Behavior Genetics

, Volume 46, Issue 1, pp 31–42 | Cite as

Cohort Effects in the Genetic Influence on Smoking

  • Benjamin W. Domingue
  • Dalton Conley
  • Jason Fletcher
  • Jason D. Boardman
Original Research


We examine the hypothesis that the heritability of smoking has varied over the course of recent history as a function of associated changes in the composition of the smoking and non-smoking populations. Classical twin-based heritability analysis has suggested that genetic basis of smoking has increased as the information about the harms of tobacco has become more prevalent—particularly after the issuance of the 1964 Surgeon General’s Report. In the present paper we deploy alternative methods to test this claim. We use data from the Health and Retirement Study to estimate cohort differences in the genetic influence on smoking using both genomic-relatedness-matrix restricted maximum likelihood and a modified DeFries–Fulker approach. We perform a similar exercise deploying a polygenic score for smoking using results generated by the Tobacco and Genetics consortium. The results support earlier claims that the genetic influence in smoking behavior has increased over time. Emphasizing historical periods and birth cohorts as environmental factors has benefits over existing GxE research. Our results provide additional support for the idea that anti-smoking policies of the 1980s may not be as effective because of the increasingly important role of genotype as a determinant of smoking status.


Smoking GCTA GREML Genome-wide Polygenic score 



This study was funded by NIH/NICHD R21 HD078031. Additional support came from R24 HD066613 which supports the CU Population Center. The Health and Retirement Study is sponsored by the National Institute on Aging (Grant Number NIA U01AG009740) and is conducted by the University of Michigan.

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.


  1. Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA et al (2012) Polygenic risk, rapid childhood growth, and the development of obesity: evidence from a 4-decade longitudinal study. Arch Pediatr Adolesc Med 166(6):515–521PubMedPubMedCentralGoogle Scholar
  2. Belsky DW, Moffitt TE, Baker TB, Biddle AK, Evans JP, Harrington H et al (2013) Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: evidence from a 4-decade longitudinal study. JAMA Psychiatry 70(5):534–542PubMedPubMedCentralCrossRefGoogle Scholar
  3. Boardman JD, Daw J, Freese J (2013) Defining the environment in gene–environment research: lessons from social epidemiology. Am J Public Health. 103(S1):S64–S72.PubMedPubMedCentralCrossRefGoogle Scholar
  4. Boardman JD, Fletcher JM (2015) To cause or not to cause? That is the question but identical twins might not have all of the answers. Soc Sci Med 127:198–200PubMedCrossRefGoogle Scholar
  5. Boardman JD, Saint Onge JM, Haberstick BC, Timberlake DS, Hewitt JK (2008) Do schools moderate the genetic determinants of smoking? Behav Genet 38(3):234–246PubMedPubMedCentralCrossRefGoogle Scholar
  6. Boardman JD, Blalock CL, Pampel FC (2010) Trends in the genetic influences on smoking. J Health Soc Behav 51(1):108–123PubMedPubMedCentralCrossRefGoogle Scholar
  7. Boardman JD, Blalock CL, Pampel FC, Hatemi PK, Heath AC, Eaves LJ (2011) Population composition, public policy, and the genetics of smoking. Demography 48(4):1517–1533PubMedPubMedCentralCrossRefGoogle Scholar
  8. Boardman JD, Barnes LL, Wilson RS, Evans DA, de Leon CFM (2012) Social disorder, APOE-E4 genotype, and change in cognitive function among older adults living in Chicago. Soc Sci Med 74(10):1584–1590PubMedPubMedCentralCrossRefGoogle Scholar
  9. Boardman JD, Domingue BW, Daw J (2015) What can genes tell us about the relationship between education and health? Soc Sci Med 127:171–180PubMedCrossRefGoogle Scholar
  10. Centers for Disease Control and Prevention (CDC) (2008) Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000–2004. MMWR Morb Mortal Wkly Rep 57(45):1226Google Scholar
  11. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2014) Second-generation PLINK: rising to the challenge of larger and richer datasets. arXiv preprint arXiv:1410.4803
  12. Clair PS, Bugliari D, Campbell N, Chien S, Hayden O, Hurd M, et al (2011) RAND HRS Data Documentation, Version LGoogle Scholar
  13. Conley D, Cesarini D, Dawes C, Domingue B, Boardman JD (2015) Is the effect of parental education on offspring biased or moderated by genotype? Sociol Sci 2:82–105CrossRefGoogle Scholar
  14. Conley D, Siegal ML, Domingue BW, Harris KM, McQueen MB, Boardman JD (2014) Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. J Hum Genet 59(6):342–345PubMedPubMedCentralCrossRefGoogle Scholar
  15. Cutler DM, Glaeser EL (2009) Why do Europeans smoke more than Americans? Developments in the economics of aging. University of Chicago Press, Chicago, pp 255–282CrossRefGoogle Scholar
  16. DeFries JC, Fulker DW (1985) Multiple regression analysis of twin data. Behav Genet 15(5):467–473PubMedCrossRefGoogle Scholar
  17. Domingue BW, Fletcher J, Conley D, Boardman JD (2014) Genetic and educational assortative mating among US adults. Proc Natl Acad Sci 111(22):7996–8000PubMedPubMedCentralCrossRefGoogle Scholar
  18. Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9(3):e1003348PubMedPubMedCentralCrossRefGoogle Scholar
  19. Escobedo LG, Peddicord JP (1996) Smoking prevalence in US birth cohorts: the influence of gender and education. Am J Public Health 86(2):231–236PubMedPubMedCentralCrossRefGoogle Scholar
  20. Hamilton AS, Lessov-Schlaggar CN, Cockburn MG, Unger JB, Cozen W, Mack TM (2006) Gender differences in determinants of smoking initiation and persistence in California twins. Cancer Epidemiol Biomark Prev 15(6):1189–1197CrossRefGoogle Scholar
  21. Jackson JS, Knight KM, Rafferty JA (2010) Race and unhealthy behaviors: chronic stress, the HPA axis, and physical and mental health disparities over the life course. Am J Public Health 100(5):933–939PubMedPubMedCentralCrossRefGoogle Scholar
  22. Li MD, Cheng R, Ma JZ, Swan GE (2003) A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction 98(1):23–31PubMedCrossRefGoogle Scholar
  23. Link, B. G., & Phelan, J. (1995) Social conditions as fundamental causes of disease. J Health Soc Behav, 80–94Google Scholar
  24. Lubke GH, Hottenga JJ, Walters R, Laurin C, De Geus EJ, Willemsen G et al (2012) Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms. Biol Psychiatry 72(8):707–709PubMedPubMedCentralCrossRefGoogle Scholar
  25. Lumley T (2004) Analysis of complex survey samples. J Stat Softw 9(1):1–19Google Scholar
  26. Perry BL, