_clean_short','CHR','P0','P1','TWAS.Z', 'TWAS.P')]
str(twas_sub)
str(twas)

#filter for sign. gene IDs only 
sign_feat<-twas_sub[twas_sub$TWAS.P < 1.368572e-06,'ID']  #to get a vector with the gene IDs of the significant features
str(sign_feat)  #find 176 features as supposed to - genes are repeated though --> you need single gene IDs

sign_genes<-unique(sign_feat)  #to identify unique gene IDs
str(sign_genes)  #94 gene IDs as expected

twas_sub[order(twas_sub$ID), ]
sign_genes[order(sign_genes$ID), ]

twas_sub <- twas_sub[(twas_sub$ID %in% sign_genes$ID), ]
str(twas_sub)  #611 observations where each observation corresponds to an ID in the sign-feat vector

#prepare a vector of gene IDs which are duplicated
duplicates_df<-sign_feat[duplicated(sign_feat)] 
duplicates <- duplicates_df$ID

# The following code is necessary to depict results from the CMC DLPFC splicing panel 
# Since in RNA-seq splicing multiple transcripts of the same gene are generally tested and we can depict just one in the heatmap, we need to pick the most significant one. This is done below. 

### 
# RETAIN THE MOST SIGN. FEATURE FROM THE CMC BRAIN SPLICING RESULTS
###

twas_sub <- twas_sub[order(twas_sub$TWAS.P), ] #we order by p-value to make sure that the most significant 
#CMC DLPFC feature for a given gene is kept and that its duplicates (which are less sign.) are excluded
head(twas_sub)
tail(twas_sub)

library(dplyr)
twas_sub  <- twas_sub %>% distinct(ID, PANEL_clean_short, .keep_all = T)  #to get rid of rows which contain duplicates based on the ID and PANEL cols 
#in our case, this is just for duplicates in the cmc brain splicing weights with the same gene id

twas_sub_temp <- twas_sub[order(twas_sub$ID), ] #to check that it worked 
#there should be just one of the same gene id from brain seq splicing weights
#this is the case, with the mmost sign. one being kept. 
#NB 43 gene IDs from the same snp weight were gotten rid of in this df for a total of 568 features instead of 611 

####
# Heatmap of genes differentially expressed across multiple SNP-weight sets
####
###
# make a list of features significant in multiple tissues
###

#order files  
twas_sub2<-twas_sub[order(twas_sub$ID),] 
duplicates<-sort(duplicates, decreasing = FALSE)   

#filter the twas_sub datatable by the duplicates vector to obtain a dt with features expressed across diff. tissues only (i.e. no unique features)
twas_sub2<-twas_sub[(twas_sub$ID %in% duplicates), ] #283 obs.
str(twas_sub2)

# Sort the data.frame by CHR and P0 
twas_sub2<-twas_sub2[order(twas_sub2$CHR,twas_sub2$P0),]

# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor 
twas_sub2$ID<-factor(twas_sub2$ID, levels=unique(twas_sub2$ID))
str(twas_sub2) #there are 36 levels (i.e. 36 unique genes differentially expressed across multiple weights)

#create a vector with all TWAS.Z values
TWAS.Z <- twas_sub2$TWAS.Z

twas_sub2_unique<-twas_sub2[!duplicated(twas_sub2$ID),]
vline_1<-min(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))
vline_2<-max(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))

#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.shared.png",width = 3500, height = 1750, units = "px", res=300)

ggplot(data = twas_sub2, aes(x = ID, y = PANEL_clean_short)) +
  #genes as x axis, panel as y axis  
  theme_bw()    +    #saying that there will be grid lines
  geom_tile(aes(fill = TWAS.Z), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
  labs(title="Genes differentially expressed across multiple SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

#title and labels shown for the x and y axes
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#### 
# Heatmap of genes differentially expressed in one SNP-weight 
####

#create a dt with the duplicates removed (!), so that we obtain only unique features
twas_unique<-twas_sub[(!twas_sub$ID %in% duplicates), ]  
str(twas_unique)   #we get 285 features from differentially expressed genes uniquely differentially expressed in one tissue

# Sort the data.frame by CHR and P0
twas_unique<-twas_unique[order(twas_unique$CHR,twas_unique$P0),]  #it's important to order by both
#if you order by pos only, the chr11 stuff will come first!

# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor 
twas_unique$ID<-factor(twas_unique$ID, levels=unique(twas_unique$ID))
str(twas_unique)

#create a vector with all TWAS.Z values
TWAS.Z <- twas_unique$TWAS.Z

twas_unique_unique<-twas_unique[!duplicated(twas_unique$ID),]
vline_1<-min(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))
vline_2<-max(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))

#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.unique.png",width = 4650, height = 1750, units = "px", res=300)

ggplot(data = twas_unique, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +    
  geom_tile(aes(fill = TWAS.Z), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in single SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

Create heatmaps for tissue groups

# Heatmaps for groups of tissues were made to show the overlap across SNP-weight panels. 

rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)

###
# Load and prepare data
###

#load
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")

#subset columns needed 
twas_sub <- twas[,c('ID','PANEL', 'PANEL_clean_short', 'CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P')]
str(twas_sub)

#turn CHR and P0 into numerical variables 
twas_sub$CHR <- as.numeric(as.character(twas_sub$CHR))
twas_sub$P0 <- as.numeric(as.character(twas_sub$P0))
str(twas_sub)

###
# Keep only the most sign. feature from the CMC brain splicing results
###
twas_sub2 <- twas_sub[order(twas_sub$TWAS.P), ]
head(twas_sub2)
tail(twas_sub2)

library(dplyr)
twas_sub  <- twas_sub2 %>% distinct(ID, PANEL, .keep_all = T)


###
# Create df for groups of tissues
###

#order by CHR and P0 first
twas2 <- twas_sub[order(twas_sub$CHR,twas_sub$P0),]
twas_sub <- twas2


#create df
twas_brain.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Amygdala", "Brain_Anterior_cingulate_cortex_BA24", "Brain_Caudate_basal_ganglia", "Brain_Cerebellar_Hemisphere", "Brain_Cerebellum", "Brain_Cortex", "Brain_Frontal_Cortex_BA9", "Brain_Hippocampus", "Brain_Hypothalamus", "Brain_Nucleus_accumbens_basal_ganglia", "Brain_Putamen_basal_ganglia", "Brain_Substantia_nigra", "CMC.BRAIN.RNASEQ", "CMC.BRAIN.RNASEQ_SPLICING","PsychENCODE"), ]
twas_blood.df <- twas_sub[twas_sub$PANEL %in% c("Whole_Blood", "NTR.BLOOD.RNAARR", "YFS.BLOOD.RNAARR"), ]
twas_HPA.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary", "Adrenal_Gland"), ]
twas_HPT.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary","Thyroid"), ]

###
#filter for gene IDs significant in a given group of tissues only 
###

#get sign. gene ID per group of tissues
sign_feat_brain<-twas_brain.df[twas_brain.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
#101 features are sign. within brain snp weight sets
str(sign_feat_brain)
sign_feat_brain <- unique(sign_feat_brain)  #111 unique genes differentially expressed in brain snp weights


sign_feat_blood<-twas_blood.df[twas_blood.df$TWAS.P < 1.368572e-06,]$ID 
str(sign_feat_blood) #26
sign_feat_blood <- unique(sign_feat_blood)#23


sign_feat_HPA<-twas_HPA.df[twas_HPA.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
str(sign_feat_HPA) #28
sign_feat_HPA <- unique(sign_feat_HPA)  #22


sign_feat_HPT<-twas_HPT.df[twas_HPT.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
str(sign_feat_HPT) #41
sign_feat_HPT <- unique(sign_feat_HPT)  #34

#filter
twas_brain.df <- twas_brain.df[(twas_brain.df$ID %in% sign_feat_brain), ]
#320 obs.
twas_blood.df <- twas_blood.df[(twas_blood.df$ID %in% sign_feat_blood), ] #32
twas_HPA.df <- twas_HPA.df[(twas_HPA.df$ID %in% sign_feat_HPA), ] #32
twas_HPT.df <- twas_HPT.df[(twas_HPT.df$ID %in% sign_feat_HPT), ] #51 features with the gene ID within the vector sign feat...

#create vectors with the z scores of the features within specific tissues
TWAS.Z.brain <- twas_brain.df$TWAS.Z
TWAS.Z.blood <- twas_blood.df$TWAS.Z
TWAS.Z.HPA <- twas_HPA.df$TWAS.Z
TWAS.Z.HPT <- twas_HPT.df$TWAS.Z

# Make ID a factor for plotting
twas_brain.df$ID<-factor(twas_brain.df$ID, levels = unique(twas_brain.df$ID))
twas_blood.df$ID<-factor(twas_blood.df$ID, levels = unique(twas_blood.df$ID))
twas_HPA.df$ID<-factor(twas_HPA.df$ID, levels = unique(twas_HPA.df$ID))
twas_HPT.df$ID<-factor(twas_HPT.df$ID, levels = unique(twas_HPT.df$ID))

#####
# Heatmap for brain SNP-weight sets
#####

twas_brain.df_unique<-twas_brain.df[!duplicated(twas_brain.df$ID),]
vline_1<-min(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))
vline_2<-max(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))

#Plot brain SNP weights
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_brain.png",width = 5100, height = 1400, units = "px", res=300)

ggplot(data = twas_brain.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.brain), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.brain,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in brain SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for blood SNP-weight sets
#####

twas_blood.df_unique<-twas_blood.df[!duplicated(twas_blood.df$ID),]
vline_1<-min(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))
vline_2<-max(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))

#Plot blood findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_blood.png",width = 2400, height = 800, units = "px", res=300)

ggplot(data = twas_blood.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.blood), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.blood,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in blood SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for HPA axis SNP-weight sets
#####

twas_HPA.df_unique<-twas_HPA.df[!duplicated(twas_HPA.df$ID),]
vline_1<-min(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))

#Plot HPA axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPA.png",width = 2400, height = 800, units = "px", res=300)

ggplot(data = twas_HPA.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.HPA), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.HPA,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in HPA axis SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for HPT axis SNP-weight sets
#####

twas_HPT.df_unique<-twas_HPT.df[!duplicated(twas_HPT.df$ID),]
vline_1<-min(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))

#Plot HPT axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPT.png",width = 3000, height = 800, units = "px", res=300)
ggplot(data = twas_HPT.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.HPT), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.HPT,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in HPT axis SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

Show plots

MDD TWAS Heatmap Shared

MDD TWAS Heatmap Shared


MDD TWAS Heatmap Unique

MDD TWAS Heatmap Unique


MDD TWAS Heatmap Brain ***

MDD TWAS Heatmap Blood

MDD TWAS Heatmap Blood


MDD TWAS Heatmap HPA

MDD TWAS Heatmap HPA


MDD TWAS Heatmap HPT

MDD TWAS Heatmap HPT



3.5 Process colocalisation results

Colocalisation identified whether TWAS and GWAS associations result from the same or distinct causal SNP.

Organise results

###
# Create a table with colocalisation results for all significant features
###
rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

library(tibble)
twas_sign <- as_data_frame(twas_sign)
colnames(twas_sign)

twas_sign$Location<-paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1)   

#transform variables into numeric and then order by them
str(twas_sign)
as.numeric(as.character(twas_sign$CHR))
as.numeric(as.character(twas_sign$P0))
twas_sign_ordered <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]

col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4")
twas_sign_ordered <- twas_sign_ordered[, col_order]
twas_sign_ordered

###
#Create a couple of additional columns specifying whether the feature is colocalised or not
###

#to specify coloc pp4 > 0.8 (see gusev et al (2019) Nat Genet on epithelial ovarian cancer)
twas_sign_ordered$High_PP4_0.8 <- NULL
twas_sign_ordered$High_PP4_0.8 <- ifelse(twas_sign_ordered$COLOC.PP4 > 0.8, "Yes", "No")
sum(twas_sign_ordered$High_PP4_0.8 == "Yes")  #97 features present a PP4 greater than 0.8

#to specify coloc pp3 < 0.2 
twas_sign_ordered$Low_PP3_0.2 <- NULL
twas_sign_ordered$Low_PP3_0.2 <- ifelse(twas_sign_ordered$COLOC.PP3 < 0.2, "Yes", "No")
sum(twas_sign_ordered$Low_PP3_0.2 == "Yes") #140 features present a PP3 smaller than 0.2

#specify whether both conditions are satisfied (NB PP4 > 0.8 is much more of a stringent threshold)
twas_sign_ordered$Colocalised <- NULL
twas_sign_ordered$Colocalised <- ifelse(twas_sign_ordered$High_PP4_0.8 == "Yes" & twas_sign_ordered$Low_PP3_0.2 == "Yes", "Yes", "No")
sum(twas_sign_ordered$Colocalised == "Yes") #97 features are colocalised

#get the number of unique genes which were colocalised
colocalised_df <- twas_sign_ordered[twas_sign_ordered$Colocalised == "Yes", ] #as expected, dim = 97, 13
colocalised_vector <- colocalised_df$ID 
unique_genes_colocalised <- unique(colocalised_vector) #57 unique genes which were colocalised


###
# Clean and Save 
###
col_order2 <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4", "Low_PP3_0.2", "High_PP4_0.8", "Colocalised")
twas_sign_ordered <- twas_sign_ordered[, col_order2]
twas_sign_ordered

write.csv(twas_sign_ordered, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_colocalisation.csv", row.names = F)

q()
n

Show colocalisation table

MDD TWAS Colocalisation Results
Location ID PANEL_clean_short TWAS.Z TWAS.P COLOC.PP0 COLOC.PP1 COLOC.PP2 COLOC.PP3 COLOC.PP4 Low_PP3_0.2 High_PP4_0.8 Colocalised
chr1:8412457-8877702 RERE GTEx Whole Blood -5.095707 3.47e-07 0.000 0.001 0.000 0.006 0.993 Yes Yes Yes
chr1:8412457-8877702 RERE YFS Blood -5.310078 1.10e-07 0.000 0.001 0.000 0.006 0.993 Yes Yes Yes
chr1:8484705-8494898 RP5-1115A15.1 GTEx Thyroid -5.175240 2.28e-07 0.000 0.001 0.000 0.004 0.995 Yes Yes Yes
chr1:8484705-8494898 RP5-1115A15.1 GTEx Whole Blood -4.866386 1.14e-06 0.012 0.001 0.088 0.008 0.891 Yes Yes Yes
chr1:36884051-36884179 SNORA63 GTEx Nucleus accumbens 4.848870 1.24e-06 0.067 0.006 0.257 0.023 0.647 Yes No No
chr1:71861623-72748417 NEGR1 GTEx Caudate 5.780100 7.47e-09 0.000 0.000 0.284 0.036 0.681 Yes No No
chr1:71861623-72748417 NEGR1 GTEx Putamen 5.548510 2.88e-08 0.000 0.000 0.018 0.014 0.968 Yes Yes Yes
chr1:71861623-72748417 NEGR1 GTEx Whole Blood 8.760622 1.94e-18 0.000 0.000 0.000 0.007 0.993 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 GTEx Cerebellar Hemispher… -7.785520 6.94e-15 0.000 0.000 0.000 0.010 0.990 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 GTEx Cerebellum -7.708820 1.27e-14 0.000 0.000 0.000 0.006 0.994 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 PsychENCODE -7.742756 9.73e-15 0.000 0.000 0.000 0.007 0.993 Yes Yes Yes
chr1:175873898-175889649 RP11-318C24.2 GTEx Thyroid -5.027510 4.97e-07 0.004 0.007 0.012 0.018 0.959 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -4.958690 7.10e-07 0.000 0.008 0.000 0.021 0.971 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing 5.039850 4.66e-07 0.000 0.007 0.000 0.020 0.973 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -5.005960 5.56e-07 0.000 0.007 0.000 0.019 0.973 Yes Yes Yes
chr1:181452685-181775921 CACNA1E CMC DLPFC Splicing -4.989390 6.06e-07 0.000 0.151 0.000 0.420 0.429 No No No
chr1:197473878-197744623 DENND1B CMC DLPFC 4.848374 1.24e-06 0.000 0.001 0.000 0.011 0.988 Yes Yes Yes
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing -5.421950 5.90e-08 0.000 0.001 0.000 0.010 0.989 Yes Yes Yes
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing 5.018050 5.22e-07 0.000 0.001 0.000 0.010 0.989 Yes Yes Yes
chr2:58386377-58468515 FANCL CMC DLPFC -5.183180 2.18e-07 0.001 0.000 0.056 0.027 0.916 Yes Yes Yes
chr2:58386377-58468515 FANCL CMC DLPFC Splicing 4.897476 9.71e-07 0.000 0.001 0.007 0.104 0.888 Yes Yes Yes
chr2:197831741-198175897 ANKRD44 YFS Blood -5.690140 1.27e-08 0.062 0.023 0.168 0.061 0.686 Yes No No
chr2:198254508-198299815 SF3B1 GTEx Hypothalamus 5.214900 1.84e-07 0.071 0.015 0.319 0.068 0.526 Yes No No
chr3:44481261-44561226 ZNF445 CMC DLPFC -5.103280 3.34e-07 0.000 0.087 0.002 0.601 0.310 No No No
chr4:41937137-41962589 TMEM33 PsychENCODE 4.837418 1.32e-06 0.000 0.001 0.000 0.074 0.925 Yes Yes Yes
chr4:41983713-41988476 DCAF4L1 GTEx Thyroid -5.128000 2.93e-07 0.003 0.001 0.291 0.091 0.615 Yes No No
chr4:41990758-41991254 RP11-814H16.2 GTEx Cerebellar Hemispher… 5.009600 5.45e-07 0.005 0.000 0.561 0.055 0.378 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Amygdala -5.253400 1.49e-07 0.004 0.001 0.476 0.070 0.450 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx ACC -5.001690 5.68e-07 0.003 0.001 0.399 0.101 0.496 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Caudate -4.854800 1.21e-06 0.003 0.001 0.388 0.086 0.521 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Cortex -5.774530 7.72e-09 0.003 0.001 0.340 0.113 0.543 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Hypothalamus -5.085140 3.67e-07 0.000 0.000 0.016 0.039 0.944 Yes Yes Yes
chr4:41992489-42092474 SLC30A9 GTEx Nucleus accumbens -5.602700 2.11e-08 0.001 0.001 0.072 0.119 0.808 Yes Yes Yes
chr4:41992489-42092474 SLC30A9 PsychENCODE -5.259200 1.45e-07 0.000 0.000 0.000 0.026 0.974 Yes Yes Yes
chr5:87564712-87732502 TMEM161B-AS1 PsychENCODE 6.091010 1.12e-09 0.000 0.000 0.000 0.117 0.883 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Adrenal Gland 5.360090 8.32e-08 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Amygdala 6.118500 9.45e-10 0.000 0.000 0.004 0.058 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx ACC 6.445500 1.15e-10 0.000 0.000 0.000 0.056 0.944 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Caudate 6.282167 3.34e-10 0.000 0.000 0.000 0.062 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellar Hemispher… 6.011700 1.84e-09 0.000 0.000 0.000 0.062 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellum 6.053050 1.42e-09 0.000 0.000 0.000 0.052 0.948 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cortex 6.021420 1.73e-09 0.000 0.000 0.000 0.070 0.930 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Frontal Cortex 6.720000 1.82e-11 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Hypothalamus 5.875800 4.21e-09 0.000 0.000 0.000 0.060 0.940 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Nucleus accumbens 6.010490 1.85e-09 0.000 0.000 0.000 0.059 0.941 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Putamen 6.372050 1.87e-10 0.000 0.000 0.000 0.054 0.946 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Substantia nigra 6.057270 1.38e-09 0.000 0.000 0.009 0.054 0.937 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Pituitary 6.048500 1.46e-09 0.000 0.000 0.000 0.050 0.950 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Thyroid 5.889760 3.87e-09 0.000 0.000 0.000 0.079 0.920 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Whole Blood 5.526440 3.27e-08 0.000 0.000 0.000 0.048 0.952 Yes Yes Yes
chr5:87729709-87794514 CTC-498M16.4 GTEx Substantia nigra 5.403610 6.53e-08 0.000 0.000 0.391 0.037 0.572 Yes No No
chr5:87988462-87989789 CTC-467M3.3 GTEx ACC -5.813700 6.11e-09 0.000 0.000 0.631 0.053 0.317 Yes No No
chr5:87988462-87989789 CTC-467M3.3 GTEx Cerebellar Hemispher… -5.861000 4.60e-09 0.000 0.000 0.056 0.051 0.893 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 GTEx Cortex -6.510990 7.47e-11 0.000 0.000 0.091 0.040 0.870 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 GTEx Frontal Cortex -7.091600 1.33e-12 0.000 0.000 0.114 0.035 0.850 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 PsychENCODE -6.097890 1.07e-09 0.000 0.000 0.035 0.251 0.715 No No No
chr5:140024947-140027370 NDUFA2 CMC DLPFC 5.190020 2.10e-07 0.104 0.037 0.170 0.060 0.629 Yes No No
chr5:140201222-140203811 PCDHA5 GTEx Thyroid -5.402970 6.55e-08 0.055 0.028 0.090 0.046 0.781 Yes No No
chr5:140220907-140223351 PCDHA8 GTEx Cerebellar Hemispher… -4.980100 6.36e-07 0.001 0.032 0.001 0.051 0.915 Yes Yes Yes
chr6:26188921-26189323 HIST1H4D NTR Blood -4.987600 6.11e-07 0.000 0.000 0.309 0.059 0.632 Yes No No
chr6:26365386-26378540 BTN3A2 NTR Blood 5.326600 1.00e-07 0.000 0.000 0.000 0.562 0.438 No No No
chr6:26365387-26378546 BTN3A2 GTEx Cerebellar Hemispher… 5.188200 2.12e-07 0.000 0.000 0.000 0.505 0.495 No No No
chr6:26365387-26378546 BTN3A2 GTEx Hippocampus 4.963000 6.96e-07 0.000 0.000 0.000 0.506 0.494 No No No
chr6:26365387-26378546 BTN3A2 GTEx Pituitary 5.898930 3.66e-09 0.000 0.000 0.000 0.501 0.499 No No No
chr6:26365387-26378546 BTN3A2 GTEx Thyroid 5.481600 4.22e-08 0.000 0.000 0.000 0.507 0.492 No No No
chr6:26365387-26378546 BTN3A2 GTEx Whole Blood 5.086960 3.64e-07 0.000 0.000 0.000 0.543 0.457 No No No
chr6:26538633-26546482 HMGN4 GTEx Cerebellum 5.395400 6.84e-08 0.000 0.000 0.404 0.299 0.297 No No No
chr6:27215480-27224250 PRSS16 GTEx Cerebellar Hemispher… -4.891200 1.00e-06 0.000 0.000 0.096 0.322 0.582 No No No
chr6:27215480-27224250 PRSS16 GTEx Cerebellum -4.947900 7.50e-07 0.000 0.001 0.000 0.962 0.037 No No No
chr6:27215480-27224250 PRSS16 GTEx Frontal Cortex -5.045000 4.54e-07 0.000 0.000 0.366 0.056 0.577 Yes No No
chr6:27215480-27224250 PRSS16 GTEx Pituitary -5.916080 3.30e-09 0.000 0.000 0.044 0.035 0.921 Yes Yes Yes
chr6:27215480-27224250 PRSS16 GTEx Whole Blood -5.335920 9.51e-08 0.000 0.001 0.047 0.474 0.479 No No No
chr6:27325604-27339304 ZNF204P GTEx Adrenal Gland -5.032700 4.84e-07 0.000 0.000 0.547 0.211 0.242 No No No
chr6:27371789-27374743 RP1-153G14.4 GTEx Hippocampus 5.354000 8.60e-08 0.000 0.000 0.676 0.142 0.182 Yes No No
chr6:27418522-27440897 ZNF184 GTEx Caudate -6.325200 2.53e-10 0.000 0.000 0.231 0.114 0.655 Yes No No
chr6:27418522-27440897 ZNF184 GTEx Hypothalamus -4.952200 7.34e-07 0.000 0.000 0.174 0.185 0.641 Yes No No
chr6:27840926-27841289 HIST1H4L NTR Blood 4.870800 1.11e-06 0.000 0.000 0.001 0.162 0.838 Yes Yes Yes
chr6:28058932-28061442 ZSCAN12P1 PsychENCODE 6.268010 3.66e-10 0.000 0.000 0.056 0.626 0.318 No No No
chr6:28058932-28061442 ZSCAN12P1 GTEx Whole Blood -4.936930 7.94e-07 0.000 0.000 0.388 0.365 0.247 No No No
chr6:28083406-28084329 RP1-265C24.5 GTEx Hippocampus 5.532000 3.16e-08 0.000 0.000 0.009 0.143 0.847 Yes Yes Yes
chr6:28092338-28097860 ZSCAN16 YFS Blood -6.109000 1.00e-09 0.000 0.000 0.013 0.054 0.933 Yes Yes Yes
chr6:28192664-28201260 ZSCAN9 GTEx Cerebellum -5.307800 1.11e-07 0.000 0.000 0.238 0.147 0.615 Yes No No
chr6:28192664-28201260 ZSCAN9 GTEx Hippocampus -6.017000 1.77e-09 0.000 0.000 0.080 0.053 0.866 Yes Yes Yes
chr6:28192664-28201260 ZSCAN9 GTEx Pituitary -6.159020 7.32e-10 0.000 0.000 0.169 0.191 0.640 Yes No No
chr6:28227098-28228736 NKAPL PsychENCODE 5.002860 5.65e-07 0.000 0.000 0.627 0.332 0.041 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Adrenal Gland 5.094600 3.49e-07 0.000 0.000 0.031 0.146 0.822 Yes Yes Yes
chr6:28234788-28245974 RP5-874C20.3 GTEx Cerebellum 5.062800 4.13e-07 0.000 0.000 0.000 0.756 0.244 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Hippocampus 5.198000 2.01e-07 0.000 0.000 0.172 0.174 0.654 Yes No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Putamen 5.739000 9.52e-09 0.000 0.000 0.018 0.051 0.931 Yes Yes Yes
chr6:28234788-28245974 RP5-874C20.3 GTEx Thyroid 5.338400 9.38e-08 0.000 0.000 0.000 0.858 0.141 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Whole Blood 5.662330 1.49e-08 0.000 0.000 0.288 0.107 0.604 Yes No No
chr6:28249314-28270326 PGBD1 GTEx Cerebellar Hemispher… -6.313100 2.74e-10 0.000 0.000 0.032 0.017 0.950 Yes Yes Yes
chr6:28292470-28324048 ZSCAN31 GTEx Amygdala -5.084150 3.69e-07 0.000 0.000 0.381 0.404 0.214 No No No
chr6:28317691-28336947 ZKSCAN3 GTEx Amygdala 4.949900 7.43e-07 0.000 0.000 0.777 0.111 0.111 Yes No No
chr6:28317691-28336947 ZKSCAN3 GTEx Hippocampus 4.951000 7.37e-07 0.000 0.000 0.389 0.273 0.338 No No No
chr6:28317691-28336947 ZKSCAN3 GTEx Thyroid 6.093300 1.11e-09 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr6:28399707-28411279 ZSCAN23 GTEx Hypothalamus -5.777500 7.58e-09 0.000 0.000 0.062 0.179 0.758 Yes No No
chr6:28399707-28411279 ZSCAN23 GTEx Putamen -4.894000 9.90e-07 0.000 0.000 0.239 0.226 0.535 No No No
chr6:28399707-28411279 ZSCAN23 GTEx Pituitary -4.953290 7.30e-07 0.000 0.000 0.002 0.275 0.723 No No No
chr6:30644166-30655672 PPP1R18 GTEx Adrenal Gland 4.910200 9.10e-07 0.106 0.007 0.130 0.007 0.750 Yes No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 1.16e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 4.04e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 7.95e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 1.16e-07 0.010 0.001 0.012 0.000 0.976 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 1.49e-07 0.016 0.001 0.020 0.000 0.963 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 2.74e-08 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30881982-30894236 VARS2 GTEx Cortex 5.922000 3.18e-09 0.105 0.004 0.158 0.005 0.727 Yes No No
chr6:30881982-30894236 VARS2 GTEx Whole Blood 6.323130 2.56e-10 0.005 0.001 0.007 0.000 0.986 Yes Yes Yes
chr6:31255287-31256741 WASF5P GTEx Pituitary -5.156240 2.52e-07 0.000 0.046 0.000 0.091 0.862 Yes Yes Yes
chr6:31368479-31445283 HCP5 GTEx Thyroid 6.400800 1.55e-10 0.000 0.008 0.000 0.015 0.976 Yes Yes Yes
chr6:31462658-31478901 MICB GTEx Thyroid -5.557000 2.74e-08 0.000 0.047 0.000 0.094 0.859 Yes Yes Yes
chr6:31606805-31620482 BAG6 CMC DLPFC Splicing -5.580000 2.40e-08 0.008 0.408 0.006 0.319 0.259 No No No
chr6:31694815-31698357 DDAH2 GTEx Frontal Cortex 5.409500 6.32e-08 0.331 0.042 0.258 0.033 0.336 Yes No No
chr6:31694816-31698039 DDAH2 CMC DLPFC 5.344500 9.07e-08 0.000 0.051 0.000 0.039 0.909 Yes Yes Yes
chr6:99817347-99842082 COQ3 CMC DLPFC Splicing 5.146560 2.65e-07 0.324 0.015 0.598 0.029 0.034 Yes No No
chr6:105404922-105531207 LIN28B CMC DLPFC -5.232050 1.68e-07 0.000 0.001 0.000 0.008 0.990 Yes Yes Yes
chr6:105404923-105531207 LIN28B PsychENCODE -5.105689 3.30e-07 0.000 0.005 0.000 0.052 0.943 Yes Yes Yes
chr6:105584224-105617820 BVES-AS1 GTEx Amygdala -5.578300 2.43e-08 0.061 0.007 0.373 0.045 0.514 Yes No No
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 3.69e-08 0.000 0.001 0.003 0.009 0.987 Yes Yes Yes
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 7.01e-09 0.000 0.001 0.000 0.054 0.945 Yes Yes Yes
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 3.18e-08 0.000 0.001 0.000 0.008 0.991 Yes Yes Yes
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 7.72e-08 0.000 0.001 0.000 0.007 0.993 Yes Yes Yes
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 1.88e-08 0.090 0.040 0.062 0.027 0.780 Yes No No
chr8:52232136-52722005 PXDNL CMC DLPFC 5.887460 3.92e-09 0.090 0.019 0.318 0.065 0.508 Yes No No
chr8:61297147-61429354 RP11-163N6.2 GTEx Thyroid -5.336530 9.47e-08 0.084 0.162 0.118 0.228 0.408 No No No
chr9:126605315-126605965 PIGFP2 PsychENCODE -5.305600 1.12e-07 0.017 0.004 0.603 0.126 0.250 Yes No No
chr11:57067112-57092426 TNKS1BP1 GTEx Adrenal Gland 4.922610 8.54e-07 0.080 0.025 0.107 0.032 0.756 Yes No No
chr11:57405497-57420263 AP000662.4 GTEx Thyroid -4.980256 6.35e-07 0.000 0.136 0.000 0.255 0.610 No No No
chr11:57424488-57429340 CLP1 GTEx Whole Blood 5.195860 2.04e-07 0.001 0.008 0.002 0.015 0.974 Yes Yes Yes
chr11:61535973-61560274 TMEM258 PsychENCODE 5.021730 5.12e-07 0.000 0.049 0.000 0.041 0.910 Yes Yes Yes
chr11:113280318-113346111 DRD2 GTEx Frontal Cortex -5.073787 3.90e-07 0.366 0.032 0.515 0.045 0.042 Yes No No
chr13:53602875-53626196 OLFM4 CMC DLPFC 5.091290 3.56e-07 0.000 0.000 0.865 0.089 0.046 Yes No No
chr14:42057064-42074059 CTD-2298J14.2 GTEx Thyroid -5.678860 1.36e-08 0.000 0.000 0.000 0.022 0.978 Yes Yes Yes
chr14:42076773-42373752 LRFN5 GTEx Cerebellar Hemispher… 5.423400 5.85e-08 0.000 0.000 0.000 0.029 0.971 Yes Yes Yes
chr14:42076773-42373752 LRFN5 GTEx Cerebellum 5.597540 2.17e-08 0.000 0.000 0.000 0.041 0.959 Yes Yes Yes
chr14:59951161-59971429 JKAMP GTEx Thyroid -5.125100 2.97e-07 0.001 0.004 0.004 0.022 0.969 Yes Yes Yes
chr14:59971779-60043549 CCDC175 GTEx Thyroid -5.478850 4.28e-08 0.000 0.004 0.000 0.018 0.979 Yes Yes Yes
chr14:60062693-60337557 RTN1 CMC DLPFC Splicing -4.874920 1.09e-06 0.001 0.006 0.007 0.033 0.953 Yes Yes Yes
chr14:60062695-60337684 RTN1 GTEx Thyroid -5.348450 8.87e-08 0.000 0.003 0.000 0.016 0.981 Yes Yes Yes
chr14:64319682-64693151 SYNE2 NTR Blood 5.609528 2.03e-08 0.000 0.000 0.000 0.016 0.984 Yes Yes Yes
chr14:64550950-64770377 ESR2 GTEx Pituitary -5.982300 2.20e-09 0.000 0.000 0.113 0.026 0.860 Yes Yes Yes
chr14:64550950-64770377 ESR2 GTEx Whole Blood -5.655371 1.56e-08 0.000 0.000 0.000 0.014 0.986 Yes Yes Yes
chr14:75120140-75179818 AREL1 PsychENCODE -5.015110 5.30e-07 0.000 0.002 0.000 0.216 0.782 No No No
chr14:75319736-75330537 PROX2 GTEx Thyroid -5.758100 8.51e-09 0.000 0.000 0.017 0.020 0.962 Yes Yes Yes
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 6.31e-07 0.000 0.001 0.000 0.047 0.952 Yes Yes Yes
chr14:75348594-75370448 DLST PsychENCODE 5.089700 3.59e-07 0.000 0.000 0.000 0.023 0.977 Yes Yes Yes
chr14:75370656-75389188 RPS6KL1 CMC DLPFC Splicing -5.023810 5.07e-07 0.003 0.001 0.205 0.082 0.708 Yes No No
chr14:75370657-75390099 RPS6KL1 PsychENCODE -4.952550 7.32e-07 0.002 0.000 0.176 0.031 0.791 Yes No No
chr14:103878456-103879098 RP11-600F24.2 PsychENCODE 5.185660 2.15e-07 0.007 0.002 0.552 0.202 0.238 No No No
chr14:103985996-103989448 CKB YFS Blood 5.346000 8.99e-08 0.000 0.001 0.000 0.005 0.995 Yes Yes Yes
chr14:103995508-104003410 TRMT61A CMC DLPFC 5.051300 4.39e-07 0.001 0.004 0.004 0.030 0.961 Yes Yes Yes
chr14:103995521-104003410 TRMT61A GTEx Whole Blood 4.977593 6.44e-07 0.006 0.010 0.049 0.081 0.854 Yes Yes Yes
chr14:104019758-104028214 RP11-894P9.2 GTEx Thyroid -5.462560 4.69e-08 0.000 0.001 0.000 0.005 0.994 Yes Yes Yes
chr14:104153913-104154464 RP11-73M18.6 PsychENCODE 5.031320 4.87e-07 0.000 0.005 0.001 0.413 0.581 No No No
chr14:104160897-104161507 RP11-73M18.7 PsychENCODE 4.856130 1.20e-06 0.000 0.006 0.000 0.513 0.480 No No No
chr14:104162690-104163500 RP11-73M18.8 GTEx Amygdala 5.142000 2.72e-07 0.010 0.002 0.082 0.019 0.887 Yes Yes Yes
chr14:104177607-104179149 AL049840.1 GTEx Cerebellum 5.029540 4.92e-07 0.001 0.003 0.008 0.026 0.962 Yes Yes Yes
chr14:104177607-104179149 AL049840.1 GTEx Cortex 5.143620 2.69e-07 0.001 0.002 0.007 0.012 0.979 Yes Yes Yes
chr14:104179904-104180441 RP11-73M18.9 GTEx Cortex 4.977330 6.45e-07 0.000 0.002 0.001 0.013 0.984 Yes Yes Yes
chr14:104179904-104180586 RP11-73M18.9 PsychENCODE 4.830100 1.36e-06 0.001 0.005 0.049 0.425 0.520 No No No
chr16:72146056-72210777 PMFBP1 PsychENCODE -5.160620 2.46e-07 0.013 0.005 0.174 0.069 0.738 Yes No No
chr17:27400528-27507430 MYO18A GTEx Adrenal Gland -5.128570 2.92e-07 0.002 0.040 0.001 0.019 0.937 Yes Yes Yes
chr17:27401933-27405875 TIAF1 GTEx Adrenal Gland -5.361200 8.27e-08 0.016 0.111 0.008 0.055 0.810 Yes Yes Yes
chr17:65520597-65521538 CTD-2653B5.1 PsychENCODE 5.105730 3.30e-07 0.000 0.441 0.000 0.028 0.531 Yes No No
chr18:52385091-52562747 RAB27B PsychENCODE 5.012900 5.36e-07 0.000 0.015 0.000 0.328 0.657 No No No
chr18:52495707-52562747 RAB27B CMC DLPFC Splicing 4.843190 1.28e-06 0.000 0.016 0.000 0.038 0.945 Yes Yes Yes
chr20:47835831-47860614 DDX27 CMC DLPFC 4.836260 1.32e-06 0.003 0.067 0.001 0.029 0.900 Yes Yes Yes
chr22:41165634-41215403 SLC25A17 GTEx Nucleus accumbens 5.076990 3.83e-07 0.007 0.001 0.547 0.097 0.348 Yes No No
chr22:41165634-41215403 SLC25A17 GTEx Thyroid 4.896100 9.78e-07 0.000 0.008 0.020 0.671 0.301 No No No
chr22:41253088-41351450 XPNPEP3 GTEx Frontal Cortex 4.951000 7.38e-07 0.009 0.001 0.731 0.091 0.168 Yes No No
chr22:41258260-41363888 XPNPEP3 CMC DLPFC 5.110000 3.21e-07 0.000 0.004 0.005 0.358 0.632 No No No
chr22:41487790-41576081 EP300 GTEx Cerebellum 5.493900 3.93e-08 0.001 0.001 0.061 0.049 0.888 Yes Yes Yes
chr22:41487790-41576081 EP300 YFS Blood 5.059100 4.21e-07 0.000 0.012 0.000 0.955 0.033 No No No
chr22:41641614-41682216 RANGAP1 CMC DLPFC Splicing 5.240100 1.61e-07 0.010 0.000 0.814 0.028 0.147 Yes No No
chr22:41641615-41682255 RANGAP1 PsychENCODE -5.575273 2.47e-08 0.000 0.004 0.000 0.705 0.290 No No No
chr22:41697526-41756151 ZC3H7B GTEx Cerebellum 5.729100 1.01e-08 0.001 0.000 0.105 0.031 0.862 Yes Yes Yes


140 of the 176 significant features presented a low posterior probability of TWAS and GWAS associations resulting from distinct causal SNPs. This is a good index of colocalisation, but PP4, should also be considered due to the possibility of other models besides PP3 and PP4 to be the most probable. When considering features with high PP4 (> 0.8), 97 of the 140 features with low PP3 also presented a high probability of GWAS and TWAS associations resulting from the same causal SNP. Therefore, based on both criteria applied, we considered 97 features as colocalised.


3.6 Process conditional analysis results

Organise coloc results

# Read in the report files
library(data.table)
setwd("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional")

# Read in the clean TWAS results
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
twas_sign$PANEL_clean<-gsub(' $','',twas_sign$PANEL_clean)

# Read in all jointly significant associations
temp = list.files(pattern=glob2rx("*chr*.report"))
report<-do.call(rbind, lapply(temp, function(x) read.table(x, header=T,stringsAsFactors=F)))
report$JOINT.ID<-NA
report$MARGIN.ID<-NA
report$JOINT.N<-NA
report$MARGIN.N<-NA
report$loc<-gsub('.*loc_','',report$FILE)
joint_res<-NULL
margin_res<-NULL

# Insert names of jointly significant genes
for(i in unique(report$CHR)){
  joint_i<-read.table(paste0('test.cond.chr',i,'.joint_included.dat'), header=T,stringsAsFactors=F)
  margin_i<-read.table(paste0('test.cond.chr',i,'.joint_dropped.dat'), header=T,stringsAsFactors=F)
  
  joint_i$path<-gsub('/[^/]+$','',joint_i$FILE)
    joint_i$path<-gsub('/[^/]+$','',joint_i$path)
    joint_i$WGT<-NA
    for(j in 1:dim(joint_i)[1]){
      joint_i$WGT[j]<-gsub(paste0(joint_i$path[j],'/'),'',joint_i$FILE[j])
  }

  if(dim(margin_i)[1] > 0){
    margin_i$path<-gsub('/[^/]+$','',margin_i$FILE)
    margin_i$path<-gsub('/[^/]+$','',margin_i$path)
    margin_i$WGT<-NA
    for(j in 1:dim(margin_i)[1]){
      margin_i$WGT[j]<-gsub(paste0(margin_i$path[j],'/'),'',margin_i$FILE[j])
    }
  }

  temp = list.files(pattern=glob2rx(paste0("*chr",i,".loc*.genes")))

  for(k in 1:length(temp)){
    loc_k<-read.table(paste0('test.cond.chr',i,'.loc_',k,'.genes'), header=T, stringsAsFactors=F)
    
    loc_k$path<-gsub('/[^/]+$','',loc_k$FILE)
    loc_k$path<-gsub('/[^/]+$','',loc_k$path)
    loc_k$WGT<-NA
    for(j in 1:dim(loc_k)[1]){
      loc_k$WGT[j]<-gsub(paste0(loc_k$path[j],'/'),'',loc_k$FILE[j])
    }

    loc_k$P0<-NULL
    loc_k$P1<-NULL
    
    loc_k<-merge(loc_k, twas_sign[,c('P0','P1','WGT','PANEL_clean')], by='WGT')
    
    loc_k_joint<-loc_k[(loc_k$WGT %in% joint_i$WGT),]
    joint_res<-rbind(joint_res,loc_k_joint)
        
    if(dim(margin_i)[1] > 0){
      loc_k_margin<-loc_k[(loc_k$WGT %in% margin_i$WGT),]
      margin_res<-rbind(margin_res,loc_k_margin)
    } else {
      loc_k_margin<-data.frame(ID=NULL)
    }
    
    g_list<-NULL
    for(g in unique(loc_k_joint$ID)){
      g_list<-c(g_list,paste0(g, " (",paste(loc_k_joint$PANEL_clean[loc_k_joint$ID == g], collapse=', '),")"))
    }
    report[report$CHR == i & report$loc == k,]$JOINT.ID<-paste(g_list,collapse=', ')

    if(dim(loc_k_margin)[1] > 0){
      g_list<-NULL
      for(g in unique(loc_k_margin$ID)){
        g_list<-c(g_list,paste0(g, " (",paste(unique(loc_k_margin$PANEL_clean[loc_k_margin$ID == g]), collapse=', '),")"))
      }
      report[report$CHR == i & report$loc == k,]$MARGIN.ID<-paste(g_list,collapse=', ')
    } else {
      report[report$CHR == i & report$loc == k,]$MARGIN.ID<-'-'
    }
    
    report[report$CHR == i & report$loc == k,]$JOINT.N<-dim(loc_k_joint)[1]
    report[report$CHR == i & report$loc == k,]$MARGIN.N<-dim(loc_k_margin)[1]
  }
}

report$LOCUS<-paste0(report$CHR,':',report$P0,':',report$P1)
report$BP<-paste0(report$P0,'-',report$P1)
report$VAR.EXP<-paste0(report$VAR.EXP*100,'%')

report<-report[,c('CHR','P0','P1','BP','LOCUS',"JOINT.N",'MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP','JOINT.ID','MARGIN.ID')]

report<-report[order(report$CHR, report$P0),]

# Save full conditional results table
write.csv(report[,c("CHR","BP","JOINT.ID","MARGIN.ID","BEST.TWAS.P","BEST.SNP.P","VAR.EXP")],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_full.csv', row.names=F, quote=T)

# Save brief conditional results table
write.csv(report[,c('CHR','BP','JOINT.ID','MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP')],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_brief.csv', row.names=F, quote=T)

# Combine gene results for marginal and joint genes
joint_res$Type<-'Joint'
margin_res$Type<-'Marginal'

gene_res<-rbind(joint_res, margin_res)

# Check number of indepenent associations
dim(joint_res) # 50

# Check number of independent associations without genome-wide significant snp
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 25

# Check number of independent associations with genome-wide significant snp but an r2 with predicted expression <0.1
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1,]) # 2

