Multiethnic polygenic risk scores improve risk prediction in diverse populations.
Multiethnic polygenic risk scores improve risk prediction in diverse populations.
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DOI：
10.1002/gepi.22083
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发表时间：
2017-12
影响因子：
2.1
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Márquez-Luna C;Loh PR;South Asian Type 2 Diabetes (SAT2D) Consortium;SIGMA Type 2 Diabetes Consortium;Price AL
遗传风险预测方法近年来得到了广泛的研究。然而，大多数可用的训练数据涉及欧洲样本，目前尚不清楚如何准确预测其他人群的疾病风险。以前的研究要么使用来自欧洲样本的大样本量训练数据，要么使用来自目标人群的小样本量训练数据，但不是两者都使用。在这里，我们引入了一个多种族多基因风险评分，它结合了来自欧洲样本的训练数据和来自目标人群的训练数据。我们应用这种方法来预测2型糖尿病（T2 D）在拉丁裔队列中使用公开可用的欧洲汇总统计量在大样本量（Neff= 40 k）和拉丁裔训练数据在小样本量（Neff= 8 k）。在这里，与仅使用一个训练数据源的方法相比，我们在预测准确性方面获得了>70%的相对提高（从R2=0.027到R2=0.047），这与模拟中的大幅相对提高一致。我们观察到T2 D风险等位基因在拉丁美洲人中的系统负荷较低，具有更多的欧洲血统，这可以解释为祖先欧洲和/或美洲原住民群体的多基因选择。我们使用欧洲（Neff= 40 k）和南亚（Neff= 16 k）训练数据预测南亚英国生物库队列中的T2 D，并在预测准确性方面获得了>70%的相对改善，并且使用欧洲（N= 113 k）和非洲（N=2k）训练数据预测非洲英国生物库队列中的身高获得了30%的相对改善。我们的工作减少了欧洲和非欧洲目标人群之间多基因风险预测准确性的差距。
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multi-ethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff=40k) and Latino training data in small sample size (Neff=8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2=0.027 to R2=0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff=40k) and South Asian (Neff=16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N=113k) and African (N=2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
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DOI：
10.1038/nrg2760
发表时间：
2010-05
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
--
影响因子：
30.8
作者：
Robinson MR;Hemani G;Medina-Gomez C;Mezzavilla M;Esko T;Shakhbazov K;Powell JE;Vinkhuyzen A;Berndt SI;Gustafsson S;Justice AE;Kahali B;Locke AE;Pers TH;Vedantam S;Wood AR;van Rheenen W;Andreassen OA;Gasparini P;Metspalu A;Berg LH;Veldink JH;Rivadeneira F;Werge TM;Abecasis GR;Boomsma DI;Chasman DI;de Geus EJ;Frayling TM;Hirschhorn JN;Hottenga JJ;Ingelsson E;Loos RJ;Magnusson PK;Martin NG;Montgomery GW;North KE;Pedersen NL;Spector TD;Speliotes EK;Goddard ME;Yang J;Visscher PM
通讯作者：
Visscher PM
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
9.8
作者：
Golan, David;Rosset, Saharon
通讯作者：
Rosset, Saharon
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH