Polygenic Prediction Across Populations Is Influenced by Ancestry, Genetic Architecture, and Methodology

Authors

Ying Wang
Masahiro Kanai
Taotao Tan
Mireille Kamariza
Kristin Tsuo
Kai Yuan
Wei Zhou
Yukinori Okada
BioBank Japan Project
Hailiang Huang
Patrick Turley
Elizabeth G Atkinson
Alicia R Martin

Publication Date

10-11-2023

Journal

Cell Genomics

DOI

10.1016/j.xgen.2023.100408

PMID

37868036

PMCID

PMC10589629

PubMedCentral® Posted Date

9-14-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

genome-wide association studies, multi-ancestry, polygenic risk scores, genetic architecture

Abstract

Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.