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DiverseAncestry.Rmd
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DiverseAncestry.Rmd
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---
title: "Prediction within Ancestral Diversity"
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
Genotype-based prediction is more accurate within European populations due to European-based GWAS being larger in sample size, and the relatively low heteroegeniety/admixture within European populations compared to some non-European populations. Apart from phenotypic heterogenity across populations, a key reason why European GWAS do not predict well in non-European populations is due to differences in LD and MAF. Although the underlying causal variant maybe the same across populations, the variant best tagging the causal variant in one population may not wel tag the causal variant in another population.
Prediction can be improved in non-European populations by:
* Performing larger GWAS in non-European populations
* Combination of European-GWAS with existing non-European GWAS
* Highlight causal genetic effects
* Modelling differences in MAF and LD between populations
* Using gene-based scores
Even within super populations, differnces in ancestry will influence the predictive utility of polygenic scores. Accounting for ancestry within the PRS model may improve prediction. Fitting main effects of PCs is one approach, which has been shown to improve prediction over PRS alone. Furthermore, including an interaction term may be beneficial, as the scale of the PRS may vary within a population for non-causal reasons.
Estimate the predictive utility of polygenic scores within each super population. UKB contains individuals within each super population.
***
# Define ancestry and QC
Global ancestry is typically defined using reference-projected genotype-based principal components. Often individuals are said to be of a population if they are within N SD of the population mean. Alternatively principal component estimates can be used in a machine learning based approach such as k-means clustering. I have used an elastic net model to predict global ancestry.
Further information can be found here: https://opain.github.io/UKB-GenoPrep/
***
# Using external GWAS sumstats
***
## Prepare phenotype files
<details><summary>Show code</summary>
```{R, eval=F, echo=T}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
library(data.table)
# Read list of individuals surviving QC in each population
# Read in fam to convert row number to application specific ID
# Row numbers in pheno file cannot be trusted
fam<-fread('/scratch/groups/ukbiobank/ukb18177_glanville/genotyped/ukb18177_glanville_binary_pre_qc.fam')
fam$V1<-seq(1:dim(fam)[1])
fam_2<-fread(paste0(UKBB_output,'/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr22.fam'))
fam_merged<-merge(fam, fam_2, by='V2')
keep<-list()
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
keep[[pop]]<-fread(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/ReQC/PostQC/UKB.postQC.',pop,'.keep'))
keep[[pop]]<-merge(keep[[pop]][,1], fam[,1:2], by='V1')
keep[[pop]]$V1<-keep[[pop]]$V2
keep_tmp<-fread(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/ReQC/PostQC/UKB.postQC.',pop,'.keep'))
keep_tmp<-merge(keep_tmp[,1], fam_merged, by.x='V1', by.y='V1.x')
keep_tmp<-keep_tmp[,c('V1.y','V2'),with=F]
# Save keep file with updated IDs
write.table(keep[[pop]], paste0(UKBB_output,'/Phenotype/',pop,'.QC.keep'), col.names=F, row.names=F, quote=F)
write.table(keep_tmp, paste0(UKBB_output,'/Phenotype/',pop,'.QC.UpdateIDs.keep'), col.names=F, row.names=F, quote=F)
}
# Read in phenotype data
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth')
pheno_file<-c('ever_depressed_pheno_final.UpdateIDs.txt','UKBB_Fluid.intelligence.score.UpdateIDs.pheno','UKBB_BMI.score.UpdateIDs.pheno','UKBB_Height.score.UpdateIDs.pheno','t2d_only_111119.UpdateIDs.txt','cad_only_111119.UpdateIDs.txt','UKBB.IBD.txt','UKBB.MultiScler.txt','UKBB.RheuArth.txt')
pheno_list<-list()
for(i in 1:length(pheno)){
pheno_list[[pheno[i]]]<-fread(paste0(UKBB_output,'/Phenotype/',pheno[i],'/',pheno_file[i]))
}
pheno_all<-Reduce(function(...) merge(..., all=T, by=c('FID','IID')), pheno_list)
names(pheno_all)<-c('FID','IID',pheno)
# Count number of non-missing values for phenotype in each population
pheno_dat_per_pop<-NULL
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
pheno_all_pop<-pheno_all[(pheno_all$IID %in% keep[[pop]]$V1),]
for(i in 1:length(pheno)){
if(length(na.omit(unique(pheno_all[[pheno[i]]]))) == 2){
pheno_dat_per_pop<-rbind(pheno_dat_per_pop, data.frame(Pop=pop,
Phenotype=pheno[i],
N=sum(!is.na(pheno_all_pop[[pheno[i]]])),
Ncase=sum(pheno_all_pop[[pheno[i]]] == 1, na.rm=T),
Ncon=sum(pheno_all_pop[[pheno[i]]] == 0, na.rm=T)))
} else {
pheno_dat_per_pop<-rbind(pheno_dat_per_pop, data.frame(Pop=pop,
Phenotype=pheno[i],
N=sum(!is.na(pheno_all_pop[[pheno[i]]])),
Ncase=NA,
Ncon=NA))
}
}
}
# Idenitfy phenotype with at least 1000 indidivuals and 50 cases with data
pheno_dat_per_pop_retain<-pheno_dat_per_pop[which((pheno_dat_per_pop$N >=500 & pheno_dat_per_pop$Ncase >= 50) | (pheno_dat_per_pop$N >=500 & is.na(pheno_dat_per_pop$Ncase))),]
