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Functionally_informed_prediction.Rmd
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Functionally_informed_prediction.Rmd
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---
title: "Evaluation of Imputed Gene Expression Risk Scores"
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
This study evaluates the predictive utility of GeRS calculated using several strategies. The predictive utility of models containing polygenic scores and GeRS is also investigated.
<br/>
***
# Aims
* Evaluate the predictive utility of GeRS based on a single eQTL data source.
* Evaluate models combining GeRS derived eQTL data from multiple tissues.
* Determine whether GeRS can improve prediction when in combination with polygenic scores.
<br/>
***
# Methods
## Samples
- UK Biobank
- TEDS
## 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)
* TEDS
* ADHD traits (continuous)
* Height (continuous)
* Body mass index (BMI - continuous)
* GCSE scores (continuous)
Note. Multiple Slerosis in UK Biobank was not included due to insufficent SNP data in the corresponding GWAS for TWAS.
## 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.
## 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')
library(data.table)
pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_031218.csv')
ukb_pheno=c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth')
ukb_gwas=c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','SCLE02','RHEU01')
ukb_prev=c(0.15,NA,NA,NA,0.05,0.03,0.013,0.00164,0.005)
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','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2 observed','h2 se','lambda GC','intercept','intercept se')]
names(pheno_ukb)<-c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_se','lambda','intercept','intercept_se')
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$h2_obs<-as.numeric(pheno_ukb$h2_obs)
pheno_ukb$h2_se<-as.numeric(pheno_ukb$h2_se)
pheno_ukb$h2_liab<-round(h2l_R2(k=pheno_ukb$pop_prev, r2=pheno_ukb$h2_obs, p=pheno_ukb$samp_prev),3)
pheno_ukb$h2_liab_se<-round(se_h2l_R2(k=pheno_ukb$pop_prev,h2=pheno_ukb$h2_obs,se=pheno_ukb$h2_se, p=pheno_ukb$samp_prev),3)
pheno_ukb$h2_obs<-paste0(pheno_ukb$h2_obs," (", pheno_ukb$h2_se,")")
pheno_ukb$h2_se<-NULL
pheno_ukb$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$intercept<-paste0(pheno_ukb$intercept," (", pheno_ukb$intercept_se,")")
pheno_ukb$intercept_se<-NULL
pheno_ukb$trait<-c('BMI','CAD','College Completion',"Crohn's Disease",'Major Depression','T2D','Height','RheuArth','MultiScler')
pheno_ukb<-pheno_ukb[match(ukb_dat$gwas,pheno_ukb$Code),]
pheno_ukb<-pheno_ukb[,c('Target_Phenotype','Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_liab','intercept','lambda')]
names(pheno_ukb)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')
write.csv(pheno_ukb, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show GWAS for UK Biobank phenotypes</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/UKBB_phenotype_GWAS_descrip.csv")
names(res)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N',"h2-obs (SE)","h2-liab (SE)",'Intercept','Lambda')
library(knitr)
kable(res, rownames = FALSE, caption='GWAS used for each UK Biobank phenotype')
```
</details>
<details><summary>Preparing GWAS sumstats table for TEDS phenotypes</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
library(data.table)
pheno<-fread('/users/k1806347/brc_scratch/Data/GWAS_sumstats/QC_sumstats_list_031218.csv')
teds_pheno=c('Height21', 'BMI21', 'GCSE', 'ADHD')
teds_gwas=c('HEIG03', 'BODY11', 'EDUC03', 'ADHD04')
teds_prev=c(NA,NA,NA,0.05)
teds_dat<-data.frame(pheno=teds_pheno,gwas=teds_gwas,prev=teds_prev)
pheno_teds<-pheno[(pheno$Code %in% teds_gwas),]
pheno_teds<-pheno_teds[,c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2 observed','h2 se','lambda GC','intercept','intercept se')]
names(pheno_teds)<-c('Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_se','lambda','intercept','intercept_se')
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$h2_obs<-as.numeric(pheno_teds$h2_obs)
pheno_teds$h2_se<-as.numeric(pheno_teds$h2_se)
pheno_teds$h2_liab<-round(h2l_R2(k=pheno_teds$pop_prev, r2=pheno_teds$h2_obs, p=pheno_teds$samp_prev),3)
pheno_teds$h2_liab_se<-round(se_h2l_R2(k=pheno_teds$pop_prev,h2=pheno_teds$h2_obs,se=pheno_teds$h2_se, p=pheno_teds$samp_prev),3)
pheno_teds$h2_obs<-paste0(pheno_teds$h2_obs," (", pheno_teds$h2_se,")")
pheno_teds$h2_se<-NULL
pheno_teds$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$intercept<-paste0(pheno_teds$intercept," (", pheno_teds$intercept_se,")")
pheno_teds$intercept_se<-NULL
pheno_teds$trait<-c("ADHD (mixed ancestry)",'BMI','Educational Attainment','Height')
pheno_teds<-pheno_teds[match(teds_dat$gwas,pheno_teds$Code),]
pheno_teds<-pheno_teds[,c('Target_Phenotype','Code','trait','year','PMID','Ncases','Ncontrols','sample_size_discovery','h2_obs','h2_liab','intercept','lambda')]
names(pheno_teds)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N','h2_obs','h2_liab','Intercept','Lambda')
pheno_teds$PMID[2]<-30124842
pheno_teds$PMID[4]<-30478444
write.csv(pheno_teds, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show GWAS for TEDS phenotypes</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Data/GWAS_sumstats/TEDS_phenotype_GWAS_descrip.csv")
names(res)<-c('Target Phenotype','Code','GWAS Phenotype','Year','PMID','Ncase','Ncontrol','N',"h2-obs (SE)","h2-liab (SE)",'Intercept','Lambda')
library(knitr)
kable(res, rownames = FALSE, caption='GWAS used for each TEDS phenotype')
```
</details>
<br/>
## TWAS
<details><summary>Preparing table showing SNP-weight sets used</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
library(data.table)
weights<-fread(paste0(TWAS_rep, '/snp_weight_list.txt'), header=F)$V1
pos_unique<-fread('/users/k1806347/brc_scratch/Data/1KG/Phase3/Predicted_expression/Tissue_specific.pos')
pos_unique$WGT<-paste0(pos_unique$PANEL,'/',gsub('.*/','',pos_unique$WGT))
weights_info<-NULL
pos_all<-NULL
for(weights_i in weights){
pos<-fread(paste0(FUSION_dir,'/SNP-weights/',weights_i,'/',weights_i,'.pos'))
if(grepl('CMC', weights_i) == T){
sample_i<-'CMC'
tissue_i<-'Brain:DLPFC'
}
if(grepl('NTR', weights_i) == T){
sample_i<-'NTR'
tissue_i<-'Peripheral Blood'
}
if(grepl('YFS', weights_i) == T){
sample_i<-'YFS'
tissue_i<-'Whole Blood'
}
if(grepl('METSIM', weights_i) == T){
sample_i<-'METSIM'
tissue_i<-'Adipose'
}
if(grepl('CMC|NTR|YFS|METSIM', weights_i) == F){
sample_i<-'GTEx'
tissue_i<-gsub('_',' ',weights_i)
}
weights_info<-rbind(weights_info, data.frame(Set=weights_i,
Sample=sample_i,
Tissue=tissue_i,
Type='Expression',
N_indiv=pos$N[1],
N_feat=dim(pos)[1],
N_feat_spec=sum(pos$WGT %in% pos_unique$WGT)))
pos_all<-rbind(pos_all, pos)
}
dim(pos_all) # 260598
length(unique(pos_all$ID)) # 26434
weights_info$Type<-as.character(weights_info$Type)
weights_info$Type[weights_info$Set=='CMC.BRAIN.RNASEQ_SPLICING']<-'Splicing'
write.csv(weights_info, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show SNP-weight characteristics</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Analyses/GeRS_comparison/snp_weights_table.csv")
library(knitr)
kable(res, rownames = FALSE, caption='SNP-weight set characteristics')
```
</details>
<details><summary>Preparing table showing TWAS descriptives</summary>
```{R, echo=T, eval=F}
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Phenotype_prep.config')
library(data.table)
weights<-fread(paste0(TWAS_rep, '/snp_weight_list.txt'), header=F)$V1
pheno=c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth','BMI','Educational Attainment','ADHD symptoms')
sample=c('UKB','UKB','UKB','Both','UKB','UKB','UKB','UKB','UKB','TEDS','TEDS','TEDS')
gwas=c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','SCLE02','RHEU01','BODY11', 'EDUC03', 'ADHD04')
twas_descript<-NULL
for(i in 1:length(gwas)){
twas<-fread(paste0(TWAS_rep,'/',gwas[i],'_withCOLOC/',gwas[i],'_res_GW.txt'))
twas_descript<-rbind(twas_descript, data.frame(Sample=sample[i],
Target_Phenotype=pheno[i],
GWAS=gwas[i],
Nfeat=dim(twas)[1],
Nfeat_imp=sum(!is.na(twas$TWAS.P))))
}
twas_descript<-twas_descript[order(twas_descript$Sample),]
write.csv(twas_descript, '/users/k1806347/brc_scratch/Analyses/GeRS_comparison/twas_descript_table.csv', row.names=F, quote=F)
```
</details>
<details><summary>Show TWAS descriptives</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/users/k1806347/brc_scratch/Analyses/GeRS_comparison/twas_descript_table.csv")
library(knitr)
kable(res, rownames = FALSE, caption='TWAS descriptives')
```
</details>
<br/>
## Gene expression risk scores (GeRS)
TWAS integrates GWAS summary statistics with multi-SNP predictors of gene expression (SNP-weights) to infer gene expression associations. Multi-SNP predictors in combination with individual-level genotype data can also be used to predict the expression level of genes within an each individual. GeRS are calculated as the TWAS-effect size weighted sum of predicted gene expression levels in each individual.
This study used SNP-weights derived from multiple panels capturing eQTL effects across a range of adult tissues. SNP-weights were downloaded from the FUSION website. TWAS was performed using FUSION and SNP-weights were used to calculated predicted expression levels using PLINK. GeRS for each panel were then calculated in R. To account for the correlation between the predicted expression of nearby features due to LD, feature clumping was used to remove features within 5Mb of lead features with a predicted expression r^2 of >0.9. Due to the complex LD structure within the MHC region, only the lead feature within this region was retained.
TWAS, gene expression prediction and GeRS calculations were carried out using LD and MAF estimations from an ancestry matched reference genotype dataset. The same SNP-weights are used to predict expression levels regardless of the target samples, using MAF imputation to account for missing variation. Predicted expression levels for each gene are then standardised based on the ancestry-matched mean and standard deviation of expression. Clumping of features is performed using predicted expression level in the reference sample.
The code used to prepare the reference data required for calculating GeRS can be found [here](https://opain.github.io/GenoPred/Pipeline_prep.html#5_functionally-informed_polygenic_scoring).
The code used for calculating predicted expression levels in the target samples can be found [here](https://opain.github.io/GenoPred/Genotype-based_scoring_in_target_samples.html#4_functionally-informed_polygenic_scoring)
<details><summary>Calculating GeRS in samples</summary>
```{bash, echo=T, eval=F}
###
# UKBB
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
for i in $(seq 1 8);do
pheno_i=$(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 ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--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 ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}
sleep 20
done
###
# TEDS
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)
for i in $(seq 1 4);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--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 ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}
sleep 5
done
```
</details>
<details><summary>Calculating GeRS in samples using PP4 to filter features</summary>
```{bash, echo=T, eval=F}
###
# UKBB
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
for i in $(seq 1 8);do
pheno_i=$(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 ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--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 ${UKBB_output}/GeRS_for_comparison/1KG_ref_withCOLOC/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}
sleep 20
done
###
# TEDS
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)
for i in $(seq 1 4);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--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 ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_withCOLOC/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}
sleep 5
done
```
</details>
<details><summary>Calculating GeRS in samples using tissue specific features</summary>
```{bash, echo=T, eval=F}
###
# UKBB
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
for i in $(seq 1 8);do
pheno_i=$(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 ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.TissueSpecific.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt
fi
done
done
for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/todo.TissueSpecific.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.scale \
--pheno_name ${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weights}/UKBB.w_hm3.EUR.TissueSpecific.${weights}.${gwas}
sleep 20
done
###
# TEDS
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)
for i in $(seq 1 4);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.TissueSpecific.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt
fi
done
done
for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/todo.TissueSpecific.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}/1KGPhase3.w_hm3.EUR.FUSION.TissueSpecific.${gwas}.${weights}.scale \
--pheno_name ${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores/EUR/${weights}/TEDS.w_hm3.EUR.TissueSpecific.${weights}.${gwas}
sleep 5
done
```
</details>
<details><summary>Calculating GeRS in samples using colocalised features</summary>
```{bash, echo=T, eval=F}
###
# UKBB
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR
> ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
for i in $(seq 1 8);do
