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Determine_optimal_polygenic_scoring_approach.Rmd
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Determine_optimal_polygenic_scoring_approach.Rmd
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---
title: "Comparison of polygenic scoring methods"
output:
html_document:
toc: true
theme: united
toc_depth: 3
number_sections: true
toc_float: true
fig_caption: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
<style>
p.caption {
font-size: 1.5em;
}
</style>
```{css, echo=F}
pre code, pre, code {
white-space: pre !important;
overflow-x: scroll !important;
word-break: keep-all !important;
word-wrap: initial !important;
}
```
***
# Introduction
In this study we compare a range of polygenic scoring approaches within a reference standardised framework, whereby the polygenic scores are derived using a previously defined set of variants, and LD and minor allele frequency (MAF) are estimated using an ancestry matched reference genotype data sample. This ensures the performance of the methods will hold true when implementing them within a clinical context, which will require reference-standardisation of polygenic scoring.
<br/>
***
# Aims
1. Compare the predictive utility of polygenic scores derived using a range of methods.
2. Compare predictive utility of models including multiple PRSs based on difference parameters, to those using a single PRS selected using 10-fold cross-validation or pseudo-validation.
<br/>
***
# Methods
## Samples
- UK Biobank
- TEDS
<br/>
## Outcomes
* UK Biobank
* Depression (binary)
* Intelligence (continuous)
* Body mass index (BMI - continuous)
* Height (continuous)
* Coronary Artery Disease (CAD - Binary)
* Type II Diabetes (T2D - Binary)
* Inflammatory Bowel Disorder (IBD - Binary)
* Rheumatoid arthritis (RheuArth - Binary)
* Multiple Slerosis (MultiScler - Binary)
* Prostate Cancer (Binary)
* Breast Cancer (Binary)
* TEDS
* ADHD traits (continuous)
* Height (continuous)
* Body mass index (BMI - continuous)
* GCSE scores (continuous)
<br/>
## Genotypic data
Both target sample underwent stringent quality control prior to imputation using the HRC-reference. After imputation, genotypes were converted to PLINK hard-calls, and only HapMap3 SNPs were retained. Eureopean individuals within the target samples were identified and retained if they were within the 3SD of the 1KG European mean of the first 100 principal components.
<br/>
## GWAS summary statistics
For each target phenotype, the largest independent phenotype-matched GWAS was selected for calculating polygenic scores. More information can be found in the table below.
<details><summary>Preparing GWAS sumstats table for UK Biobank phenotypes</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config')
library(data.table)
pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_270121.csv')
ukb_pheno=c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth','Breast_Cancer','Prostate_Cancer')
ukb_gwas=c('DEPR06','COLL01','BODY04','HEIG03','DIAB05','COAD01','CROH01','SCLE03','RHEU02','BRCA01','PRCA01')
ukb_prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005,0.125,0.125)
ukb_dat<-data.frame(pheno=ukb_pheno,gwas=ukb_gwas,prev=ukb_prev)
pheno_ukb<-pheno[(pheno$code %in% ukb_gwas),]
pheno_ukb<-pheno_ukb[,c('code','phenotype_name','reference_year','pmid','n_cases','n_controls','n_total')]
names(pheno_ukb)<-c('code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery')
ldsc_res<-NULL
for(i in ukb_gwas){
tmp<-read.table(paste0(gwas_rep_munged,'/',i,'_herit.log'), sep='&')
}
pheno_ukb$ldsc_h2_observed<-NA
pheno_ukb$ldsc_h2_se<-NA
pheno_ukb$ldsc_lambda_GC<-NA
pheno_ukb$ldsc_intercept<-NA
pheno_ukb$ldsc_intercept_se<-NA
ldsc_logs<-list.files(path='/scratch/groups/ukbiobank/sumstats/munged', pattern='_herit.log')
ldsc_logs<-gsub('_herit.log','',ldsc_logs)
for(i in 1:dim(pheno_ukb)[1]){
if((pheno_ukb$code[i] %in% ldsc_logs)){
ldsc_logs_i<-read.fwf(paste0(gwas_rep_munged,'/',pheno_ukb$code[i],'_herit.log'),widths = 1000000)
pheno_ukb$ldsc_h2_observed[i]<-as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ','',ldsc_logs_i[grepl('Total Observed scale h2: ', ldsc_logs_i$V1),])))
pheno_ukb$ldsc_h2_se[i]<-as.numeric(gsub("\\)",'', gsub(".*\\(",'', gsub('Total Observed scale h2: ','',ldsc_logs_i[grepl('Total Observed scale h2: ', ldsc_logs_i$V1),]))))
pheno_ukb$ldsc_lambda_GC[i]<-as.numeric(gsub('Lambda GC: ','',ldsc_logs_i[grepl('Lambda GC: ', ldsc_logs_i$V1),]))
pheno_ukb$ldsc_intercept[i]<-as.numeric(gsub(' .*','', gsub("Intercept: ",'',ldsc_logs_i[grepl("Intercept: ", ldsc_logs_i$V1),])))
pheno_ukb$ldsc_intercept_se[i]<-as.numeric(gsub("\\)",'', gsub(".*\\(",'', gsub("Intercept: ",'',ldsc_logs_i[grepl("Intercept: ", ldsc_logs_i$V1),]))))
}
}
pheno_ukb$pop_prev<-ukb_dat$prev[match(pheno_ukb$code, ukb_dat$gwas)]
pheno_ukb$Target_Phenotype<-ukb_dat$pheno[match(pheno_ukb$code, ukb_dat$gwas)]
pheno_ukb$Ncases<-as.numeric(gsub(',','',pheno_ukb$Ncases))
pheno_ukb$Ncontrols<-as.numeric(gsub(',','',pheno_ukb$Ncontrols))
pheno_ukb$samp_prev<-pheno_ukb$Ncases/(pheno_ukb$Ncases+pheno_ukb$Ncontrols)
h2l_R2 <- function(k, r2, p) {
# K baseline disease risk
# r2 from a linear regression model attributable to genomic profile risk score
# P proportion of sample that are cases
# calculates proportion of variance explained on the liability scale
#from ABC at http://www.complextraitgenomics.com/software/
#Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
x= qnorm(1-k)
z= dnorm(x)
i=z/k
C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
h2l_R2 = C*r2 / (1 + C*theta*r2)
}
se_h2l_R2 <- function(k,h2,se, p) {
# K baseline disease risk
# r2 from a linear regression model attributable to genomic profile risk score
# P proportion of sample that are cases
# calculates proportion of variance explained on the liability scale
#from ABC at http://www.complextraitgenomics.com/software/
#Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
#SE on the liability (From a Taylor series expansion)
#var(h2l_r2) = [d(h2l_r2)/d(R2v)]^2*var(R2v) with d being calculus differentiation
x= qnorm(1-k)
z= dnorm(x)
i=z/k
C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
se_h2l_R2 = C*(1-h2*theta)*se
}
pheno_ukb$ldsc_h2_observed<-as.numeric(pheno_ukb$ldsc_h2_observed)
pheno_ukb$ldsc_h2_se<-as.numeric(pheno_ukb$ldsc_h2_se)
pheno_ukb$h2_liab<-round(h2l_R2(k=pheno_ukb$pop_prev, r2=pheno_ukb$ldsc_h2_observed, p=pheno_ukb$samp_prev),3)
pheno_ukb$h2_liab_se<-round(se_h2l_R2(k=pheno_ukb$pop_prev, h2=pheno_ukb$ldsc_h2_observed, se=pheno_ukb$ldsc_h2_se, p=pheno_ukb$samp_prev),3)
