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GWAS Study

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

Pasman JA, Demange PA, Guloksuz S et al.

34855049 PubMed ID
GWAS Study Type
499738 Participants
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

PJ
Pasman JA
DP
Demange PA
GS
Guloksuz S
WA
Willemsen AHM
AA
Abdellaoui A
TH
Ten Have M
HJ
Hottenga JJ
BD
Boomsma DI
DG
de Geus E
BM
Bartels M
DG
de Graaf R
VK
Verweij KJH
SD
Smit DJ
NM
Nivard M
VJ
Vink JM
Chapter II

Abstract

Summary of the research findings

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.

272,943 European or unknown ancestry cases, 226,795 European or unknown ancestry controls

Chapter III

Study Statistics

Key metrics and study information

499738
Total Participants
GWAS
Study Type
No
Replicated
European, NR
Ancestry
U.K.
Recruitment Country
Chapter IV

AI-Generated Summary

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