Texas Academy of Mathematics & Science, University of North Texas, Denton, TX, United States of America.
Department of Mathematics, University of North Texas, Denton, TX, United States of America.
PLoS One. 2020 Mar 10;15(3):e0229217. doi: 10.1371/journal.pone.0229217. eCollection 2020.
The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions' effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions' effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study.
许多复杂疾病的风险是由遗传和环境因素的复杂相互作用决定的。先进的下一代测序技术使得常见和罕见变异体的基因-环境（GE）相互作用的识别成为可能。然而，大多数现有的方法侧重于测试常见和/或罕见遗传变异体的主要效应。开发的方法有限，只能测试罕见变异体或罕见和常见变异体同时的 GE 相互作用的影响。在这项研究中，我们开发了新的方法来测试测序关联研究中罕见和/或常见风险和/或保护变异体的 GE 相互作用的影响。我们提出了两种方法：1）测试罕见变异体（TOW-GE）的最优加权组合的 GE 相互作用的影响；2）测试罕见和常见变异体的 GE 相互作用的加权组合的影响（可变权重 TOW-GE，VW-TOW-GE）。基于遗传分析研讨会 17 数据的广泛模拟研究表明，所提出方法的Ⅰ型错误率得到了很好的控制。与现有的交互序列核关联测试（ISKAT）相比，当存在罕见风险和/或保护变异体的 GE 相互作用效应时，TOW-GE 更有效；当存在罕见和常见风险和保护变异体的 GE 相互作用效应时，VW-TOW-GE 更有效。TOW-GE 和 VW-TOW-GE 均对因果 GE 相互作用的效应方向具有鲁棒性。我们使用 COPDGene 研究中的一个推断数据演示了 TOW-GE 和 VW-TOW-GE 的应用。
