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check_TWASres_June11.R
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check_TWASres_June11.R
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#!/usr/bin/env Rscript
qqplot=function(pvalue=NULL,fwer=NULL,main="",xlim=NULL,ylim=NULL)
{
n=length(pvalue)
plot(-log((1:n)/n,base=10),-log(pvalue[order(pvalue)],base=10),xlab="Expected p-value (-log base 10)",
ylab="Observed p-value (-log base 10)",main=main,xlim=xlim,ylim=ylim,cex.lab=1.3,cex.axis=1.3)
abline(0,1,lty=2)
pvalue_order=pvalue[order(pvalue)]
if (is.null(fwer))
{
fwer=p.adjust(pvalue_order,method="bonferroni")
idx=which(fwer<0.05)
if (length(idx)>0)
{
points(-log((1:n)/n,base=10)[idx],-log(pvalue[order(pvalue)],base=10)[idx],pch=16,col="red")
print(paste0("#FWER<0.05:",length(idx)))
legend("topleft",legend = "FWER<0.05",pch=16,col="red")
}
}else
{
idx1=sum(fwer<0.05)
if (length(idx1)>0)
{
idx=1:idx1
points(-log((1:n)/n,base=10)[idx],-log(pvalue[order(pvalue)],base=10)[idx],pch=16,col="red")
print(paste0("#FWER<0.05:",length(idx)))
legend("topleft",legend = "FWER<0.05",pch=16,col="red")
}
}
}
compute_fwer_fdr=function(dat=skat_min_code,cutoff=0.05)
{
compute_fdr=function(dat,namecol="BE_p",cutoff1=cutoff)
{
res=NULL
pvalues=dat[,which(colnames(dat)==namecol)]
names(pvalues)=rownames(dat)
tmp=p.adjust(pvalues,method="fdr")
if (sum(tmp<cutoff1,na.rm = T)>0)
{
res=tmp[which(tmp<cutoff)]
res=res[order(res)]
}
return(res)
}
compute_fwer=function(dat,namecol="BE_p",cutoff1=cutoff)
{
res=NULL
pvalues=dat[,which(colnames(dat)==namecol)]
names(pvalues)=rownames(dat)
tmp=p.adjust(pvalues,method = "bonferroni")
if (sum(tmp<cutoff,na.rm = T)>0)
{
res=tmp[which(tmp<cutoff1)]
res=res[order(res)]
}
return(res)
}
BE_fdr=compute_fdr(dat,namecol="BE_p")
BE_fwer=compute_fwer(dat,namecol="BE_p")
EA_fdr=compute_fdr(dat,namecol="EA_p")
EA_fwer=compute_fwer(dat,namecol="EA_p")
BEA_fdr=compute_fdr(dat,namecol="BEA_p")
BEA_fwer=compute_fwer(dat,namecol="BEA_p")
BEEA_fdr=compute_fdr(dat,namecol="BEEA_p")
BEEA_fwer=compute_fwer(dat,namecol="BEEA_p")
return(list(BE_fdr=BE_fdr,BE_fwer=BE_fwer,EA_fdr=EA_fdr,EA_fwer=EA_fwer,
BEA_fdr=BEA_fdr,BEA_fwer=BEA_fwer,BEEA_fdr=BEEA_fdr,BEEA_fwer=BEEA_fwer))
}
load("/fh/fast/stanford_j/Xiaoyu/QTL/data/GTEx/gtexv8_ge_anno.RData")
proteingenes=unique(gtexv8_ge_anno$Symbol[gtexv8_ge_anno$gene_type=="protein_coding"])
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_June11" #models stored for GTEx, came from prediction_michigan_models6_GTExV8.R
check_skatres=function(dat=skat_min1,opt="plot")
{
colnames(dat)=c("BE_p","EA_p","BEA_p","BEEA_p")
dat_code=dat[rownames(dat) %in% proteingenes,]
tmp=compute_fwer_fdr(dat=dat_code)
if (opt=="plot")
{
for(i in 1:length(tmp))
{
if (!is.null(tmp[[i]]))
{
print(paste0(names(tmp)[i],":"))
print(tmp[[i]])
}
}
par(mfrow=c(2,2))
qqplot(dat_code$BE_p,main="BE")
qqplot(dat_code$EA_p,main="EA")
qqplot(dat_code$BEA_p,main="BE vs EA")
qqplot(dat_code$BEEA_p,main="BEEA")
}
return(tmp)
}
check_model=function(mod=res_min,r2cut=0)
{
mod=mod[which(mod$r2>=r2cut),]
print(nrow(mod))
print(quantile(mod$r2))
print(quantile(mod$numselectedsnp))
}
scinumber=function(num)
{
formatC(num,format="e",digits = 2)
}
