Lim Elise, Chen Han, Dupuis Josée, Liu Ching-Ti
Department of Biostatistics, Boston University, Boston, Massachusetts.
Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas.
Stat Med. 2020 Mar 15;39(6):801-813. doi: 10.1002/sim.8446. Epub 2019 Dec 4.
Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.
全基因组测序的先进技术为全面研究遗传因素，尤其是罕见变异对复杂性状的贡献提供了契机。已经开发了几种基于区域的测试方法来联合模拟罕见变异的边际效应，但检测基因-环境（GE）相互作用的方法仍不完善。识别环境因素对遗传风险的修饰作用是一项相当大的挑战。为应对这一挑战，我们开发了一种使用广义线性混合效应模型检测罕见变异的基因-环境相互作用的方法。所提出的方法可以适用于相关或不相关样本中的二元或连续性状。在此模型下，遗传主效应、基因-环境相互作用和样本相关性被建模为随机效应。我们采用基于核的方法来利用罕见变异之间的联合信息，并实施方差分量得分检验以减轻计算负担。我们对连续和二元性状的模拟研究表明，所提出的方法在各种情况下都能保持正确的I型错误率和适当的检验效能，例如基因型主效应和基因-环境相互作用效应方向相反以及模型中因果变异比例不同的情况。我们将我们的方法应用于弗雷明汉心脏研究，以测试吸烟对体重指数或超重状态的基因-环境相互作用，并重复了先前大型联盟对单核苷酸多态性-吸烟相互作用的荟萃分析中报道的胆碱能受体烟碱型β4基因关联。我们提出的基于集合的基因-环境检验计算效率高，适用于二元和连续表型，同时适当考虑了家族或隐性相关性。
