Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent’s age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard “main effect” GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

基因-环境相互作用（GxE）在遗传因素和复杂性状之间的理论关系中起着核心作用。虽然人类行为的全基因组 GxE 研究仍未得到充分利用，部分原因是方法学限制，但现有的模式生物 GxE 研究强调了在环境背景下解释遗传关联的重要性。在本文中，我们提出了一个框架，用于使用来自全基因组关联研究 (GWAS) 的原始数据进行 GxE 分析，并应用该技术分析酒精使用频率的基因-年龄相互作用。为了说明此程序的有效性，我们从 GxE GWAS 分析中计算遗传边际效应，以从 UK Biobank 数据集中对酒精使用频率进行有序测量，将受访者的年龄视为持续调节的环境。遗传边际效应阐明了 GxE 关联的解释，并提供了对遗传关联如何随年龄（环境）变化的直接和清晰的理解。为了突出我们提出的用于呈现 GxE GWAS 结果的方法的优势，我们将边际遗传效应的解释与狭隘地关注相互作用系数的重要性的解释进行了比较。结果表明，与酒精使用频率的遗传关联在不同年龄段有很大差异，这一结论从原始回归或交互系数中可能并不明显。GxE GWAS 不如标准“主效应”GWAS 方法强大，因此需要更大的样本来检测显着的调节关联。