pheno_ukb$ldsc_h2_observed<-paste0(pheno_ukb$ldsc_h2_observed," (", pheno_ukb$ldsc_h2_se,")")
pheno_ukb$ldsc_h2_se<-NULL
pheno_ukb$h2_liab[!is.na(pheno_ukb$Ncases)]<-paste0(pheno_ukb$h2_liab[!is.na(pheno_ukb$Ncases)]," (", pheno_ukb$h2_liab_se[!is.na(pheno_ukb$Ncases)],")")
pheno_ukb$h2_liab_se<-NULL
pheno_ukb$ldsc_intercept<-paste0(pheno_ukb$ldsc_intercept," (", pheno_ukb$ldsc_intercept_se,")")
pheno_ukb$ldsc_intercept_se<-NULL
pheno_ukb$Trait<-c('BMI','CAD','Intelligence',"Crohn's Disease",'Major Depression','T2D','Height','RheuArth','MultiScler','Prostate Cancer','Breast Cancer')
pheno_ukb<-pheno_ukb[match(ukb_dat$gwas,pheno_ukb$code),]
pheno_ukb<-pheno_ukb[,c('Trait','code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','ldsc_h2_observed','h2_liab','ldsc_intercept','ldsc_lambda_GC')]
names(pheno_ukb)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')
write.csv(pheno_ukb, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show GWAS for UK Biobank phenotypes</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv")
names(res)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N',"h2-obs (SE)","h2-liab (SE)",'Intercept','Lambda')
library(knitr)
kable(res, rownames = FALSE, caption='GWAS used for each UK Biobank phenotype')
```
</details>
<details><summary>Preparing GWAS sumstats table for TEDS phenotypes</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config')
library(data.table)
pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_270121.csv')
teds_pheno=c('Height21', 'BMI21', 'GCSE', 'ADHD')
teds_gwas=c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
teds_prev=c(NA,NA,NA,0.05)
teds_dat<-data.frame(pheno=teds_pheno,gwas=teds_gwas,prev=teds_prev)
pheno_teds<-pheno[(pheno$code %in% teds_gwas),]
pheno_teds<-pheno_teds[,c('code','phenotype_name','reference_year','pmid','n_cases','n_controls','n_total')]
names(pheno_teds)<-c('code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery')
ldsc_res<-NULL
for(i in teds_gwas){
tmp<-read.table(paste0(gwas_rep_munged,'/',i,'_herit.log'), sep='&')
}
pheno_teds$ldsc_h2_observed<-NA
pheno_teds$ldsc_h2_se<-NA
pheno_teds$ldsc_lambda_GC<-NA
pheno_teds$ldsc_intercept<-NA
pheno_teds$ldsc_intercept_se<-NA
ldsc_logs<-list.files(path='/scratch/groups/ukbiobank/sumstats/munged', pattern='_herit.log')
ldsc_logs<-gsub('_herit.log','',ldsc_logs)
for(i in 1:dim(pheno_teds)[1]){
if((pheno_teds$code[i] %in% ldsc_logs)){
ldsc_logs_i<-read.fwf(paste0(gwas_rep_munged,'/',pheno_teds$code[i],'_herit.log'),widths = 1000000)
pheno_teds$ldsc_h2_observed[i]<-as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ','',ldsc_logs_i[grepl('Total Observed scale h2: ', ldsc_logs_i$V1),])))
pheno_teds$ldsc_h2_se[i]<-as.numeric(gsub("\\)",'', gsub(".*\\(",'', gsub('Total Observed scale h2: ','',ldsc_logs_i[grepl('Total Observed scale h2: ', ldsc_logs_i$V1),]))))
pheno_teds$ldsc_lambda_GC[i]<-as.numeric(gsub('Lambda GC: ','',ldsc_logs_i[grepl('Lambda GC: ', ldsc_logs_i$V1),]))
pheno_teds$ldsc_intercept[i]<-as.numeric(gsub(' .*','', gsub("Intercept: ",'',ldsc_logs_i[grepl("Intercept: ", ldsc_logs_i$V1),])))
pheno_teds$ldsc_intercept_se[i]<-as.numeric(gsub("\\)",'', gsub(".*\\(",'', gsub("Intercept: ",'',ldsc_logs_i[grepl("Intercept: ", ldsc_logs_i$V1),]))))