# Check number of independent novel associations
dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 27

# Check number of novel associations
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8,]) # 68

gene_res$Novel<-'No'
gene_res$Novel[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8]<-'Yes'

gene_res$BP<-paste0(gene_res$P0,'-',gene_res$P1)
gene_res$BEST.GWAS.P<-2*pnorm(-abs(gene_res$BEST.GWAS.Z))

gene_res<-gene_res[order(gene_res$CHR, gene_res$P0),]

gene_res$Colocalised<-F
gene_res$Colocalised[gene_res$COLOC.PP4 >0.8]<-T

# Check number of independent novel associations which colocalise for joint genes
joint_res$Colocalised<-F
joint_res$Colocalised[joint_res$COLOC.PP4 >0.8]<-T

dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1 & joint_res$Colocalised == T) | (2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8 & joint_res$Colocalised == T),]) # 12

# Check number of novel associations which colocalise
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1 & gene_res$Colocalised == T) | (2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8 & gene_res$Colocalised == T),]) # 45

gene_res<-gene_res[,c('CHR','BP','P0','P1','ID','PANEL_clean','WGT','TWAS.Z','TWAS.P','BEST.GWAS.P','TOP.SNP.COR','Type','Novel','COLOC.PP3','COLOC.PP4','Colocalised')]

# Save table showing whether gene associations are novel
write.csv(gene_res,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv', row.names=F, quote=T)

Show full conditional analysis table

MDD TWAS Full Conditional Results
CHR BP Jointly sign. Features (SNP-weight set) Marginally sign. Features (SNP-weight set) Top TWAS p-value Top GWAS p-value Variance Explained
1 7413452-9875347 RERE (YFS Blood) RP5-1115A15.1 (GTEx Thyroid, GTEx Whole Blood), RERE (GTEx Whole Blood) 1.10e-07 3.18e-08 100%
1 35885799-37876701 SNORA63 (GTEx Nucleus accumbens) - 1.24e-06 6.27e-08 69.3%
1 71753372-73766162 NEGR1 (GTEx Whole Blood) NEGR1 (GTEx Caudate, GTEx Putamen), RPL31P12 (GTEx Cerebellar Hemisphere, GTEx Cerebellum, PsychENCODE) 1.94e-18 4.54e-15 97.8%
1 174891875-177103690 RFWD2 (CMC DLPFC Splicing) RFWD2 (CMC DLPFC Splicing), RP11-318C24.2 (GTEx Thyroid) 4.66e-07 2.30e-07 90%
1 180725304-182724241 CACNA1E (CMC DLPFC Splicing) - 6.06e-07 1.08e-07 64%
1 196478918-198741422 DENND1B (CMC DLPFC Splicing) DENND1B (CMC DLPFC Splicing, CMC DLPFC) 5.90e-08 3.11e-08 92.6%
2 57388379-59467945 FANCL (CMC DLPFC Splicing, CMC DLPFC) - 2.18e-07 4.68e-09 85%
2 196832647-199295649 ANKRD44 (YFS Blood) SF3B1 (GTEx Hypothalamus) 1.27e-08 3.52e-07 82.8%
3 43487406-45561063 ZNF445 (CMC DLPFC) - 3.34e-07 6.34e-08 74.7%
4 40937584-43090938 SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) SLC30A9 (GTEx Amygdala, GTEx ACC, GTEx Caudate, GTEx Hypothalamus, GTEx Nucleus accumbens, PsychENCODE), RP11-814H16.2 (GTEx Cerebellar Hemisphere), DCAF4L1 (GTEx Thyroid) 7.72e-09 3.59e-09 92.3%
5 86565927-88989352 TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) TMEM161B-AS1 (GTEx Adrenal Gland, GTEx Amygdala, GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cerebellum, GTEx Cortex, GTEx Frontal Cortex, GTEx Hypothalamus, GTEx Nucleus accumbens, GTEx Putamen, GTEx Substantia nigra, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), CTC-467M3.3 (GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cortex, PsychENCODE) 1.33e-12 1.07e-10 -2250%
5 139030460-141219083 PCDHA5 (GTEx Thyroid) PCDHA8 (GTEx Cerebellar Hemisphere), NDUFA2 (CMC DLPFC) 6.55e-08 1.37e-06 87.7%
6 25193720-29216321 ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) RP5-874C20.3 (GTEx Adrenal Gland, GTEx Cerebellum, GTEx Hippocampus, GTEx Putamen, GTEx Thyroid, GTEx Whole Blood), ZNF204P (GTEx Adrenal Gland), ZKSCAN3 (GTEx Amygdala, GTEx Hippocampus, GTEx Thyroid), ZSCAN31 (GTEx Amygdala), PGBD1 (GTEx Cerebellar Hemisphere), BTN3A2 (GTEx Cerebellar Hemisphere, GTEx Hippocampus, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), PRSS16 (GTEx Cerebellum, GTEx Frontal Cortex, GTEx Whole Blood), HMGN4 (GTEx Cerebellum), ZSCAN9 (GTEx Hippocampus, GTEx Pituitary), RP1-265C24.5 (GTEx Hippocampus), RP1-153G14.4 (GTEx Hippocampus), ZNF184 (GTEx Hypothalamus), ZSCAN23 (GTEx Putamen, GTEx Pituitary), HIST1H4D (NTR Blood), HIST1H4L (NTR Blood), NKAPL (PsychENCODE), ZSCAN12P1 (GTEx Whole Blood), ZSCAN16 (YFS Blood) 2.53e-10 1.34e-10 100%
6 30577966-32580366 BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) PPP1R18 (GTEx Adrenal Gland), FLOT1 (GTEx Cerebellum, CMC DLPFC Splicing, GTEx Pituitary, GTEx Thyroid), VARS2 (GTEx Cortex, GTEx Whole Blood), DDAH2 (GTEx Frontal Cortex, CMC DLPFC), WASF5P (GTEx Pituitary) 1.55e-10 3.95e-08 86%
6 98832858-100829135 COQ3 (CMC DLPFC Splicing) - 2.65e-07 9.09e-08 35.1%
6 104405706-106583999 BVES-AS1 (GTEx Amygdala) LIN28B (CMC DLPFC, PsychENCODE) 2.43e-08 9.50e-08 92.9%
7 11252396-13282905 TMEM106B (PsychENCODE) TMEM106B (GTEx Adrenal Gland, GTEx Whole Blood, YFS Blood) 7.01e-09 2.55e-08 100%
7 24021857-26019767 OSBPL3 (GTEx Pituitary) - 1.88e-08 6.49e-07 77.7%
8 51238261-53720740 PXDNL (CMC DLPFC) - 3.92e-09 1.34e-07 83.8%
8 60435234-62428932 RP11-163N6.2 (GTEx Thyroid) - 9.47e-08 5.25e-07 89.8%
9 125606617-127604411 PIGFP2 (PsychENCODE) - 1.12e-07 2.73e-08 63.8%
11 56092913-58422547 TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) AP000662.4 (GTEx Thyroid) 2.04e-07 1.47e-07 95.2%
11 60540194-62557903 TMEM258 (PsychENCODE) - 5.12e-07 4.26e-07 83.9%
11 112346414-114345882 DRD2 (GTEx Frontal Cortex) - 3.90e-07 4.90e-07 0.414%
13 52652520-54625616 OLFM4 (CMC DLPFC) - 3.56e-07 6.06e-19 29.9%
14 41077086-43073683 CTD-2298J14.2 (GTEx Thyroid) LRFN5 (GTEx Cerebellar Hemisphere, GTEx Cerebellum) 1.36e-08 2.57e-09 88.1%
14 58952573-61334943 CCDC175 (GTEx Thyroid) RTN1 (CMC DLPFC Splicing, GTEx Thyroid), JKAMP (GTEx Thyroid) 4.28e-08 2.18e-07 82.3%
14 63322572-65770213 ESR2 (GTEx Pituitary) SYNE2 (NTR Blood), ESR2 (GTEx Whole Blood) 2.20e-09 7.60e-10 80%
14 74120633-76388050 PROX2 (GTEx Thyroid) RPS6KL1 (CMC DLPFC Splicing, PsychENCODE), DLST (CMC DLPFC, PsychENCODE), AREL1 (PsychENCODE) 8.51e-09 6.71e-09 93.5%
14 102878783-105180229 RP11-894P9.2 (GTEx Thyroid) RP11-73M18.8 (GTEx Amygdala), AL049840.1 (GTEx Cerebellum, GTEx Cortex), RP11-73M18.9 (GTEx Cortex, PsychENCODE), TRMT61A (CMC DLPFC, GTEx Whole Blood), RP11-600F24.2 (PsychENCODE), RP11-73M18.7 (PsychENCODE), RP11-73M18.6 (PsychENCODE), CKB (YFS Blood) 4.69e-08 3.05e-09 84.6%
16 71147494-73210261 PMFBP1 (PsychENCODE) - 2.46e-07 3.35e-08 76.3%
17 26406423-28478661 TIAF1 (GTEx Adrenal Gland) MYO18A (GTEx Adrenal Gland) 8.27e-08 8.51e-09 58.5%
17 64524284-66521332 CTD-2653B5.1 (PsychENCODE) - 3.30e-07 5.39e-06 25.8%
18 51385406-53561919 RAB27B (PsychENCODE) RAB27B (CMC DLPFC Splicing) 5.36e-07 3.62e-11 14.6%
20 46838019-48853908 DDX27 (CMC DLPFC) - 1.32e-06 3.54e-06 91%
22 40218102-42697216 ZC3H7B (GTEx Cerebellum) EP300 (GTEx Cerebellum, YFS Blood), XPNPEP3 (GTEx Frontal Cortex, CMC DLPFC), SLC25A17 (GTEx Nucleus accumbens, GTEx Thyroid), RANGAP1 (CMC DLPFC Splicing, PsychENCODE) 1.01e-08 7.56e-09 95.5%

Show brief conditional analysis table

MDD TWAS Brief Conditional Results
CHR BP Jointly sign. Features (SNP-weight set) N Marginal Top TWAS p-value Top GWAS p-value Variance Explained
1 7413452-9875347 RERE (YFS Blood) 3 1.10e-07 3.18e-08 100%
1 35885799-37876701 SNORA63 (GTEx Nucleus accumbens) 0 1.24e-06 6.27e-08 69.3%
1 71753372-73766162 NEGR1 (GTEx Whole Blood) 5 1.94e-18 4.54e-15 97.8%
1 174891875-177103690 RFWD2 (CMC DLPFC Splicing) 3 4.66e-07 2.30e-07 90%
1 180725304-182724241 CACNA1E (CMC DLPFC Splicing) 0 6.06e-07 1.08e-07 64%
1 196478918-198741422 DENND1B (CMC DLPFC Splicing) 2 5.90e-08 3.11e-08 92.6%
2 57388379-59467945 FANCL (CMC DLPFC Splicing, CMC DLPFC) 0 2.18e-07 4.68e-09 85%
2 196832647-199295649 ANKRD44 (YFS Blood) 1 1.27e-08 3.52e-07 82.8%
3 43487406-45561063 ZNF445 (CMC DLPFC) 0 3.34e-07 6.34e-08 74.7%
4 40937584-43090938 SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) 8 7.72e-09 3.59e-09 92.3%
5 86565927-88989352 TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) 18 1.33e-12 1.07e-10 -2250%
5 139030460-141219083 PCDHA5 (GTEx Thyroid) 2 6.55e-08 1.37e-06 87.7%
6 25193720-29216321 ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) 33 2.53e-10 1.34e-10 100%
6 30577966-32580366 BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) 12 1.55e-10 3.95e-08 86%
6 98832858-100829135 COQ3 (CMC DLPFC Splicing) 0 2.65e-07 9.09e-08 35.1%
6 104405706-106583999 BVES-AS1 (GTEx Amygdala) 2 2.43e-08 9.50e-08 92.9%
7 11252396-13282905 TMEM106B (PsychENCODE) 3 7.01e-09 2.55e-08 100%
7 24021857-26019767 OSBPL3 (GTEx Pituitary) 0 1.88e-08 6.49e-07 77.7%
8 51238261-53720740 PXDNL (CMC DLPFC) 0 3.92e-09 1.34e-07 83.8%
8 60435234-62428932 RP11-163N6.2 (GTEx Thyroid) 0 9.47e-08 5.25e-07 89.8%
9 125606617-127604411 PIGFP2 (PsychENCODE) 0 1.12e-07 2.73e-08 63.8%
11 56092913-58422547 TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) 1 2.04e-07 1.47e-07 95.2%
11 60540194-62557903 TMEM258 (PsychENCODE) 0 5.12e-07 4.26e-07 83.9%
11 112346414-114345882 DRD2 (GTEx Frontal Cortex) 0 3.90e-07 4.90e-07 0.414%
13 52652520-54625616 OLFM4 (CMC DLPFC) 0 3.56e-07 6.06e-19 29.9%
14 41077086-43073683 CTD-2298J14.2 (GTEx Thyroid) 2 1.36e-08 2.57e-09 88.1%
14 58952573-61334943 CCDC175 (GTEx Thyroid) 3 4.28e-08 2.18e-07 82.3%
14 63322572-65770213 ESR2 (GTEx Pituitary) 2 2.20e-09 7.60e-10 80%
14 74120633-76388050 PROX2 (GTEx Thyroid) 5 8.51e-09 6.71e-09 93.5%
14 102878783-105180229 RP11-894P9.2 (GTEx Thyroid) 11 4.69e-08 3.05e-09 84.6%
16 71147494-73210261 PMFBP1 (PsychENCODE) 0 2.46e-07 3.35e-08 76.3%
17 26406423-28478661 TIAF1 (GTEx Adrenal Gland) 1 8.27e-08 8.51e-09 58.5%
17 64524284-66521332 CTD-2653B5.1 (PsychENCODE) 0 3.30e-07 5.39e-06 25.8%
18 51385406-53561919 RAB27B (PsychENCODE) 1 5.36e-07 3.62e-11 14.6%
20 46838019-48853908 DDX27 (CMC DLPFC) 0 1.32e-06 3.54e-06 91%
22 40218102-42697216 ZC3H7B (GTEx Cerebellum) 8 1.01e-08 7.56e-09 95.5%

3.7 Novel findings

Show novelty table

MDD TWAS Results Novelty compared to GWAS
CHR BP ID PANEL WGT TWAS.P BEST.GWAS.P TOP.SNP.COR TYPE NOVEL
1 8412457-8877702 RERE YFS Blood YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat 1.10e-07 3.59e-08 -0.97 Joint No
1 8412457-8877702 RERE GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat 3.47e-07 3.59e-08 -0.98 Marginal No
1 8484705-8494898 RP5-1115A15.1 GTEx Thyroid Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat 2.28e-07 3.59e-08 -0.94 Marginal No
1 8484705-8494898 RP5-1115A15.1 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat 1.14e-06 3.59e-08 -0.75 Marginal No
1 36884051-36884179 SNORA63 GTEx Nucleus accumbens Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat 1.24e-06 7.45e-08 0.64 Joint Yes
1 71861623-72748417 NEGR1 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat 1.94e-18 5.72e-15 -0.82 Joint No
1 71861623-72748417 NEGR1 GTEx Caudate Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat 7.47e-09 5.72e-15 -0.69 Marginal No
1 71861623-72748417 NEGR1 GTEx Putamen Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat 2.88e-08 5.72e-15 -0.82 Marginal No
1 72767155-72767512 RPL31P12 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat 6.94e-15 5.72e-15 0.99 Marginal No
1 72767155-72767512 RPL31P12 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat 1.27e-14 5.72e-15 1.00 Marginal No
1 72767155-72767512 RPL31P12 PsychENCODE PEC_TWAS_weights/ENSG00000227207.wgt.RDat 9.73e-15 4.54e-15 0.99 Marginal No
1 175873898-175889649 RP11-318C24.2 GTEx Thyroid Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat 4.97e-07 2.34e-07 0.88 Marginal Yes
1 175913966-176176370 RFWD2 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat 4.66e-07 2.34e-07 -0.86 Joint Yes
1 175913966-176176370 RFWD2 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat 7.10e-07 2.34e-07 0.93 Marginal Yes
1 175913966-176176370 RFWD2 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat 5.56e-07 2.34e-07 0.84 Marginal Yes
1 181452685-181775921 CACNA1E CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat 6.06e-07 1.10e-07 -0.52 Joint Yes
1 197473878-197744623 DENND1B CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat 5.90e-08 8.80e-08 0.90 Joint Yes
1 197473878-197744623 DENND1B CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat 5.22e-07 8.80e-08 -0.89 Marginal Yes
1 197473878-197744623 DENND1B CMC DLPFC CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat 1.24e-06 8.80e-08 -0.78 Marginal Yes
2 58386377-58468515 FANCL CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat 9.71e-07 4.63e-09 0.52 Joint No
2 58386377-58468515 FANCL CMC DLPFC CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat 2.18e-07 4.63e-09 -0.64 Joint No
2 197831741-198175897 ANKRD44 YFS Blood YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat 1.27e-08 7.81e-07 -0.60 Joint Yes
2 198254508-198299815 SF3B1 GTEx Hypothalamus Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat 1.84e-07 4.90e-07 0.81 Marginal Yes
3 44481261-44561226 ZNF445 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat 3.34e-07 9.82e-08 -0.66 Joint Yes
4 41937137-41962589 TMEM33 PsychENCODE PEC_TWAS_weights/ENSG00000109133.wgt.RDat 1.32e-06 2.27e-08 -0.42 Joint No
4 41983713-41988476 DCAF4L1 GTEx Thyroid Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat 2.93e-07 3.64e-09 0.64 Marginal No
4 41990758-41991254 RP11-814H16.2 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat 5.45e-07 3.64e-09 -0.74 Marginal No
4 41992489-42092474 SLC30A9 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat 7.72e-09 3.64e-09 0.73 Joint No
4 41992489-42092474 SLC30A9 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat 1.49e-07 3.64e-09 0.79 Marginal No
4 41992489-42092474 SLC30A9 GTEx ACC Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat 5.68e-07 3.64e-09 0.81 Marginal No
4 41992489-42092474 SLC30A9 GTEx Caudate Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat 1.21e-06 3.64e-09 0.78 Marginal No
4 41992489-42092474 SLC30A9 GTEx Hypothalamus Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat 3.67e-07 3.64e-09 0.83 Marginal No
4 41992489-42092474 SLC30A9 GTEx Nucleus accumbens Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat 2.11e-08 3.64e-09 0.82 Marginal No
4 41992489-42092474 SLC30A9 PsychENCODE PEC_TWAS_weights/ENSG00000014824.wgt.RDat 1.45e-07 2.27e-08 0.76 Marginal No
5 87564712-87732502 TMEM161B-AS1 PsychENCODE PEC_TWAS_weights/ENSG00000247828.wgt.RDat 1.12e-09 1.88e-10 0.57 Joint No
5 87564888-87732502 TMEM161B-AS1 GTEx Caudate Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat 3.34e-10 1.66e-10 0.67 Joint No
5 87564888-87732502 TMEM161B-AS1 GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat 8.32e-08 1.66e-10 0.52 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat 9.45e-10 1.66e-10 0.58 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx ACC Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat 1.15e-10 1.66e-10 0.69 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat 1.84e-09 1.66e-10 0.61 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat 1.42e-09 1.66e-10 0.58 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat 1.73e-09 1.66e-10 0.61 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat 1.82e-11 1.66e-10 0.70 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Hypothalamus Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat 4.21e-09 1.66e-10 0.57 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Nucleus accumbens Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat 1.85e-09 1.66e-10 0.61 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Putamen Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat 1.87e-10 1.66e-10 0.65 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Substantia nigra Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat 1.38e-09 1.66e-10 0.60 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Pituitary Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat 1.46e-09 1.66e-10 0.58 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Thyroid Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat 3.87e-09 1.66e-10 0.61 Marginal No
5 87564888-87732502 TMEM161B-AS1 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat 3.27e-08 1.66e-10 0.53 Marginal No
5 87729709-87794514 CTC-498M16.4 GTEx Substantia nigra Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat 6.53e-08 1.66e-10 0.82 Joint No
5 87988462-87989789 CTC-467M3.3 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat 1.33e-12 1.66e-10 -0.45 Joint No
5 87988462-87989789 CTC-467M3.3 GTEx ACC Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat 6.11e-09 1.66e-10 -0.67 Marginal No
5 87988462-87989789 CTC-467M3.3 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat 4.60e-09 1.66e-10 -0.30 Marginal Yes
5 87988462-87989789 CTC-467M3.3 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat 7.47e-11 1.66e-10 -0.41 Marginal No
5 87988462-87989789 CTC-467M3.3 PsychENCODE PEC_TWAS_weights/ENSG00000250377.wgt.RDat 1.07e-09 1.88e-10 -0.12 Marginal Yes
5 140024947-140027370 NDUFA2 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat 2.10e-07 1.37e-06 -0.86 Marginal Yes
5 140201222-140203811 PCDHA5 GTEx Thyroid Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat 6.55e-08 1.37e-06 0.66 Joint Yes
5 140220907-140223351 PCDHA8 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat 6.36e-07 1.37e-06 0.86 Marginal Yes
6 26188921-26189323 HIST1H4D NTR Blood NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat 6.11e-07 2.15e-10 0.42 Marginal No
6 26365386-26378540 BTN3A2 NTR Blood NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat 1.00e-07 2.15e-10 -0.61 Joint No
6 26365387-26378546 BTN3A2 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat 2.12e-07 2.15e-10 -0.61 Marginal No
6 26365387-26378546 BTN3A2 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat 6.96e-07 2.15e-10 -0.62 Marginal No
6 26365387-26378546 BTN3A2 GTEx Pituitary Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat 3.66e-09 2.15e-10 -0.64 Marginal No
6 26365387-26378546 BTN3A2 GTEx Thyroid Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat 4.22e-08 2.15e-10 -0.61 Marginal No
6 26365387-26378546 BTN3A2 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat 3.64e-07 2.15e-10 -0.61 Marginal No
6 26538633-26546482 HMGN4 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat 6.84e-08 2.15e-10 -0.32 Marginal No
6 27215480-27224250 PRSS16 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat 1.00e-06 2.79e-10 0.56 Joint No
6 27215480-27224250 PRSS16 GTEx Pituitary Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat 3.30e-09 2.79e-10 0.30 Joint Yes
6 27215480-27224250 PRSS16 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat 7.50e-07 2.79e-10 0.55 Marginal No
6 27215480-27224250 PRSS16 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat 4.54e-07 2.79e-10 0.48 Marginal No
6 27215480-27224250 PRSS16 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat 9.51e-08 2.79e-10 0.47 Marginal No
6 27325604-27339304 ZNF204P GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat 4.84e-07 2.79e-10 0.60 Marginal No
6 27371789-27374743 RP1-153G14.4 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat 8.60e-08 2.79e-10 -0.28 Marginal Yes
6 27418522-27440897 ZNF184 GTEx Caudate Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat 2.53e-10 2.79e-10 0.53 Joint No
6 27418522-27440897 ZNF184 GTEx Hypothalamus Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat 7.34e-07 2.79e-10 0.66 Marginal No
6 27840926-27841289 HIST1H4L NTR Blood NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat 1.11e-06 1.66e-10 -0.78 Marginal No
6 28058932-28061442 ZSCAN12P1 PsychENCODE PEC_TWAS_weights/ENSG00000219891.wgt.RDat 3.66e-10 1.36e-10 -0.49 Joint No
6 28058932-28061442 ZSCAN12P1 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat 7.94e-07 1.66e-10 0.64 Marginal No
6 28083406-28084329 RP1-265C24.5 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat 3.16e-08 1.66e-10 -0.73 Marginal No
6 28092338-28097860 ZSCAN16 YFS Blood YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat 1.00e-09 1.66e-10 0.80 Marginal No
6 28192664-28201260 ZSCAN9 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat 1.11e-07 1.66e-10 0.73 Joint No
6 28192664-28201260 ZSCAN9 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat 1.77e-09 1.66e-10 0.65 Marginal No
6 28192664-28201260 ZSCAN9 GTEx Pituitary Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat 7.32e-10 1.66e-10 0.70 Marginal No
6 28227098-28228736 NKAPL PsychENCODE PEC_TWAS_weights/ENSG00000189134.wgt.RDat 5.65e-07 1.36e-10 -0.45 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat 3.49e-07 1.66e-10 -0.72 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat 4.13e-07 1.66e-10 -0.80 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat 2.01e-07 1.66e-10 -0.69 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Putamen Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat 9.52e-09 1.66e-10 -0.84 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Thyroid Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat 9.38e-08 1.66e-10 -0.82 Marginal No
6 28234788-28245974 RP5-874C20.3 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat 1.49e-08 1.66e-10 -0.77 Marginal No
6 28249314-28270326 PGBD1 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat 2.74e-10 1.66e-10 0.99 Marginal No
6 28292470-28324048 ZSCAN31 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat 3.69e-07 1.66e-10 0.57 Marginal No
6 28317691-28336947 ZKSCAN3 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat 7.43e-07 1.66e-10 -0.51 Marginal No
6 28317691-28336947 ZKSCAN3 GTEx Hippocampus Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat 7.37e-07 1.66e-10 -0.74 Marginal No
6 28317691-28336947 ZKSCAN3 GTEx Thyroid Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat 1.11e-09 1.66e-10 -0.78 Marginal No
6 28399707-28411279 ZSCAN23 GTEx Hypothalamus Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat 7.58e-09 1.66e-10 0.74 Joint No
6 28399707-28411279 ZSCAN23 GTEx Putamen Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat 9.90e-07 1.66e-10 0.70 Marginal No
6 28399707-28411279 ZSCAN23 GTEx Pituitary Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat 7.30e-07 1.66e-10 0.72 Marginal No
6 30644166-30655672 PPP1R18 GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat 9.10e-07 1.16e-07 -0.02 Marginal Yes
6 30695485-30710682 FLOT1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat 1.16e-07 1.16e-07 0.43 Marginal Yes
6 30695485-30710682 FLOT1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat 4.04e-07 1.16e-07 0.43 Marginal Yes
6 30695485-30710682 FLOT1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat 7.95e-07 1.16e-07 -0.40 Marginal Yes
6 30695486-30710510 FLOT1 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat 1.16e-07 1.16e-07 0.42 Marginal Yes
6 30695486-30710510 FLOT1 GTEx Pituitary Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat 1.49e-07 1.16e-07 0.41 Marginal Yes
6 30695486-30710510 FLOT1 GTEx Thyroid Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat 2.74e-08 1.16e-07 0.44 Marginal Yes
6 30881982-30894236 VARS2 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat 3.18e-09 1.16e-07 -0.66 Marginal Yes
6 30881982-30894236 VARS2 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat 2.56e-10 1.16e-07 -0.60 Marginal Yes
6 31255287-31256741 WASF5P GTEx Pituitary Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat 2.52e-07 1.16e-07 0.54 Marginal Yes
6 31368479-31445283 HCP5 GTEx Thyroid Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat 1.55e-10 1.16e-07 -0.58 Joint Yes
6 31462658-31478901 MICB GTEx Thyroid Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat 2.74e-08 1.16e-07 0.20 Joint Yes
6 31606805-31620482 BAG6 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat 2.40e-08 5.44e-07 0.40 Joint Yes
6 31694815-31698357 DDAH2 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat 6.32e-08 5.44e-07 -0.30 Marginal Yes
6 31694816-31698039 DDAH2 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat 9.07e-08 5.44e-07 -0.46 Marginal Yes
6 99817347-99842082 COQ3 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat 2.65e-07 1.70e-07 0.22 Joint Yes
6 105404922-105531207 LIN28B CMC DLPFC CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat 1.68e-07 1.16e-07 0.97 Marginal Yes
6 105404923-105531207 LIN28B PsychENCODE PEC_TWAS_weights/ENSG00000187772.wgt.RDat 3.30e-07 9.29e-08 0.73 Marginal Yes
6 105584224-105617820 BVES-AS1 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat 2.43e-08 1.16e-07 0.81 Joint Yes
7 12250867-12282993 TMEM106B PsychENCODE PEC_TWAS_weights/ENSG00000106460.wgt.RDat 7.01e-09 5.33e-08 -0.95 Joint Yes
7 12250867-12282993 TMEM106B GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat 3.69e-08 5.96e-08 0.98 Marginal Yes
7 12250867-12282993 TMEM106B GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat 3.18e-08 5.96e-08 0.82 Marginal Yes
7 12250867-12276886 TMEM106B YFS Blood YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat 7.72e-08 5.96e-08 0.95 Marginal Yes
7 24836158-25021253 OSBPL3 GTEx Pituitary Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat 1.88e-08 7.05e-07 -0.53 Joint Yes
8 52232136-52722005 PXDNL CMC DLPFC CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat 3.92e-09 1.52e-07 0.61 Joint Yes
8 61297147-61429354 RP11-163N6.2 GTEx Thyroid Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat 9.47e-08 5.17e-07 -0.74 Joint Yes
9 126605315-126605965 PIGFP2 PsychENCODE PEC_TWAS_weights/ENSG00000234881.wgt.RDat 1.12e-07 2.70e-08 -0.50 Joint No
11 57067112-57092426 TNKS1BP1 GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat 8.54e-07 6.70e-07 0.49 Joint Yes
11 57405497-57420263 AP000662.4 GTEx Thyroid Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat 6.35e-07 6.70e-07 -0.62 Marginal Yes
11 57424488-57429340 CLP1 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat 2.04e-07 6.70e-07 0.88 Joint Yes
11 61535973-61560274 TMEM258 PsychENCODE PEC_TWAS_weights/ENSG00000134825.wgt.RDat 5.12e-07 4.19e-07 0.73 Joint Yes
11 113280318-113346111 DRD2 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat 3.90e-07 4.90e-07 0.00 Joint Yes
13 53602875-53626196 OLFM4 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat 3.56e-07 6.11e-19 0.42 Joint No
14 42057064-42074059 CTD-2298J14.2 GTEx Thyroid Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat 1.36e-08 2.85e-09 -0.87 Joint No
14 42076773-42373752 LRFN5 GTEx Cerebellar Hemisphere Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat 5.85e-08 2.85e-09 0.78 Marginal No
14 42076773-42373752 LRFN5 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat 2.17e-08 2.85e-09 0.89 Marginal No
14 59951161-59971429 JKAMP GTEx Thyroid Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat 2.97e-07 3.77e-07 -0.62 Marginal Yes
14 59971779-60043549 CCDC175 GTEx Thyroid Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat 4.28e-08 3.77e-07 -0.64 Joint Yes
14 60062693-60337557 RTN1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat 1.09e-06 3.77e-07 -0.85 Marginal Yes
14 60062695-60337684 RTN1 GTEx Thyroid Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat 8.87e-08 3.77e-07 -0.77 Marginal Yes
14 64319682-64693151 SYNE2 NTR Blood NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat 2.03e-08 7.75e-10 -0.94 Marginal No
14 64550950-64770377 ESR2 GTEx Pituitary Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat 2.20e-09 7.75e-10 0.70 Joint No
14 64550950-64770377 ESR2 GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat 1.56e-08 7.75e-10 0.91 Marginal No
14 75120140-75179818 AREL1 PsychENCODE PEC_TWAS_weights/ENSG00000119682.wgt.RDat 5.30e-07 6.63e-09 -0.77 Marginal No
14 75319736-75330537 PROX2 GTEx Thyroid Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat 8.51e-09 1.13e-08 -0.89 Joint No
14 75348593-75370450 DLST CMC DLPFC CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat 6.31e-07 1.13e-08 0.89 Marginal No
14 75348594-75370448 DLST PsychENCODE PEC_TWAS_weights/ENSG00000119689.wgt.RDat 3.59e-07 6.63e-09 0.80 Marginal No
14 75370656-75389188 RPS6KL1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat 5.07e-07 1.13e-08 -0.57 Marginal No
14 75370657-75390099 RPS6KL1 PsychENCODE PEC_TWAS_weights/ENSG00000198208.wgt.RDat 7.32e-07 6.63e-09 -0.87 Marginal No
14 103878456-103879098 RP11-600F24.2 PsychENCODE PEC_TWAS_weights/ENSG00000244691.wgt.RDat 2.15e-07 3.03e-09 -0.64 Marginal No
14 103985996-103989448 CKB YFS Blood YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat 8.99e-08 7.87e-08 -0.97 Marginal Yes
14 103995508-104003410 TRMT61A CMC DLPFC CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat 4.39e-07 7.87e-08 -0.57 Marginal Yes
14 103995521-104003410 TRMT61A GTEx Whole Blood Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat 6.44e-07 7.87e-08 -0.64 Marginal Yes
14 104019758-104028214 RP11-894P9.2 GTEx Thyroid Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat 4.69e-08 7.87e-08 0.94 Joint Yes
14 104153913-104154464 RP11-73M18.6 PsychENCODE PEC_TWAS_weights/ENSG00000270108.wgt.RDat 4.87e-07 3.03e-09 -0.63 Marginal No
14 104160897-104161507 RP11-73M18.7 PsychENCODE PEC_TWAS_weights/ENSG00000269940.wgt.RDat 1.20e-06 3.03e-09 -0.65 Marginal No
14 104162690-104163500 RP11-73M18.8 GTEx Amygdala Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat 2.72e-07 7.87e-08 -0.70 Marginal Yes
14 104177607-104179149 AL049840.1 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat 4.92e-07 7.87e-08 -0.60 Marginal Yes
14 104177607-104179149 AL049840.1 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat 2.69e-07 7.87e-08 -0.72 Marginal Yes
14 104179904-104180441 RP11-73M18.9 GTEx Cortex Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat 6.45e-07 7.87e-08 -0.69 Marginal Yes
14 104179904-104180586 RP11-73M18.9 PsychENCODE PEC_TWAS_weights/ENSG00000269963.wgt.RDat 1.36e-06 3.03e-09 -0.62 Marginal No
16 72146056-72210777 PMFBP1 PsychENCODE PEC_TWAS_weights/ENSG00000118557.wgt.RDat 2.46e-07 4.76e-08 -0.70 Joint No
17 27400528-27507430 MYO18A GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat 2.92e-07 4.19e-07 -0.62 Marginal Yes
17 27401933-27405875 TIAF1 GTEx Adrenal Gland Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat 8.27e-08 4.19e-07 -0.46 Joint Yes
17 65520597-65521538 CTD-2653B5.1 PsychENCODE PEC_TWAS_weights/ENSG00000264754.wgt.RDat 3.30e-07 2.92e-05 0.13 Joint Yes
18 52385091-52562747 RAB27B PsychENCODE PEC_TWAS_weights/ENSG00000041353.wgt.RDat 5.36e-07 7.47e-09 0.11 Joint Yes
18 52495707-52562747 RAB27B CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat 1.28e-06 3.06e-07 0.11 Marginal Yes
20 47835831-47860614 DDX27 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat 1.32e-06 3.48e-06 -0.78 Joint Yes
22 41165634-41215403 SLC25A17 GTEx Nucleus accumbens Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat 3.83e-07 1.00e-08 0.74 Marginal No
22 41165634-41215403 SLC25A17 GTEx Thyroid Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat 9.78e-07 1.00e-08 0.47 Marginal No
22 41253088-41351450 XPNPEP3 GTEx Frontal Cortex Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat 7.38e-07 1.00e-08 0.45 Marginal No
22 41258260-41363888 XPNPEP3 CMC DLPFC CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat 3.21e-07 1.00e-08 0.64 Marginal No
22 41487790-41576081 EP300 GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat 3.93e-08 1.00e-08 0.85 Marginal No
22 41487790-41576081 EP300 YFS Blood YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat 4.21e-07 1.00e-08 0.61 Marginal No
22 41641614-41682216 RANGAP1 CMC DLPFC Splicing CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat 1.61e-07 1.00e-08 0.57 Marginal No
22 41641615-41682255 RANGAP1 PsychENCODE PEC_TWAS_weights/ENSG00000100401.wgt.RDat 2.47e-08 7.47e-09 -0.81 Marginal No
22 41697526-41756151 ZC3H7B GTEx Cerebellum Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat 1.01e-08 1.00e-08 0.93 Joint No

3.8 FOCUS

Merge the relevent FOCUS databases

# Create a list of databases to be merged.
cat << 'EOF' > /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Adrenal_Gland.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Amygdala.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Anterior_cingulate_cortex_BA24.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Caudate_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellar_Hemisphere.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellum.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cortex.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Frontal_Cortex_BA9.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hippocampus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hypothalamus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Putamen_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Spinal_cord_cervical_c-1.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Substantia_nigra.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/NTR.BLOOD.RNAARR.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/PEC_TWAS_weights.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Pituitary.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Thyroid.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Whole_Blood.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/YFS.BLOOD.RNAARR.db
EOF

####
# Merge the databases
####
# I have written a script to do this in R
/users/k1806347/brc_scratch/Software/Rscript.sh /scratch/users/k1806347/Software/MyGit/FOCUS_db_merger/FOCUS_db_merger.r \
--dbs /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt \
--out /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS

# This approach is giving strange results.
# Delete the output

Create FOCUS database for MDD TWAS

# Merging the databases causes some strange warnings
# Use the standard approach for the time being

########
# Import the FUSION SNP-weights to FOCUS format
########

# Import with all tissues in the MDD TWAS combined
# A shell script to do this has been written
mkdir -p /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db

# Fusion weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_fusion.sh

# Onyl continue once the FUSION SNP-weights are in the database
# Psych ENCODE weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_psychENCODE.sh

Run FOCUS

########
# Finemap TWAS associations
########

# Use a threshold that will run FOCUS for all TWAS significant loci (max GWAS.P = 5e-6)
for chr in $(seq 1 22); do
sbatch -p brc,shared --mem=10G /users/k1806347/brc_scratch/Software/focus.sh finemap /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz  /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr${chr} /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS.db --chr ${chr} --p-threshold 5e-6 --plot --out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD.FOCUS.MDD_TWAS_db.chr${chr}
done

Process the FOCUS results

library(data.table)

fusion <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

focus.files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/', pattern=glob2rx("MDD.FOCUS.MDD_TWAS_db.chr*.focus.tsv"))
length(focus.files)
focus<-NULL
for(i in focus.files){
focus<-rbind(focus,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/',i)))
}

# Update the feature IDs with gene names
pos<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos')
focus<-merge(focus, pos[,c('WGT','ID')], by.x='mol_name', by.y='WGT',all.x=T)
focus<-focus[order(focus$chrom, focus$region, 1-focus$pip),]

# I noticed a bug in the output where features that should be in the 90% credible set are not
focus_bug<-NULL
for(i in unique(focus$region)){
    focus_temp<-focus[focus$region == i,]
    if(sum(focus_temp$in_cred_set) == 0 & max(focus_temp$pip) != focus_temp$pip[focus_temp$ens_gene_id == 'NULL.MODEL']){
    print(head(focus_temp))
        focus_bug<-rbind(focus_bug, focus_temp)
    }
}

# This shows several features should be in the credible set
focus$in_cred_set[focus$ID == 'OLFM4' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '13:53339645-13:54682393'] <- 1
focus$in_cred_set[focus$ID == 'ENSG00000229267' & focus$tissue == 'pec_twas_weights' & focus$region == '2:214014511-2:215573795'] <- 1
focus$in_cred_set[focus$ID == 'COQ3' & focus$tissue == 'cmc.brain.rnaseq_splicing' & focus$region == '6:97842747-6:100629728'] <- 1
focus$in_cred_set[focus$ID == 'OSBPL3' & focus$tissue == 'pituitary' & focus$region == '7:23471523-7:25077097'] <- 1
focus$in_cred_set[focus$ID == 'PXDNL' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '8:50082470-8:53302930'] <- 1

# Update tissue for psychencode features
focus_psychencode<-focus[focus$tissue == 'pec_twas_weights',]
focus_fusion<-focus[focus$tissue != 'pec_twas_weights',]
focus_psychencode$tissue<-'psychencode'

# Update PsychENCODE gene IDs from ensembl to gene names
library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)

focus_psychencode<-merge(focus_psychencode, Genes, by.x='ID', by.y='ensembl_gene_id')
focus_psychencode$ID<-focus_psychencode$external_gene_name
focus_psychencode$external_gene_name<-NULL
focus_psychencode<-focus_psychencode[,names(focus),with=F]
focus<-rbind(focus_fusion,focus_psychencode)

fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('WGT'), by.y=c('mol_name'), all.x=T)
fusion_focus<-fusion_focus[,c('WGT','CHR','P0','P1','PANEL_clean_short','ID','TWAS.Z','TWAS.P','twas_z','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('WGT','CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)   
fusion_focus<-fusion_focus[,c('Location','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region'),with=F]

write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)

Investigate FANCL locus

# The FANCL gene is considered twice in the FOCUS analysis. Why? It is likely due to multiple independent SNP associations in that region. This may be genuine or due to complex LD structure.

######
# Estimate LD for this locus.
######

# Calculate LD
/users/k1806347/brc_scratch/Software/plink1.9.sh \
--bfile /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/LDREF/1000G.EUR.2 \
--r2 square \
--chr 2 \
--write-snplist \
--from-bp 57429100 \
--to-bp 60292000 \
--out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/FANCL_region_LD

# Plot LD
module add apps/R
R

library(data.table)
ld<-as.matrix(fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/FANCL_region_LD.ld'))
snps<-read.table('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/FANCL_region_LD.snplist')$V1

dimnames(ld)<-list(snps,snps)

ld_melt<-melt(ld)

library(ggplot2)

ggplot(data = ld_melt, aes(Var1, Var2, fill = value))+
  geom_tile() +
  scale_fill_gradient(low = "white", high = "red")

# The r2 patterns don't look strange. There is no long range LD.

######
# Perform LD clumping in this locus as well.
######

/users/k1806347/brc_scratch/Software/plink1.9.sh \
--bfile /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/LDREF/1000G.EUR.2 \
--chr 2 \
--from-bp 57429100 \
--to-bp 60292000 \
--clump /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP \
--clump-p1 5e-6 \
--clump-p2 1 \
--clump-kb 250 \
--clump-r2 0.2 \
--out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/FANCL_region_LD_clump

./plink --bfile mydata
        --clump myresults-a.assoc,myresults-b.assoc 
        --clump-best
        --clump-replicate
        --clump-index-first
        --clump-allow-overlap
        --clump-p1 1e-4 
        --clump-p2 1
        --clump-kb 250 
        --clump-r2 0.2

Combine FOCUS results with novelty table

library(data.table)

focus<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv')

fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('WGT'), by.y=c('mol_name'), all.x=T)

fusion_focus<-fusion_focus[,c('WGT','CHR','P0','P1','PANEL_clean','ID','TWAS.Z','TWAS.P','Novel','Colocalised','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('WGT','CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','Novel','Colocalised','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)   

# Remove the MHC region
fusion_focus_noMHC<-fusion_focus[!(fusion_focus$CHR == 6 & fusion_focus$P1 > 26e6 & fusion_focus$P0 < 34e6),]

# Subset those which are high confidence
fusion_focus_highConf<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 3.685926e-08,]

# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$TWAS.P < 1.368572e-06,]

sum(duplicated(fusion_focus_TWsig$WGT)) # 2
fusion_focus_TWsig[(fusion_focus_TWsig$WGT %in% fusion_focus_TWsig$WGT[duplicated(fusion_focus_TWsig$WGT)]),]
# The FANCL feature is duplicated due to FOCUS defining two nearby regions of association. A splice variant of FANCL is in the credible set for one region but not the other.

# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig_coloc<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 1.368572e-06,]
dim(fusion_focus_TWsig_coloc)[1] # 11
length(unique(fusion_focus_TWsig_coloc$ID)) #11

# Subset transcriptom-wide significant with pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 1.368572e-06,]
dim(fusion_focus_TWsig)[1] # 23
length(unique(fusion_focus_TWsig$ID)) # 23

write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)
write.csv(fusion_focus_TWsig,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.TWSig_PIP5.csv', row.names=F, quote=F)
write.csv(fusion_focus_TWsig_coloc,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.TWSig_coloc_PIP5.csv', row.names=F, quote=F)
write.csv(fusion_focus_highConf,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_HighConf_results.csv', row.names=F, quote=F)

Show FOCUS and other combined results

MDD TWAS Results with FOCUS
WGT CHR P0 P1 SNP.weight.Set ID TWAS.Z TWAS.P Novel Colocalised FOCUS_in_cred_set FOCUS_pip FOCUS_region Location
Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat 1 8412457 8877702 GTEx Whole Blood RERE -5.095707 3.47e-07 No TRUE 1 0.110000 1:7247335-1:9365199 chr1:8412457-8877702
YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat 1 8412457 8877702 YFS Blood RERE -5.310078 1.10e-07 No TRUE 1 0.504000 1:7247335-1:9365199 chr1:8412457-8877702
Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat 1 8484705 8494898 GTEx Thyroid RP5-1115A15.1 -5.175240 2.28e-07 No TRUE 1 0.269000 1:7247335-1:9365199 chr1:8484705-8494898
Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat 1 8484705 8494898 GTEx Whole Blood RP5-1115A15.1 -4.866386 1.14e-06 No TRUE 0 0.078700 1:7247335-1:9365199 chr1:8484705-8494898
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat 1 36884051 36884179 GTEx Nucleus accumbens SNORA63 4.848870 1.24e-06 Yes FALSE 1 0.702000 1:34799758-1:37549183 chr1:36884051-36884179
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Caudate NEGR1 5.780100 7.47e-09 No FALSE 0 0.000217 1:71684606-1:74326484 chr1:71861623-72748417
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Putamen NEGR1 5.548510 2.88e-08 No TRUE 0 0.000186 1:71684606-1:74326484 chr1:71861623-72748417
Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Whole Blood NEGR1 8.760622 1.94e-18 No TRUE 1 1.000000 1:71684606-1:74326484 chr1:71861623-72748417
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat 1 72767155 72767512 GTEx Cerebellar Hemisphere RPL31P12 -7.785520 6.94e-15 No TRUE 0 0.000325 1:71684606-1:74326484 chr1:72767155-72767512
Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat 1 72767155 72767512 GTEx Cerebellum RPL31P12 -7.708820 1.27e-14 No TRUE 0 0.000410 1:71684606-1:74326484 chr1:72767155-72767512
PEC_TWAS_weights/ENSG00000227207.wgt.RDat 1 72767155 72767512 PsychENCODE RPL31P12 -7.742756 9.73e-15 No TRUE 0 0.000648 1:71684606-1:74326484 chr1:72767155-72767512
Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat 1 175873898 175889649 GTEx Thyroid RP11-318C24.2 -5.027510 4.97e-07 Yes TRUE 1 0.286000 1:175089768-1:177433331 chr1:175873898-175889649
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat 1 175913966 176176370 CMC DLPFC Splicing RFWD2 -4.958690 7.10e-07 Yes TRUE 0 0.184000 1:175089768-1:177433331 chr1:175913966-176176370
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat 1 175913966 176176370 CMC DLPFC Splicing RFWD2 5.039850 4.66e-07 Yes TRUE 1 0.208000 1:175089768-1:177433331 chr1:175913966-176176370
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat 1 175913966 176176370 CMC DLPFC Splicing RFWD2 -5.005960 5.56e-07 Yes TRUE 1 0.188000 1:175089768-1:177433331 chr1:175913966-176176370
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat 1 181452685 181775921 CMC DLPFC Splicing CACNA1E -4.989390 6.06e-07 Yes FALSE 1 0.518000 1:181144121-1:182755356 chr1:181452685-181775921
CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat 1 197473878 197744623 CMC DLPFC DENND1B 4.848374 1.24e-06 Yes TRUE 1 0.725000 1:197311514-1:199239815 chr1:197473878-197744623
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat 1 197473878 197744623 CMC DLPFC Splicing DENND1B -5.421950 5.90e-08 Yes TRUE 1 0.165000 1:197311514-1:199239815 chr1:197473878-197744623
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat 1 197473878 197744623 CMC DLPFC Splicing DENND1B 5.018050 5.22e-07 Yes TRUE 0 0.100000 1:197311514-1:199239815 chr1:197473878-197744623
CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat 2 58386377 58468515 CMC DLPFC FANCL -5.183180 2.18e-07 No TRUE 0 0.007500 2:57429100-2:58296890 chr2:58386377-58468515
CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat 2 58386377 58468515 CMC DLPFC FANCL -5.183180 2.18e-07 No TRUE 0 0.398000 2:58297664-2:60292000 chr2:58386377-58468515
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat 2 58386377 58468515 CMC DLPFC Splicing FANCL 4.897476 9.71e-07 No TRUE 1 0.800000 2:57429100-2:58296890 chr2:58386377-58468515
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat 2 58386377 58468515 CMC DLPFC Splicing FANCL 4.897476 9.71e-07 No TRUE 0 0.000688 2:58297664-2:60292000 chr2:58386377-58468515
YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat 2 197831741 198175897 YFS Blood ANKRD44 -5.690140 1.27e-08 Yes FALSE 1 0.308000 2:198078110-2:199311125 chr2:197831741-198175897
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat 2 198254508 198299815 GTEx Hypothalamus SF3B1 5.214900 1.84e-07 Yes FALSE 1 0.126000 2:198078110-2:199311125 chr2:198254508-198299815
CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat 3 44481261 44561226 CMC DLPFC ZNF445 -5.103280 3.34e-07 Yes FALSE 1 0.308000 3:42540002-3:45165153 chr3:44481261-44561226
PEC_TWAS_weights/ENSG00000109133.wgt.RDat 4 41937137 41962589 PsychENCODE TMEM33 4.837418 1.32e-06 No TRUE 0 0.009820 4:40202609-4:42213058 chr4:41937137-41962589
Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat 4 41983713 41988476 GTEx Thyroid DCAF4L1 -5.128000 2.93e-07 No FALSE 0 0.010600 4:40202609-4:42213058 chr4:41983713-41988476
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat 4 41990758 41991254 GTEx Cerebellar Hemisphere RP11-814H16.2 5.009600 5.45e-07 No FALSE 0 0.004000 4:40202609-4:42213058 chr4:41990758-41991254
Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Amygdala SLC30A9 -5.253400 1.49e-07 No FALSE 1 0.034900 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx ACC SLC30A9 -5.001690 5.68e-07 No FALSE 1 0.052500 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Caudate SLC30A9 -4.854800 1.21e-06 No FALSE 0 0.006000 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Cortex SLC30A9 -5.774530 7.72e-09 No FALSE 1 0.534000 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Hypothalamus SLC30A9 -5.085140 3.67e-07 No TRUE 0 0.029800 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Nucleus accumbens SLC30A9 -5.602700 2.11e-08 No TRUE 1 0.271000 4:40202609-4:42213058 chr4:41992489-42092474
PEC_TWAS_weights/ENSG00000014824.wgt.RDat 4 41992489 42092474 PsychENCODE SLC30A9 -5.259200 1.45e-07 No TRUE 0 0.002660 4:40202609-4:42213058 chr4:41992489-42092474
PEC_TWAS_weights/ENSG00000247828.wgt.RDat 5 87564712 87732502 PsychENCODE TMEM161B-AS1 6.091010 1.12e-09 No TRUE 0 0.003040 5:87390031-5:88891530 chr5:87564712-87732502
Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Adrenal Gland TMEM161B-AS1 5.360090 8.32e-08 No TRUE 0 0.008360 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Amygdala TMEM161B-AS1 6.118500 9.45e-10 No TRUE 1 0.117000 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx ACC TMEM161B-AS1 6.445500 1.15e-10 No TRUE 1 0.136000 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Caudate TMEM161B-AS1 6.282167 3.34e-10 No TRUE 1 0.082400 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Cerebellar Hemisphere TMEM161B-AS1 6.011700 1.84e-09 No TRUE 0 0.025000 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Cerebellum TMEM161B-AS1 6.053050 1.42e-09 No TRUE 1 0.088000 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Cortex TMEM161B-AS1 6.021420 1.73e-09 No TRUE 0 0.048200 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Frontal Cortex TMEM161B-AS1 6.720000 1.82e-11 No TRUE 1 0.126000 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Hypothalamus TMEM161B-AS1 5.875800 4.21e-09 No TRUE 1 0.057200 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Nucleus accumbens TMEM161B-AS1 6.010490 1.85e-09 No TRUE 1 0.056900 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Putamen TMEM161B-AS1 6.372050 1.87e-10 No TRUE 1 0.059300 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Substantia nigra TMEM161B-AS1 6.057270 1.38e-09 No TRUE 0 0.049800 5:87390031-5:88891530 chr5:87564888-87732502
Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Pituitary TMEM161B-AS1 6.048500 1.46e-09 No TRUE 1 0.070200 5:87390031-5:88891530 chr5:87564888-87732502
Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Thyroid TMEM161B-AS1 5.889760 3.87e-09 No TRUE 0 0.035900 5:87390031-5:88891530 chr5:87564888-87732502
Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat 5 87564888 87732502 GTEx Whole Blood TMEM161B-AS1 5.526440 3.27e-08 No TRUE 0 0.008200 5:87390031-5:88891530 chr5:87564888-87732502
Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat 5 87729709 87794514 GTEx Substantia nigra CTC-498M16.4 5.403610 6.53e-08 No FALSE 0 0.004490 5:87390031-5:88891530 chr5:87729709-87794514
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx ACC CTC-467M3.3 -5.813700 6.11e-09 No FALSE 0 0.000351 5:87390031-5:88891530 chr5:87988462-87989789
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Cerebellar Hemisphere CTC-467M3.3 -5.861000 4.60e-09 Yes TRUE 1 0.135000 5:87390031-5:88891530 chr5:87988462-87989789
Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Cortex CTC-467M3.3 -6.510990 7.47e-11 No TRUE 0 0.004320 5:87390031-5:88891530 chr5:87988462-87989789
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Frontal Cortex CTC-467M3.3 -7.091600 1.33e-12 No TRUE 1 0.839000 5:87390031-5:88891530 chr5:87988462-87989789
PEC_TWAS_weights/ENSG00000250377.wgt.RDat 5 87988462 87989789 PsychENCODE CTC-467M3.3 -6.097890 1.07e-09 Yes FALSE 0 0.021700 5:87390031-5:88891530 chr5:87988462-87989789
CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat 5 140024947 140027370 CMC DLPFC NDUFA2 5.190020 2.10e-07 Yes FALSE 1 0.326000 5:139265072-5:140645971 chr5:140024947-140027370
Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat 5 140201222 140203811 GTEx Thyroid PCDHA5 -5.402970 6.55e-08 Yes FALSE 1 0.011400 5:139265072-5:140645971 chr5:140201222-140203811
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat 5 140220907 140223351 GTEx Cerebellar Hemisphere PCDHA8 -4.980100 6.36e-07 Yes TRUE 1 0.087600 5:139265072-5:140645971 chr5:140220907-140223351
NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat 6 26188921 26189323 NTR Blood HIST1H4D -4.987600 6.11e-07 No FALSE 1 0.187000 6:25684587-6:26789628 chr6:26188921-26189323
NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat 6 26365386 26378540 NTR Blood BTN3A2 5.326600 1.00e-07 No FALSE 1 0.038900 6:25684587-6:26789628 chr6:26365386-26378540
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat 6 26365387 26378546 GTEx Cerebellar Hemisphere BTN3A2 5.188200 2.12e-07 No FALSE 1 0.016800 6:25684587-6:26789628 chr6:26365387-26378546
Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat 6 26365387 26378546 GTEx Hippocampus BTN3A2 4.963000 6.96e-07 No FALSE 1 0.008270 6:25684587-6:26789628 chr6:26365387-26378546
Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat 6 26365387 26378546 GTEx Pituitary BTN3A2 5.898930 3.66e-09 No FALSE 1 0.277000 6:25684587-6:26789628 chr6:26365387-26378546
Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat 6 26365387 26378546 GTEx Thyroid BTN3A2 5.481600 4.22e-08 No FALSE 1 0.069000 6:25684587-6:26789628 chr6:26365387-26378546
Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat 6 26365387 26378546 GTEx Whole Blood BTN3A2 5.086960 3.64e-07 No FALSE 1 0.037800 6:25684587-6:26789628 chr6:26365387-26378546
Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat 6 26538633 26546482 GTEx Cerebellum HMGN4 5.395400 6.84e-08 No FALSE 1 0.043100 6:25684587-6:26789628 chr6:26538633-26546482
Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat 6 26538633 26546482 GTEx Cerebellum HMGN4 5.395400 6.84e-08 No FALSE 0 0.000403 6:26791421-6:28017629 chr6:26538633-26546482
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat 6 27215480 27224250 GTEx Cerebellar Hemisphere PRSS16 -4.891200 1.00e-06 No FALSE 0 0.001250 6:26791421-6:28017629 chr6:27215480-27224250
Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat 6 27215480 27224250 GTEx Cerebellum PRSS16 -4.947900 7.50e-07 No FALSE 0 0.001470 6:26791421-6:28017629 chr6:27215480-27224250
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat 6 27215480 27224250 GTEx Frontal Cortex PRSS16 -5.045000 4.54e-07 No FALSE 0 0.000813 6:26791421-6:28017629 chr6:27215480-27224250
Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat 6 27215480 27224250 GTEx Pituitary PRSS16 -5.916080 3.30e-09 Yes TRUE 1 0.077000 6:26791421-6:28017629 chr6:27215480-27224250
Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat 6 27215480 27224250 GTEx Whole Blood PRSS16 -5.335920 9.51e-08 No FALSE 1 0.006580 6:26791421-6:28017629 chr6:27215480-27224250
Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat 6 27325604 27339304 GTEx Adrenal Gland ZNF204P -5.032700 4.84e-07 No FALSE 1 0.008260 6:26791421-6:28017629 chr6:27325604-27339304
Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat 6 27371789 27374743 GTEx Hippocampus RP1-153G14.4 5.354000 8.60e-08 Yes FALSE 1 0.018300 6:26791421-6:28017629 chr6:27371789-27374743
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat 6 27418522 27440897 GTEx Caudate ZNF184 -6.325200 2.53e-10 No FALSE 1 0.111000 6:26791421-6:28017629 chr6:27418522-27440897
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat 6 27418522 27440897 GTEx Hypothalamus ZNF184 -4.952200 7.34e-07 No FALSE 0 0.000884 6:26791421-6:28017629 chr6:27418522-27440897
NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat 6 27840926 27841289 NTR Blood HIST1H4L 4.870800 1.11e-06 No TRUE 0 0.000344 6:26791421-6:28017629 chr6:27840926-27841289
NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat 6 27840926 27841289 NTR Blood HIST1H4L 4.870800 1.11e-06 No TRUE 0 0.003600 6:28018353-6:28917525 chr6:27840926-27841289
PEC_TWAS_weights/ENSG00000219891.wgt.RDat 6 28058932 28061442 PsychENCODE ZSCAN12P1 6.268010 3.66e-10 No FALSE 1 0.018100 6:26791421-6:28017629 chr6:28058932-28061442
PEC_TWAS_weights/ENSG00000219891.wgt.RDat 6 28058932 28061442 PsychENCODE ZSCAN12P1 6.268010 3.66e-10 No FALSE 1 0.303000 6:28018353-6:28917525 chr6:28058932-28061442
Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat 6 28058932 28061442 GTEx Whole Blood ZSCAN12P1 -4.936930 7.94e-07 No FALSE 0 0.000506 6:26791421-6:28017629 chr6:28058932-28061442
Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat 6 28058932 28061442 GTEx Whole Blood ZSCAN12P1 -4.936930 7.94e-07 No FALSE 0 0.003560 6:28018353-6:28917525 chr6:28058932-28061442
Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat 6 28083406 28084329 GTEx Hippocampus RP1-265C24.5 5.532000 3.16e-08 No TRUE 1 0.007530 6:26791421-6:28017629 chr6:28083406-28084329
Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat 6 28083406 28084329 GTEx Hippocampus RP1-265C24.5 5.532000 3.16e-08 No TRUE 1 0.007950 6:28018353-6:28917525 chr6:28083406-28084329
YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat 6 28092338 28097860 YFS Blood ZSCAN16 -6.109000 1.00e-09 No TRUE 1 0.052900 6:26791421-6:28017629 chr6:28092338-28097860
YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat 6 28092338 28097860 YFS Blood ZSCAN16 -6.109000 1.00e-09 No TRUE 1 0.087200 6:28018353-6:28917525 chr6:28092338-28097860
Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Cerebellum ZSCAN9 -5.307800 1.11e-07 No FALSE 1 0.011800 6:26791421-6:28017629 chr6:28192664-28201260
Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Cerebellum ZSCAN9 -5.307800 1.11e-07 No FALSE 0 0.000608 6:28018353-6:28917525 chr6:28192664-28201260
Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Hippocampus ZSCAN9 -6.017000 1.77e-09 No TRUE 1 0.014300 6:26791421-6:28017629 chr6:28192664-28201260
Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Hippocampus ZSCAN9 -6.017000 1.77e-09 No TRUE 0 0.000726 6:28018353-6:28917525 chr6:28192664-28201260
Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Pituitary ZSCAN9 -6.159020 7.32e-10 No FALSE 0 0.000231 6:26791421-6:28017629 chr6:28192664-28201260
Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat 6 28192664 28201260 GTEx Pituitary ZSCAN9 -6.159020 7.32e-10 No FALSE 1 0.057800 6:28018353-6:28917525 chr6:28192664-28201260
PEC_TWAS_weights/ENSG00000189134.wgt.RDat 6 28227098 28228736 PsychENCODE NKAPL 5.002860 5.65e-07 No FALSE 0 0.000507 6:26791421-6:28017629 chr6:28227098-28228736
PEC_TWAS_weights/ENSG00000189134.wgt.RDat 6 28227098 28228736 PsychENCODE NKAPL 5.002860 5.65e-07 No FALSE 1 0.021200 6:28018353-6:28917525 chr6:28227098-28228736
Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Adrenal Gland RP5-874C20.3 5.094600 3.49e-07 No TRUE 1 0.005860 6:26791421-6:28017629 chr6:28234788-28245974
Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Adrenal Gland RP5-874C20.3 5.094600 3.49e-07 No TRUE 0 0.003240 6:28018353-6:28917525 chr6:28234788-28245974
Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Cerebellum RP5-874C20.3 5.062800 4.13e-07 No FALSE 0 0.000540 6:26791421-6:28017629 chr6:28234788-28245974
Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Cerebellum RP5-874C20.3 5.062800 4.13e-07 No FALSE 1 0.025800 6:28018353-6:28917525 chr6:28234788-28245974
Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Hippocampus RP5-874C20.3 5.198000 2.01e-07 No FALSE 1 0.546000 6:26791421-6:28017629 chr6:28234788-28245974
Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Hippocampus RP5-874C20.3 5.198000 2.01e-07 No FALSE 1 0.018300 6:28018353-6:28917525 chr6:28234788-28245974
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Putamen RP5-874C20.3 5.739000 9.52e-09 No TRUE 1 0.005060 6:26791421-6:28017629 chr6:28234788-28245974
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Putamen RP5-874C20.3 5.739000 9.52e-09 No TRUE 1 0.033100 6:28018353-6:28917525 chr6:28234788-28245974
Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Thyroid RP5-874C20.3 5.338400 9.38e-08 No FALSE 1 0.010900 6:26791421-6:28017629 chr6:28234788-28245974
Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Thyroid RP5-874C20.3 5.338400 9.38e-08 No FALSE 0 0.003450 6:28018353-6:28917525 chr6:28234788-28245974
Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Whole Blood RP5-874C20.3 5.662330 1.49e-08 No FALSE 0 0.001660 6:26791421-6:28017629 chr6:28234788-28245974
Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat 6 28234788 28245974 GTEx Whole Blood RP5-874C20.3 5.662330 1.49e-08 No FALSE 1 0.030100 6:28018353-6:28917525 chr6:28234788-28245974
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat 6 28249314 28270326 GTEx Cerebellar Hemisphere PGBD1 -6.313100 2.74e-10 No TRUE 1 0.140000 6:28018353-6:28917525 chr6:28249314-28270326
Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat 6 28292470 28324048 GTEx Amygdala ZSCAN31 -5.084150 3.69e-07 No FALSE 0 0.000740 6:26791421-6:28017629 chr6:28292470-28324048
Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat 6 28292470 28324048 GTEx Amygdala ZSCAN31 -5.084150 3.69e-07 No FALSE 0 0.004050 6:28018353-6:28917525 chr6:28292470-28324048
Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Amygdala ZKSCAN3 4.949900 7.43e-07 No FALSE 0 0.001330 6:26791421-6:28017629 chr6:28317691-28336947
Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Amygdala ZKSCAN3 4.949900 7.43e-07 No FALSE 1 0.056200 6:28018353-6:28917525 chr6:28317691-28336947
Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Hippocampus ZKSCAN3 4.951000 7.37e-07 No FALSE 0 0.000232 6:26791421-6:28017629 chr6:28317691-28336947
Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Hippocampus ZKSCAN3 4.951000 7.37e-07 No FALSE 0 0.004150 6:28018353-6:28917525 chr6:28317691-28336947
Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Thyroid ZKSCAN3 6.093300 1.11e-09 No TRUE 1 0.008910 6:26791421-6:28017629 chr6:28317691-28336947
Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat 6 28317691 28336947 GTEx Thyroid ZKSCAN3 6.093300 1.11e-09 No TRUE 1 0.037500 6:28018353-6:28917525 chr6:28317691-28336947
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Hypothalamus ZSCAN23 -5.777500 7.58e-09 No FALSE 1 0.006160 6:26791421-6:28017629 chr6:28399707-28411279
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Hypothalamus ZSCAN23 -5.777500 7.58e-09 No FALSE 1 0.067400 6:28018353-6:28917525 chr6:28399707-28411279
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Putamen ZSCAN23 -4.894000 9.90e-07 No FALSE 0 0.002130 6:26791421-6:28017629 chr6:28399707-28411279
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Putamen ZSCAN23 -4.894000 9.90e-07 No FALSE 1 0.011300 6:28018353-6:28917525 chr6:28399707-28411279
Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Pituitary ZSCAN23 -4.953290 7.30e-07 No FALSE 0 0.001390 6:26791421-6:28017629 chr6:28399707-28411279
Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat 6 28399707 28411279 GTEx Pituitary ZSCAN23 -4.953290 7.30e-07 No FALSE 1 0.017500 6:28018353-6:28917525 chr6:28399707-28411279
Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat 6 30644166 30655672 GTEx Adrenal Gland PPP1R18 4.910200 9.10e-07 Yes FALSE 1 0.009620 6:30798168-6:31571218 chr6:30644166-30655672
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat 6 30695485 30710682 CMC DLPFC Splicing FLOT1 -5.299700 1.16e-07 Yes TRUE 0 0.001230 6:30798168-6:31571218 chr6:30695485-30710682
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat 6 30695485 30710682 CMC DLPFC Splicing FLOT1 -5.067100 4.04e-07 Yes TRUE 0 0.000653 6:30798168-6:31571218 chr6:30695485-30710682
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat 6 30695485 30710682 CMC DLPFC Splicing FLOT1 4.936600 7.95e-07 Yes TRUE 0 0.000801 6:30798168-6:31571218 chr6:30695485-30710682
Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat 6 30695486 30710510 GTEx Cerebellum FLOT1 -5.299000 1.16e-07 Yes TRUE 0 0.001200 6:30798168-6:31571218 chr6:30695486-30710510
Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat 6 30695486 30710510 GTEx Pituitary FLOT1 -5.253270 1.49e-07 Yes TRUE 0 0.001010 6:30798168-6:31571218 chr6:30695486-30710510
Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat 6 30695486 30710510 GTEx Thyroid FLOT1 -5.557400 2.74e-08 Yes TRUE 1 0.002400 6:30798168-6:31571218 chr6:30695486-30710510
Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat 6 30881982 30894236 GTEx Cortex VARS2 5.922000 3.18e-09 Yes FALSE 1 0.030600 6:30798168-6:31571218 chr6:30881982-30894236
Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat 6 30881982 30894236 GTEx Whole Blood VARS2 6.323130 2.56e-10 Yes TRUE 1 0.184000 6:30798168-6:31571218 chr6:30881982-30894236
Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat 6 31255287 31256741 GTEx Pituitary WASF5P -5.156240 2.52e-07 Yes TRUE 1 0.003470 6:30798168-6:31571218 chr6:31255287-31256741
Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat 6 31255287 31256741 GTEx Pituitary WASF5P -5.156240 2.52e-07 Yes TRUE 1 0.009760 6:31571218-6:32682664 chr6:31255287-31256741
Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat 6 31368479 31445283 GTEx Thyroid HCP5 6.400800 1.55e-10 Yes TRUE 1 0.777000 6:30798168-6:31571218 chr6:31368479-31445283
Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat 6 31368479 31445283 GTEx Thyroid HCP5 6.400800 1.55e-10 Yes TRUE 1 0.061900 6:31571218-6:32682664 chr6:31368479-31445283
Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat 6 31462658 31478901 GTEx Thyroid MICB -5.557000 2.74e-08 Yes TRUE 1 0.091400 6:30798168-6:31571218 chr6:31462658-31478901
Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat 6 31462658 31478901 GTEx Thyroid MICB -5.557000 2.74e-08 Yes TRUE 0 0.000364 6:31571218-6:32682664 chr6:31462658-31478901
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat 6 31606805 31620482 CMC DLPFC Splicing BAG6 -5.580000 2.40e-08 Yes FALSE 0 0.000540 6:30798168-6:31571218 chr6:31606805-31620482
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat 6 31606805 31620482 CMC DLPFC Splicing BAG6 -5.580000 2.40e-08 Yes FALSE 1 0.221000 6:31571218-6:32682664 chr6:31606805-31620482
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat 6 31694815 31698357 GTEx Frontal Cortex DDAH2 5.409500 6.32e-08 Yes FALSE 1 0.006230 6:30798168-6:31571218 chr6:31694815-31698357
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat 6 31694815 31698357 GTEx Frontal Cortex DDAH2 5.409500 6.32e-08 Yes FALSE 1 0.012700 6:31571218-6:32682664 chr6:31694815-31698357
CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat 6 31694816 31698039 CMC DLPFC DDAH2 5.344500 9.07e-08 Yes TRUE 1 0.003160 6:30798168-6:31571218 chr6:31694816-31698039
CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat 6 31694816 31698039 CMC DLPFC DDAH2 5.344500 9.07e-08 Yes TRUE 1 0.016200 6:31571218-6:32682664 chr6:31694816-31698039
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat 6 99817347 99842082 CMC DLPFC Splicing COQ3 5.146560 2.65e-07 Yes FALSE 1 0.974000 6:97842747-6:100629728 chr6:99817347-99842082
CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat 6 105404922 105531207 CMC DLPFC LIN28B -5.232050 1.68e-07 Yes TRUE 1 0.127000 6:103983395-6:106056733 chr6:105404922-105531207
PEC_TWAS_weights/ENSG00000187772.wgt.RDat 6 105404923 105531207 PsychENCODE LIN28B -5.105689 3.30e-07 Yes TRUE 0 0.092400 6:103983395-6:106056733 chr6:105404923-105531207
Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat 6 105584224 105617820 GTEx Amygdala BVES-AS1 -5.578300 2.43e-08 Yes FALSE 1 0.742000 6:103983395-6:106056733 chr6:105584224-105617820
Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat 7 12250867 12282993 GTEx Adrenal Gland TMEM106B 5.505026 3.69e-08 Yes TRUE 0 0.062900 7:11299198-7:12635461 chr7:12250867-12282993
PEC_TWAS_weights/ENSG00000106460.wgt.RDat 7 12250867 12282993 PsychENCODE TMEM106B -5.790690 7.01e-09 Yes TRUE 1 0.259000 7:11299198-7:12635461 chr7:12250867-12282993
Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat 7 12250867 12282993 GTEx Whole Blood TMEM106B 5.531000 3.18e-08 Yes TRUE 1 0.614000 7:11299198-7:12635461 chr7:12250867-12282993
YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat 7 12250867 12276886 YFS Blood TMEM106B 5.373600 7.72e-08 Yes TRUE 0 0.057400 7:11299198-7:12635461 chr7:12250867-12276886
Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat 7 24836158 25021253 GTEx Pituitary OSBPL3 -5.622890 1.88e-08 Yes FALSE 1 0.983000 7:23471523-7:25077097 chr7:24836158-25021253
CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat 8 52232136 52722005 CMC DLPFC PXDNL 5.887460 3.92e-09 Yes FALSE 1 0.998000 8:50082470-8:53302930 chr8:52232136-52722005
Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat 8 61297147 61429354 GTEx Thyroid RP11-163N6.2 -5.336530 9.47e-08 Yes FALSE 1 0.923000 8:59728421-8:62328644 chr8:61297147-61429354
PEC_TWAS_weights/ENSG00000234881.wgt.RDat 9 126605315 126605965 PsychENCODE PIGFP2 -5.305600 1.12e-07 No FALSE 1 0.944000 9:124871322-9:126971353 chr9:126605315-126605965
Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat 11 57067112 57092426 GTEx Adrenal Gland TNKS1BP1 4.922610 8.54e-07 Yes FALSE 1 0.214000 11:55082693-11:58457495 chr11:57067112-57092426
Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat 11 57405497 57420263 GTEx Thyroid AP000662.4 -4.980256 6.35e-07 Yes FALSE 1 0.205000 11:55082693-11:58457495 chr11:57405497-57420263
Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat 11 57424488 57429340 GTEx Whole Blood CLP1 5.195860 2.04e-07 Yes TRUE 1 0.449000 11:55082693-11:58457495 chr11:57424488-57429340
PEC_TWAS_weights/ENSG00000134825.wgt.RDat 11 61535973 61560274 PsychENCODE TMEM258 5.021730 5.12e-07 Yes TRUE 1 0.223000 11:58780549-11:62223771 chr11:61535973-61560274
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat 11 113280318 113346111 GTEx Frontal Cortex DRD2 -5.073787 3.90e-07 Yes FALSE 1 0.967000 11:112459488-11:114256749 chr11:113280318-113346111
CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat 13 53602875 53626196 CMC DLPFC OLFM4 5.091290 3.56e-07 No FALSE 1 0.992000 13:53339645-13:54682393 chr13:53602875-53626196
Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat 14 42057064 42074059 GTEx Thyroid CTD-2298J14.2 -5.678860 1.36e-08 No TRUE 1 0.402000 14:41615719-14:43137256 chr14:42057064-42074059
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat 14 42076773 42373752 GTEx Cerebellar Hemisphere LRFN5 5.423400 5.85e-08 No TRUE 0 0.093300 14:41615719-14:43137256 chr14:42076773-42373752
Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat 14 42076773 42373752 GTEx Cerebellum LRFN5 5.597540 2.17e-08 No TRUE 1 0.504000 14:41615719-14:43137256 chr14:42076773-42373752
Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat 14 59951161 59971429 GTEx Thyroid JKAMP -5.125100 2.97e-07 Yes TRUE 1 0.139000 14:59448336-14:61680201 chr14:59951161-59971429
Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat 14 59971779 60043549 GTEx Thyroid CCDC175 -5.478850 4.28e-08 Yes TRUE 1 0.609000 14:59448336-14:61680201 chr14:59971779-60043549
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat 14 60062693 60337557 CMC DLPFC Splicing RTN1 -4.874920 1.09e-06 Yes TRUE 0 0.039700 14:59448336-14:61680201 chr14:60062693-60337557
Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat 14 60062695 60337684 GTEx Thyroid RTN1 -5.348450 8.87e-08 Yes TRUE 1 0.189000 14:59448336-14:61680201 chr14:60062695-60337684
NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat 14 64319682 64693151 NTR Blood SYNE2 5.609528 2.03e-08 No TRUE 1 0.253000 14:63790015-14:65220117 chr14:64319682-64693151
Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat 14 64550950 64770377 GTEx Pituitary ESR2 -5.982300 2.20e-09 No TRUE 1 0.593000 14:63790015-14:65220117 chr14:64550950-64770377
Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat 14 64550950 64770377 GTEx Whole Blood ESR2 -5.655371 1.56e-08 No TRUE 0 0.157000 14:63790015-14:65220117 chr14:64550950-64770377
PEC_TWAS_weights/ENSG00000119682.wgt.RDat 14 75120140 75179818 PsychENCODE AREL1 -5.015110 5.30e-07 No FALSE 1 0.018200 14:72889754-14:76444767 chr14:75120140-75179818
Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat 14 75319736 75330537 GTEx Thyroid PROX2 -5.758100 8.51e-09 No TRUE 1 0.718000 14:72889754-14:76444767 chr14:75319736-75330537
CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat 14 75348593 75370450 CMC DLPFC DLST 4.981400 6.31e-07 No TRUE 1 0.019200 14:72889754-14:76444767 chr14:75348593-75370450
PEC_TWAS_weights/ENSG00000119689.wgt.RDat 14 75348594 75370448 PsychENCODE DLST 5.089700 3.59e-07 No TRUE 1 0.210000 14:72889754-14:76444767 chr14:75348594-75370448
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat 14 75370656 75389188 CMC DLPFC Splicing RPS6KL1 -5.023810 5.07e-07 No FALSE 1 0.032000 14:72889754-14:76444767 chr14:75370656-75389188
PEC_TWAS_weights/ENSG00000198208.wgt.RDat 14 75370657 75390099 PsychENCODE RPS6KL1 -4.952550 7.32e-07 No FALSE 0 0.012900 14:72889754-14:76444767 chr14:75370657-75390099
PEC_TWAS_weights/ENSG00000244691.wgt.RDat 14 103878456 103879098 PsychENCODE RP11-600F24.2 5.185660 2.15e-07 No FALSE 1 0.039200 14:103012102-14:105001723 chr14:103878456-103879098
YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat 14 103985996 103989448 YFS Blood CKB 5.346000 8.99e-08 Yes TRUE 1 0.145000 14:103012102-14:105001723 chr14:103985996-103989448
CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat 14 103995508 104003410 CMC DLPFC TRMT61A 5.051300 4.39e-07 Yes TRUE 1 0.039000 14:103012102-14:105001723 chr14:103995508-104003410
Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat 14 103995521 104003410 GTEx Whole Blood TRMT61A 4.977593 6.44e-07 Yes TRUE 1 0.025300 14:103012102-14:105001723 chr14:103995521-104003410
Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat 14 104019758 104028214 GTEx Thyroid RP11-894P9.2 -5.462560 4.69e-08 Yes TRUE 1 0.232000 14:103012102-14:105001723 chr14:104019758-104028214
PEC_TWAS_weights/ENSG00000270108.wgt.RDat 14 104153913 104154464 PsychENCODE RP11-73M18.6 5.031320 4.87e-07 No FALSE 1 0.180000 14:103012102-14:105001723 chr14:104153913-104154464
PEC_TWAS_weights/ENSG00000269940.wgt.RDat 14 104160897 104161507 PsychENCODE RP11-73M18.7 4.856130 1.20e-06 No FALSE 1 0.015900 14:103012102-14:105001723 chr14:104160897-104161507
Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat 14 104162690 104163500 GTEx Amygdala RP11-73M18.8 5.142000 2.72e-07 Yes TRUE 1 0.077900 14:103012102-14:105001723 chr14:104162690-104163500
Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat 14 104177607 104179149 GTEx Cerebellum AL049840.1 5.029540 4.92e-07 Yes TRUE 1 0.031600 14:103012102-14:105001723 chr14:104177607-104179149
Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat 14 104177607 104179149 GTEx Cortex AL049840.1 5.143620 2.69e-07 Yes TRUE 1 0.034400 14:103012102-14:105001723 chr14:104177607-104179149
Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat 14 104179904 104180441 GTEx Cortex RP11-73M18.9 4.977330 6.45e-07 Yes TRUE 1 0.032000 14:103012102-14:105001723 chr14:104179904-104180441
PEC_TWAS_weights/ENSG00000269963.wgt.RDat 14 104179904 104180586 PsychENCODE RP11-73M18.9 4.830100 1.36e-06 No FALSE 1 0.011500 14:103012102-14:105001723 chr14:104179904-104180586
PEC_TWAS_weights/ENSG00000118557.wgt.RDat 16 72146056 72210777 PsychENCODE PMFBP1 -5.160620 2.46e-07 No FALSE 1 0.964000 16:71054116-16:72935146 chr16:72146056-72210777
Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat 17 27400528 27507430 GTEx Adrenal Gland MYO18A -5.128570 2.92e-07 Yes TRUE 1 0.184000 17:27334244-17:29786491 chr17:27400528-27507430
Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat 17 27401933 27405875 GTEx Adrenal Gland TIAF1 -5.361200 8.27e-08 Yes TRUE 1 0.746000 17:27334244-17:29786491 chr17:27401933-27405875
PEC_TWAS_weights/ENSG00000264754.wgt.RDat 17 65520597 65521538 PsychENCODE CTD-2653B5.1 5.105730 3.30e-07 Yes FALSE NA NA NA chr17:65520597-65521538
PEC_TWAS_weights/ENSG00000041353.wgt.RDat 18 52385091 52562747 PsychENCODE RAB27B 5.012900 5.36e-07 Yes FALSE 1 0.469000 18:51554430-18:55213838 chr18:52385091-52562747
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat 18 52495707 52562747 CMC DLPFC Splicing RAB27B 4.843190 1.28e-06 Yes TRUE 1 0.122000 18:51554430-18:55213838 chr18:52495707-52562747
CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat 20 47835831 47860614 CMC DLPFC DDX27 4.836260 1.32e-06 Yes TRUE 1 0.353000 20:47199980-20:49239658 chr20:47835831-47860614
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat 22 41165634 41215403 GTEx Nucleus accumbens SLC25A17 5.076990 3.83e-07 No FALSE 1 0.017600 22:40545828-22:42690262 chr22:41165634-41215403
Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat 22 41165634 41215403 GTEx Thyroid SLC25A17 4.896100 9.78e-07 No FALSE 1 0.018500 22:40545828-22:42690262 chr22:41165634-41215403
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat 22 41253088 41351450 GTEx Frontal Cortex XPNPEP3 4.951000 7.38e-07 No FALSE 1 0.003980 22:40545828-22:42690262 chr22:41253088-41351450
CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat 22 41258260 41363888 CMC DLPFC XPNPEP3 5.110000 3.21e-07 No FALSE 1 0.022800 22:40545828-22:42690262 chr22:41258260-41363888
Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat 22 41487790 41576081 GTEx Cerebellum EP300 5.493900 3.93e-08 No TRUE 1 0.319000 22:40545828-22:42690262 chr22:41487790-41576081
YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat 22 41487790 41576081 YFS Blood EP300 5.059100 4.21e-07 No FALSE 1 0.058800 22:40545828-22:42690262 chr22:41487790-41576081
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat 22 41641614 41682216 CMC DLPFC Splicing RANGAP1 5.240100 1.61e-07 No FALSE 1 0.150000 22:40545828-22:42690262 chr22:41641614-41682216
PEC_TWAS_weights/ENSG00000100401.wgt.RDat 22 41641615 41682255 PsychENCODE RANGAP1 -5.575273 2.47e-08 No FALSE 1 0.011900 22:40545828-22:42690262 chr22:41641615-41682255
Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat 22 41697526 41756151 GTEx Cerebellum ZC3H7B 5.729100 1.01e-08 No TRUE 1 0.382000 22:40545828-22:42690262 chr22:41697526-41756151

