Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores.
Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores.
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DOI：
10.1038/s41588-022-01036-9
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发表时间：
2022-04
期刊：
影响因子：
30.8
通讯作者：
Price, Alkes L.
中科院分区：
文献类型：
--
作者：
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.
多基因风险评分（PRS）在非欧洲人群中的准确性降低，加剧了健康差异。我们提出了PolyPred，这是一种通过结合两个预测因子来改善交叉人群PRS的方法：一个新的预测因子，利用功能性信息精细映射来估计因果效应（而不是标签效应），解决LD差异;和BOLT-LMM，一个已发表的预测因子。当非欧洲目标人群中有大量训练样本时，我们提出了PolyPred+，它进一步整合了非欧洲训练数据。我们使用UK Biobank British训练数据将PolyPred应用于4个UK Biobank人群中的49种疾病/性状，并观察到与BOLT-LMM相比的相对改善，从南亚人的+7%到非洲人的+32%，与模拟一致。我们使用英国生物银行和日本生物银行的训练数据将PolyPred+应用于英国生物银行东亚人的23种疾病/性状，并观察到与BOLT-LMM相比+24%的改善和与PolyPred相比+12%的改善。PolyPred和PolyPred+的基于药理学的类似物获得了类似的改善。
Polygenic risk scores (PRS) suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population PRS by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing LD 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 4 UK Biobank populations using UK Biobank British training data, and observed relative improvements vs. 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% vs. BOLT-LMM and +12% vs. PolyPred. Summary statistic-based analogues of PolyPred and PolyPred+ attained similar improvements.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
9.8
作者：
Chun, Sung;Imakaev, Maxim;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.