# BMI and Height are the only phenotypes meeting these criteria. This is fine for a preliminary analysis, but other phenotypes that are more available across ancestries should be explored. Intelligence was also available in EUR, AFR, and SAS.
# Save keep file with updated IDs for each phenotype, restricting the sample size to 50K
# Read in fam file with ID to match
system(paste0('mkdir ',UKBB_output,'/DiverseAncestry/Phenotype_subsets/'))
set.seed(1)
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
for(pheno_i in c('BMI','Height')){
pheno_all_pop<-pheno_all[(pheno_all$IID %in% keep[[pop]]$V1),c('FID','IID',pheno_i), with=F]
pheno_all_pop<-pheno_all_pop[complete.cases(pheno_all_pop),]
if(dim(pheno_all_pop)[1] > 50000){
pheno_all_pop<-pheno_all_pop[sample(1:50000),]
}
write.table(pheno_all_pop[,1:2], paste0(UKBB_output,'/DiverseAncestry/Phenotype_subsets/UKB.',pheno_i,'.',pop,'.QC.keep'), col.names=F, row.names=F, quote=F)
}
}
```
</details>
***
## Calculate scores
***
### PRS
<details><summary>pT + clump: Sparse</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch --mem 2G -p brc,shared -J pT_clump /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 ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/pt_clump/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--plink ${plink1_9} \
--pheno_name ${gwas_i} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>lassosum</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 5 --mem 5G -p brc,shared /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 ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/lassosum/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/lassosum/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--n_cores 5 \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/lassosum/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>PRScs</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 1 --mem 5G -p brc,shared /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 ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/PRScs/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/PRScs/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/PRScs/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>SBLUP</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 1 --mem 5G -p brc,shared /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 ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/SBLUP/${gwas_i}/GWAS_sumstats_SBLUP.sblup.cojo \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/SBLUP/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/SBLUP/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>SBayesR</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 1 --mem 5G -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_SBayesR/Scaled_polygenic_scorer_SBayesR.R \
\
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/SBayesR/${gwas_i}_GCTB_203_robust/GWAS_sumstats_SBayesR.GW.snpRes \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/SBayesR/${gwas_i}_GCTB_203_robust/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/SBayesR/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>LDPred</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 1 --mem 5G -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_LDPred/Scaled_polygenic_scorer_LDPred.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/LDPred/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/LDPred/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/LDPred/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>LDPred2</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch -n 6 --mem 10G -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_LDPred2/Scaled_polygenic_scorer_LDPred2.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/LDPred2/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/LDPred2/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--n_cores 6 \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/LDPred2/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
<details><summary>DBSLMM</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch --mem 10G -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Scaled_polygenic_scorer_DBSLMM/Scaled_polygenic_scorer_DBSLMM.R \
--target_plink_chr ${UKBB_output}/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.${pheno_i}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_polygenic/DBSLMM/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.dbslmm.GW.txt \
--ref_scale ${Geno_1KG_dir}/Score_files_for_polygenic/DBSLMM/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.${pop}.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/${pop}/1KGPhase3.w_hm3.${pop}.chr \
--pheno_name ${gwas_i} \
--plink ${plink1_9} \
--output ${UKBB_output}/DiverseAncestry/1KG_ref/DBSLMM/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}
done
done
```
</details>
***
### GeRS
<details><summary>GeRS</summary>
```{bash, echo=T, eval=F}
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for pop in $(echo AFR AMR EAS EUR SAS);do
mkdir -p ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}
> ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt
fi
done
done
cat <<EOF >${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo_array.sh
#!/bin/bash
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -n 1
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
