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PRS_LDpred_cambridge_BEEA.R
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#!/usr/bin/env Rscript
# generate data for LDpred,for imputed validation data
#use Cambridge+BEACONcontrol as the validation, only work on BEEA
#BEACONcase+AMOS+other BEACONcontrol for discovery
library(data.table)
#generate disc/valid samples
sampletable=readxl::read_excel("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/PLINKinputCombo_bca_07Feb2018.xls",1)
sampletable=as.data.frame(sampletable)
#site 30:Cambridge, 55:AMOS
idx=which(sampletable$site<30)
table(sampletable$phenoEA_bca[idx])
# -9 1 2
# 2566 2185 1512
idx=which(sampletable$site==30)
table(sampletable$phenoEA_bca[idx])
# -9 2
# 936 1003
idx=which(sampletable$site==55)
table(sampletable$phenoEA_bca[idx])
# 1
# 1022
table(sampletable$site,sampletable$phenoEA_bca)
# -9 1 2
# 11 174 0 102
# 12 303 214 0
# 13 2 116 63
# 14 822 0 502
# 15 0 88 42
# 16 202 218 193
# 17 332 323 0
# 18 36 259 247
# 19 101 0 0
# 20 160 167 0
# 21 122 92 54
# 22 2 26 14
# 23 8 437 59
# 25 0 245 236
# 27 302 0 0
# 30 936 0 1003
# 55 0 1022 0
# NA 15 0 0
#sites: control will be picked to combine with Cambridge to form validationset
controlsites=c(12,17,20,23)
table(sampletable$phenoEA_bca[sampletable$site %in% controlsites])
# -9 1 2
# 803 1141 59
for (i in 1:ncol(sampletable)) sampletable[which(sampletable[,i]==-9),i]=NA
validation_cases=sampletable$localid[sampletable$site %in% 30 & !is.na(sampletable$phenoEABE_bca)]
idx=match(validation_cases,sampletable$localid)
table(sampletable$phenoEABE_bca[idx])
validation_controls=sampletable$localid[which(sampletable$site %in% controlsites & sampletable$phenoEABE_bca==1)]
validationsamples=c(validation_cases,validation_controls)
discoverysamples=sampletable$localid[!is.na(sampletable$phenoEABE_bca)]
discoverysamples=discoverysamples[! discoverysamples %in% validationsamples]
idx=match(discoverysamples,sampletable$localid)
table(sampletable$phenoEABE_bca[idx])
# 1 2
# 2065 3921
idx=which(sampletable$localid %in% discoverysamples)
sampletable1=sampletable[idx,]
discsamples=sampletable[idx,1:2]
#write.table(discsamples,file="../result/discsamplesBEEA_plink.txt",row.names = F,col.names = F,sep="\t",quote=F)
BEEAsamples=sampletable1$localid[which(sampletable1$phenoEABE_bca==2)]
COsamples=sampletable1$localid[which(sampletable1$phenoEABE_bc==1)]
fam=read.table("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/imputation/bca_1000g/bca_filter_noambiguous.fam",header = F,stringsAsFactors = F)
COsamples=COsamples[COsamples %in% fam$V2]
BEEAsamples=BEEAsamples[BEEAsamples %in% fam$V2]
filesforGWAS=function(casesamples=BEEAsamples,controlsamples=COsamples,prefix="Discovery_BEEA_CO")
{
idx1=match(casesamples,fam$V2)
tmp1=data.frame(FID=fam$V1[idx1],IID=fam$V2[idx1],affected=2,stringsAsFactors = F)
idx2=match(controlsamples,fam$V2)
tmp2=data.frame(FID=fam$V1[idx2],IID=fam$V2[idx2],affected=1,stringsAsFactors = F)
tmp=rbind(tmp1,tmp2)
idx=match(tmp$IID,sampletable1$localid)
tmp$pc1=sampletable1$ev1_bca[idx]
tmp$pc2=sampletable1$ev2_bca[idx]
tmp$pc3=sampletable1$ev3_bca[idx]
tmp$pc4=sampletable1$ev4_bca[idx]
tmp$age=sampletable1$age[idx]
tmp$sex=sampletable1$sex[idx]
#for validate_twas, ind ID changed
