Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

Abstract

Polygenic risk scores (PRS) developed from multi-ancestry genome-wide association studies (GWAS), 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 to PRS constructed from single-ancestry GWAS (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. Notably, for traits with large-effect ancestry-enriched variants, such as mean corpuscular volume, using substantially fewer samples from Biobank Japan achieved comparable accuracies to a much larger European cohort. Furthermore, integrating PRS based on local ancestry-informed GWAS and large-scale European-based PRS improved predictive performance in understudied African populations, especially for less polygenic traits with large 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 PRS from multiple studies.