生命银行
连锁不平衡
多基因风险评分
人口
生物
人口学
统计
环境卫生
遗传学
医学
基因型
数学
单倍型
基因
社会学
单核苷酸多态性
作者
Omer Weissbrod,Masahiro Kanai,Huwenbo Shi,Steven Gazal,Wouter J. Peyrot,Amit V. Khera,Yukinori Okada,Martin Ar,Hilary Finucane,Alkes L. Price
出处
期刊:Nature Genetics
[Springer Nature]
日期:2022-04-01
卷期号:54 (4): 450-458
被引量:65
标识
DOI:10.1038/s41588-022-01036-9
摘要
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.
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