Culverhouse Robert C, Hinrichs Anthony L, Suarez Brian K
Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S101. doi: 10.1186/1753-6561-5-S9-S101.
The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype.
遗传分析研讨会17中的非亲属个体样本由来自8个群体样本的少量受试者以及主要由罕见变异组成的遗传数据构成。我们比较了两种在基因内合并罕见变异的简单方法,以评估它们在识别影响表型的基因方面的效用。我们还将分层分析的结果与使用种族作为协变量的汇总分析结果进行了比较。我们发现,这两种合并方法在识别这些数据中包含致病变异的基因方面同样有效。然而,当只有一个亚群包含与表型相关的罕见变异时,将群体作为协变量并不能有效替代对亚群进行单独分析。