}
}
pheno_teds$pop_prev<-teds_dat$prev[match(pheno_teds$code, teds_dat$gwas)]
pheno_teds$Target_Phenotype<-teds_dat$pheno[match(pheno_teds$code, teds_dat$gwas)]
pheno_teds$Ncases<-as.numeric(gsub(',','',pheno_teds$Ncases))
pheno_teds$Ncontrols<-as.numeric(gsub(',','',pheno_teds$Ncontrols))
pheno_teds$samp_prev<-pheno_teds$Ncases/(pheno_teds$Ncases+pheno_teds$Ncontrols)
h2l_R2 <- function(k, r2, p) {
# K baseline disease risk
# r2 from a linear regression model attributable to genomic profile risk score
# P proportion of sample that are cases
# calculates proportion of variance explained on the liability scale
#from ABC at http://www.complextraitgenomics.com/software/
#Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
x= qnorm(1-k)
z= dnorm(x)
i=z/k
C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
h2l_R2 = C*r2 / (1 + C*theta*r2)
}
se_h2l_R2 <- function(k,h2,se, p) {
# K baseline disease risk
# r2 from a linear regression model attributable to genomic profile risk score
# P proportion of sample that are cases
# calculates proportion of variance explained on the liability scale
#from ABC at http://www.complextraitgenomics.com/software/
#Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
#SE on the liability (From a Taylor series expansion)
#var(h2l_r2) = [d(h2l_r2)/d(R2v)]^2*var(R2v) with d being calculus differentiation
x= qnorm(1-k)
z= dnorm(x)
i=z/k
C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
se_h2l_R2 = C*(1-h2*theta)*se
}
pheno_teds$ldsc_h2_observed<-as.numeric(pheno_teds$ldsc_h2_observed)
pheno_teds$ldsc_h2_se<-as.numeric(pheno_teds$ldsc_h2_se)
pheno_teds$h2_liab<-round(h2l_R2(k=pheno_teds$pop_prev, r2=pheno_teds$ldsc_h2_observed, p=pheno_teds$samp_prev),3)
pheno_teds$h2_liab_se<-round(se_h2l_R2(k=pheno_teds$pop_prev, h2=pheno_teds$ldsc_h2_observed, se=pheno_teds$ldsc_h2_se, p=pheno_teds$samp_prev),3)
pheno_teds$ldsc_h2_observed<-paste0(pheno_teds$ldsc_h2_observed," (", pheno_teds$ldsc_h2_se,")")
pheno_teds$ldsc_h2_se<-NULL
pheno_teds$h2_liab[!is.na(pheno_teds$Ncases)]<-paste0(pheno_teds$h2_liab[!is.na(pheno_teds$Ncases)]," (", pheno_teds$h2_liab_se[!is.na(pheno_teds$Ncases)],")")
pheno_teds$h2_liab_se<-NULL
pheno_teds$ldsc_intercept<-paste0(pheno_teds$ldsc_intercept," (", pheno_teds$ldsc_intercept_se,")")
pheno_teds$ldsc_intercept_se<-NULL
pheno_teds$Trait<-c("ADHD symptoms",'BMI','GCSE','Height')
pheno_teds<-pheno_teds[match(teds_dat$gwas,pheno_teds$code),]
pheno_teds<-pheno_teds[,c('Trait','code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','ldsc_h2_observed','h2_liab','ldsc_intercept','ldsc_lambda_GC')]
names(pheno_teds)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')
write.csv(pheno_teds, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show GWAS for TEDS phenotypes</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv")
names(res)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N',"h2-obs (SE)","h2-liab (SE)",'Intercept','Lambda')
library(knitr)
kable(res, rownames = FALSE, caption='GWAS used for each TEDS phenotype')
```
</details>
<br/>
## Polygenic scoring
Polygenic scores were derived using the following methods:
1. p-value thresholding and clumping (pT + clump).
2. lassosum - Lasso-based shrinkage method.
