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

Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRS(multi), 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 PRS(multi) compared with PRSs constructed from single-ancestry GWASs (PRS(single)). Through extensive simulations and empirical analyses, we showed that PRS(multi) overall outperformed PRS(single) 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.