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## PRSice2
module load lang/R/3.5.1-Python-3.8.5-Anaconda3-2020.11
R --vanilla
library(ggplot2)
library(optparse)
library(method)
library(tools)
library(data.table)
library(grDevices)
library(RColorBrewer)
q()
Rscript workdir/PRSice.R --dir workdir --prsice ./workdir/PRSice_linux --base workdir/source_gwas_sumstat_info_9_prsicein.txt --target indir/target_gwas_cleaned_plink --bar-levels 5e-8,0.00001,0.00005,0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.2,0.3,0.4,0.5,1 --seed 1234 --perm 10000 --fastscore --all-score --no-regress T --out outdir/target_gwas_cleaned_source_gwas_prs

## PRS-CS
python workdir/PRScs/PRScs.py --ref_dir=workdir/ldblk_1kg_eur --bim_prefix=indir/target_gwas_cleaned_plink --sst_file=workdir/source_gwas_sumstat_prscsin.txt --n_gwas=[neff] --seed=1234 --out_dir=outdir/target_gwas_source_gwas_sumstat_prscsout
# Merge sumstat outputs from PRS-CS chr1-22 in R:
R
mci<-c()
mcall<-c()
for (i in 1:22) {
	mci<-read.table(paste("outdir//target_gwas_source_gwas_sumstat_prscsout_pst_eff_a1_b0.5_phiauto_chr",i,".txt",sep=""),header=F)
	mcall<-rbind(mcall,mci)
}
colnames(mcall)<-c("CHR","SNP","BP","A1","A2","B")
write.table(mcall,"outdir//target_gwas_source_gwas_sumstat_prscsout_pst_eff_a1_b0.5_phiauto_chr1-22.txt", row.names=F, col.names=T, quote=F, sep='\t')
mcalla=subset(mcall,select=c("SNP","A1","B"))
colnames(mcalla)<-c("SNP","A1","Score")
write.table(mcalla,"outdir//target_gwas_source_gwas_sumstat_prscsout_pst_eff_a1_b0.5_phiauto_chr1-22.raw", row.names=F, col.names=T, quote=F, sep='\t')
# Run polygenic scoring in PLINK:
plink --bfile indir/target_gwas_cleaned_plink --score outdir/target_gwas_source_gwas_sumstat_prscsout_pst_eff_a1_b0.5_phiauto_chr1-22.raw --out outdir/target_gwas_source_gwas_sumstat_prscsout