3. PRScs - Bayesian shrinkage method.
4. SBLUP - Summary statistic-based BLUP estimates
5. SBayesR - Bayesian shrinkage method
6. LDpred - Bayesian shrinkage method
7. DBSLMM - Bayesian shrinkage method
8. LDpred2 - Bayesian shrinkage method
Polygenic scores were derived using a reference standardised pipeline. Two references were used for all methods, the European subset of the 1KG reference ([described here](https://opain.github.io/GenoPred/Pipeline_prep.html#4_polygenic_scoring)), and an independant subset of 10K European individuals from UK Biobank ([described here](https://opain.github.io/GenoPred/Pipeline_prep_withUKBB_ref.html#3_polygenic_scoring)). In brief, all scores were derived using HapMap3 SNPs only, modelling LD based on these references Any HapMap3 missing in the target sample are imputed using the reference estimated allele frequency.
<br/>
### UK Biobank
<details><summary>pT + clump: Sparse</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J pt_clump
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump/${gwas}/1KGPhase3.w_hm3.${gwas} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/sbatch.sh
######
# UKBB ref
######
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir -p ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J pt_clump
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/pt_clump/${gwas}/UKBB.noPheno.EUR.10K.w_hm3.${gwas} \
--ref_scale ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/pt_clump/${gwas}/UKBB.noPheno.EUR.10K.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${UKBB_output}/UKBB_ref/genotype/UKBB.noPheno.EUR.10K.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/UKBB_ref/pt_clump/sbatch.sh
```
</details>
<details><summary>pT + clump: Dense</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 25G
#SBATCH -t 72:00:00
#SBATCH -J pt_clump_dense
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_dense/Scaled_polygenic_scorer_dense.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.GWAS_sumstats_clumped.txt \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--prsice_path ${prsice_path} \
--rscript ${rscript} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/todo.txt | cut -d' ' -f1)%5 ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch.sh
# BODY04 and PRCA01 aren't finishing
# Run in batches
# Split phenotype files into batches
```
```{R, eval=F, echo=T}
library(data.table)
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config')
pheno<-c('BMI', 'Prostate_Cancer')
for(i in pheno){
pheno_file<-fread(paste0(UKBB_output,'/Phenotype/PRS_comp_subset/UKBB.',i,'.txt'))
pheno_file_split<-split(pheno_file, (as.numeric(rownames(pheno_file))-1) %/% 5000)
for(k in 1:length(pheno_file_split)){
write.table(pheno_file_split[[k]], paste0(UKBB_output,'/Phenotype/PRS_comp_subset/UKBB.',i,'.subset_',k,'.txt'), col.names=T, row.names=F, quote=F)
}
}
```
```{bash, eval=F, echo=T}
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
# BODY04
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch_BODY04.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -t 72:00:00
#SBATCH -J pt_clump_dense
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=BODY04
pheno=BMI
subset=${SLURM_ARRAY_TASK_ID}
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_dense/Scaled_polygenic_scorer_dense.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.subset_${subset}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.GWAS_sumstats_clumped.txt \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--prsice_path ${prsice_path} \
--rscript ${rscript} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/${gwas}.subset_${subset}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-10 ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch_BODY04.sh
# PRCA01
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch_PRCA01.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -t 72:00:00
#SBATCH -J pt_clump_dense
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=PRCA01
pheno=Prostate_Cancer
subset=${SLURM_ARRAY_TASK_ID}
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_dense/Scaled_polygenic_scorer_dense.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.subset_${subset}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.GWAS_sumstats_clumped.txt \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_dense/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--prsice_path ${prsice_path} \
--rscript ${rscript} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/${gwas}.subset_${subset}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-10 ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_dense/sbatch_PRCA01.sh
```
```{R, eval=F,echo=T}
# Combine the score from the different batches
library(data.table)
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config')
gwas<-c('BODY04', 'PRCA01')
for(i in gwas){
tmp<-NULL
for(k in 1:10){
print(k)
tmp<-rbind(tmp, fread(paste0(UKBB_output,'/PRS_for_comparison/1KG_ref/pt_clump_dense/',i,'.subset_',k,'/UKBB.subset.w_hm3.',i,'.profiles')))
