A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.
A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.
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作者：
Chen W;Ren C;Qin H;Archer KJ;Ouyang W;Liu N;Chen X;Luo X;Zhu X;Sun S;Gao G
为非裔美国人等混合人群的全基因组关联研究（GWAS）建立有效的方法。研究表明，在检验检验单核苷酸多态性（SNP）与因果变异不存在背景连锁不平衡（LD）的原假设时，现有的几种方法不能很好地控制GWAS中强意义上的家庭误差率（FWER）；现有的方法包括调整全局祖先的关联测试和联合关联测试，联合关联测试结合了来自混合映射测试的统计数据和校正本地祖先的关联测试。此外，我们描述了一种用于GWAS的广义顺序Bonferroni （smooth-GSB）程序，该程序将从混合映射测试计算的平滑权重合并到校正本地血统的关联测试中。我们应用平滑gsb程序分析来自社区动脉粥样硬化风险（ARIC）研究的美国非洲人GWAS数据。我们的仿真研究表明，与校正本地血统的关联检验相比，平滑gsb程序不仅可以控制FWER，而且可以提高统计能力。在混合种群中，光滑gsb程序比现有的几种GWAS方法具有更好的性能。
To develop effective methods for genome wide association studies (GWAS) in admixed populations, such as African Americans. We show that when testing the null hypothesis that the test single nucleotide polymorphism (SNP) is not in background linkage disequilibrium (LD) with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS; the existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. Our simulation studies indicate that the smooth-GSB procedure not only can control the FWER, but also improve statistical power compared with association tests correcting for local ancestry. The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.