Jeanne E. Savage, Christiaan A. de Leeuw, Josefin Werme, Spit for Science Working Group, Danielle M. Dick, Danielle Posthuma, Sophie van der Sluis
{"title":"Refining the scope of genetic influences on alcohol misuse through environmental stratification and gene–environment interaction","authors":"Jeanne E. Savage, Christiaan A. de Leeuw, Josefin Werme, Spit for Science Working Group, Danielle M. Dick, Danielle Posthuma, Sophie van der Sluis","doi":"10.1111/acer.15425","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Gene–environment interaction (G × E) 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 use 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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (<i>N</i> = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>GWEIS models showed few genetic variants with significant interaction effects across gene–environment pairs. Enrichment analyses identified moderation by SES of the genes <i>NOXA1</i>, <i>DLGAP1</i>, and <i>UBE2L3</i> on drinking quantity and the gene <i>IFIT1B</i> on drinking frequency. Except for <i>DLGAP1</i>, these genes have not previously been linked to AM. The most robust results (GWEIS interaction <i>p</i> = 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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.</p>\n </section>\n </div>","PeriodicalId":72145,"journal":{"name":"Alcohol (Hanover, York County, Pa.)","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acer.15425","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alcohol (Hanover, York County, Pa.)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/acer.15425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Gene–environment interaction (G × E) 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 use 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 genome-wide association (GWAS) and GWEIS modeling 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 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
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. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.
背景:基因-环境相互作用(G×E)可能是形成酒精滥用(AM)个体差异的重要影响因素,但分子遗传学研究尚未对其进行广泛研究。在本研究中,我们采用了一系列全基因组范围的基因-环境相互作用(GWEIS)和硅标注方法,旨在提高对酒精滥用的基因鉴定和生物学理解:我们在英国生物库(UK Biobank)大样本(N = 360,314)中对四种急性髓系白血病表型进行了全基因组-环境相互作用(GWEIS)研究,将创伤暴露和社会经济地位(SES)作为遗传效应的调节因子。探索性分析比较了分层全基因组关联(GWAS)和 GWEIS 建模方法。我们应用功能注释、基因和基因组富集以及多基因评分分析来解释 GWEIS 的结果:结果:GWEIS 模型显示,在基因-环境配对中具有显著交互效应的基因变异很少。富集分析发现,NOXA1、DLGAP1 和 UBE2L3 基因以及 IFIT1B 基因对饮酒量和饮酒频率的影响受 SES 的调节。除 DLGAP1 外,这些基因以前从未与 AM 联系在一起。最稳健的结果(GWEIS交互作用 p = 4.59e-09)是,社会经济地位调节了与免疫相关基因有关的变异对有餐饮酒与无餐饮酒模式的影响:我们的研究结果凸显了 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。尽管 GWEIS 似乎是比分层 GWAS 更受青睐的方法,但即使在大样本中,在分子水平上建立 G × E 效应模型仍然是一项挑战。要了解这些影响,需要大量的努力和更深入的表型测量。