Fischer S Taylor, Jiang Yunxuan, Broadaway K Alaine, Conneely Karen N, Epstein Michael P
Department of Human Genetics and Center for Computational and Quantitative Genetics, Emory University, Atlanta, Georgia, United States of America.
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
Genet Epidemiol. 2018 Jul;42(5):447-458. doi: 10.1002/gepi.22116. Epub 2018 Feb 20.
There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross-phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family-based designs, including the valuable case-parent trio design. In this paper, we describe a robust gene-based association test of multiple phenotypes collected in a case-parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case-parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome-wide significant gene demonstrating cross-phenotype effects that was not identified using standard univariate approaches.
人们对识别人类基因组中影响多种不同表型的基因越来越感兴趣。在存在多效性的情况下，对这些表型进行联合检测不仅在生物学上有意义，而且在统计学上比单独对每个表型进行单变量分析（考虑多重检验）更具效力。尽管存在许多跨表型关联检验方法，但大多数此类方法假定样本由无关个体组成，因此不适用于基于家系的设计，包括有价值的病例-父母三联体设计。在本文中，我们描述了一种在病例-父母三联体研究中对收集的多种表型进行稳健的基于基因的关联检验方法。我们的方法基于核距离协方差（KDC）方法，即我们首先为多个表型构建一个相似性矩阵，为一个基因中的遗传变异构建一个相似性矩阵；然后我们检验这两个相似性矩阵之间的依赖性。该方法适用于基因中的常见变异或罕见变异，并且该方法产生的检验在设计上对由于群体分层导致的混杂具有稳健性。我们通过模拟研究评估了我们的方法，观察到该方法比每个单独表型的标准单变量检验更具效力。我们还将我们的方法应用于作为糖尿病肾脏遗传学（GoKinD）研究一部分的病例-父母三联体中收集的表型和基因型数据，并鉴定出一个全基因组显著的基因，该基因显示出跨表型效应，而使用标准单变量方法未鉴定出该效应。
