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#### PRSice2 ####
##Some quality control steps before using summary statistics file in PRSice2
## *** make sure to review any documents that come with the summary statistics to understand how the summary statistics were derived and what the column names stand for ***
# In R:
ss=read.table("BMI_1kgph3_chr16_snps_summarystat_2024.txt",header=T)
nrow(ss) #[1] 68385
head(ss)
summary(ss$INFO) #imputation quality
summary(ss$N) #N for each SNP in source GWAS
summary(ss$Freq1.Hapmap) #minor allele frequency for each SNP
nrow(ss[ss$INFO<0.8,]) #[1] 199
nrow(ss[ss$N<0.5*max(ss$N),]) #[1] 2629
nrow(ss[ss$Freq1.Hapmap<=0.01 | is.na(ss$Freq1.Hapmap),]) #[1] 1590
#Some quality control for imputation quality, N, and minor allele frequency:
ss1=ss[ss$INFO>0.8 & ss$N>0.5*max(ss$N) & ss$Freq1.Hapmap>0.01 & !is.na(ss$Freq1.Hapmap),]
nrow(ss1) #[1] 64933
colnames(ss1)<-c("SNP","A1","A2","Freq1.Hapmap","BETA","SE","P","N","INFO")
head(ss1)
write.table(ss1,"BMI_1kgph3_chr16_snps_summarystat_2024_short.txt", col.names=T, row.names=F, quote=F, sep='\t')
##Now we are ready to try PRSice2!
##Need to set up the following modules in R for PRSice2 to run properly:
module load lang/R/3.5.1-Python-3.8.5-Anaconda3-2020.11
R --vanilla
library(ggplot2)
library(optparse) #may need to install this package manually
library(methods)
library(tools)
library(data.table)
library(grDevices)
library(RColorBrewer)
q()
## Linux -- Using our tutorial files:
Rscript PRSice.R --prsice ./PRSice_linux --base BMI_1kgph3_chr16_snps_summarystat_2024_short.txt --target 1kgph3_chr16 --pheno 1kgph3_dummybmi20200804.txt --pheno-col dummybmi --thread 1 --stat BETA --pvalue P --binary-target F --out dummybmi_nocov2024
## Windows (in R) -- Using our tutorial files:
system("Rscript.exe PRSice.R --prsice ./PRSice_win64.exe --base BMI_1kgph3_chr16_snps_summarystat_2024_short.txt --target 1kgph3_chr16 --pheno 1kgph3_dummybmi20200804.txt --pheno-col dummybmi --thread 1 --stat BETA --pvalue P --binary-target F --out dummybmi_nocov2024")
## Linux -- Adding a covariate:
Rscript PRSice.R --prsice ./PRSice_linux --base BMI_1kgph3_chr16_snps_summarystat_2024_short.txt --target 1kgph3_chr16 --pheno 1kgph3_dummybmi20200804.txt --pheno-col dummybmi --cov 1kgph3_dummybmi20200804.txt --cov-col sex --thread 1 --stat BETA --pvalue P --binary-target F --out dummybmi_sexcov2024
## Windows (in R) -- Adding a covariate:
system("Rscript.exe PRSice.R --prsice ./PRSice_win64.exe --base BMI_1kgph3_chr16_snps_summarystat_2024_short.txt --target 1kgph3_chr16 --pheno 1kgph3_dummybmi20200804.txt --pheno-col dummybmi --cov 1kgph3_dummybmi20200804.txt --cov-col sex --thread 1 --stat BETA --pvalue P --binary-target F --out dummybmi_sexcov2024")
##Additional Notes/Examples:
##This is sample script (***Need to change the actual file names***):
#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
## PRSice2 within R on Windows (from tutorial):
system("Rscript.exe PRSice.R --prsice ./PRSice_win64.exe --base TOY_BASE_GWAS.assoc --target TOY_TARGET_DATA --thread 1 --stat OR --binary-target T")
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