shaprs's Introduction

shaprs

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

Installation:

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

To find the shaPRS weighted meta-analysis of a proximal and adjunct data, simply run:

proximalLoc <- system.file("extdata", "phenoA_sumstats", package = "shaPRS")
adjunctLoc <- system.file("extdata", "phenoB_sumstats", package = "shaPRS")
shaPRS(proximalLoc, adjunctLoc, "<YOUR_OUTPUT_FOLDER>")

This will output your final summary statistics file with the postfix "_shaprs" that you may use in your favourite PRS generation tool.
The above will also output a few other files that may be of interest: "_meta" (fixed fixed effect meta-analysis) and "_SNP_lFDR" (lFDR estimates and Q-values for each SNP).

Blend LD ref matrices (cross-ancestry analysis):

Pop1LDRefLoc <- paste0(system.file("extdata", "", package = "shaPRS"), "/")
Pop2LDRefLoc <- paste0(system.file("extdata", "", package = "shaPRS"), "/")
blendFactorLoc <- system.file("extdata", "pop_SNP_lFDR", package = "shaPRS")
adjustinputLoc <- system.file("extdata", "pop_adjustinput", package = "shaPRS")
outputLoc <- "<YOUR LOCATION>"
shaPRS_LDGen(Pop1LDRefLoc, Pop2LDRefLoc, blendFactorLoc, adjustinputLoc, outputLoc)

This runs the shaPRS cross-ancestry analysis on the included toy dataset and generates the LD-reference panel for the 22 autosomes, along with a map.rds file.
To run it on real data, first run the main shaPRS() to generate the "_SNP_lFDR" and "_adjustinput" files (see above) for "blendFactorLoc" and "adjustinputLoc". Finally, specify two LDpred2 formatted LD-reference panels, appropriate to your proximal/adjunct datasets ("Pop1LDRefLoc" and "Pop2LDRefLoc").
The output data in the results folder (), can then be used by LDpred2, processed by the same scripts that you have been using on the standard LD-ref matrices.

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