Gauderman W James, Mukherjee Bhramar, Aschard Hugues, Hsu Li, Lewinger Juan Pablo, Patel Chirag J, Witte John S, Amos Christopher, Tai Caroline G, Conti David, Torgerson Dara G, Lee Seunggeun, Chatterjee Nilanjan
Am J Epidemiol. 2017 Oct 1;186(7):762-770. doi: 10.1093/aje/kwx228.
The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.
基因-环境相互作用（G×E）分析可能是进一步理解许多复杂性状病因的关键所在。当前大量遗传数据的可得性、可测量的环境数据类型的广泛多样性以及多项研究联盟的形成，既为识别基因-环境相互作用提供了新机遇，也带来了新的分析挑战。在本文中，我们总结了几种可用于在全基因组关联研究中检测基因-环境相互作用的统计方法。这些方法包括病例对照或数量性状研究中传统的基因-环境相互作用模型，以及能提供更大检验效能的替代方法。总结了用基因集和在联盟环境下的数据分析基因-环境相互作用的最新方法，以及因环境数据复杂性而产生的问题。我们对为何在全基因组关联研究中检测基因-环境相互作用至今仍很困难进行了一些推测。最后，我们描述了可用于实施本文所述大多数方法的软件程序。
