Background: Gene-environment interaction (GxE) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we utilize a series of genome-wide gene-environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. Methods: We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified GWAS and GWEIS modelling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. Results: GWEIS models showed few genetic variants with significant interaction effects across all gene-environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3, on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. Conclusions: Even in large samples, GxE effects are difficult to detect at the molecular level. Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. While GWEIS seems to be a preferred approach over stratified GWAS, modelling molecular GxE effects remains a challenge that will require larger consortia and more in-depth phenotypic measurement.

中文翻译：

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通过环境分层和基因-环境相互作用细化遗传对酒精滥用的影响范围

背景：基因-环境相互作用（GxE）可能是塑造酒精滥用（AM）个体差异的重要影响因素，但在分子遗传学研究中尚未得到广泛研究。在这项研究中，我们利用一系列全基因组基因-环境相互作用（GWEIS）和计算机注释方法，旨在提高基因识别和对 AM 的生物学理解。方法：我们对英国生物银行大型样本 (N = 360,314) 中的四种 AM 表型进行了 GWEIS，以创伤暴露和社会经济地位 (SES) 作为遗传效应的调节因素。探索性分析比较了分层 GWAS 和 GWEIS 建模方法。我们应用功能注释、基因和基因集富集以及多基因评分分析来解释 GWEIS 结果。结果：GWEIS 模型显示很少有遗传变异，在所有基因-环境对中具有显着的相互作用效应。富集分析确定了 SES 对 NOXA1、DLGAP1 和 UBE2L3 基因对饮酒量的调节作用以及基因 IFIT1B 对饮酒频率的调节作用。除DLGAP1外，这些基因此前并未与AM相关。最稳健的结果（GWEIS 相互作用 p = 4.59e-09）是 SES 调节与免疫相关基因相关的变异对随餐饮酒和不随餐饮酒模式的影响。结论：即使在大样本中，GxE 效应也很难在分子水平上检测到。我们的结果强调了 SES 对 AM 遗传影响的调节作用背后的几个基因和免疫系统功能的潜在机制。虽然 GWEIS 似乎是优于分层 GWAS 的首选方法，但分子 GxE 效应建模仍然是一个挑战，需要更大的联盟和更深入的表型测量。