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遗传因素与吸烟行为:解析基因与社会经济地位的相互作用。

Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status.

机构信息

Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands.

Department of Medical Epidemiology and Biostatistics, Karolinska Institute, PO Box 281, 171 77, Stockholm, Sweden.

出版信息

Behav Genet. 2022 Mar;52(2):92-107. doi: 10.1007/s10519-021-10094-4. Epub 2021 Dec 2.

DOI:10.1007/s10519-021-10094-4
PMID:34855049
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8860781/
Abstract

This study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA ('smoking-without-EA'). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene-environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

摘要

本研究旨在厘清遗传易感性、受教育程度(EA)及其在终身吸烟中的重叠和相互作用对终身吸烟的贡献。我们在英国生物银行(UK Biobank)进行了全基因组关联研究(GWAS),以(i)捕获与终身吸烟相关的变异,(ii)与 EA 相关的变异,以及(iii)独立于 EA 对终身吸烟有贡献的变异(“吸烟不依赖 EA”)。基于 GWAS,我们为荷兰双胞胎登记处(NTR,N=17805)和荷兰心理健康调查和发病率研究-2(NEMESIS-2,N=3090)的个体创建了三个多基因评分(PGS)。我们测试了每个 PGS 与环境(G×E)之间的相互作用、邻里社会经济地位(SES)和 EA 对终身吸烟的影响。为了评估 PGS 效应是否特定于吸烟或具有更广泛的影响,我们用心理健康指标重复了这些分析。从吸烟 GWAS 中减去 EA 效应后,基于 SNP 的遗传力从 9.2%下降到 7.2%。吸烟与 SES 特征之间的遗传相关性降低,而减去 EA 后,与吸烟特征的重叠受影响较小。吸烟、EA 和吸烟不依赖 EA 的 PGS 均能预测吸烟。对于心理健康,只有 EA 的 PGS 是可靠的预测指标。一些关系存在 G×E 的迹象,但每种 PGS 类型都没有明确的模式。本研究表明,吸烟的遗传结构除了其他可能更直接的因素外,还有一个 EA 成分。基于 EA 和吸烟不依赖 EA 的 PGS 具有不同的预测特征。本研究展示了如何分解不同的遗传易感性模型和相互作用可以帮助我们理解吸烟的病因。

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