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

Autor: 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.
Zdroj: Nature Genetics; April 2022, Vol. 54 Issue: 4 p450-458, 9p
Abstrakt: 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.
Databáze: Supplemental Index
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