Zhou Fei, Ren Jie, Lu Xi, Ma Shuangge, Wu Cen
Department of Statistics, Kansas State University, Manhattan, KS, USA.
Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.
Methods Mol Biol. 2021;2212:191-223. doi: 10.1007/978-1-0716-0947-7_13.
Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.
基因-环境相互作用对于阐明复杂疾病的遗传基础具有重要意义，这超出了多个遗传因素及其相互作用（或上位性）的联合作用。过去，基因-环境相互作用主要是在遗传关联研究的框架内进行的。由于环境效应形式复杂以及存在大量包括基因表达和单核苷酸多态性（SNP）在内的遗传因素，基因-环境相互作用具有高维度性，这推动了用于剖析基因-环境相互作用的惩罚变量选择方法的发展，而在大多数已发表的关于遗传相互作用研究的综述中，这一点被忽视了。在本文中，我们首先综述了关于基因-环境和基因-基因相互作用的现有研究。然后，在简要介绍变量选择方法之后，我们分别在各种概念模型下，回顾了边际范式和联合范式中的惩罚及相关变量选择方法。还提供了针对基因-环境研究的变量选择方法的优缺点以及计算方面的讨论。
