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Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status
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 E...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860781/ https://www.ncbi.nlm.nih.gov/pubmed/34855049 http://dx.doi.org/10.1007/s10519-021-10094-4 |
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author | Pasman, Joëlle A. Demange, Perline A. Guloksuz, Sinan Willemsen, A. H. M. Abdellaoui, Abdel ten Have, Margreet Hottenga, Jouke-Jan Boomsma, Dorret I. de Geus, Eco Bartels, Meike de Graaf, Ron Verweij, Karin J. H. Smit, Dirk J. Nivard, Michel Vink, Jacqueline M. |
author_facet | Pasman, Joëlle A. Demange, Perline A. Guloksuz, Sinan Willemsen, A. H. M. Abdellaoui, Abdel ten Have, Margreet Hottenga, Jouke-Jan Boomsma, Dorret I. de Geus, Eco Bartels, Meike de Graaf, Ron Verweij, Karin J. H. Smit, Dirk J. Nivard, Michel Vink, Jacqueline M. |
author_sort | Pasman, Joëlle A. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10519-021-10094-4. |
format | Online Article Text |
id | pubmed-8860781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-88607812022-02-23 Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status Pasman, Joëlle A. Demange, Perline A. Guloksuz, Sinan Willemsen, A. H. M. Abdellaoui, Abdel ten Have, Margreet Hottenga, Jouke-Jan Boomsma, Dorret I. de Geus, Eco Bartels, Meike de Graaf, Ron Verweij, Karin J. H. Smit, Dirk J. Nivard, Michel Vink, Jacqueline M. Behav Genet Original Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10519-021-10094-4. Springer US 2021-12-02 2022 /pmc/articles/PMC8860781/ /pubmed/34855049 http://dx.doi.org/10.1007/s10519-021-10094-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Pasman, Joëlle A. Demange, Perline A. Guloksuz, Sinan Willemsen, A. H. M. Abdellaoui, Abdel ten Have, Margreet Hottenga, Jouke-Jan Boomsma, Dorret I. de Geus, Eco Bartels, Meike de Graaf, Ron Verweij, Karin J. H. Smit, Dirk J. Nivard, Michel Vink, Jacqueline M. Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title | Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title_full | Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title_fullStr | Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title_full_unstemmed | Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title_short | Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status |
title_sort | genetic risk for smoking: disentangling interplay between genes and socioeconomic status |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860781/ https://www.ncbi.nlm.nih.gov/pubmed/34855049 http://dx.doi.org/10.1007/s10519-021-10094-4 |
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