Department of Mathematics, University of North Texas, Denton, TX, United States of America.
Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America.
PLoS One. 2019 Aug 9;14(8):e0220914. doi: 10.1371/journal.pone.0220914. eCollection 2019.
There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study.
越来越多的证据表明，多效性是一种广泛存在的现象，对于涉及多种相关特征的复杂疾病，通常会对这些特征进行测量。联合分析多种特征可以通过汇总多个微弱效应来提高统计效力。现有的多特征关联测试方法通常分别研究每一个多特征，然后结合单变量测试统计量或合并单变量测试的 p 值来识别与疾病相关的遗传变异。然而，忽略表型之间的相关性可能会导致效力损失。此外，一个基因中的遗传变异（包括常见和罕见变异）通常被视为一个整体，因为遗传的基本功能单位是基因而不是遗传变异，因此会影响潜在的疾病。因此，基因水平关联测试的结果可以更方便地与下游的功能和发病机制研究相结合，而许多现有的多特征关联测试方法仅关注于测试单个常见变异，而不是一个基因。在本文中，我们提出了一种统计方法，即通过测试多个特征的最佳加权组合（TOW-CM）来测试基因组区域（一个基因或通路）中的多个特征和多个变异之间的关联。我们通过广泛的模拟研究来评估所提出方法的性能。我们的模拟研究表明，所提出的方法具有正确的Ⅰ型错误率，并且要么是最有力的检验方法，要么与最有力的检验方法相当。此外，我们还基于 COPDGene 研究说明了 TOW-CM 的实用性。