#GTEx junction------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8junctiondata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mar=c(5,5.5,2,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
genesr2=rownames(res_min_code)[res_min_code$r2>0.05]
genesr2=rownames(res_min_code)[res_min_code$r2>0.1]
check_res=function(r2cutoff=0)
{
print(paste0("r2cutoff=",r2cutoff))
genesr2=rownames(res_min_code)[res_min_code$r2>r2cutoff]
idx=which(rownames(assoc_min_code) %in% genesr2)
# tmp=compute_fwer_fdr(dat=assoc_min_code[idx,])
# print(paste0("regular TWAS:"))
# print(tmp)
# par(mfrow=c(2,2),mar=c(5,5.5,2,1))
# qqplot(assoc_min_code$BE_p[idx],main="BE")
# qqplot(assoc_min_code$EA_p[idx],main="EA")
# qqplot(assoc_min_code$BEA_p[idx],main="BEA") #EA vs BE
# qqplot(assoc_min_code$BEEA_p[idx],main="BEEA")
# idx=which(rownames(skat_min2_code) %in% genesr2)
# print("skat-TWAS result:")
# tmp=p.adjust(skat_min2_code$BEA_p[idx],method="fdr")
# print(paste0("BE/EA,fdr:",min(tmp)))
tmp=check_skatres(dat=skat_min2_code[idx,])
genes=NULL
for (i in 1:length(tmp))
{
genes=unique(c(genes,names(tmp[[i]])))
}
for (i in 1:length(genes))
{
gene=genes[i]
idx1=which(gtexv8_ge_anno$Symbol==gene & gtexv8_ge_anno$V3=="gene")[1]
idx2=which(rownames(res_min)==gene)
print(paste0(gene," ",gtexv8_ge_anno$Chromosome[idx1],":",gtexv8_ge_anno$start[idx1],
",R2=",scinumber(res_min$r2[idx2]),",#SNP=",res_min$numselectedsnp[idx2]))
}
return(skat_min2_code[idx,])
}
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
load(paste0(outfolder,"/skat_res.RData"))
load(paste0(outfolder,"/skat_GERD1_res.RData")) #GERD=1
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
tmp=check_res(r2cutoff=0.05)
tmp=check_res(r2cutoff=0.1)
idx=order(tmp$BEA_p)
genes=rownames(tmp)[idx[1:2]]
load(paste0(outfolder,"/skat_3gwas_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp1=check_res(r2cutoff=0.1)
idx=match(genes,rownames(tmp))
tmp1[idx,]
# BE_p EA_p BEA_p BEEA_p
# PLA2G4C 0.161076689 0.02505544 6.220921e-06 0.4973432
# LRPAP1 0.007484662 0.16598405 1.475615e-05 0.2398256
#check v7 genes
genes=c("KXD1","UBAC1","ISYNA1")
for (gene in genes)
{
idx=which(rownames(skat_min2_code)==gene)
print(paste0(gene,": BE-p ",scinumber(skat_min2_code$BE_p[idx])))
print(paste0(gene,": EA-p ",scinumber(skat_min2_code$EA_p[idx])))
print(paste0(gene," BEEA-p ",scinumber(skat_min2_code$BEEA_p[idx])))
}
#GTEx stomach------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_stomach_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8stomachdata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mfrow=c(1,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
#SKAT TWAS result
load(paste0(outfolder,"/skat_res.RData"))
load(paste0(outfolder,"/skat_GERD1_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
tmp=check_res(r2cutoff=0.05)
tmp=check_res(r2cutoff=0.1)
#GTEx blood------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_blood_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8blooddata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mfrow=c(1,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
#SKAT TWAS result
load(paste0(outfolder,"/skat_res.RData"))
load(paste0(outfolder,"/skat_GERD1_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
