Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: evidence from a 4-decade longitudinal study.
- Authors
- Belsky, Daniel W; Moffitt, Terrie E; Baker, Timothy B; Biddle, Andrea K; Evans, James P; Harrington, HonaLee; Houts, Renate; Meier, Madeline; Sugden, Karen; Williams, Benjamin; Poulton, Richie; Caspi, Avshalom
- Year
- 2013
- Journal
- JAMA psychiatry
- PMID
- 23536134
- DOI
- 10.1001/jamapsychiatry.2013.736
- PMCID
- PMC3644004
IMPORTANCE: Genome-wide hypothesis-free discovery methods have identified loci that are associated with heavy smoking in adulthood. Research is needed to understand developmental processes that link newly discovered genetic risks with adult heavy smoking. OBJECTIVE: To test how genetic risks discovered in genome-wide association studies of adult smoking influence the developmental progression of smoking behavior from initiation through conversion to daily smoking, progression to heavy smoking, nicotine dependence, and struggles with cessation. DESIGN: A 38-year, prospective, longitudinal study of a representative birth cohort. SETTING: The Dunedin Multidisciplinary Health and Development Study of New Zealand. PARTICIPANTS: The study included 1037 male and female participants. EXPOSURE: We assessed genetic risk with a multilocus genetic risk score. The genetic risk score was composed of single-nucleotide polymorphisms identified in 3 meta-analyses of genome-wide association studies of smoking quantity phenotypes. MAIN OUTCOMES AND MEASURES: Smoking initiation, conversion to daily smoking, progression to heavy smoking, nicotine dependence (FagerstrΓΆm Test of Nicotine Dependence), and cessation difficulties were evaluated at 8 assessments spanning the ages of 11 to 38 years. RESULTS: Genetic risk score was unrelated to smoking initiation. However, individuals at higher genetic risk were more likely to convert to daily smoking as teenagers, progressed more rapidly from smoking initiation to heavy smoking, persisted longer in smoking heavily, developed nicotine dependence more frequently, were more reliant on smoking to cope with stress, and were more likely to fail in their cessation attempts. Further analysis revealed that 2 adolescent developmental phenotypes-early conversion to daily smoking and rapid progression to heavy smoking-mediated associations between the genetic risk score and mature phenotypes of persistent heavy smoking, nicotine dependence, and cessation failure. The genetic risk score predicted smoking risk over and above family history. CONCLUSIONS AND RELEVANCE: Initiatives that disrupt the developmental progression of smoking behavior among adolescents may mitigate genetic risks for developing adult smoking problems. Future genetic research may maximize discovery potential by focusing on smoking behavior soon after smoking initiation and by studying young smokers.
Genetic risk and the developmental progression of smoking behaviorIn the hypothesized model, genetic risk influences the mature phenotypes of heavy smoking persistence, nicotine dependence, and cessation failure through a pathway mediated by three developmental phenotypes: smoking initiation, conversion to daily smoking; and progression to heavy smoking.
Smoking behavior in the Dunedin cohortPanel A. Developmental Progression of Smoking Behavior in the Dunedin cohort. Study members reported their smoking status during in-person assessments at ages 11 (percent ever-smokers=7%), 13 (13%), 15 (62%), 18 (66%), 21 (70%), 26 (70%), 32 (71%), and 38 years (71%) and their daily cigarette consumption at ages 13 (percent daily smokers=1%), 15 (14%), 18 (31%), 21 (34%), 26 (35%), 32 (30%), and 38 years (20%). We assessed nicotine dependence using the Fagerstrom Test of Nicotine Dependence (FTND),73 completed by study members at the age-21, -26, and -38 assessments. We assessed cessation failure using study membersβ reports of quit attempts and outcomes at the ages 18, 21, 26, 32, and 38 assessments. Panel B. Measurements of Developmental and Mature Smoking Phenotypes
A genetic risk score derived from GWAS of smoking quantity is associated with the developmental progression of smoking behavior in a birth cohort of European-descent individualsPanel A shows that individuals at higher genetic risk progressed more rapidly from smoking initiation to heavy smoking. Panel A graphs hazard functions for onset of heavy smoking among individuals at low genetic risk (genetic risk Z-score=-1, green line), average genetic risk (genetic risk Z-score=0, black line), and high genetic risk (genetic risk Z-score=1, red line). The dashed gray line marks the cumulative hazard for individuals at average genetic risk. The hazard function was estimated from a Cox proportional hazard model with time since onset of ever-smoking as the exposure time and the first assessment a study member reported smoking β₯20 cigarettes/day as the failure event. The hazard model included all individuals who ever initiated smoking (N=627). Individuals at higher genetic risk progressed more rapidly from smoking initiation to smoking β₯20 cigarettes/day (Hazard Ratio=1.35 [1.14-1.58]). Panel B shows that genetic risk was highest among individuals who progressed to heavy smoking and lowest among individuals who initiated smoking but who did not progress to heavy smoking. The figure shows the genetic risk Z-sores (+/- 1 standard error) for each group. βCPDβ is βcigarettes per day.β A genetic risk Z-score of 0 corresponds to the average genetic risk in the cohort. Error bars reflect standard errors of the sub-group means.