tmp1=tmp
tmp1$IID=paste0(tmp$FID,"_",tmp$IID)
tmp1$FID=0
write.table(tmp1[,c(1,2)],file=paste0("../result/GWAS/",prefix,"_selectedsamples_plink1.txt"),row.names = F,col.names = F,sep="\t",quote=F)
write.table(tmp[,c(1,2)],file=paste0("../result/GWAS/",prefix,"_selectedsamples_plink.txt"),row.names = F,col.names = F,sep="\t",quote=F)
write.table(tmp,file=paste0("../result/GWAS/",prefix,"_selectedsamples_pheno_plink.txt"),row.names = F,col.names = T,sep="\t",quote=F)
write.table(tmp1,file=paste0("../result/GWAS/",prefix,"_selectedsamples_pheno_plink1.txt"),row.names = F,col.names = T,sep="\t",quote=F)
}
filesforGWAS()
#check files:
tmp=read.table("../result/GWAS/Discovery_BEEA_CO_gwas.fam")
tmp1=read.table("../result/GWAS/Discovery_BEEA_CO_selectedsamples_pheno_plink.txt",header = T)
all(tmp$V2 %in% tmp1$IID) #T
udpate_fam=function(famfile="../result/GWAS/Discovery_BEEA_CO_gwas.fam",phenofile="../result/GWAS/Discovery_BEEA_CO_selectedsamples_pheno_plink.txt")
{
fam=read.table(famfile,stringsAsFactors = F)
pheno=read.table(phenofile,header=T,stringsAsFactors = F)
idx=match(fam$V2,pheno$IID)
fam$V5=pheno$sex[idx]
fam$V6=pheno$affected[idx]
write.table(fam,file=famfile,col.names=F,row.names = F,quote=F)
}
udpate_fam()
#after running gwas, update the result
#after running gwas, update the result
update_gwas=function(gwasfile="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/GWAS/Discovery_BEEA_CO_gwas.assoc.logistic",
bimfile="../result/GWAS/Discovery_BEEA_CO_gwas.bim")
{
gwas=data.frame(fread(gwasfile,sep=" "))
#check gwas
# tmp=as.data.frame(fread("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/EA_Beacon_autosomes_comsnp_N.txt"))
# tmp1=merge(gwas,tmp,by="SNP")
bim=data.frame(fread(bimfile))
all(gwas$SNP==bim$V2)
all(gwas$A1 %in% bim$V5)
idx=match(bim$V2,gwas$SNP)
gwas$A2=bim$V6[idx]
colnames(gwas)[colnames(gwas)=="A1"]="effect_allele"
colnames(gwas)[colnames(gwas)=="A2"]="non_effect_allele"
colnames(gwas)[colnames(gwas)=="BETA"]="beta"
colnames(gwas)[colnames(gwas)=="SE"]="se"
colnames(gwas)[colnames(gwas)=="BP"]="position"
gwas$N=gwas$NMISS
gwas1=as.data.frame(fread("/fh/fast/dai_j/BEACON/Meta_summary_stat/BEEA_BEACON_autosomes.txt"))
posname1_gwas=paste0(gwas$CHR,"_",gwas$position,"_",gwas$effect_allele,"_",gwas$non_effect_allele)
posname2_gwas=paste0(gwas$CHR,"_",gwas$position,"_",gwas$non_effect_allele,"_",gwas$effect_allele)
posname1_gwas1=paste0(gwas1$CHR,"_",gwas1$position,"_",gwas1$effect_allele,"_",gwas1$non_effect_allele)
tmp=intersect(posname1_gwas, posname1_gwas1)
idx1=match(tmp,posname1_gwas)
idx2=match(tmp,posname1_gwas1)
gwas$SNP[idx1]=gwas1$SNP[idx2]
tmp=intersect(posname2_gwas, posname1_gwas1)
idx1=match(tmp,posname2_gwas)
idx2=match(tmp,posname1_gwas1)
gwas$SNP[idx1]=gwas1$SNP[idx2]
idx=is.na(gwas$P)
gwas=gwas[!idx,]
fwrite(gwas,file=gwasfile,quote=F,sep=" ")
}
#run Meta_Analysis.R and Meta_Analysis.sh to get meta results
#step0 meta analysis