idx=order(tmp$BEEA_p)
genes=rownames(tmp)[idx[1:5]]
tmp=check_res(r2cutoff=0.05)
tmp1=p.adjust(tmp$EA_p)
min(tmp1)
tmp=check_res(r2cutoff=0.1)
load(paste0(outfolder,"/skat_3gwas_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp1=check_res()
idx=match(genes,rownames(tmp))
tmp1[idx,]
# BE_p EA_p BEA_p BEEA_p
# KLHL26 0.4915183 0.9735564 0.2844699 0.8912872
# PGPEP1 0.4032439 0.8299318 0.3935246 0.5317461
# LSM4 0.3838819 0.9321208 0.7118257 0.4740115
# GDF15 0.5593960 0.7814989 0.3670895 0.5893278
# HOMER3 0.7362117 0.5671278 0.2337186 0.7806328
#GTEx mucosa------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_mucosa_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8mucosadata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mfrow=c(1,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
#SKAT TWAS result
load(paste0(outfolder,"/skat_res.RData"))
rownames(tmp)[which.min(tmp1)]
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
idx=order(tmp$EA_p)
genes=rownames(tmp)[idx[1:2]]
tmp=check_res(r2cutoff=0.05)
tmp=check_res(r2cutoff=0.1)
tmp1=p.adjust(tmp$EA_p,method="fdr")
min(tmp1)
load(paste0(outfolder,"/skat_3gwas_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp1=check_res()
idx=match(genes,rownames(tmp))
tmp1[idx,]
# BE_p EA_p BEA_p BEEA_p
# SLC25A42 0.2745835 0.03689584 0.2225576 0.10134638
# TMEM161A 0.1393658 0.04392426 0.1934317 0.06722125
#GTEx muscularis------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_muscularis_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8muscularisdata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mfrow=c(1,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
#SKAT TWAS result
load(paste0(outfolder,"/skat_res.RData"))
rownames(tmp)[which.min(tmp1)]
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
tmp=check_res(r2cutoff=0.05)
tmp=check_res(r2cutoff=0.1)
#GTEx adipose------
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_adipose_June11"
#load("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GTExV8adiposedata_ambiguous_TPM_for_prediction.RData")
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
res_min_code=res_min[rownames(res_min) %in% proteingenes,]
par(mfrow=c(1,1))
hist(res_min_code$r2[res_min_code$glmflag==1],main="",xlab="R-squared",col="blue")
check_model(mod=res_min_code)
check_model(mod=res_min_code,r2cut=0.05)
#Regular TWAS result
load(paste0(outfolder,"/bca_assoc.RData"))
assoc_min_code=assoc_min[rownames(assoc_min) %in% proteingenes,]
#SKAT TWAS result
load(paste0(outfolder,"/skat_res.RData"))
load(paste0(outfolder,"/skat_GERD1_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
tmp=check_res()
tmp1=p.adjust(tmp$EA_p,method = "fdr")
min(tmp1,na.rm=T)
tmp=check_res(r2cutoff=0.05)
tmp=check_res(r2cutoff=0.1)
get_genelist=function(tissues=c("junction","stomach","mucosa","muscularis","blood","adipose"),
r2cutoff=0)
{
genetable=NULL
for (i in 1:length(tissues))
{
tissue=tissues[i]
outfolder=paste0("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_",tissue,"_June11")
if (tissue=="junction")
{
outfolder=paste0("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_June11")
}
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
