Beyond the main genetic and environmental effects, gene–environment (G–E) interactions have been demonstrated to significantly contribute to the development and progression of complex diseases. Published analyses of G–E interactions have primarily used a supervised framework to model both low-dimensional environmental factors and high-dimensional genetic factors in relation to disease outcomes. In this article, we aim to provide a selective review of methodological developments in G–E interaction analysis from a statistical perspective. The three main families of techniques are hypothesis testing, variable selection, and dimension reduction, which lead to three general frameworks: testing-based, estimation-based, and prediction-based. Linear- and nonlinear-effects analysis, fixed- and random-effects analysis, marginal and joint analysis, and Bayesian and frequentist analysis are reviewed to facilitate the conduct of interaction analysis in a wide range of situations with various assumptions and objectives. Statistical properties, computations, applications, and future directions are also discussed.

中文翻译：

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高维基因-环境相互作用分析


除了主要的遗传和环境影响外，基因-环境 （G-E） 相互作用已被证明对复杂疾病的发展和进展有重大贡献。已发表的 G-E 相互作用分析主要使用监督框架来模拟与疾病结果相关的低维环境因素和高维遗传因素。在本文中，我们旨在从统计角度对 G-E 交互分析的方法学发展进行选择性回顾。技术有三个主要系列是假设检验、变量选择和降维，这导致了三个一般框架：基于测试、基于估计和基于预测。回顾了线性和非线性效应分析、固定和随机效应分析、边际和联合分析以及贝叶斯和频率分析，以促进在具有各种假设和目标的广泛情况下进行交互作用分析。还讨论了统计特性、计算、应用和未来方向。