一种用于混合人群全基因组关联研究的广义序贯邦费罗尼程序，将混合映射信息纳入关联测试。
A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.
作者信息
Chen Wenan, Ren Chunfeng, Qin Huaizhen, Archer Kellie J, Ouyang Weiwei, Liu Nianjun, Chen Xiangning, Luo Xingguang, Zhu Xiaofeng, Sun Shumei, Gao Guimin
机构信息
Department of Biostatistics, Virginia Commonwealth University, Richmond, Va., USA.
出版信息
Hum Hered. 2015;79(2):80-92. doi: 10.1159/000381474. Epub 2015 Jun 13.
OBJECTIVE
To develop effective methods for GWAS in admixed populations such as African Americans.
METHODS
We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These 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.
RESULTS
Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry.
CONCLUSION
The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.
目的
开发适用于非洲裔美国人等混合人群全基因组关联研究（GWAS）的有效方法。
方法
我们发现，在检验测试单核苷酸多态性（SNP）与因果变异不存在背景连锁不平衡的原假设时，几种现有方法在GWAS中无法严格控制家族性错误率（FWER）。这些现有方法包括针对全球祖先进行调整的关联检验，以及将混合映射检验的统计量与针对本地祖先进行校正的关联检验相结合的联合关联检验。此外，我们描述了一种用于GWAS的广义序贯邦费罗尼（smooth-GSB）程序，该程序将从混合映射检验计算出的平滑权重纳入针对本地祖先进行校正的关联检验中。我们已将smooth-GSB程序应用于社区动脉粥样硬化风险（ARIC）研究中美国非洲人的GWAS数据分析。
结果
我们的模拟研究表明，与针对本地祖先进行校正的关联检验相比，smooth-GSB程序不仅能控制FWER，还能提高统计效能。
结论
对于混合人群的GWAS，smooth-GSB程序比几种现有方法性能更优。
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