# BEmeta=as.data.frame(fread("../result/Bonn_Oxford_Cambridge_METAANALYSIS_BE_comsnp1.tbl"))
# colnames(BEmeta)[which(colnames(BEmeta)=="P-value")]="P"
# BECambridge=as.data.frame(fread("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/BE_Cambridge_autosomes_comsnp_N.txt"))
# idx=match(BEmeta$MarkerName,BECambridge$SNP)
# BEmeta$chr=BECambridge$CHR[idx]
# BEmeta$pos=BECambridge$position[idx]
# write.table(BEmeta,file="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Bonn_Oxford_Cambridge_metastat.txt",col.names = T,
# row.names=F,quote=F,sep="\t")
# cmd="gzip /fh/fast/dai_j/BEACON/BEACON_GRANT/result/Bonn_Oxford_Cambridge_metastat.txt"
# system(cmd)
#
BEEAmeta=as.data.frame(fread("../result/Discovery_Bonn_METAANALYSIS_BEEA_comsnp1.tbl"))
colnames(BEEAmeta)[which(colnames(BEEAmeta)=="P-value")]="P"
BEEABeacon=as.data.frame(fread("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Discovery_BD_autosomes_comsnp_N.txt"))
idx=match(BEEAmeta$MarkerName,BEEABeacon$SNP)
BEEAmeta$chr=BEEABeacon$CHR[idx]
BEEAmeta$pos=BEEABeacon$position[idx]
write.table(BEEAmeta,file="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Discovery_Bonn_imp_metastat.txt",col.names = T,
row.names=F,quote=F,sep="\t")
cmd="gzip /fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Discovery_Bonn_imp_metastat.txt"
system(cmd)
#
# BEEAmeta=as.data.frame(fread("../result/Bonn_Cambridge_METAANALYSIS_BEEA_comsnp1.tbl"))
# colnames(BEEAmeta)[which(colnames(BEEAmeta)=="P-value")]="P"
# BEEACambridge=as.data.frame(fread("/fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Cambridge_autosomes_comsnp_N.txt"))
# idx=match(BEEAmeta$MarkerName,BEEACambridge$SNP)
# BEEAmeta$chr=BEEACambridge$CHR[idx]
# BEEAmeta$pos=BEEACambridge$position[idx]
# write.table(BEEAmeta,file="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Bonn_Cambridge_metastat.txt",col.names = T,
# row.names=F,quote=F,sep="\t")
# system("rm /fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Bonn_Cambridge_metastat.txt.gz")
# cmd="gzip /fh/fast/dai_j/BEACON/BEACON_GRANT/result/BEEA_Bonn_Cambridge_metastat.txt"
# system(cmd)
#step2-------
#work on validation------
#Pick validation samples
library(readxl)
idx=which(sampletable$localid %in% validationsamples)
sampletable1=sampletable[idx,]
valisamples=sampletable[idx,1:2]
table(sampletable$phenoEABE_bca[idx])
# 1 2
# 1140 1885
write.table(valisamples,file="../result/validationBEEAsamples_plink.txt",row.names = F,col.names = F,sep="\t",quote=F)
keep_nohetsamples=function(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/data/imputation/bca_1000g/validationBEEA_filter_noambiguous_QC")
{
dat <- read.table(paste0(prefix,".het"), header=T) # Read in the EUR.het file, specify it has header
m <- mean(dat$F) # Calculate the mean
s <- sd(dat$F) # Calculate the SD
valid <- subset(dat, F <= m+3*s & F >= m-3*s) # Get any samples with F coefficient within 3 SD of the population mean
write.table(valid[,c(1,2)], paste0(prefix,".valid.sample"), quote=F, row.names=F)
}
generate_plink=function(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/data/imputation/bca_1000g/validationBEEA_filter_noambiguous_QC",
opt="BEEA",prefix1="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Validation_BEEA",metadat=BEEAmeta)
{
#
bim <- read.table(paste0(prefix,".bim"),stringsAsFactors = F)
colnames(bim) <- c("CHR", "SNP", "CM", "BP", "B.A1", "B.A2")
metadat$Allele1=toupper(metadat$Allele1) #Allele1 is non-effect
metadat$Allele2=toupper(metadat$Allele2)
metadat$A2=metadat$Allele1
metadat$A1=metadat$Allele2
colnames(metadat)[which(colnames(metadat)=="MarkerName")]="SNP"
