shaprs

ShaPRS: Leveraging shared genetic effects across traits and ancestries improves accuracy of polygenic scores

Installation:

install_github("mkelcb/shaprs")
library("shaPRS")

Step 1: adjust your summary statistics, run:

inputDataLoc <- system.file("extdata", "shapersToydata.txt", package = "shaPRS")
inputData= read.table(inputDataLoc, header = T)
results = shaPRS_adjust(inputData)

• the results object will have a table,'lFDRTable', which provides the lFDR estimates and Q-values for each SNP.

Step 2: Blend summary statistics, run:

subphenoLoc <- system.file("extdata", "phenoA_sumstats", package = "shaPRS")
subpheno_otherLoc <- system.file("extdata", "phenoB_sumstats", package = "shaPRS")
blendFactorLoc <- system.file("extdata", "myOutput_SNP_lFDR", package = "shaPRS")
subpheno= read.table(subphenoLoc, header = TRUE)
subpheno_other= read.table(subpheno_otherLoc, header = TRUE)
blendingFactors= read.table(blendFactorLoc, header = TRUE)
blendedSumstats = shaPRS_blend_overlap(subpheno, subpheno_other, blendingFactors)

• 'blendedSumstats' is a summary statistics dataframe with the following columns: chr pos SNP A1 A2 Freq1.Hapmap b se p N

That's it. You may now then use this in your favourite PRS generation tool.

Step 3: Blend LD ref matrices (optional):

sumstatsData = readRDS(file = system.file("extdata", "sumstatsData_toy.rds", package = "shaPRS") )

read SNP map files ( same toy data for the example)

pop1_map_rds = readRDS(file = system.file("extdata", "my_data.rds", package = "shaPRS") )
pop2_map_rds = readRDS(file = system.file("extdata", "my_data2.rds", package = "shaPRS") )

use chrom 21 as an example

chromNum=21

load the two chromosomes from each population ( same toy data for the example)

pop1LDmatrix = readRDS(file = system.file("extdata", "LDref.rds", package = "shaPRS") )
pop2LDmatrix = readRDS(file = system.file("extdata", "LDref2.rds", package = "shaPRS") )

2. grab the RSids from the map for the SNPS on this chrom, each LD mat has a potentiall different subset of SNPs this is guaranteed to be the same order as the pop1LDmatrix

pop1_chrom_SNPs = pop1_map_rds[ which(pop1_map_rds$chr == chromNum),]

this is guaranteed to be the same order as the pop2LDmatrix

pop2_chrom_SNPs = pop2_map_rds[ which(pop2_map_rds$chr == chromNum),]
pop1_chrom_SNPs$pop1_id = 1:nrow(pop1_chrom_SNPs)
pop2_chrom_SNPs$pop2_id = 1:nrow(pop2_chrom_SNPs)

intersect the 2 SNP lists so that we only use the ones common to both LD matrices by merging them

chrom_SNPs_df  <- merge(pop1_chrom_SNPs,pop2_chrom_SNPs, by = "rsid")

align the two LD matrices

chrom_SNPs_df = alignStrands(chrom_SNPs_df, A1.x ="a1.x", A2.x ="a0.x", A1.y ="a1.y", A2.y ="a0.y")

align the summary for phe A and B

sumstatsData = alignStrands(sumstatsData)

subset sumstats data to the same chrom

sumstatsData = sumstatsData[which(sumstatsData$CHR == chromNum ),]

merge sumstats with common LD map data

sumstatsData  <- merge(chrom_SNPs_df,sumstatsData, by.x="rsid", by.y = "SNP")

remove duplicates

sumstatsData = sumstatsData[ !duplicated(sumstatsData$rsid) ,]

use the effect alleles for the sumstats data with the effect allele of the LD mat as we are aligning the LD mats against each other, not against the summary stats we only use the lFDR /SE from the sumstats, which are directionless, so those dont need to be aligned

sumstatsData$A1.x =sumstatsData$a1.x
sumstatsData$A1.y =sumstatsData$a1.y

make sure the sumstats is ordered the same way as the LD matrix:

sumstatsData = sumstatsData[order(sumstatsData$pop1_id), ]

(it doesn't matter which matrix to use to order the sumstats as they are the same)

subset the LD matrices to the SNPs we actualy have

pop1LDmatrix = pop1LDmatrix[sumstatsData$pop1_id,sumstatsData$pop1_id]
pop2LDmatrix = pop2LDmatrix[sumstatsData$pop2_id,sumstatsData$pop2_id]

generate the blended LD matrix

cormat = LDRefBlend(pop1LDmatrix,pop2LDmatrix, sumstatsData)

create a new map file that matches the SNPs common to both LD panels

map_rds_new = pop1_map_rds[which(pop1_map_rds$chr == chromNum),]

map_rds_new2 = map_rds_new[which(map_rds_new$rsid %in% sumstatsData$rsid),]

save the new LD matrix to a location of your choice

saveRDS(cormat,file =paste0(<YOUR LOCATION>,"/LD_chr",chromNum,".rds"))

save its Map file too

saveRDS(map_rds_new2,file = paste0(<YOUR LOCATION>,"/LD_chr",chromNum,"_map.rds"))

• The cormat is a 29x29 dense matrix of SNP-SNP correlations, which are saved to a location of your choice, together with its map file.