pheno_i=$(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 ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $pheno_i $weights >> ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)
pheno=$(awk -v var="$i" 'NR == var {print $2}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $3}' ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${UKBB_output}/Predicted_expression/FUSION/EUR/${weights}/UKBB.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--target_keep ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
--pheno_name ${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${UKBB_output}/GeRS_for_comparison/1KG_ref_pT_withColoc/EUR/${weights}/UKBB.w_hm3.EUR.${weights}.${gwas}
sleep 20
done
###
# TEDS
###
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
mkdir -p ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR
> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)
for i in $(seq 1 4);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
for weights in $(cat ~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt);do
if [ ! -f ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas_i}.fiprofile ]; then
echo $gwas_i $weights >> ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt
fi
done
done
for i in $(seq 1 $(wc -l ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt | cut -d ' ' -f 1));do
gwas=$(awk -v var="$i" 'NR == var {print $1}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt)
weights=$(awk -v var="$i" 'NR == var {print $2}' ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/todo.txt)
sbatch -p brc,shared --mem 10G -n 1 /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 ${TEDS_output_dir}/Predicted_expression/FUSION/EUR/${weights}/TEDS.w_hm3.QCd.AllSNP.FUSION.${weights}.predictions.gz \
--ref_score ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.score \
--ref_scale ${Geno_1KG_dir}/Score_files_for_functionally_informed_risk_scores/${gwas}_pT_withColoc/1KGPhase3.w_hm3.EUR.FUSION.${gwas}.${weights}.scale \
--pheno_name ${gwas} \
--n_cores 1 \
--pigz ${pigz_binary} \
--output ${TEDS_output_dir}/FunctionallyInformedPolygenicScores_pT_withColoc/EUR/${weights}/TEDS.w_hm3.EUR.${weights}.${gwas}
sleep 5
done
```
</details>
<br/>
## Functionally-informed polygenic scoring
Functionally informed polygenic scores were derived as follows:
1. TWAS SNP-weight-stratified p-value thresholding and clumping (eQTL pT+clump)
<details><summary>Calculating GeRS in samples</summary>
```{bash, echo=T, eval=F}
# Set required variables
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
#######
# UKBB
#######
# Create variable listing phenotypes and corresponding GWAS
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 8);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch --mem 10G -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 ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}_withCOLOC/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}_withCOLOC/1KGPhase3.w_hm3.${gwas_i}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas_i} \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref_withCOLOC/pt_clump_stratified_TWAS_Gene/${gwas_i}/UKBB.subset.w_hm3.${gwas_i}
done
#######
# TEDS
#######
# Create variable listing phenotypes and corresponding GWAS
gwas=$(echo HEIG03 EDUC03 ADHD04 BODY11)
# Calculate polygenic scores using 1KG reference
for i in $(seq 1 4);do
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
sbatch --mem 10G -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 ${TEDS_output_dir}/Genotype/Harmonised/TEDS.w_hm3.QCd.AllSNP.chr \
--target_keep ${TEDS_output_dir}/Projected_PCs/Ancestry_idenitfier/TEDS.w_hm3.AllAncestry.EUR.keep \
--ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}/1KGPhase3.w_hm3.${gwas_i} \
--ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic_stratified_TWAS_Gene/${gwas_i}/1KGPhase3.w_hm3.${gwas_i}.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name ${gwas_i} \
--output ${TEDS_output_dir}/PolygenicScores_stratified_TWAS_Gene/${gwas_i}/TEDS.subset.w_hm3.${gwas_i}
done
```
</details>
<br/>
## Functionally-agnostic polygenic scoring
Polygenic scores were derived using PRScs-auto, a Bayesian shrinkage method that I have shown to perform well previously.
Polygenic scores were derived using a reference standardised pipeline. The European subset of the 1KG reference was used ([described here](https://opain.github.io/GenoPred/Pipeline_prep.html#4_polygenic_scoring)). In brief, all scores were derived using HapMap3 SNPs only, modelling LD based on the reference. Any HapMap3 missing in the target sample are imputed using the reference estimated allele frequency.
Polygenic scoring in target samples has been previously documented [here](https://opain.github.io/GenoPred/Determine_optimal_polygenic_scoring_approach.html)
<br/>
### Derive pT+clump polygenic scores without retaining single variant in the MHC
Only do this as a sensitivity analysis for Rheumatoid arthritis
<details><summary>Show code</summary>
```{R, eval=F, echo=T}
# Generate scoring files
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Pipeline_prep.config
sbatch -p shared,brc --mem=6G /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/polygenic_score_file_creator/polygenic_score_file_creator.R \
--ref_plink_chr ${Geno_1KG_dir}/1KGPhase3.w_hm3.chr \
--ref_keep ${Geno_1KG_dir}/keep_files/EUR_samples.keep \
--sumstats ${gwas_rep}/RHEU01.sumstats.gz \
--plink ${plink1_9} \
--memory 3000 \
--prune_hla F \
--output ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump \
--ref_pop_scale ${Geno_1KG_dir}/super_pop_keep.list
# Calculate scores in UKB
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
sbatch --mem 10G -p brc,shared /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.RheuArth.txt \
--ref_score ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump \
--ref_scale ${Geno_1KG_dir}/Score_files_for_poylygenic/RHEU01.noMHCClump/1KGPhase3.w_hm3.RHEU01.noMHCClump.EUR.scale \
--ref_freq_chr ${Geno_1KG_dir}/freq_files/EUR/1KGPhase3.w_hm3.EUR.chr \
--plink ${plink1_9} \
--pheno_name RHEU01 \
--output ${UKBB_output}/PRS_for_comparison/1KG_ref/pt_clump/RHEU01.noMHCClump/UKBB.subset.w_hm3.RHEU01.noMHCClump
```
</details>
<br/>
## Estimating predictive ability
Models containing a single predictor were derived using generalised linear model (GLM). Models containing multiple predictors were derived using elastic-net regularisation to reduce the likelihood of overfitting and account for multicollinearity when modelling highly correlated predictors. Nested cross validation was used to estimate the variance explained by models to avoid overfitting. This involves an outer loop, splitting the data into training and test datasets, deriving the model using 10-fold cross validation in the training dataset, saving model predictions for the test dataset, combining the test predictions for each outer loop and estimating the variance explained.
Model building and evaluation was performed using an Rscript called Model_builder_V2_nested.R (more information [here](https://github.com/opain/GenoPred/tree/master/Scripts/Model_builder)).
### UK Biobank
<details><summary>Single-pT vs. Multi-pT</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of GeRS 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 $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done
# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
for i in $(seq 1 8);do
for weight in ${weights};do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
cat > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups <<EOF
predictors
${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile
EOF
done
done
# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
# 1KG reference
for i in $(seq 1 8);do
for weight in ${weights};do
pheno_i=$(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 ' ')
sbatch --mem 10G -n 2 -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 ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
--keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
--out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs \
--n_core 2 \
--compare_predictors T \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.${weight}.${gwas_i}.EUR-GeRSs.predictor_groups
done
sleep 200
done
```
</details>
<details><summary>Single-tissue vs. Multi-tissue</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of GeRS across multiple tissues individually and in combination
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done
# Create a file listing the predictors files
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
weights=$(cat ${TWAS_rep}/snp_weight_list.txt)
for i in $(seq 1 8);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
echo "predictors group" > /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
for weight in ${weights}; do
echo ${UKBB_output}/GeRS_for_comparison/1KG_ref/EUR/${weight}/UKBB.w_hm3.EUR.${weight}.${gwas_i}.fiprofile ${weight} >> /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
done
done
# Derive and evaluate models
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD RheuArth)
gwas=$(echo DEPR06 COLL01 BODY03 HEIG03 DIAB05 COAD01 CROH01 RHEU01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005)
weights=YFS.BLOOD.RNAARR
# 1KG reference
for i in $(seq 1 8);do
pheno_i=$(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 ' ')
sbatch --mem 10G -n 2 -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 ${UKBB_output}/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.txt \
--keep /users/k1806347/brc_scratch/Analyses/PRS_comparison/UKBB_outcomes_for_prediction/ukb18177_glanville_post_qc_id_list.UpdateIDs.fam \
--out /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs \
--n_core 2 \
--compare_predictors F \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs/UKBB.w_hm3.AllTissue.${gwas_i}.EUR-GeRSs.predictor_groups
done
```
</details>
<details><summary>Multi-tissue per pT</summary>
```{bash, echo=T, eval=F}
##############################
# Evaluating predictive utility of GeRS across multiple tissues for each pT seperately
##############################
. /users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config
# Make required directories
for pheno_i in $(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth);do
mkdir -p /users/k1806347/brc_scratch/Analyses/GeRS_comparison/UKBB_outcomes_for_prediction/${pheno_i}/Association_withGeRSs
done
# Split the GeRS files by pT
module add apps/R
R
source('/users/k1806347/brc_scratch/Software/MyGit/GenoPred/config_used/Target_scoring.config')
gwas<-c('DEPR06','COLL01','BODY03','HEIG03','DIAB05','COAD01','CROH01','RHEU01')
weights<-read.table('~/brc_scratch/Data/TWAS_sumstats/FUSION/snp_weight_list.txt', stringsAsFactors=F)$V1
library(data.table)
for(gwas_i in gwas){
print(gwas_i)
for(weights_i in weights){