Genetic risk predicts mature phenotypes of smoking behaviorPanel A shows that among individuals who initiated smoking, those at higher genetic risk smoked more cigarettes by age 38 years. Ever-smokers were all individuals who initiated smoking by age 38 years (N=627). The bars of the histogram graph the percentages of the sample carrying 1-12 risk alleles. The dots and standard-error bars reflect average lifetime cigarette consumption (in pack-years) for ever-smokers carrying 1-3, 4, 5, 6, 7, 8, 9, 10, and 11-12 risk alleles. The regression line shows the association between the genetic risk score and pack-years smoked by age 38 years (Pearson Correlation r=0.12, p=0.003). Panel B shows that ever-smokers at higher genetic risk were more likely to be nicotine dependent. The bars of the chart graph the proportion of ever-smokers at low (n=157), average (n=292), and high (n=178) genetic risk (left side) who became nicotine dependent (β₯4 Fagerstrom symptoms) by age 38 years; and (right side) who were nicotine dependent at two or more assessments. Panel C shows that smokers at higher genetic risk were more likely to experience cessation failure during their 30s. The bars of the chart graph the proportions of daily smokers at low, average, and high genetic risk (left side) who experienced relapse following a quit attempt lasting β₯1 month; and (right side) who achieved successful cessation (abstinence β₯1 year) through age 38 years. Percent with relapse was calculated from cohort members who quit smoking for β₯1 month during ages 32-38 years (n= 36 for the low genetic risk group; n= 61 for the average genetic risk group; n=34 for the high genetic risk group). Percent with successful cessation was calculated for cohort members who smoked daily during their 30s (n=65 for the low genetic risk group; n=120 for the average genetic risk group; n=77 for the high genetic risk group). In panels B and C, low genetic risk individuals had GRSs more than 0.5 standard deviations below the cohort mean; average genetic risk individuals had GRSs within 0.5 standard deviations of the cohort mean; and high genetic risk individuals had GRSs more than 0.5 standard deviations above the cohort mean. Error bars reflect standard errors.
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| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Synergistic Effects of Early Psychosocial Factors and Polygenic Risk for Smoking: a Cross-Sectional Analysis of a Sample of Older Adults in the United States. | Dyer WG et al. | β | 2026 | β |
| Nicotine Metabolism-Related Genetic Polymorphisms Associated with Smoking Cessation in Korean Men: A Candidate Gene Association Study in a Korean Cohort. | Park JM et al. | β | 2025 | β |
| Post-traumatic stress and genetic interactions affect tobacco and alcohol use after trauma: findings from a multi-ancestry cohort. | Garrison-Desany HM et al. | β | 2025 | β |
| Research Review: A review of the past decade ofΒ family and genomic studies on adolescent mentalΒ health. | Morneau-Vaillancourt G et al. | β | 2025 | β |
| A comprehensive analysis of the health effects associated with smoking in the largest population using UK Biobank genotypic and phenotypic data. | Lin Z et al. | β | 2024 | β |
| Considerations, Caveats, and Suggestions for the Use of Polygenic Scores for Social and Behavioral Traits. | Non AL et al. | β | 2024 | β |
| Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders. | Kember RL et al. | β | 2024 | β |
| Nicotinic Receptor Alpha-5 Subunit Gene Polymorphism is Associated With Heavy Smoking Under a Range of Nicotine Dosing Conditions. | Zuo Y et al. | β | 2024 | β |
| Global burden of cancers attributable to tobacco smoking, 1990-2019: an ecological study. | Sharma R et al. | β | 2023 | β |
| How has the brain disease model of addiction contributed to tobacco control? | Hall W et al. | β | 2023 | β |
| Role of polygenic risk scores in the association between chronotype and health risk behaviors. | Zhang Y et al. | β | 2023 | β |
| The Dunedin study after half a century: reflections on the past, and course for the future. | Poulton R et al. | β | 2023 | β |
| The shared genetic architecture of smoking behaviours and psychiatric disorders: evidence from a population-based longitudinal study in England. | Ajnakina O et al. | β | 2023 | β |
| A genetic association study of tobacco withdrawal endophenotypes in African Americans. | Leventhal AM et al. | β | 2022 | β |
| Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood. | Deak JD et al. | β | 2022 | β |
| Association of Treatable Health Conditions During Adolescence With Accelerated Aging at Midlife. | Bourassa KJ et al. | β | 2022 | β |
| Building causal knowledge in behavior genetics. | Madole JW et al. | β | 2022 | β |
| Genetic influences impacting nicotine use and abuse during adolescence: Insights from human and rodent studies. | Goldberg LR et al. | β | 2022 | β |
| Incarceration, polygenic risk, and depressive symptoms among males in late adulthood. | Liu H et al. | β | 2022 | β |
| Polygenic Score for Physical Activity Is Associated with Multiple Common Diseases. | SillanpÀÀ E et al. | β | 2022 | β |
| The potential of DNA methylation as a biomarker for obesity and smoking. | Heikkinen A et al. | β | 2022 | β |
| The Promise of Polygenic Risk Prediction in Smoking Cessation: Evidence From Two Treatment Trials. | Bray M et al. | β | 2022 | β |
| Evaluating the genetic effects of sex hormone traits on the development of mental traits: a polygenic score analysis and gene-environment-wide interaction study in UK Biobank cohort. | Liang X et al. | β | 2021 | β |
| Multi-Polygenic Analysis of Nicotine Dependence in Individuals of European Ancestry. | Risner VA et al. | β | 2021 | β |
| Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort. | Bray MJ et al. | β | 2021 | β |
| Genetic susceptibility to nicotine addiction: Advances and shortcomings in our understanding of the CHRNA5/A3/B4 gene cluster contribution. | Icick R et al. | β | 2020 | β |
| Genomic prediction of alcohol-related morbidity and mortality. | Kiiskinen T et al. | β | 2020 | β |
| Stability in effects of different smoking-related polygenic risk scores over age and smoking phenotypes. | Deutsch AR et al. | β | 2020 | β |
| The relevance of animal models of addiction. | Deroche-Gamonet V | β | 2020 | β |
| Associations between polygenic risk for tobacco and alcohol use and liability to tobacco and alcohol use, and psychiatric disorders in an independent sample of 13,999 Australian adults. | Chang LH et al. | β | 2019 | β |
| Establishing a generalized polyepigenetic biomarker for tobacco smoking. | Sugden K et al. | β | 2019 | β |
| Every contact leaves a trace: contact with the criminal justice system, life outcomes, and the intersection with genetics. | Motz RT et al. | β | 2019 | β |
| Gene set enrichment analysis to create polygenic scores: a developmental examination of aggression. | Elam KK et al. | β | 2019 | β |
| Genetics and the geography of health, behaviour and attainment. | Belsky DW et al. | β | 2019 | β |
| Genetics of alcohol use disorder: a review. | Deak JD et al. | β | 2019 | β |
| New and Emerging Tobacco Products and the Nicotine Endgame: The Role of Robust Regulation and Comprehensive Tobacco Control and Prevention: A Presidential Advisory From the American Heart Association. | Bhatnagar A et al. | β | 2019 | β |
| Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down. | Belsky DW et al. | β | 2019 | β |
| Polygenic Risk: Predicting Depression Outcomes in Clinical and Epidemiological Cohorts of Youths. | Halldorsdottir T et al. | β | 2019 | β |
| Quantifying the Potential for Future Gene Therapy to Lower Lifetime Risk of Polygenic Late-Onset Diseases. | Oliynyk RT | β | 2019 | β |
| The Impact of Genes on Adolescent Substance Use: A Developmental Perspective. | Trucco EM et al. | β | 2019 | β |
| The Neuroscience of Drug Reward and Addiction. | Volkow ND et al. | β | 2019 | β |
| Associations between alcohol dehydrogenase genes and alcohol use across early and middle adolescence: Moderation Γ Preventive intervention. | Cleveland HH et al. | β | 2018 | β |
| CYP2A6 metabolism in the development of smoking behaviors in young adults. | Olfson E et al. | β | 2018 | β |
| Genetic nature or genetic nurture? Introducing social genetic parameters to quantify bias in polygenic score analyses. | Trejo S et al. | β | 2018 | β |
| Genetic polymorphisms associated with smoking behaviour predict the risk of surgery in patients with Crohn's disease. | Lang BM et al. | β | 2018 | β |
| Genetics & the Geography of Health, Behavior, and Attainment | Belsky DW et al. | β | 2018 | β |
| Increased Risk of Smoking in Female Adolescents Who Had Childhood ADHD. | Elkins IJ et al. | β | 2018 | β |
| Is the FagerstrΓΆm test for nicotine dependence invariant across secular trends in smoking? A question for cross-birth cohort analysis of nicotine dependence. | Glasheen C et al. | β | 2018 | β |
| Missing single nucleotide polymorphisms in Genetic Risk Scores: A simulation study. | Chagnon M et al. | β | 2018 | β |
| Overlap of heritable influences between cannabis use disorder, frequency of use and opportunity to use cannabis: trivariate twin modelling and implications for genetic design. | Hines LA et al. | β | 2018 | β |
| The Speed of Progression to Tobacco and Alcohol Dependence: A Twin Study. | Huggett SB et al. | β | 2018 | β |
| Evaluation of a Novel Difficulty of Smoking Cessation Phenotype Based on Number of Quit Attempts. | Stevens VL et al. | β | 2017 | β |
| Genotype Γ Environment Interaction in Smoking Behaviors: A Systematic Review. | Do EK et al. | β | 2017 | β |
| Identification of a cancer stem cell-specific function for the histone deacetylases, HDAC1 and HDAC7, in breast and ovarian cancer. | Witt AE et al. | β | 2017 | β |
| The 2016 Ferno Award Address: Three Things. | Baker TB | β | 2017 | β |
| The Influence of Men's Military Service on Smoking Across the Life Course. | London AS et al. | β | 2017 | β |
| An Adolescent Substance Prevention Model Blocks the Effect of CHRNA5 Genotype on Smoking During High School. | Vandenbergh DJ et al. | β | 2016 | β |
| Changing Polygenic Penetrance on Phenotypes in the 20(th) Century Among Adults in the US Population. | Conley D et al. | β | 2016 | β |
| Cohort Effects in the Genetic Influence on Smoking. | Domingue BW et al. | β | 2016 | β |
| Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing. | Fox E et al. | β | 2016 | β |
| Genetic Modification of the Relationship between Parental Rejection and Adolescent Alcohol Use. | Stogner JM et al. | β | 2016 | β |
| Genetic scores of smoking behaviour in a Chinese population. | Yang S et al. | β | 2016 | β |
| Genome-wide time-to-event analysis on smoking progression stages in a family-based study. | He L et al. | β | 2016 | β |
| Nondaily, Low-Rate Daily, and High-Rate Daily Smoking in Young Adults: A 17-Year Follow-Up. | Robertson L et al. | β | 2016 | β |
| Opportunities and challenges of big data for the social sciences: The case of genomic data. | Liu H et al. | β | 2016 | β |
| Progression of nicotine dependence, mood level, and mood variability in adolescent smokers. | Piasecki TM et al. | β | 2016 | β |
| Role of Cytokine Gene Score in Risk Prediction of Premature Coronary Artery Disease. | Omer W et al. | β | 2016 | β |
| The Bell Curve Revisited: Testing Controversial Hypotheses with Molecular Genetic Data. | Conley D et al. | β | 2016 | β |
| The Long-Term Consequences of Vietnam-Era Conscription and Genotype on Smoking Behavior and Health. | Schmitz L et al. | β | 2016 | β |
| Additive genetic risk from five serotonin system polymorphisms interacts with interpersonal stress to predict depression. | Vrshek-Schallhorn S et al. | β | 2015 | β |
| A guide on gene prioritization in studies of psychiatric disorders. | Stringer S et al. | β | 2015 | β |
| A preliminary exploration of college smokers' reactions to nicotine dependence genetic susceptibility feedback. | Lipkus IM et al. | β | 2015 | β |
| Cardiorespiratory fitness and cognitive function in midlife: neuroprotection or neuroselection? | Belsky DW et al. | β | 2015 | β |
| Developmental differences in early adolescent aggression: a geneΒ ΓΒ environmentΒ ΓΒ intervention analysis. | Schlomer GL et al. | β | 2015 | β |
| Developmental mediation of genetic variation in response to the Fast Track prevention program. | Albert D et al. | β | 2015 | β |
| Family Economic Hardship, Corticotropin-Releasing Hormone Receptor Polymorphisms, and Depressive Symptoms in Rural African-American Youths. | Chen YF et al. | β | 2015 | β |
| Genetic and Environmental Interplay in Adolescent Substance Use Disorders. | Hines LA et al. | β | 2015 | β |
| Is the Effect of Parental Education on Offspring Biased or Moderated by Genotype? | Conley D et al. | β | 2015 | β |
| Polygenic Influence on Educational Attainment: New evidence from The National Longitudinal Study of Adolescent to Adult Health. | Domingue BW et al. | β | 2015 | β |
| Polygenic risk for externalizing disorders: Gene-by-development and gene-by-environment effects in adolescents and young adults. | Salvatore JE et al. | β | 2015 | β |
| Polygenic score Γ intervention moderation: an application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth. | Musci RJ et al. | β | 2015 | β |
| The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future. | Poulton R et al. | β | 2015 | β |
| A glimpse into the future - Personalized medicine for smoking cessation. | Bierut LJ et al. | β | 2014 | β |
| An ADH1B variant and peer drinking in progression to adolescent drinking milestones: evidence of a gene-by-environment interaction. | Olfson E et al. | β | 2014 | β |
| A polygenic risk score associated with measures of depressive symptoms among older adults. | Levine ME et al. | β | 2014 | β |
| Finding genomic function for genetic associations in nicotine addiction research: the ENCODE project's role in future pharmacogenomic analysis. | Vandenbergh DJ et al. | β | 2014 | β |
| Gene-environment interaction research in psychiatric epidemiology: a framework and implications for study design. | Belsky DW et al. | β | 2014 | β |
| Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview. | Iacono WG et al. | β | 2014 | β |
| Integrating genetics and social science: genetic risk scores. | Belsky DW et al. | β | 2014 | β |
| Is chronic asthma associated with shorter leukocyte telomere length at midlife? | Belsky DW et al. | β | 2014 | β |
| On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis. | Peterson RE et al. | β | 2014 | β |
| Parental smoking during pregnancy and ADHD in children: the Danish national birth cohort. | Zhu JL et al. | β | 2014 | β |
| Polygenic risk scores for smoking: predictors for alcohol and cannabis use? | Vink JM et al. | β | 2014 | β |
| Quit interest, quit attempt and recent cigarette smoking cessation in the US working population, 2010. | Yong LC et al. | β | 2014 | β |
| Rare nonsynonymous exonic variants in addiction and behavioral disinhibition. | Vrieze SI et al. | β | 2014 | β |
| SLC6A4 STin2 VNTR genetic polymorphism is associated with tobacco use disorder, but not with successful smoking cessation or smoking characteristics: a case control study. | Pizzo de Castro MR et al. | β | 2014 | β |
| Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. | Conley D et al. | β | 2014 | β |
| The emerging molecular architecture of schizophrenia, polygenic risk scores and the clinical implications for GxE research. | Iyegbe C et al. | β | 2014 | β |
| Are addictions diseases or choices? | Leyton M | β | 2013 | β |
| Communicating genetics and smoking through social media: are we there yet? | de Viron S et al. | β | 2013 | β |
| Genetics in population health science: strategies and opportunities. | Belsky DW et al. | β | 2013 | β |
| Interaction between polygenic risk for cigarette use and environmental exposures in the Detroit Neighborhood Health Study. | Meyers JL et al. | β | 2013 | β |
| Polygenic risk and the development and course of asthma: an analysis of data from a four-decade longitudinal study. | Belsky DW et al. | β | 2013 | β |