Coram Marc A, Candille Sophie I, Duan Qing, Chan Kei Hang K, Li Yun, Kooperberg Charles, Reiner Alex P, Tang Hua
Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.
Department of Genetics, Stanford University School of Medicine, Stanford CA 94305, USA.
Am J Hum Genet. 2015 May 7;96(5):740-52. doi: 10.1016/j.ajhg.2015.03.008. Epub 2015 Apr 16.
Elucidating the genetic basis of complex traits and diseases in non-European populations is particularly challenging because US minority populations have been under-represented in genetic association studies. We developed an empirical Bayes approach named XPEB (cross-population empirical Bayes), designed to improve the power for mapping complex-trait-associated loci in a minority population by exploiting information from genome-wide association studies (GWASs) from another ethnic population. Taking as input summary statistics from two GWASs-a target GWAS from an ethnic minority population of primary interest and an auxiliary base GWAS (such as a larger GWAS in Europeans)-our XPEB approach reprioritizes SNPs in the target population to compute local false-discovery rates. We demonstrated, through simulations, that whenever the base GWAS harbors relevant information, XPEB gains efficiency. Moreover, XPEB has the ability to discard irrelevant auxiliary information, providing a safeguard against inflated false-discovery rates due to genetic heterogeneity between populations. Applied to a blood-lipids study in African Americans, XPEB more than quadrupled the discoveries from the conventional approach, which used a target GWAS alone, bringing the number of significant loci from 14 to 65. Thus, XPEB offers a flexible framework for mapping complex traits in minority populations.
在非欧洲人群中阐明复杂性状和疾病的遗传基础尤其具有挑战性，因为美国少数族裔人群在基因关联研究中的代表性不足。我们开发了一种名为XPEB（跨人群经验贝叶斯）的经验贝叶斯方法，旨在通过利用来自另一个种族人群的全基因组关联研究（GWAS）信息，提高在少数族裔人群中定位复杂性状相关基因座的能力。以来自两个GWAS的汇总统计数据为输入——一个来自主要关注的少数族裔人群的目标GWAS和一个辅助基础GWAS（例如欧洲人中规模更大的GWAS）——我们的XPEB方法对目标人群中的单核苷酸多态性（SNP）重新进行优先级排序，以计算局部错误发现率。我们通过模拟证明，只要基础GWAS包含相关信息，XPEB就能提高效率。此外，XPEB有能力舍弃不相关的辅助信息，防止因人群间遗传异质性导致错误发现率虚高。应用于一项针对非裔美国人的血脂研究时，XPEB使仅使用目标GWAS的传统方法的发现数量增加了四倍多，将显著基因座的数量从14个增加到65个。因此，XPEB为在少数族裔人群中定位复杂性状提供了一个灵活的框架。
