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## PRS-CS
## setting up:
git clone https://github.com/getian107/PRScs.git
wget https://www.dropbox.com/s/mt6var0z96vb6fv/ldblk_1kg_eur.tar.gz?dl=0
tar -zxvf ldblk_1kg_eur.tar.gz
#downloading and installing h5py and scipy:
pip install scipy h5py
#
#need effective sample size of source GWAS (neff =2*N_cases*N_controls/(N_cases+N_controls))
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")
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