Hsieh Ai-Ru, Chen Li-Shiun, Li Ying-Ju, Fann Cathy S J
Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.
Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.
Psychiatr Genet. 2019 Aug;29(4):111-119. doi: 10.1097/YPG.0000000000000227.
Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of 'missing heritability' and have a proven role in dissecting the etiology for human diseases and complex traits.
We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as 'str-CMC' and 'str-WSS'. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran-Mantel-Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes.
The Cochran-Mantel-Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O.
Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.
罕见变异（次要等位基因频率<1%或5%）有助于研究人员解决“遗传力缺失”这一混杂问题，并且在剖析人类疾病和复杂性状的病因方面已被证明具有重要作用。
我们扩展了联合多变量和压缩法（CMC）以及加权和统计法（WSS），并通过主成分分析进行分层分析来考虑群体分层和亚组效应的影响，在此将其命名为“str-CMC”和“str-WSS”。为了评估扩展方法的有效性，我们分析了吸烟与戒烟遗传结构数据库，该数据库包含在Illumina Human Omni2.5芯片上进行基因分型的非裔美国人和欧裔美国人，并且我们将结果与通过序列核关联检验（SKAT）及其改进方法SKAT-O（将群体分层和亚组效应作为协变量）所获得的结果进行了比较。我们利用 Cochr an-Mantel-Haenszel检验来检查基因内亚组之间单核苷酸多态性等位基因频率的可能差异。我们旨在检测罕见变异，并考虑包含39个乙酰胆碱受体相关基因的基因组区域中的群体分层和亚组效应。
应用于GABRG2的Cochran-Mantel-Haenszel检验（P = 0.001）具有显著性。然而，在非裔美国人中，str-CMC（P = 8.04E-06）和str-WSS（P = 0.046）均检测到了GABRG2，但SKAT或SKAT-O未检测到。
我们的结果表明，如果相关的罕见变异仅特定于一个亚组，那么分层分析可能比联合分析是一种更好的方法。