gwas=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$1}' ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt)
pheno=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$2}' ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt)
weights=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$3}' ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt)
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
--targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/${pop}/\${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.\${weights}.predictions.gz \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.\${pheno}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/\${gwas}/1KGPhase3.w_hm3.EUR.FUSION.\${gwas}.\${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/\${gwas}/1KGPhase3.w_hm3.EUR.FUSION.\${gwas}.\${weights}.scale \
--pheno_name \${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/\${weights}/UKBB.w_hm3.EUR.\${weights}.\${gwas}
EOF
done
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --array=1-$(wc -l ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo.txt | cut -d ' ' -f 1)%10 ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/todo_array.sh
done
```
</details>
<details><summary>GeRS (coloc)</summary>
```{bash, echo=T, eval=F}
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
# Calculate polygenic scores using 1KG reference
for pop in $(echo AFR AMR EAS EUR SAS);do
mkdir -p ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}
> ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt
fi
done
done
cat <<EOF >${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo_array.sh
#!/bin/bash
#SBATCH -p shared,brc
#SBATCH --mem 10G
#SBATCH -n 1
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
gwas=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$1}' ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt)
pheno=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$2}' ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt)
weights=\$(awk -v var=\${SLURM_ARRAY_TASK_ID} 'NR == var {print \$3}' ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt)
/users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/scaled_functionally_informed_risk_scorer/scaled_functionally_informed_risk_scorer.R \
--targ_feature_pred ${UKBB_output}/Predicted_expression/FUSION/${pop}/\${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.\${weights}.predictions.gz \
--target_keep ${UKBB_output}/DiverseAncestry/Phenotype_subsets/UKB.\${pheno}.${pop}.QC.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/\${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.\${gwas}.\${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/\${gwas}_COLOC_PP4/1KGPhase3.w_hm3.EUR.FUSION.\${gwas}.\${weights}.scale \
--pheno_name \${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/\${weights}/UKBB.w_hm3.EUR.\${weights}.\${gwas}
EOF
done
for pop in $(echo EUR AFR SAS EAS AMR);do
sbatch --array=1-$(wc -l ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo.txt | cut -d ' ' -f 1)%10 ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/todo_array.sh
done
```
</details>
Note. Some GeRS are not being calculated. Investigate why.
***
### PCs
Reference project PCs have already been calculated, as described in the 'Define ancestry...' section. We just need to update the IDs to match the genetic data.
<details><summary>PCs</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
library(data.table)
fam<-fread('/scratch/groups/ukbiobank/ukb18177_glanville/genotyped/ukb18177_glanville_binary_pre_qc.fam')
fam$V1<-seq(1:dim(fam)[1])
fam_2<-fread(paste0(UKBB_output,'/Genotype/Harmonised/UKBB.w_hm3.QCd.AllSNP.chr22.fam'))
fam_merged<-merge(fam, fam_2, by='V2')
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
PCs<-fread(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/ReQC/defining_ancestry/UKBB.Ancestry.',pop,'.eigenvec'))
PCs<-merge(PCs, fam_merged, by.x='FID', by.y='V1.x')
PCs<-PCs[,c('V1.y','V2','PC1','PC2','PC3','PC4','PC5', 'PC6'),with=F]
names(PCs)<-c('FID','IID','PC1','PC2','PC3','PC4','PC5', 'PC6')
write.table(PCs, paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PCs/UKBB.Ancestry.',pop,'.UpdateIDs.eigenvec'), col.names=T, row.names=F, quote=F)
}
```
</details>
***
## Evaluate scores
***
### pT + clump comparison
<details><summary>pT + clump comparison</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of pT + clump PRSs across multiple pTs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
cat > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups <<EOF
predictors
${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles
EOF
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
# pT + clump (sparse)
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 10G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs \
--n_core 1 \
--compare_predictors T \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs.predictor_groups
done
done
```
</details>
***
### Comparison of PRS methods
<details><summary>Comparison of PRS methods</summary>
```{R, eval=F, echo=T}
##############################
# Evaluating predictive utility of pT + clump PRSs across multiple pTs individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
cat > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs.AllMethodComp.predictor_groups <<EOF
