Harnessing functional annotation to improve the accuracy and transferability of polygenic scores

Polygenic scores (PGS) have shown promise in predicting complex traits and disease risk, but their accuracy remains limited and poorly transferable across ancestries. Integrating functional annotations with whole-genome sequencing data can improve prediction by prioritizing likely causal variants shared across populations and by assigning greater weight to variants in biologically relevant regions. The accuracy of polygenic scores (PGS) remains limited and poorly transferable across ancestries. In this Comment, Zeng and Visscher discuss how integrating functional annotations with whole-genome sequencing data can improve PGS by prioritizing likely causal variants shared across populations and by assigning greater weight to variants in biologically relevant regions.