}
dir.create(paste0(UKBB_output,'/PRS_for_comparison/1KG_ref/pt_clump_dense/',i))
fwrite(tmp, paste0(UKBB_output,'/PRS_for_comparison/1KG_ref/pt_clump_dense/',i,'/UKBB.subset.w_hm3.',i,'.profiles'), col.names=T, row.names=F, quote=F, sep=' ')
}
```
</details>
<details><summary>pT + clump: Sparse (non-nested)</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J pt_clump_nonnested
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer/Scaled_polygenic_scorer.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_nonnested/${gwas}/1KGPhase3.w_hm3.${gwas} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump_nonnested/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump_nonnested/sbatch.sh
```
</details>
<details><summary>lassosum</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.lassosum_profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 15G
#SBATCH -n 5
#SBATCH -J lassosum
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_lassosum/Scaled_polygenic_scorer_lassosum.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/lassosum/${gwas}/1KGPhase3.w_hm3.${gwas} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/lassosum/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--pheno_name ${gwas} \
--n_cores 5 \
--plink ${plink1_9} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/1KG_ref/lassosum/sbatch.sh
######
# UKBB ref
######
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.lassosum_profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 15G
#SBATCH -n 5
#SBATCH -J lassosum
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_lassosum/Scaled_polygenic_scorer_lassosum.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/lassosum/${gwas}/UKBB.noPheno.EUR.10K.w_hm3.${gwas} \
--ref_scale ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/lassosum/${gwas}/UKBB.noPheno.EUR.10K.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${UKBB_output}/UKBB_ref/genotype/UKBB.noPheno.EUR.10K.chr \
--pheno_name ${gwas} \
--n_cores 5 \
--plink ${plink1_9} \
--output ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/UKBB_ref/lassosum/sbatch.sh
```
</details>
<details><summary>PRScs</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.PRScs_profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J PRScs
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_PRScs/Scaled_polygenic_scorer_PRScs.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/PRScs/${gwas}/1KGPhase3.w_hm3.${gwas} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/PRScs/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/1KG_ref/PRScs/sbatch.sh
```
</details>
<details><summary>SBLUP</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.SBLUP_profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J SBLUP
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_SBLUP/Scaled_polygenic_scorer_SBLUP.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/SBLUP/${gwas}/GWAS_sumstats_SBLUP.sblup.cojo \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/SBLUP/${gwas}/1KGPhase3.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/1KG_ref/SBLUP/sbatch.sh
######
# UKBB ref
######
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
# Create directory
mkdir ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP
# Create file listing GWAS that haven't been processed.
> ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/todo.txt
for i in $(seq 1 11);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
if [ ! -f ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}.SBLUP_profiles ]; then
echo ${gwas_i} ${pheno_i} >> ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/todo.txt
fi
done
# Create shell script to run using sbatch
cat > ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/sbatch.sh << 'EOF'
#!/bin/sh
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -J SBLUP
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
gwas=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $1}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/todo.txt)
pheno=$(awk -v var="$SLURM_ARRAY_TASK_ID" 'NR == var {print $2}' ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/todo.txt)
echo $gwas
echo $pheno
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_SBLUP/Scaled_polygenic_scorer_SBLUP.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/SBLUP/${gwas}/GWAS_sumstats_SBLUP.sblup.cojo \
--ref_scale ${UKBB_output}/UKBB_ref/Score_files_for_polygenic/SBLUP/${gwas}/UKBB.noPheno.EUR.10K.w_hm3.${gwas}.EUR.scale \
--ref_freq_chr ${UKBB_output}/UKBB_ref/genotype/UKBB.noPheno.EUR.10K.chr \
--plink ${plink1_9} \
--pheno_name ${gwas} \
--output ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/${gwas}/UKBB.subset.w_hm3.${gwas}
EOF
sbatch --array 1-$(wc -l ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/todo.txt | cut -d' ' -f1)%3 ${UKBB_output}/PRS_for_comparison/UKBB_ref/SBLUP/sbatch.sh
```
</details>
<details><summary>SBayesR</summary>
```{bash, echo=T, eval=F}
########
# 1KG ref
########
####
# Standard
####
# Set required variables