predictors group
${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles pt_clump
${UKBB_output}/DiverseAncestry/1KG_ref/lassosum/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.lassosum_profiles lassosum
${UKBB_output}/DiverseAncestry/1KG_ref/PRScs/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.PRScs_profiles PRScs
${UKBB_output}/DiverseAncestry/1KG_ref/SBLUP/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.SBLUP_profiles SBLUP
${UKBB_output}/DiverseAncestry/1KG_ref/SBayesR/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.SBayesR_profiles SBayesR
${UKBB_output}/DiverseAncestry/1KG_ref/LDPred/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.LDPred_profiles LDPred
${UKBB_output}/DiverseAncestry/1KG_ref/LDPred2/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.LDPred_profiles LDPred2
${UKBB_output}/DiverseAncestry/1KG_ref/DBSLMM/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.DBSLMM_profiles DBSLMM
EOF
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 20G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs.AllMethodComp \
--n_core 1 \
--compare_predictors T \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRSs/UKBB.${pop}.w_hm3.${gwas_i}.EUR-PRSs.AllMethodComp.predictor_groups
done
done
# Note. The nested cross validation model builder is not ideal when there are multiple parameters for each method. Previously we have identifying the best single parameter in the training sample, and then testing it out of sample. With nested cross validation, we are selecting the best pT based on results out of sample. Probably doesn't make a big diffrence, but will lead to slight inflation of R2 of single parameters PRS. I like using the full sample though, so I could add an option select best predictor within each group, rather than nested cross validate single predictors which is quite pointless. Leave as is for now.
```
</details>
***
### GeRS + PRS
<details><summary>GeRS + PRS</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
for i in $(seq 1 2);do
for pop in $(echo AFR AMR EAS EUR SAS);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs.${pop}-PRSs.pt_clump.predictor_groups
for weight in ${weights}; do
if [ -f ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ]; then
echo ${UKBB_output}/GeRS_for_diversity/1KG_ref/${pop}/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs.${pop}-PRSs.pt_clump.predictor_groups
fi
done
echo ${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs.${pop}-PRSs.pt_clump.predictor_groups
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
# 1KG reference
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 10G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs.${pop}-PRSs.pt_clump \
--n_core 1 \
--compare_predictors F \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs.${pop}-PRSs.pt_clump.predictor_groups
done
done
```
</details>
***
### GeRS (coloc) + PRS
<details><summary>GeRS (coloc) + PRS</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of GeRS and PRS individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
for i in $(seq 1 2);do
for pop in $(echo EUR EAS AMR SAS AFR);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs_coloc.${pop}-PRSs.pt_clump.predictor_groups
for weight in ${weights}; do
if [ -f ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ]; then
echo ${UKBB_output}/GeRS_for_diversity/1KG_ref_withCOLOC/${pop}/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile GeRS >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs_coloc.${pop}-PRSs.pt_clump.predictor_groups
fi
done
echo ${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs_coloc.${pop}-PRSs.pt_clump.predictor_groups
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
# 1KG reference
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 10G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs_coloc.${pop}-PRSs.pt_clump \
--n_core 1 \
--compare_predictors F \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_GeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.${pop}-GeRSs_coloc.${pop}-PRSs.pt_clump.predictor_groups
done
done
```
</details>
***
### PCs
<details><summary>PCs</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of PCs in combination with PRS
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
for i in $(seq 1 2);do
for pop in $(echo EUR EAS AMR SAS AFR);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump.predictor_groups
echo ${UKBB_output}/DiverseAncestry/1KG_ref/PCs/UKBB.Ancestry.${pop}.UpdateIDs.eigenvec PCs >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump.predictor_groups
echo ${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles PRS >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump.predictor_groups
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
# 1KG reference
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 10G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump \
--n_core 1 \
--compare_predictors F \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump.predictor_groups
done
done
```
</details>
***
### PCs with interactions
<details><summary>PCs with interactions</summary>
Rather than modifying the model builder script. Calculate PRS and PC interactions in advance.
```{R, eval=F, echo=T}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')
dir.create(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PC_PRS_int'))
for(gwas in c('BODY04','HEIG03')){
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
# Read in prs and PC data
pcs<-fread(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PCs/UKBB.Ancestry.',pop,'.UpdateIDs.eigenvec'))
prs<-fread(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/pt_clump/',pop,'/',gwas,'/UKBB.',pop,'.w_hm3.',gwas,'.profiles'))
names(prs)[-1:-2]<-paste0('PRS',1:(dim(prs)[2]-2))
both<-merge(prs, pcs, by=c('FID','IID'))
interaction_list<-NULL
for(i in names(pcs[,-1:-2])){
interaction_list<-rbind(interaction_list, data.frame(x1=i, x2=names(prs[,-1:-2])))
}
interaction_list$x1<-as.character(interaction_list$x1)
interaction_list$x2<-as.character(interaction_list$x2)
int_dat<-NULL
for(i in 1:dim(interaction_list)[1]){
tmp<-as.numeric(scale(both[[interaction_list$x1[i]]]*both[[interaction_list$x2[i]]]))
int_dat<-data.frame(cbind(int_dat, tmp))
names(int_dat)[i]<-paste0(interaction_list$x1[i],'_',interaction_list$x2[i])
}
int_dat<-cbind(both[,1:2],int_dat)
fwrite(int_dat, paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PC_PRS_int/UKBB.',gwas,'.',pop,'.prs_pc_interactions.txt'), quote=F, na='NA', sep=' ')
}
}
```
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of PCs in combination with PRS
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo BMI Height);do
mkdir -p /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs
done
# Create a file listing the predictors files
pheno=$(echo BMI Height)
gwas=$(echo BODY04 HEIG03)
for i in $(seq 1 2);do
for pop in $(echo EUR EAS AMR SAS AFR);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions.predictor_groups
echo ${UKBB_output}/DiverseAncestry/1KG_ref/PCs/UKBB.Ancestry.${pop}.UpdateIDs.eigenvec main >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions.predictor_groups
echo ${UKBB_output}/DiverseAncestry/1KG_ref/pt_clump/${pop}/${gwas_i}/UKBB.${pop}.w_hm3.${gwas_i}.profiles main >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions.predictor_groups
echo ${UKBB_output}/DiverseAncestry/1KG_ref/PC_PRS_int/UKBB.${gwas_i}.${pop}.prs_pc_interactions.txt interaction >> /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions.predictor_groups
done
done
# Derive and evaluate models
pheno=$(echo BMI Height)
pheno_file=$(echo UKBB_BMI.score.UpdateIDs.pheno UKBB_Height.score.UpdateIDs.pheno)
gwas=$(echo BODY04 HEIG03)
prev=$(echo NA NA)
# 1KG reference
for i in $(seq 1 2);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
pheno_file_i=$(echo ${pheno_file} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
for pop in $(echo AFR AMR EAS EUR SAS);do
sbatch --mem 10G -n 1 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2_nested.R \
--pheno ${UKBB_output}/Phenotype/${pheno_i}/${pheno_file_i} \
--out /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions \
--n_core 1 \
--compare_predictors F \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/DiverseAncestry/UKBB_outcomes_for_prediction/${pheno_i}/Association_withPRS_and_PCs/UKBB.w_hm3.${gwas_i}.${pop}-PCs-PRSs.pt_clump-interactions.predictor_groups
done
done
```
I expected interaction terms to improve prediction. They do not, and by including many predicors increase risk of overfitting.
</details>
***
### PCs with PRS residuals
<details><summary>PCs with PRS residuals</summary>
Rather than modifying the model builder script. Calculate PRS and PC interactions in advance.
```{R, eval=F, echo=T}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')
dir.create(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PRS_residuals'))
for(gwas in c('BODY04','HEIG03')){
for(pop in c('EUR','AFR','SAS','EAS','AMR')){
# Read in prs and PC data
pcs<-fread(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/PCs/UKBB.Ancestry.',pop,'.UpdateIDs.eigenvec'))
prs<-fread(paste0(UKBB_output,'/DiverseAncestry/1KG_ref/pt_clump/',pop,'/',gwas,'/UKBB.',pop,'.w_hm3.',gwas,'.profiles'))
names(prs)[-1:-2]<-paste0('PRS',1:(dim(prs)[2]-2))