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

2021/12/02 by Joëlle A. Pasman, Perline Demange, Perline A. Demange +19 · 44 citations
Biochemistry, Genetics and Molecular Biology · Medicine · Psychology · #BRCA gene mutations in cancer #Biobank #Biology #Demography #Environmental health #Gene #Genetic Associations and Epidemiology #Genetic association #Genetics #Genome-wide association study #Genotype #Heritability #Medicine #Mental health #Population #Psychiatry #Psychology #Single-nucleotide polymorphism #Smoking Behavior and Cessation #Socioeconomic status

paper · pdf · doi:10.1007/s10519-021-10094-4

published in Behavior Genetics 52(2), 92-107 (Springer Science+Business Media)

openalex publication_date 2021/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• GCTA: A Tool for Genome-wide Complex Trait Analysis
• Heritability of Smoking Initiation and Nicotine Dependence
• PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
• Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

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