Show TW-significant results with PIP > 0.5

MDD TWAS:TW-significant results with PIP > 0.5
WGT CHR P0 P1 SNP.weight.Set ID TWAS.Z TWAS.P Novel Colocalised FOCUS_in_cred_set FOCUS_pip FOCUS_region Location
YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat 1 8412457 8877702 YFS Blood RERE -5.310078 1.10e-07 No TRUE 1 0.504 1:7247335-1:9365199 chr1:8412457-8877702
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat 1 36884051 36884179 GTEx Nucleus accumbens SNORA63 4.848870 1.24e-06 Yes FALSE 1 0.702 1:34799758-1:37549183 chr1:36884051-36884179
Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Whole Blood NEGR1 8.760622 1.94e-18 No TRUE 1 1.000 1:71684606-1:74326484 chr1:71861623-72748417
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat 1 181452685 181775921 CMC DLPFC Splicing CACNA1E -4.989390 6.06e-07 Yes FALSE 1 0.518 1:181144121-1:182755356 chr1:181452685-181775921
CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat 1 197473878 197744623 CMC DLPFC DENND1B 4.848374 1.24e-06 Yes TRUE 1 0.725 1:197311514-1:199239815 chr1:197473878-197744623
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat 2 58386377 58468515 CMC DLPFC Splicing FANCL 4.897476 9.71e-07 No TRUE 1 0.800 2:57429100-2:58296890 chr2:58386377-58468515
Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat 4 41992489 42092474 GTEx Cortex SLC30A9 -5.774530 7.72e-09 No FALSE 1 0.534 4:40202609-4:42213058 chr4:41992489-42092474
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Frontal Cortex CTC-467M3.3 -7.091600 1.33e-12 No TRUE 1 0.839 5:87390031-5:88891530 chr5:87988462-87989789
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat 6 99817347 99842082 CMC DLPFC Splicing COQ3 5.146560 2.65e-07 Yes FALSE 1 0.974 6:97842747-6:100629728 chr6:99817347-99842082
Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat 6 105584224 105617820 GTEx Amygdala BVES-AS1 -5.578300 2.43e-08 Yes FALSE 1 0.742 6:103983395-6:106056733 chr6:105584224-105617820
Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat 7 12250867 12282993 GTEx Whole Blood TMEM106B 5.531000 3.18e-08 Yes TRUE 1 0.614 7:11299198-7:12635461 chr7:12250867-12282993
Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat 7 24836158 25021253 GTEx Pituitary OSBPL3 -5.622890 1.88e-08 Yes FALSE 1 0.983 7:23471523-7:25077097 chr7:24836158-25021253
CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat 8 52232136 52722005 CMC DLPFC PXDNL 5.887460 3.92e-09 Yes FALSE 1 0.998 8:50082470-8:53302930 chr8:52232136-52722005
Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat 8 61297147 61429354 GTEx Thyroid RP11-163N6.2 -5.336530 9.47e-08 Yes FALSE 1 0.923 8:59728421-8:62328644 chr8:61297147-61429354
PEC_TWAS_weights/ENSG00000234881.wgt.RDat 9 126605315 126605965 PsychENCODE PIGFP2 -5.305600 1.12e-07 No FALSE 1 0.944 9:124871322-9:126971353 chr9:126605315-126605965
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat 11 113280318 113346111 GTEx Frontal Cortex DRD2 -5.073787 3.90e-07 Yes FALSE 1 0.967 11:112459488-11:114256749 chr11:113280318-113346111
CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat 13 53602875 53626196 CMC DLPFC OLFM4 5.091290 3.56e-07 No FALSE 1 0.992 13:53339645-13:54682393 chr13:53602875-53626196
Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat 14 42076773 42373752 GTEx Cerebellum LRFN5 5.597540 2.17e-08 No TRUE 1 0.504 14:41615719-14:43137256 chr14:42076773-42373752
Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat 14 59971779 60043549 GTEx Thyroid CCDC175 -5.478850 4.28e-08 Yes TRUE 1 0.609 14:59448336-14:61680201 chr14:59971779-60043549
Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat 14 64550950 64770377 GTEx Pituitary ESR2 -5.982300 2.20e-09 No TRUE 1 0.593 14:63790015-14:65220117 chr14:64550950-64770377
Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat 14 75319736 75330537 GTEx Thyroid PROX2 -5.758100 8.51e-09 No TRUE 1 0.718 14:72889754-14:76444767 chr14:75319736-75330537
PEC_TWAS_weights/ENSG00000118557.wgt.RDat 16 72146056 72210777 PsychENCODE PMFBP1 -5.160620 2.46e-07 No FALSE 1 0.964 16:71054116-16:72935146 chr16:72146056-72210777
Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat 17 27401933 27405875 GTEx Adrenal Gland TIAF1 -5.361200 8.27e-08 Yes TRUE 1 0.746 17:27334244-17:29786491 chr17:27401933-27405875

Show TW-significant results with COLOC PP4 > 0.8 and PIP > 0.5

MDD TWAS:TW-significant results with COLOC PP4 > 0.8 and PIP > 0.5
WGT CHR P0 P1 SNP.weight.Set ID TWAS.Z TWAS.P Novel Colocalised FOCUS_in_cred_set FOCUS_pip FOCUS_region Location
YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat 1 8412457 8877702 YFS Blood RERE -5.310078 1.10e-07 No TRUE 1 0.504 1:7247335-1:9365199 chr1:8412457-8877702
Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Whole Blood NEGR1 8.760622 1.94e-18 No TRUE 1 1.000 1:71684606-1:74326484 chr1:71861623-72748417
CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat 1 197473878 197744623 CMC DLPFC DENND1B 4.848374 1.24e-06 Yes TRUE 1 0.725 1:197311514-1:199239815 chr1:197473878-197744623
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat 2 58386377 58468515 CMC DLPFC Splicing FANCL 4.897476 9.71e-07 No TRUE 1 0.800 2:57429100-2:58296890 chr2:58386377-58468515
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Frontal Cortex CTC-467M3.3 -7.091600 1.33e-12 No TRUE 1 0.839 5:87390031-5:88891530 chr5:87988462-87989789
Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat 7 12250867 12282993 GTEx Whole Blood TMEM106B 5.531000 3.18e-08 Yes TRUE 1 0.614 7:11299198-7:12635461 chr7:12250867-12282993
Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat 14 42076773 42373752 GTEx Cerebellum LRFN5 5.597540 2.17e-08 No TRUE 1 0.504 14:41615719-14:43137256 chr14:42076773-42373752
Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat 14 59971779 60043549 GTEx Thyroid CCDC175 -5.478850 4.28e-08 Yes TRUE 1 0.609 14:59448336-14:61680201 chr14:59971779-60043549
Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat 14 64550950 64770377 GTEx Pituitary ESR2 -5.982300 2.20e-09 No TRUE 1 0.593 14:63790015-14:65220117 chr14:64550950-64770377
Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat 14 75319736 75330537 GTEx Thyroid PROX2 -5.758100 8.51e-09 No TRUE 1 0.718 14:72889754-14:76444767 chr14:75319736-75330537
Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat 17 27401933 27405875 GTEx Adrenal Gland TIAF1 -5.361200 8.27e-08 Yes TRUE 1 0.746 17:27334244-17:29786491 chr17:27401933-27405875

3.9 High-confidence findings

Show High-confidence associations results

MDD TWAS: High confidence results
WGT CHR P0 P1 SNP.weight.Set ID TWAS.Z TWAS.P Novel Colocalised FOCUS_in_cred_set FOCUS_pip FOCUS_region Location
Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat 1 71861623 72748417 GTEx Whole Blood NEGR1 8.760622 1.94e-18 No TRUE 1 1.000 1:71684606-1:74326484 chr1:71861623-72748417
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat 5 87988462 87989789 GTEx Frontal Cortex CTC-467M3.3 -7.091600 1.33e-12 No TRUE 1 0.839 5:87390031-5:88891530 chr5:87988462-87989789
Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat 7 12250867 12282993 GTEx Whole Blood TMEM106B 5.531000 3.18e-08 Yes TRUE 1 0.614 7:11299198-7:12635461 chr7:12250867-12282993
Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat 14 42076773 42373752 GTEx Cerebellum LRFN5 5.597540 2.17e-08 No TRUE 1 0.504 14:41615719-14:43137256 chr14:42076773-42373752
Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat 14 64550950 64770377 GTEx Pituitary ESR2 -5.982300 2.20e-09 No TRUE 1 0.593 14:63790015-14:65220117 chr14:64550950-64770377
Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat 14 75319736 75330537 GTEx Thyroid PROX2 -5.758100 8.51e-09 No TRUE 1 0.718 14:72889754-14:76444767 chr14:75319736-75330537

3.10 TWAS-GSEA

Combine the predicted expression files for FUSION and PsychENCODE

library(data.table)

FUSION<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Predicted_expression/FUSION_1KG/FUSION_1KG_Expr_AllSets.csv.gz')
PsychENCODE<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/Predicted_expression/FeaturePredictions.csv.gz')

both<-merge(FUSION, PsychENCODE, by=c('FID','IID'))

fwrite(both, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv', row.names=F, quote=F)
gzip /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv

TWAS-GSEA: All tissues

# Using TWAS from all PANELs, removing duplicate genes.
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 3 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray

# hypothesis-free analysis
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free

# brainspan
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --linear_p_thresh 1 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_brainspan

TWAS-GSEA: Tissue groups

library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')
Brain_res<-res[grepl('Brain|BRAIN|PsychENCODE', res$PANEL),]
HPA_res<-res[grepl('Adrenal|Pituitary|Hypothalamus', res$PANEL),]
HPT_res<-res[grepl('Thyroid|Pituitary|Hypothalamus', res$PANEL),]
BLOOD_res<-res[grepl('BLOOD', res$PANEL),]

write.table(Brain_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BRAIN.GW', row.names=F, col.names=T, quote=F)
write.table(HPA_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPA.GW', row.names=F, col.names=T, quote=F)
write.table(HPT_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPT.GW', row.names=F, col.names=T, quote=F)
write.table(BLOOD_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BLOOD.GW', row.names=F, col.names=T, quote=F)
# Candidate
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --linear_p_thresh 1 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Candidate_Wray
done

# Hypo-free
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Hypo_free
done

# brainspan
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --n_cores 3 \
  --linear_p_thresh 1 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_brainspan

done

TWAS-GSEA: Tissue-specific

library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')

for(i in unique(res$PANEL)){
  write.table(res[res$PANEL == i,], paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_',i,'.GW'), row.names=F, col.names=T, quote=F)
}
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --linear_p_thresh 1 \
  --competitive T \
  --min_r2 0.05 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Candidate_Wray

done

for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --competitive T \
  --min_r2 0.05 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Hypo_free

done

# brainspan
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --linear_p_thresh 1 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_brainspan

done

# Note. The individual tissue analyses return more significant findings. Instead of aggregating tissues, perhaps meta-analysis of per tissue results would be more effective.

Tabulate the results

library(data.table)

#####
# AllTissue results
#####
##
# Candidate
##

res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray.competitive.txt')

res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
                            PMID=gsub('.*\\.','',res$GeneSet),
                            res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])

write.csv(res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/AllTissue_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

##
# Hpothesis free
##

res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free.competitive.txt')

res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
                            PMID=gsub('.*\\.','',res$GeneSet),
                            res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])

####
# Tissue-set analysis
####

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('Candidate', res_files)]

tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
                            PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
                            tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL

write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/TissueSet_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('Hypo_free', res_files)]

tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
                            tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL

####
# Tissue specific analyses
####

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('Candidate', res_files)]

tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
                            PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
                            tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL

write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('Hypo_free', res_files)]

tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
                            tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL

write.csv(tissue_hypo_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Hypo_free_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

############
# Table and make figures for brainspan enrichment analysis
############

##
# All tissue
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle('All Tissues') +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_AllTissue_BrainSpan.png', units='px', res=300, width=1500, height=1000)
plot_grid(plotlist=res_plot, ncol=1)
dev.off()

##
# Tissue set
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle(gsub('_brainspan.competitive.txt','',i)) +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/MDD_TWAS_ByPanelSet_BrainSpan.png', units='px', res=300, width=2500, height=1500)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()

##
# Tissue-specific
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle(gsub('_brainspan.competitive.txt','',i)) +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/MDD_TWAS_ByPanel_BrainSpan.png', units='px', res=300, width=3000, height=7000)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()

Show candidate TWAS-GSEA results

All tissue results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR
RBFOX2 24613350 0.08092875 0.02482267 3.260276 2445 3031 0.0005565194 0.03951288
Tissue-Set results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
RBFOX2 24613350 0.10697919 0.02587590 4.134318 2373 3031 1.780048e-05 0.001263834 BRAIN
SCZ.COMPOSITE 24463508 0.11324126 0.03114801 3.635586 1343 1787 1.386748e-04 0.004922957 BRAIN
RBFOX1.RBFOX3 24613350 0.08366670 0.02424599 3.450744 2627 3400 2.795220e-04 0.006615355 BRAIN
FMRP 21784246 0.12461728 0.03715019 3.354419 937 1240 3.976596e-04 0.007058457 BRAIN
POTENTIALLY.SYNAPTIC.ALL 27694994 0.06031635 0.01988561 3.033166 4384 5736 1.210011e-03 0.017182151 BRAIN
PGC.BP.P10.4 21926972 0.18130846 0.06366349 2.847919 324 629 2.200306e-03 0.026036954 BRAIN
NEURONAL.PSD 23071613 0.08920852 0.03345738 2.666333 1131 1444 3.834183e-03 0.038889571 BRAIN
Tissue-Specific results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
MIR.137 24463508 0.3550824 0.09912228 3.582266 130 421 0.0001703133 0.01021880 CMC.BRAIN.RNASEQ
SCZ.DENOVO.NONSYN 24463508 0.4039377 0.11510465 3.509308 83 604 0.0002246369 0.01168112 Pituitary
SCZ.COMPOSITE 24463508 0.2323825 0.07087671 3.278687 233 1787 0.0005214569 0.01355788 Pituitary
SCZ.COMPOSITE 24463508 0.2677617 0.08385283 3.193234 188 1787 0.0007034446 0.03235845 Brain_Caudate_basal_ganglia
CONSTRAINED 25086666 0.2667025 0.08985060 2.968289 157 1003 0.0014973135 0.03436180 CMC.BRAIN.RNASEQ
RBFOX1.RBFOX3 24613350 0.1219388 0.04167769 2.925758 938 3400 0.0017180898 0.03436180 CMC.BRAIN.RNASEQ
PGC.SCZ.P10.4 24463508 0.2749434 0.10082558 2.726921 155 442 0.0031964205 0.04794631 CMC.BRAIN.RNASEQ

Show hypothesis-free TWAS-GSEA results

Tissue-Specific results
GeneSet Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
GO.MACROMOLECULAR.COMPLEX.BINDING 0.4625439 0.09122296 5.070477 152 1365 1.984100e-07 0.000598603 Brain_Caudate_basal_ganglia
GO.MICROTUBULE.BINDING 1.0368947 0.22172666 4.676455 25 191 1.459384e-06 0.002201480 Brain_Caudate_basal_ganglia
GO.ALCOHOL.BINDING 1.7881848 0.38352213 4.662534 8 99 1.561701e-06 0.005161421 Pituitary
GO.CHROMATIN.BINDING 0.7897970 0.18867269 4.186070 35 422 1.419132e-05 0.014077837 Brain_Caudate_basal_ganglia
GO.PROTEIN.COMPLEX.BINDING 0.4429150 0.10741456 4.123417 110 913 1.866468e-05 0.014077837 Brain_Caudate_basal_ganglia
GO.LIGAND.DEPENDENT.NUCLEAR.RECEPTOR.BINDING 1.9756846 0.49658009 3.978582 5 22 3.466376e-05 0.020916114 Brain_Caudate_basal_ganglia
GO.REGULATION.OF.INTRINSIC.APOPTOTIC.SIGNALING.PATHWAY 1.7918397 0.43272770 4.140802 6 138 1.730469e-05 0.032982733 Brain_Amygdala

Show developmental stage enrichment plots

MDD TWAS: All Tissues BrainSpan

MDD TWAS: All Tissues BrainSpan


MDD TWAS: Tissue-sets BrainSpan

MDD TWAS: Tissue-sets BrainSpan


MDD TWAS: Tissue-specific BrainSpan ***


4 Comparison with previous literature

Results files of the previous studies used for this comparison were obtained from the supplementary tables of each study.


4.1 TWAS literature

Our study results were compared with Wray et al. (2018), Gaspar et al. (2019), and Gerring et al. (2019)

Show code

########################################
# Comparing our results to previous TWASs
###############################################
# our TWAS, Wray et al TWAS, Gaspar et al. TWAS, Gerring et al. (2019)

###
# Load data
###
library(data.table)

#these are all files with significant features only (except for our results)
our_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/findings/raw findings/AllTissues_CLEAN.txt")
our_hits_sign <- fread("C:/Users/loryd/Desktop/MSc dissertation/findings/raw findings/signtest.AllTissues_CLEAN.txt")
Gaspar_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Gaspar et al. hits.txt")
Wray_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Wray et al. hits.csv")
Gerring_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Gerring et al. hits.csv")

###
# Structure df so that they are similar
###

#1. GASPAR (NB this includes info on TWAS.Z values not p values)
#melt the gaspar hits df so that it presents one column with all SNP-weigths and one with the values
Gaspar_hits2 <- melt(Gaspar_hits, id = c("target", "CHROMOSOME"))
head(Gaspar_hits2)

#rename cols
Gaspar_hits2$PANEL <- Gaspar_hits2$variable
Gaspar_hits2$TWAS.Z <- Gaspar_hits2$value

#2. Wray et al 
#keep panel, ID, and TWAS.P & TWAS.Z info only 
library(tidyverse)
Wray_hits <- as_tibble(Wray_hits)
Wray_hits
Wray_hits_filt <- Wray_hits %>% select("Gene", "CHR", "TWAS.Z", "TWAS.P")

#3. Gerring et al. 
#the gene needs to be called ID so that we can merge based on that. Filter for the columns needed
Gerring_hits$ID <- Gerring_hits$GENE
Gerring_hits <- as_tibble(Gerring_hits)
Gerring_hits_filt <- Gerring_hits %>% select ('ID', 'ZSTAT', 'P')

#ALL
#rename columns so they all correspond 
Wray_hits_filt$ID <- Wray_hits_filt$Gene

#filter our findings for the  cols needed only 
our_hits <- as_tibble(our_hits)
colnames(our_hits)
our_hits_filt <- our_hits %>% select("PANEL", "PANEL_clean_short", "ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P")

###
# Fix SNP-weights 
###

###
# Filter our findings for any gene significant in either study 
###
str(our_hits_sign)
sign_genes_our <- our_hits_sign$ID
str(sign_genes_our)
sign_genes_Wray <- Wray_hits_filt$ID
str(sign_genes_Wray)

Gaspar_hits2$ID <- Gaspar_hits2$target
Gaspar_hits2<-Gaspar_hits2[!is.na(Gaspar_hits2$TWAS.Z),]
sign_genes_Gaspar <- Gaspar_hits2$ID
str(sign_genes_Gaspar)

sign_genes_Gerring <- Gerring_hits_filt$ID

Gaspar <- unique(sign_genes_Gaspar) #25 unique genes
Wray <- unique(sign_genes_Wray) #17 unique genes
Us <- unique(sign_genes_our) #91 unique genes 
Gerring <- unique(sign_genes_Gerring) #57 unique genes

#create a general variable with all sign. genes in any paper
All <- c(Gaspar, Wray, Us, Gerring)
All #190 genes - from any of the examined publications (i.e. there might still be some duplicates, across studies)

#keep only unique gene IDs
All_unique <- unique(All)
All_unique #133 unique genes considered as significant across all examined publications 


###
#Keep only genes sign, in either of the three studies
###

#filter by genes which are in the vector containing sign. features in any of the studies
our_hits_final <- our_hits_filt[(our_hits_filt$ID %in% All_unique), ]  #817 observations

#keep only unique genes (those with the greatest absolute z-score)
#to do so, after excluding for missing values, you order genes by z-scores (decreasing order) and keep only unique values
#since you ordered from the largest z-scores, the genes with the largest z-scores will be kept and their duplicates, with lower
# z-scores, discarded. 
our_hits_final<-our_hits_final[!is.na(our_hits_final$TWAS.Z),]
our_hits_final2 <- our_hits_final[order(abs(our_hits_final$TWAS.Z), decreasing = T), ]
library(dplyr)
our_hits_final3  <- our_hits_final2 %>% distinct(ID, .keep_all = T) #125 obs.

our_hits_correct <- our_hits_final3

#keep only unique genes in the Gaspar et al study (different snp-weight sets were tested)
Gaspar_hits2b<-Gaspar_hits2[!is.na(Gaspar_hits2$TWAS.Z),]
Gaspar_hits3 <- Gaspar_hits2b[order(abs(Gaspar_hits2b$TWAS.Z), decreasing = T), ]
Gaspar_hits4  <- Gaspar_hits3 %>% distinct(target, .keep_all = T) #25 gene IDs, as expected 

Gaspar_correct <- Gaspar_hits4

#NB no need to keep only unique genes for the Wray et al TWAS as that is tested in one tissue only
#exclude missings in the Wray et al 
Wray_hits_correct <- Wray_hits_filt[!is.na(Wray_hits_filt$TWAS.Z), ] 
Wray_hits_correct

#keep only unique genes in the Gerring et al. study (different snp-weight sets were tested)
Gerring_hits_filt2<-Gerring_hits_filt[!is.na(Gerring_hits_filt$ZSTAT),]
Gerring_hits_filt3 <- Gerring_hits_filt2[order(abs(Gerring_hits_filt2$ZSTAT), decreasing = T), ]
Gerring_correct <- Gerring_hits_filt3 %>% distinct(ID, .keep_all = T) #57 genes

###
# Change the names of variables which have the same col name but distinct values across the three df
###
Gaspar_correct$Gaspar_TWAS.Z <- Gaspar_correct$TWAS.Z
Gaspar_correct$TWAS.Z <- NULL

Wray_hits_correct$Wray_TWAS.Z <- Wray_hits_correct$TWAS.Z
Wray_hits_correct$TWAS.Z <- NULL

Gerring_correct$Gerring_TWAS.Z <- Gerring_correct$ZSTAT
Gerring_correct$ZSTAT <- NULL

Wray_hits_correct$Wray_TWAS.P <- Wray_hits_correct$TWAS.P
Wray_hits_correct$TWAS.P <- NULL

our_hits_correct$DallAglio_TWAS.Z <- our_hits_correct$TWAS.Z
our_hits_correct$DallAglio_TWAS.P <- our_hits_correct$TWAS.P

our_hits_correct$TWAS.Z <- NULL
our_hits_correct$TWAS.P <- NULL
our_hits_correct$PANEL <- NULL
our_hits_correct$PANEL_clean_short <- NULL


Wray_hits_correct$CHR <- NULL
Wray_hits_correct$Wray_TWAS.P <- NULL
Wray_hits_correct$TWAS.P <- NULL

Gaspar_correct$PANEL <- NULL


###
#join your hits with the hits from the wray et al. paper and Gaspar et al paper
###
#since the three tibbles do not have the same nrow, we cannot use merge. But we can use full_join

Gaspar_correct <- as_tibble(Gaspar_correct) #transform all df into tibbles to use full join. the other 3
##df are already tibbles. this was the only one left to convert


#join hits bw Gaspar and our study
Gaspar_correct$ID <- Gaspar_correct$target
table_correct<- full_join(our_hits_correct, Gaspar_correct) #dim 129, 10

#merge the newly created table with the wray et al findings too
table_correct2 <- full_join(table_correct, Wray_hits_correct)  #dim 129, 11

#merge the newly created table with the Gerring et al. findings
table_correct3 <- full_join(table_correct2, Gerring_correct) #dim 133, 13

#check that no gene is repeated and that z-scores are present for the three studies (should be high z-scores)
table_correct3 <- table_correct3[order(table_correct3$ID), ]


###
# Clean up and save
###

my_data <- as_data_frame(table_correct3)
colnames(my_data)

col_order <- c("ID", "Gaspar_TWAS.Z", "Wray_TWAS.Z", "Gerring_TWAS.Z", "DallAglio_TWAS.Z", "Transcriptome_wide_sign_DallAglio")
my_data2 <- my_data[, col_order]
my_data2


#save as csv file (each df col is treated as independent col, +excel format)
write.csv(my_data2, 'C:/Users/loryd/Desktop/MSc dissertation/findings/table/comparison_previousTWASs.csv', row.names=F)

Show comparison with previous TWAS

Comparison with previous TWAS
ID Gaspar_TWAS.Z Wray_TWAS.Z DallAglio_TWAS.Z Transcriptome_wide_sign_DallAglio
AL049840.1 NA NA 5.143620 Sign.
ANKRD44 NA NA -5.690140 Sign.
AP000662.4 NA NA -4.980256 Sign.
AREL1 NA NA -5.015110 Sign.
BAG5 5.24 NA 4.630780 Not Sign.
BAG6 NA NA -5.580000 Sign.
BEND4 NA -4.64 -4.645980 Not Sign.
BTN1A1 -5.91 NA NA NA
BTN3A2 5.38 NA 5.898930 Sign.
BVES-AS1 NA NA -5.578300 Sign.
CACNA1E NA NA -4.989390 Sign.
CCDC175 NA NA -5.478850 Sign.
CKB NA NA 5.346000 Sign.
CLP1 NA NA 5.195860 Sign.
COQ3 NA NA 5.146560 Sign.
CSE1L NA -4.71 -4.714000 Not Sign.
CTC-467M3.3 NA NA -7.091600 Sign.
CTC-498M16.4 NA NA 5.403610 Sign.
CTD-2298J14.2 NA NA -5.678860 Sign.
CTD-2653B5.1 NA NA 5.105730 Sign.
DCAF4L1 NA NA -5.128000 Sign.
DDAH2 NA 5.34 5.409500 Sign.
DDX27 NA 4.84 4.836260 Sign.
DENND1B NA 4.85 -5.421950 Sign.
DLST NA 4.98 5.089700 Sign.
DNAJC24 NA 4.46 4.498630 Not Sign.
DRD2 NA NA -5.073787 Sign.
EP300 NA NA 5.493900 Sign.
ESR2 -5.43 NA -5.982300 Sign.
FANCL NA -5.18 -5.183180 Sign.
FLOT1 NA NA -5.557400 Sign.
HCP5 NA NA 6.400800 Sign.
HIST1H2AK 6.50 NA -0.805100 Not Sign.
HIST1H4D NA NA -4.987600 Sign.
HIST1H4L NA NA 4.870800 Sign.
HMGN4 NA NA 5.395400 Sign.
JKAMP NA NA -5.125100 Sign.
KLC1 5.34 NA -4.714100 Not Sign.
LIN28B NA -5.23 -5.232050 Sign.
LRFN5 5.23 NA 5.597540 Sign.
MFF 4.98 NA 4.601028 Not Sign.
MICB NA NA -5.557000 Sign.
MYO18A NA NA -5.128570 Sign.
NDUFA2 NA 5.19 5.190020 Sign.
NEGR1 7.35 NA 8.760622 Sign.
NKAPL NA NA 5.002860 Sign.
OLFM4 NA 5.09 5.091290 Sign.
OSBPL3 NA NA -5.622890 Sign.
PCDHA5 NA NA -5.402970 Sign.
PCDHA8 NA NA -4.980100 Sign.
PCMTD1 -5.38 NA -4.612900 Not Sign.
PGBD1 -5.80 NA -6.313100 Sign.
PIGFP2 NA NA -5.305600 Sign.
PMFBP1 NA NA -5.160620 Sign.
PPP1R18 NA NA 4.910200 Sign.
PROX2 NA NA -5.758100 Sign.
PRRC2A 6.29 NA 3.755600 Not Sign.
PRSS16 NA NA -5.916080 Sign.
PXDNL NA 5.89 5.887460 Sign.
RAB27B NA NA 5.012900 Sign.
RANGAP1 NA NA -5.575273 Sign.
RERE -5.46 NA -5.310078 Sign.
RFWD2 NA NA 5.039850 Sign.
RP1-153G14.4 NA NA 5.354000 Sign.
RP1-265C24.5 NA NA 5.532000 Sign.
RP11-163N6.2 NA NA -5.336530 Sign.
RP11-318C24.2 NA NA -5.027510 Sign.
RP11-600F24.2 NA NA 5.185660 Sign.
RP11-73M18.6 NA NA 5.031320 Sign.
RP11-73M18.7 NA NA 4.856130 Sign.
RP11-73M18.8 NA NA 5.142000 Sign.
RP11-73M18.9 NA NA 4.977330 Sign.
RP11-814H16.2 NA NA 5.009600 Sign.
RP11-894P9.2 NA NA -5.462560 Sign.
RP5-1115A15.1 NA NA -5.175240 Sign.
RP5-874C20.3 NA NA 5.739000 Sign.
RPL31P12 NA NA -7.785520 Sign.
RPS6KL1 NA NA -5.023810 Sign.
RTN1 NA NA -5.348450 Sign.
SF3B1 NA 4.45 5.214900 Sign.
SLC25A17 NA -4.73 5.076990 Sign.
SLC30A9 -5.46 NA -5.774530 Sign.
SNORA63 NA NA 4.848870 Sign.
SYNE2 NA NA 5.609528 Sign.
TIAF1 NA NA -5.361200 Sign.
TMEM106B NA NA -5.790690 Sign.
TMEM161B-AS1 NA NA 6.720000 Sign.
TMEM258 NA NA 5.021730 Sign.
TMEM33 5.16 NA 4.837418 Sign.
TMX2 5.52 NA -0.130340 Not Sign.
TNKS1BP1 NA NA 4.922610 Sign.
TRMT61A NA 5.05 5.051300 Sign.
VARS2 NA NA 6.323130 Sign.
WASF5P NA NA -5.156240 Sign.
XPNPEP3 NA 5.11 5.110000 Sign.
ZC3H7B NA NA 5.729100 Sign.
ZKSCAN3 5.05 NA 6.093300 Sign.
ZKSCAN4 -6.08 NA -3.889300 Not Sign.
ZMYND8 4.95 NA -0.204800 Not Sign.
ZNF165 -4.96 NA 4.186300 Not Sign.
ZNF184 -5.19 NA -6.325200 Sign.
ZNF204P NA NA -5.032700 Sign.
ZNF445 NA -5.11 -5.103280 Sign.
ZSCAN12P1 NA NA 6.268010 Sign.
ZSCAN16 -5.47 NA -6.109000 Sign.
ZSCAN23 -5.66 NA -5.777500 Sign.
ZSCAN31 -5.59 NA -5.084150 Sign.
ZSCAN9 -5.14 NA -6.159020 Sign.

4.2 Observed gene expression TWAS of depression

Our findings compared to Jansen et al. (2016)

##################
# Comparing our findings to the findings from Jansen et al. (TWAS of observed gene expression)
##################

###
# Load data
###

library(data.table)
library(dplyr)

our_hits <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
Jansen_alloutput <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Previous_literature/Jansen\ et\ al.\ hits.csv")
our_alloutput <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")

#filter for significant features only in the Jansen paper
colnames(Jansen_alloutput)
Jansen_sign <- filter(Jansen_alloutput, Jansen_alloutput$`FDR control vs current`< 0.1) #this is for the Jansenvsour study comparison

#keep only needed columns in both df 
library(tidyverse)
our_hits <- as_tibble(our_hits)
our_hits
our_hits_filt <- our_hits %>% select("ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P", "PANEL_clean_short")

###
#Comparing our results vs Jansen et al's 
###
our_hits[order(our_hits$ID), ]
Jansen_alloutput[order(Jansen_alloutput$Gene), ]

table_ourvsJansen2 <- merge(our_hits, Jansen_alloutput, by.x = "ID", by.y = "Gene")

validated <- table_ourvsJansen2[(table_ourvsJansen2$`P control vs current`< 0.05), ]
dim(validated)[1] # 44
#44 features which were significant in our study, were also nominally significant in their study (for either one of the three comparison types)

unique(validated$ID)
#these 44 hits corresponded to 14 unique genes
#[1] "ANKRD44"  "CKB"      "COQ3"     "DLST"     "EP300"    "FLOT1"
#[7] "OSBPL3"   "PCDHA8"   "RAB27B"   "RERE"     "SYNE2"    "TMEM106B"
#[13] "TRMT61A"  "ZSCAN16"

nrow(validated)  #44 rows

###
#create a table comparing our findings to theirs
###

#Order cols as you like
library(tibble)
validated <- as_data_frame(validated)
colnames(validated)

#order the table by CHR and then P0
str(validated)
validated$CHR <- as.numeric(as.character(validated$CHR))
validated$P0 <- as.numeric(as.character(validated$P0))
validated <- validated[order(validated$CHR, validated$P0), ]


#fix columns
validated$Location <- paste0('chr',validated$CHR,':', validated$P0,'-',validated$P1)
validated$DallAglio_Zscore <- validated$TWAS.Z
validated$DallAglio_pvalue <- validated$TWAS.P
validated$Jansen_pvalue_controlsvscurrent <- validated$`P control vs current` 
validated$Jansen_zscore_controlsvscurrent <- validated$`B control vs current`

#set a column order
col_order <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")

validated <- validated[, col_order]
validated

#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies

#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 14    #Bonf. significance = nominal p value / number of unique genes
# = 0.003571429

validated$Bonf_validated <- ifelse(validated$Jansen_pvalue_controlsvscurrent < 0.003571429, "Yes", "No")

#2) add the column specifying whether direction of effects is consistent

validated$Consistent_dir_effect <- ifelse((validated$Jansen_zscore_controlsvscurrent > 0 & validated$DallAglio_Zscore > 0) | (validated$Jansen_zscore_controlsvscurrent < 0 & validated$DallAglio_Zscore <0), "Yes", "No")

sum(validated$Consistent_dir_effect == "Yes")  #16 associations present the same direction of effect
sum(validated$Consistent_dir_effect == "No") #28 associations present a different direction of effect

sum(validated$Bonf_validated == "Yes") #12 associations Bonferroni validated
print(validated$ID[validated$Bonf_validated == "Yes"]) 
#[1] "RERE"     "RERE"     "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B"
#[8] "TMEM106B" "TMEM106B" "TMEM106B" "EP300"    "EP300"

#these 12 validated associations come from 3 unique genes = RERE, TMEM106B, EP300

#change col order again
col_order_2 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")

validated <- validated[, col_order_2]
head(validated)

#save as csv file (each df col is treated as independent col, +excel format)
write.csv(validated, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Previous_literature/OurFindingsvsJansen_correct.csv', row.names = F)

validated

Jansen et al. (2016) findings compared to ours

####
# Comparing the Jansen et al results to ours 
####
#this script is the continuation of the one before

#order both df by gene ID
our_alloutput[order(our_alloutput$ID), ]
Jansen_sign[order(Jansen_sign$Gene), ]

#merge
table_Jansenvsours <- merge(our_alloutput, Jansen_sign, by.x = "ID", by.y = "Gene")

Replicated_byus <- table_Jansenvsours[(table_Jansenvsours$TWAS.P < 0.05), ]
dim(Replicated_byus)[1]
#66 observations

unique(Replicated_byus$ID)
#pertain to 32 IDs:
# [1] "AMICA1"  "ARHGEF7" "ARL4C"   "ASPH"    "CCDC116" "CD47"    "COA1"    "CPEB4"  
# [9] "DDHD1"   "DENND4C" "DYRK2"   "DYSF"    "FBXO3"   "GNPTAB"  "GOT2"    "IL6R"   
#[17] "INVS"    "KTN1"    "MBNL1"   "MEFV"    "MTSS1"   "MYH9"    "NAPG"    "NCALD"  
#[25] "NUPL2"   "OSTM1"   "PAPPA2"  "SP4"     "TGFBR3"  "TMED10"  "TMEM136" "TMEM64"


###
#FIx the table
###

#Order cols as you like
library(tibble)
Replicated_byus <- as_data_frame(Replicated_byus)
colnames(Replicated_byus)

#order the table by CHR and then P0
str(Replicated_byus)
Replicated_byus$CHR <- as.numeric(as.character(Replicated_byus$CHR))
Replicated_byus$P0 <- as.numeric(as.character(Replicated_byus$P0))
Replicated_byus$P1 <- as.numeric(as.character(Replicated_byus$P1))

Replicated_byus <- Replicated_byus[order(Replicated_byus$CHR, Replicated_byus$P0), ]

#fix columns
Replicated_byus$Location <- paste0('chr',Replicated_byus$CHR,':', Replicated_byus$P0,'-',Replicated_byus$P1)
Replicated_byus$DallAglio_Zscore <- Replicated_byus$TWAS.Z
Replicated_byus$DallAglio_pvalue <- Replicated_byus$TWAS.P
Replicated_byus$Jansen_pvalue_controlsvscurrent <- Replicated_byus$`P control vs current` 
Replicated_byus$Jansen_zscore_controlsvscurrent <- Replicated_byus$`B control vs current`

#set a column order
col_order3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")

Replicated_byus <- Replicated_byus[, col_order3]
Replicated_byus

#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies

#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 32    #Bonf. significance = nominal p value / number of unique genes
# = 0.0015625

Replicated_byus$Bonf_validated <- ifelse(Replicated_byus$DallAglio_pvalue < 0.0015625, "Yes", "No")

#2) add the column specifying whether direction of effects is consistent

Replicated_byus$Consistent_dir_effect <- ifelse((Replicated_byus$Jansen_zscore_controlsvscurrent > 0 & Replicated_byus$DallAglio_Zscore > 0) | (Replicated_byus$Jansen_zscore_controlsvscurrent < 0 & Replicated_byus$DallAglio_Zscore <0), "Yes", "No")

sum(Replicated_byus$Consistent_dir_effect == "Yes")  #33 associations present the same direction of effect
sum(Replicated_byus$Consistent_dir_effect == "No") #33 associations present a different direction of effect

sum(Replicated_byus$Bonf_validated == "Yes") #11 associations Bonferroni validated
print(Replicated_byus$ID[Replicated_byus$Bonf_validated == "Yes"]) 
#[1] "PAPPA2" "MBNL1"  "TMEM64" "TMEM64" "TMEM64" "TMEM64" "GNPTAB" "KTN1"   "KTN1"  
#[10] "KTN1"   "TMED10"

#these come from 6 unique genes = PAPPA2, MBNL1, TMEM64, GNPTAB, KTN1, TMED10

#change col order again
col_order_3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")

Replicated_byus <- Replicated_byus[, col_order_3]
head(Replicated_byus)

#save as csv file 
write.csv(Replicated_byus, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Previous_literature/Jansenvsourfindings_correct.csv', row.names = F)

Replicated_byus

Show comparison with Jansen et al.

Our Findings vs Jansen
Location ID PANEL_clean_short DallAglio_Zscore Jansen_zscore_controlsvscurrent DallAglio_pvalue Jansen_pvalue_controlsvscurrent Consistent_dir_effect Bonf_validated
chr1:8412457-8877702 RERE GTEx Whole Blood -5.095707 0.194 3.47e-07 0.000998 No Yes
chr1:8412457-8877702 RERE YFS Blood -5.310078 0.194 1.10e-07 0.000998 No Yes
chr2:197831741-198175897 ANKRD44 YFS Blood -5.690140 -0.129 1.27e-08 0.036400 Yes No
chr5:140220907-140223351 PCDHA8 GTEx Cerebellar Hemispher… -4.980100 0.135 6.36e-07 0.027700 No No
chr6:28092338-28097860 ZSCAN16 YFS Blood -6.109000 -0.146 1.00e-09 0.016700 Yes No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 0.130 1.16e-07 0.030700 No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 0.132 1.16e-07 0.031100 No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 0.130 4.04e-07 0.030700 No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 0.132 4.04e-07 0.031100 No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 0.130 7.95e-07 0.030700 Yes No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 0.132 7.95e-07 0.031100 Yes No
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 0.130 1.16e-07 0.030700 No No
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 0.132 1.16e-07 0.031100 No No
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 0.130 1.49e-07 0.030700 No No
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 0.132 1.49e-07 0.031100 No No
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 0.130 2.74e-08 0.030700 No No
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 0.132 2.74e-08 0.031100 No No
chr6:99817347-99842082 COQ3 CMC DLPFC Splicing 5.146560 0.146 2.65e-07 0.030500 Yes No
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 -0.187 3.69e-08 0.000894 No Yes
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 -0.175 3.69e-08 0.007620 No No
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 -0.178 3.69e-08 0.001790 No Yes
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 -0.187 7.72e-08 0.000894 No Yes
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 -0.175 7.72e-08 0.007620 No No
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 -0.178 7.72e-08 0.001790 No Yes
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 -0.187 7.01e-09 0.000894 Yes Yes
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 -0.175 7.01e-09 0.007620 Yes No
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 -0.178 7.01e-09 0.001790 Yes Yes
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 -0.187 3.18e-08 0.000894 No Yes
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 -0.175 3.18e-08 0.007620 No No
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 -0.178 3.18e-08 0.001790 No Yes
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 -0.138 1.88e-08 0.025700 Yes No
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 -0.158 1.88e-08 0.007180 Yes No
chr14:64319682-64693151 SYNE2 NTR Blood 5.609528 0.168 2.03e-08 0.009600 Yes No
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 -0.141 6.31e-07 0.029600 No No
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 -0.134 6.31e-07 0.040200 No No
chr14:75348594-75370448 DLST PsychENCODE 5.089700 -0.141 3.59e-07 0.029600 No No
chr14:75348594-75370448 DLST PsychENCODE 5.089700 -0.134 3.59e-07 0.040200 No No
chr14:103985996-103989448 CKB YFS Blood 5.346000 0.129 8.99e-08 0.043300 Yes No
chr14:103995508-104003410 TRMT61A CMC DLPFC 5.051300 0.134 4.39e-07 0.035600 Yes No
chr14:103995521-104003410 TRMT61A GTEx Whole Blood 4.977593 0.134 6.44e-07 0.035600 Yes No
chr18:52385091-52562747 RAB27B PsychENCODE 5.012900 -0.152 5.36e-07 0.012400 No No
chr18:52495707-52562747 RAB27B CMC DLPFC Splicing 4.843190 -0.152 1.28e-06 0.012400 No No
chr22:41487790-41576081 EP300 GTEx Cerebellum 5.493900 0.186 3.93e-08 0.003380 Yes Yes
chr22:41487790-41576081 EP300 YFS Blood 5.059100 0.186 4.21e-07 0.003380 Yes Yes
Jansen vs our findings
Location ID PANEL_clean_short DallAglio_Zscore Jansen_zscore_controlsvscurrent DallAglio_pvalue Jansen_pvalue_controlsvscurrent Consistent_dir_effect Bonf_validated
chr1:92145902-92371892 TGFBR3 PsychENCODE 1.969894 -0.283 0.0489000 8.62e-06 No No
chr1:154377669-154441926 IL6R GTEx Whole Blood -2.562862 0.205 0.0104000 1.96e-04 No No
chr1:154377669-154441926 IL6R GTEx Whole Blood -2.562862 0.210 0.0104000 2.14e-04 No No
chr1:154377669-154441926 IL6R YFS Blood -2.121342 0.205 0.0339000 1.96e-04 No No
chr1:154377669-154441926 IL6R YFS Blood -2.121342 0.210 0.0339000 2.14e-04 No No
chr1:176432307-176814735 PAPPA2 GTEx Substantia nigra 3.910660 0.238 0.0000920 2.74e-04 Yes Yes
chr2:71680852-71913898 DYSF PsychENCODE 2.009264 0.254 0.0445000 1.40e-05 Yes No
chr2:71680852-71913898 DYSF PsychENCODE 2.009264 0.231 0.0445000 1.08e-04 Yes No
chr2:71680852-71913898 DYSF PsychENCODE 2.009264 0.231 0.0445000 1.92e-04 Yes No
chr2:235401681-235405693 ARL4C NTR Blood -2.642760 -0.235 0.0082200 1.07e-04 Yes No
chr2:235401685-235405697 ARL4C YFS Blood -2.342520 -0.235 0.0192000 1.07e-04 Yes No
chr3:107762145-107809872 CD47 YFS Blood -2.087970 -0.227 0.0368000 1.30e-04 Yes No
chr3:151961617-152183569 MBNL1 PsychENCODE -2.382370 -0.162 0.0172000 2.73e-04 Yes No
chr3:151985828-152183569 MBNL1 CMC DLPFC -2.754761 -0.162 0.0058700 2.73e-04 Yes No
chr3:151985828-152183569 MBNL1 CMC DLPFC Splicing 4.155040 -0.162 0.0000325 2.73e-04 No Yes
chr5:173315283-173388979 CPEB4 GTEx Whole Blood 2.672010 0.293 0.0075400 1.77e-06 Yes No
chr6:108362613-108487058 OSTM1 GTEx Cortex -1.966030 -0.231 0.0492950 2.47e-04 Yes No
chr7:21467651-21554162 SP4 NTR Blood -3.045580 -0.242 0.0023220 1.64e-04 Yes No
chr7:21467652-21554440 SP4 PsychENCODE 2.048560 -0.242 0.0405000 1.64e-04 No No
chr7:21467652-21554440 SP4 GTEx Pituitary 2.843150 -0.242 0.0044700 1.64e-04 No No
chr7:21467652-21554440 SP4 GTEx Thyroid 2.969400 -0.242 0.0029800 1.64e-04 No No
chr7:21467688-21554151 SP4 CMC DLPFC Splicing -2.543660 -0.242 0.0110000 1.64e-04 Yes No
chr7:23221237-23240630 NUPL2 NTR Blood -2.247980 -0.221 0.0245770 1.28e-04 Yes No
chr7:23221446-23240628 NUPL2 GTEx Pituitary -2.035400 -0.221 0.0418000 1.28e-04 Yes No
chr7:23221446-23240630 NUPL2 YFS Blood -2.055800 -0.221 0.0398000 1.28e-04 Yes No
chr7:43648055-43769316 COA1 GTEx Whole Blood -2.099800 -0.222 0.0357000 2.24e-04 Yes No
chr8:62414205-62627155 ASPH GTEx Whole Blood 2.630040 0.236 0.0085400 1.25e-04 Yes No
chr8:91634223-91803777 TMEM64 GTEx Adrenal Gland 3.393900 -0.255 0.0006890 9.19e-05 No Yes
chr8:91634223-91803860 TMEM64 PsychENCODE 3.358660 -0.255 0.0007830 9.19e-05 No Yes
chr8:91634223-91803777 TMEM64 GTEx Thyroid 3.124600 -0.255 0.0017800 9.19e-05 No No
chr8:91634223-91803777 TMEM64 GTEx Whole Blood 4.326630 -0.255 0.0000151 9.19e-05 No Yes
chr8:91634227-91803859 TMEM64 NTR Blood 3.585800 -0.255 0.0003360 9.19e-05 No Yes
chr8:102698769-103137135 NCALD CMC DLPFC Splicing 2.296500 -0.260 0.0216470 1.43e-05 No No
chr8:102698771-103137135 NCALD YFS Blood -1.998400 -0.260 0.0456780 1.43e-05 Yes No
chr8:125563031-125740730 MTSS1 GTEx Whole Blood 2.001870 -0.256 0.0453000 1.13e-04 No No
chr8:125563031-125740730 MTSS1 YFS Blood 2.150700 -0.256 0.0315000 1.13e-04 No No
chr9:19230762-19374137 DENND4C CMC DLPFC Splicing -2.128000 -0.226 0.0333370 2.08e-04 Yes No
chr9:102861510-103063426 INVS NTR Blood 2.465700 -0.249 0.0137000 1.88e-04 No No
chr9:102861538-103063282 INVS GTEx Cortex 1.963500 -0.249 0.0495800 1.88e-04 No No
chr9:102861538-103063282 INVS GTEx Nucleus accumbens 1.960990 -0.249 0.0498800 1.88e-04 No No
chr9:102861538-103063282 INVS GTEx Whole Blood 1.967980 -0.249 0.0491000 1.88e-04 No No
chr11:33762485-33796089 FBXO3 YFS Blood 2.689500 -0.242 0.0071600 1.75e-04 No No
chr11:33762489-33796071 FBXO3 NTR Blood 2.768300 -0.242 0.0056350 1.75e-04 No No
chr11:118064455-118095809 AMICA1 GTEx Whole Blood -2.773880 0.248 0.0055400 4.34e-05 No No
chr11:120195837-120204388 TMEM136 CMC DLPFC 2.799790 0.242 0.0051100 2.08e-04 Yes No
chr11:120195838-120204391 TMEM136 GTEx Adrenal Gland 2.314160 0.242 0.0207000 2.08e-04 Yes No
chr11:120195838-120204391 TMEM136 PsychENCODE 2.569810 0.242 0.0102000 2.08e-04 Yes No
chr12:68042118-68059186 DYRK2 PsychENCODE 2.022380 -0.232 0.0431000 2.87e-04 No No
chr12:102139275-102224716 GNPTAB GTEx Caudate 2.714700 -0.239 0.0066300 1.05e-04 No No
chr12:102139275-102224716 GNPTAB GTEx Thyroid -3.440580 -0.239 0.0005800 1.05e-04 Yes Yes
chr13:111767623-111947542 ARHGEF7 CMC DLPFC Splicing -2.395100 -0.223 0.0166160 1.51e-04 Yes No
chr13:111767623-111947542 ARHGEF7 CMC DLPFC Splicing 2.482300 -0.223 0.0130540 1.51e-04 No No
chr14:53503457-53620046 DDHD1 CMC DLPFC Splicing -2.190690 -0.226 0.0285000 5.95e-05 Yes No
chr14:56025790-56168244 KTN1 PsychENCODE -3.357870 -0.212 0.0007850 2.38e-04 Yes Yes
chr14:56046924-56151302 KTN1 CMC DLPFC Splicing -3.841650 -0.212 0.0001220 2.38e-04 Yes Yes
chr14:56046925-56168244 KTN1 GTEx Cerebellum -3.341430 -0.212 0.0008330 2.38e-04 Yes Yes
chr14:75598173-75643334 TMED10 PsychENCODE -3.214700 -0.250 0.0013100 2.57e-05 Yes Yes
chr16:3292028-3306627 MEFV GTEx Whole Blood -2.208440 0.254 0.0272100 8.29e-05 No No
chr16:58741035-58768261 GOT2 GTEx Adrenal Gland -2.009775 -0.242 0.0445000 2.63e-04 Yes No
chr16:58741035-58768261 GOT2 GTEx Thyroid 2.504040 -0.242 0.0123000 2.63e-04 No No
chr16:58741035-58768261 GOT2 GTEx Whole Blood 2.134320 -0.242 0.0328200 2.63e-04 No No
chr18:10525902-10552757 NAPG GTEx Frontal Cortex -2.021900 -0.240 0.0431840 3.09e-04 Yes No
chr22:21987005-21991616 CCDC116 GTEx Frontal Cortex 2.031400 0.244 0.0422000 1.87e-04 Yes No
chr22:36677322-36784063 MYH9 CMC DLPFC -2.540000 0.223 0.0111000 2.42e-04 No No
chr22:36677327-36784063 MYH9 PsychENCODE -3.110229 0.223 0.0018700 2.42e-04 No No
chr22:36677327-36784063 MYH9 YFS Blood -2.885900 0.223 0.0039000 2.42e-04 No No

Report ended