shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores

Martin Kelemen; Elena Vigorito; Laura Fachal; Carl A Anderson; Chris Wallace

doi:10.1016/j.ajhg.2024.04.009

shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores

Am J Hum Genet. 2024 Apr 27:S0002-9297(24)00124-1. doi: 10.1016/j.ajhg.2024.04.009. Online ahead of print.

Authors

Martin Kelemen¹, Elena Vigorito², Laura Fachal³, Carl A Anderson³, Chris Wallace⁴

Affiliations

¹ Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK; Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK. Electronic address: mk907@cam.ac.uk.
² MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
³ Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK.
⁴ Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

PMID: 38703768
DOI: 10.1016/j.ajhg.2024.04.009

Abstract

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Keywords: PGS; PRS; cross-ancestry; genetic correlation; pleiotropy; polygenic score; shaPRS; trans-ethnic.