Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

• Published in: Nature genetics Vol. 54; no. 4; pp. 450 - 458
• Main Authors: Weissbrod, Omer, Kanai, Masahiro, Shi, Huwenbo, Gazal, Steven, Peyrot, Wouter J., Khera, Amit V., Okada, Yukinori, Martin, Alicia R., Finucane, Hilary K., Price, Alkes L.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2022; Nature Publishing Group
• Subjects: 631/208/2489; 692/699; Accuracy; Agriculture; Animal Genetics and Genomics; Biobanks; Biomedical and Life Sciences; Biomedicine; Cancer Research; Estimates; Gene Function; Genome-Wide Association Study; Genomes; Human Genetics; Humans; Linkage Disequilibrium; Mapping; Methods; Multifactorial Inheritance - genetics; Polygenic inheritance; Polymorphism, Single Nucleotide - genetics; Populations; Risk; Risk Factors; Simulation; Training; United Kingdom > UK

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate ca...