load(paste0(outfolder,"/skat_res.RData"))
colnames(skat_min2)=c("BE_p","EA_p","BEA_p","BEEA_p") #weights.beta=c(1,1), the one picked before
skat_min2_code=skat_min2[rownames(skat_min2) %in% proteingenes,]
genesr2=rownames(res_min)[which(res_min$r2>r2cutoff)]
skat_min2_code=skat_min2_code[rownames(skat_min2_code) %in% genesr2,]
res=check_skatres(dat=skat_min2_code,opt="noplot")
for (j in 1:(length(res)/2))
{
if (j==1)
{
comparison="BEvsCO"
}
if (j==2)
{
comparison="EAvsCO"
}
if (j==3)
{
comparison="EAvsBE"
}
if (j==4)
{
comparison="BEEAvsCO"
}
k=(j-1)*2+1 #fdr
if (length(res[[k]])>0)
{
genesfdr=names(res[[k]])
genesfwer=names(res[[k+1]])
genesfdr=genesfdr[!genesfdr %in% genesfwer]
if (length(genesfwer)>0)
{
for (l in 1:length(genesfwer))
{
gene=genesfwer[l]
idx1=which(rownames(res_min)==gene)
idx2=which(gtexv8_ge_anno$Symbol==gene & gtexv8_ge_anno$V3=="gene")[1]
generes=data.frame(gene=gene,type=comparison,cutoff="FWER",value=res[[k+1]][l],
tissue=tissue,R2=res_min$r2[idx1],numsnp=res_min$numselectedsnp[idx1],
chr=gtexv8_ge_anno$Chromosome[idx2],position=gtexv8_ge_anno$start[idx2],stringsAsFactors = F)
genetable=rbind(genetable,generes)
}
}
if (length(genesfdr)>0)
{
for (l in 1:length(genesfdr))
{
gene=genesfdr[l]
idx1=which(rownames(res_min)==gene)
idx2=which(gtexv8_ge_anno$Symbol==gene & gtexv8_ge_anno$V3=="gene")[1]
generes=data.frame(gene=gene,type=comparison,cutoff="FDR",value=res[[k]][l],
tissue=tissue,R2=res_min$r2[idx1],numsnp=res_min$numselectedsnp[idx1],
chr=gtexv8_ge_anno$Chromosome[idx2],position=gtexv8_ge_anno$start[idx2],stringsAsFactors = F)
genetable=rbind(genetable,generes)
}
}
}
}
}
return(genetable)
}
genelist=get_genelist()
genelist05=get_genelist(r2cutoff = 0.05)
genelist1=get_genelist(r2cutoff = 0.1)
#list result for each gene
get_genetable=function(genelist)
{
genes=unique(genelist$gene)
genetable=data.frame(gene=genes,tissue=NA,type=NA,
chr=NA,position=NA,numtissue=NA,numtype=NA,stringsAsFactors = F)
for (i in 1:nrow(genetable))
{
idx=which(genelist$gene==genes[i])
genetable$chr[i]=genelist$chr[idx[1]]
genetable$position[i]=genelist$position[idx[1]]
genetable$tissue[i]=paste0(genelist$tissue[idx],collapse = "|")
genetable$type[i]=paste0(genelist$type[idx],collapse = "|")
genetable$numtissue[i]=length(unique(genelist$tissue[idx]))
genetable$numtype[i]=length(unique(genelist$type[idx]))
}
return(genetable)
}
genetable=get_genetable(genelist)
genetable05=get_genetable(genelist05)
genetable1=get_genetable(genelist1)
write.csv(genelist,file="../result/SKAT_TWAS_genes_r2_0_.csv",row.names = F)
write.csv(genelist05,file="../result/SKAT_TWAS_genes_r2_0.05_.csv",row.names = F)
write.csv(genelist1,file="../result/SKAT_TWAS_genes_r2_0.1_.csv",row.names = F)
#check the overlap of eQTLs
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_June11"
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
snp_junction=unique(unlist(strsplit(res_min$selectedsnps,"|",fixed=T)))
outfolder="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/dist500K_GTEx_blood_June11"
load(paste0(outfolder,"/preidiction_michigan_model.RData"))
snp_blood=unique(unlist(strsplit(res_min$selectedsnps,"|",fixed=T)))
sum(snp_junction %in% snp_blood) #194059