colnames(metadat)[which(colnames(metadat)=="chr")]="CHR"
colnames(metadat)[which(colnames(metadat)=="pos")]="BP"
#update bim SNP ID based on metadat
bim_posname1=paste0(bim$CHR,":",bim$BP,"_",bim$B.A1,"_",bim$B.A2)
bim_posname2=paste0(bim$CHR,":",bim$BP,"_",bim$B.A2,"_",bim$B.A1)
meta_posname=paste0(metadat$CHR,":",metadat$BP,"_",metadat$Allele1,"_",metadat$Allele2)
idx1=which(bim_posname1 %in% meta_posname)
idx2=which(bim_posname2 %in% meta_posname)
tmp=bim_posname1[idx1]
idx3=match(tmp,bim_posname1)
idx4=match(tmp,meta_posname)
bim$SNP[idx3]=metadat$SNP[idx4]
tmp=bim_posname2[idx2]
idx3=match(tmp,bim_posname2)
idx4=match(tmp,meta_posname)
bim$SNP[idx3]=metadat$SNP[idx4]
print(sum(bim$SNP %in% metadat$SNP))#number of overlapped SNPs
write.table(bim,file=paste0(prefix1,".bim"),col.names = F,row.names = F,sep=" ",quote=F)
# #Identify SNPs that require strand flipping
# info <- merge(bim, metadat, by = c("SNP", "CHR", "BP"))
# # Function for finding the complementary allele
# complement <- function(x) {
# switch (
# x,
# "A" = "T",
# "C" = "G",
# "T" = "A",
# "G" = "C",
# return(NA)
# )
# }
# # Get SNPs that have the same alleles across base and target
# info.match <- subset(info, A1 == B.A1 & A2 == B.A2)
# # Identify SNPs that are complementary between base and target
# info$C.A1 <- sapply(info$B.A1, complement)
# info$C.A2 <- sapply(info$B.A2, complement)
# info.complement <- subset(info, A1 == C.A1 & A2 == C.A2)
# # Update the complementary alleles in the bim file
# # This allow us to match the allele in subsequent analysis
# complement.snps <- bim$SNP %in% info.complement$SNP
# bim[complement.snps,]$B.A1 <-
# sapply(bim[complement.snps,]$B.A1, complement)
# bim[complement.snps,]$B.A2 <-
# sapply(bim[complement.snps,]$B.A2, complement)
#
#
# # Identify SNPs that require recoding in the target (to ensure the coding allele in the target data is the effective allele in the base summary statistic)
# info.recode <- subset(info, A1 == B.A2 & A2 == B.A1)
# # Update the recode SNPs
# recode.snps <- bim$SNP %in% info.recode$SNP
# tmp <- bim[recode.snps,]$B.A1
# bim[recode.snps,]$B.A1 <- bim[recode.snps,]$B.A2
# bim[recode.snps,]$B.A2 <- tmp
#
# # identify SNPs that need recoding & complement
# info.crecode <- subset(info, A1 == C.A2 & A2 == C.A1)
# # Update the recode + strand flip SNPs
# com.snps <- bim$SNP %in% info.crecode$SNP
# tmp <- bim[com.snps,]$B.A1
# bim[com.snps,]$B.A1 <- as.character(sapply(bim[com.snps,]$B.A2, complement))
# bim[com.snps,]$B.A2 <- as.character(sapply(tmp, complement))
#
# # Output updated bim file
# write.table(
# bim,
# paste0(prefix1,"_QC.adj.bim"),
# quote = F,
# row.names = F,
# col.names = F,
# sep="\t"
# )
# cmd=paste0("cp ",prefix,".bim ",prefix1,".bim.bk")
# system(cmd,wait=T)
# cmd=paste0("cp ",prefix1,"_QC.adj.bim ",prefix1,".bim")
# system(cmd,wait=T)
for (ext in c(".bed",".fam"))
{
#file.copy(paste0(prefix,ext),paste0(prefix1,ext))
cmd=paste0("cp ",paste0(prefix,ext)," ",paste0(prefix1,ext))
system(cmd,wait = T)
}
fam=read.table(paste0(prefix1,".fam"))
idx=match(fam$V2,sampletable$localid)
if (opt=="BE")
{
fam$V6=sampletable$phenoBE_bca[idx]
}
if (opt=="EA")
{
fam$V6=sampletable$phenoEA_bca[idx]
}
if (opt=="BEEA")
{
fam$V6=sampletable$phenoEABE_bca[idx]
}
