Open AccessLeveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores
Author(s) -
Omer Weissbrod,
Masahiro Kanai,
Huwenbo Shi,
Steven Gazal,
Wouter J. Peyrot,
Amit V. Khera,
Yukinori Okada,
Martin Ar,
Hilary Finucane,
Alkes L. Price
Publication year - 2022
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/s41588-022-01036-9
Subject(s) - biobank , linkage disequilibrium , polygenic risk score , population , biology , demography , genetics , environmental health , medicine , genotype , haplotype , gene , sociology , single nucleotide polymorphism
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 causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred + , which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred + to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred + attained similar improvements.