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

Joëlle A. Pasman; Perline A. Demange; Sinan Guloksuz; A. H. M. Willemsen; Abdel Abdellaoui; Margreet Ten Have; Jouke Jan Hottenga; Dorret I. Boomsma; Eco de Geus; Meike Bartels; Ron De Graaf; Karin J. H. Verweij; Dirk J. Smit; Michel Nivard; Jacqueline M. Vink

doi:10.1007/s10519-021-10094-4

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

Joëlle A. Pasman, Perline A. Demange, Sinan Guloksuz, A. H. M. Willemsen, Abdel Abdellaoui, Margreet Ten Have, Jouke Jan Hottenga, Dorret I. Boomsma, Eco de Geus, Meike Bartels, Ron De Graaf, Karin J. H. Verweij, Dirk J. Smit, Michel Nivard, Jacqueline M. Vink

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	92-107
Number of pages	16
Journal	Behavior Genetics
Volume	52
Issue number	2
Early online date	2 Dec 2021
DOIs	https://doi.org/10.1007/s10519-021-10094-4
Publication status	Published - Mar 2022

Funding

Open access funding provided by Karolinska Institute. M.G.N. is supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH120219, ZonMW Grants 849200011 and 531003014 from The Netherlands Organisation for Health Research and Development, a VENI grant awarded by The Dutch Research Council (NWO) (VI.Veni.191G.030) and is a Jacobs Foundation Fellow. The NTR is supported by: \u2018Twin-family database for behavior genetics and genomics studies\u2019 (NWO 480-04-004), Longitudinal data collection from teachers of Dutch twins and their siblings (NWO-481-08-011); Twin-family study of individual differences in school achievement (NWO 056-32-010) and Gravitation program of the Dutch Ministry of Education, Culture and Science and the Netherlands Organization for Scientific Research (NWO 0240-001-003); NWO Groot (480-15-001/674): Netherlands Twin Registry Repository: researching the interplay between genome and environment; NWO-Spi-56-464-14192 Biobanking and Biomolecular Resources Research Infrastructure (BBMRI\u2014NL, 184.021.007 and 184.033.111); European Research Council (ERC-230374); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157-01A); and the National Institute of Mental Health Grand Opportunity grants (grant nos. 1RC2MH089951-01 and 1RC2 MH089995-01). The Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2) is conducted by the Netherlands Institute of Mental Health and Addiction (Trimbos Institute) in Utrecht, The Netherlands. Financial support has been received from the Ministry of Health, Welfare and Sport, with supplemental support from the Netherlands Organization for Health Research and Development (ZonMw) and the Genetic Risk and Outcome of Psychosis (GROUP) investigators. A.A. and K.J.H.V. are supported by the Foundation Volksbond Rotterdam. A.A. is also supported by ZonMw Grant No. 849200011 from The Netherlands Organisation for Health Research and Development.

Funders	Funder number
Erasmus Universiteit Rotterdam
Seventh Framework Programme
NWO-Spi-56-464-14192 Biobanking and Biomolecular Resources Research Infrastructure
Office of Research Infrastructure Programs, National Institutes of Health
National Institutes of Health
Ministerie van Volksgezondheid, Welzijn en Sport
Karolinska Institutet
Office of Extramural Research, National Institutes of Health
Foundation Volksbond Rotterdam
European Research Council
Ministerie van Onderwijs, Cultuur en Wetenschap
Not added	0240-001-003, 480-04-004, 481-08-011, NWO-Spi-56-464-14192, NWO 480-04-004, VI.Veni.191G.030, NWO-481-08-011, 480-15-001/674, NWO 056-32-010
National Institute of Mental Health Grand Opportunity	1RC2MH089951-01, 1RC2 MH089995-01
ZonMw	531003014, 849200011
National Institute of Mental Health	R01MH120219
Avera Institute for Human Genetics	R01D0042157-01A
European Commission	230374
BBMRI	184.033.111, 184.021.007

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Pasman, J. A., Demange, P. A., Guloksuz, S., Willemsen, A. H. M., Abdellaoui, A., Ten Have, M., Hottenga, J. J., Boomsma, D. I., de Geus, E., Bartels, M., De Graaf, R., Verweij, K. J. H., Smit, D. J., Nivard, M., & Vink, J. M. (2022). Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status. Behavior Genetics, 52(2), 92-107. https://doi.org/10.1007/s10519-021-10094-4

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title = "Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status",

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 ({\textquoteleft}smoking-without-EA{\textquoteright}). 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.",

author = "Pasman, {Jo{\"e}lle A.} and Demange, {Perline A.} and Sinan Guloksuz and Willemsen, {A. H. M.} and Abdel Abdellaoui and {Ten Have}, Margreet and Hottenga, {Jouke Jan} and Boomsma, {Dorret I.} and {de Geus}, Eco and Meike Bartels and {De Graaf}, Ron and Verweij, {Karin J. H.} and Smit, {Dirk J.} and Michel Nivard and Vink, {Jacqueline M.}",

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Pasman, JA, Demange, PA, Guloksuz, S, Willemsen, AHM, Abdellaoui, A, Ten Have, M, Hottenga, JJ , Boomsma, DI , de Geus, E , Bartels, M, De Graaf, R, Verweij, KJH, Smit, DJ, Nivard, M & Vink, JM 2022, 'Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status', Behavior Genetics, vol. 52, no. 2, pp. 92-107. https://doi.org/10.1007/s10519-021-10094-4

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T1 - Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status

AU - Pasman, Joëlle A.

AU - Demange, Perline A.

AU - Guloksuz, Sinan

AU - Willemsen, A. H. M.

AU - Abdellaoui, Abdel

AU - Ten Have, Margreet

AU - Hottenga, Jouke Jan

AU - Boomsma, Dorret I.

AU - de Geus, Eco

AU - Bartels, Meike

AU - De Graaf, Ron

AU - Verweij, Karin J. H.

AU - Smit, Dirk J.

AU - Nivard, Michel

AU - Vink, Jacqueline M.

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

U2 - 10.1007/s10519-021-10094-4

DO - 10.1007/s10519-021-10094-4

M3 - Article

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SP - 92

EP - 107

JO - Behavior Genetics

JF - Behavior Genetics

IS - 2

ER -

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

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