write.table(fam,file=paste0(prefix1,".fam"),row.names = F,col.names = F,sep=" ",quote=F)
#generate phenotype file
tmp=fam[,c(1,2,6)]
write.table(tmp,file=paste0(prefix1,".pheno"),row.names = F,col.names = F,sep=" ",quote=F)
}
generate_plink()
generate_covariate=function(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BE")
{
fam=read.table(paste0(prefix,".fam"),stringsAsFactors = F)
idx=match(fam$V2,sampletable$localid)
Covariate=sampletable[idx,colnames(sampletable) %in% c("sex","age","ev1_bca","ev2_bca","ev3_bca","ev4_bca")]
colnames(Covariate)[which(colnames(Covariate)=="ev1_bca")]="pc1"
colnames(Covariate)[which(colnames(Covariate)=="ev2_bca")]="pc2"
colnames(Covariate)[which(colnames(Covariate)=="ev3_bca")]="pc3"
colnames(Covariate)[which(colnames(Covariate)=="ev4_bca")]="pc4"
rownames(Covariate)=sampletable$localid[idx]
Covariate$age[is.na(Covariate$age)]=mean(Covariate$age,na.rm = T)
Covariate=data.frame(IID=rownames(Covariate),Covariate)
write.table(Covariate,file=paste0(prefix,".covariate"),col.names = T,row.names = F,sep=" ",quote=F)
}
#generate_covariate()
generate_covariate(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Validation_BEEA")
#generate_covariate(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BEEA")
#after runing PRS_LDpred.sh, read results:
#library(DescTools)
#p.threshold <- c(1,0.3,0.1)
p.threshold <- c(1,0.3,0.1,0.03,0.01,0.003,0.001,0.0003,0.0001)
# Read in the covariates
read_LDpred=function(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BEEA")
{
# Read in the phenotype file
phenotype <- read.table(paste0(prefix,".pheno"), header=F)
colnames(phenotype)=c("FID","IID","case")
covariate <- read.table(paste0(prefix,".covariate"), header=T,stringsAsFactors = F)
# Now merge the files
pheno <- merge(phenotype, covariate, by=c("IID"))
# We can then calculate the null model (model with PRS) using a linear regression
null.model <- glm(I(case==1)~., data=pheno[,!colnames(pheno)%in%c("FID","IID")],family = "binomial")
null.model1 <- glm(I(case==1)~1, data=pheno[,!colnames(pheno)%in%c("FID","IID")],family = "binomial")
# And the R2 of the null model is
null.r2 <- 1-logLik(null.model)/logLik(null.model1)
prs.result <- NULL
for(i in p.threshold){
# Go through each p-value threshold .score_LDpred_p1.0000e-01.txt
tmp=formatC(i, format = "e", digits =4)
LDpredfile=paste0(prefix,".score_LDpred_p",tmp,".txt")
if (file.exists(LDpredfile))
{
#prs <- read.table(paste0(prefix,".score_LDpred_p",tmp,".txt"), header=T,sep=",")
prs <- read.table(paste0(prefix,".score_LDpred_p",tmp,".txt.adj"), header=T,sep=",")
# Merge the prs with the phenotype matrix
# We only want the FID, IID and PRS from the PRS file, therefore we only select the
# relevant columns
pheno.prs <- merge(pheno, prs, by=c("IID"))
# Now perform a linear regression on Height with PRS and the covariates
# ignoring the FID and IID from our model
model <- glm(I(case==1)~., data=pheno.prs[,!colnames(pheno.prs)%in%c("FID","IID","true_phens","cov_prs")],family = "binomial")
model1 <- glm(I(case==1)~1, data=pheno.prs[,!colnames(pheno.prs)%in%c("FID","IID","true_phens","cov_prs")],family = "binomial")
# model R2 is obtained as
model.r2 <- 1-logLik(model)/logLik(model1)
# R2 of PRS is simply calculated as the model R2 minus the null R2
prs.r2 <- model.r2-null.r2
#prs.r2=1-logLik(model)/logLik(null.model)
# We can also obtain the coeffcient and p-value of association of PRS as follow
prs.coef <- summary(model)$coeff["PRS",]
#prs.coef <- summary(model)$coeff["cov_prs",]
prs.beta <- as.numeric(prs.coef[1])
prs.se <- as.numeric(prs.coef[2])
prs.p <- as.numeric(prs.coef[4])
# We can then store the results
prs.result <- rbind(prs.result, data.frame(Threshold=i, R2=prs.r2, P=prs.p, BETA=prs.beta,SE=prs.se))
}
}
# Best result is:
prs.result[which.max(prs.result$R2),]
return(prs.result)
}
plot_LDpred=function(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BEEA")
{
prs.result=read_LDpred(prefix=prefix)
#pdf(paste0(prefix,".LDpred.pdf"),width=12,height=8)
png(paste0(prefix,".LDpred.png"), height=10, width=10, res=300, unit="in")
# First, obtain the colorings based on the p-value
col <- suppressWarnings(colorRampPalette(c("dodgerblue", "firebrick")))
# We want the color gradient to match the ranking of p-values
prs.result <- prs.result[order(-log10(prs.result$P)),]
prs.result$color <- col(nrow(prs.result))
prs.result <- prs.result[order(prs.result$Threshold),]
# generate a pretty format for p-value output
prs.result$print.p <- round(prs.result$P, digits = 3)
prs.result$print.p[!is.na(prs.result$print.p) & prs.result$print.p == 0 ] <-
format(prs.result$P[!is.na(prs.result$print.p) & prs.result$print.p == 0 ], digits = 2)
prs.result$print.p <- sub("e", "*x*10^", prs.result$print.p)
# Generate the axis labels
xlab <- expression(italic(P) - value ~ threshold ~ (italic(P)[T]))
ylab <- expression(paste("PRS model fit: ", R ^ 2))
# Setup the drawing area
layout(t(1:2), widths=c(8.8,1.2))
par( cex.lab=1.5, cex.axis=1.25, font.lab=2,
oma=c(0,0.5,0,0),
mar=c(4,6,0.5,0.5))
# Plotting the bars
b<- barplot(height=prs.result$R2,
col=prs.result$color,
border=NA,
ylim=c(0, max(prs.result$R2)*1.25),
axes = F, ann=F)
# Plot the axis labels and axis ticks
odd <- seq(0,nrow(prs.result)+1,2)
even <- seq(1,nrow(prs.result),2)
axis(side=1, at=b[odd], labels=prs.result$Threshold[odd], lwd=2)
axis(side=1, at=b[even], labels=prs.result$Threshold[even],lwd=2)
axis(side=1, at=c(0,b[1],2*b[length(b)]-b[length(b)-1]), labels=c("","",""), lwd=2, lwd.tick=0)
# Write the p-value on top of each bar
text( parse(text=paste(
prs.result$print.p)),
x = b+0.1,
y = prs.result$R2+ (max(prs.result$R2)*1.05-max(prs.result$R2)),
srt = 45)
# Now plot the axis lines
box(bty='L', lwd=2)
axis(2,las=2, lwd=2)
# Plot the axis titles
title(ylab=ylab, line=4, cex.lab=1.5, font=2 )
title(xlab=xlab, line=2.5, cex.lab=1.5, font=2 )
# Generate plot area for the legend
par(cex.lab=1.5, cex.axis=1.25, font.lab=2,
mar=c(20,0,20,4))
prs.result <- prs.result[order(-log10(prs.result$P)),]
image(1, -log10(prs.result$P), t(seq_along(-log10(prs.result$P))), col=prs.result$color, axes=F,ann=F)
axis(4,las=2,xaxs='r',yaxs='r', tck=0.2, col="white")
# plot legend title
title(bquote(atop(-log[10] ~ model, italic(P) - value), ),
line=2, cex=1.5, font=2, adj=0)
dev.off()
return(prs.result)
}
BE_LDpred=plot_LDpred(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BE")
BEEA_LDpred=plot_LDpred(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_BEEA")
EA_LDpred=plot_LDpred(prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/Beacon_EA")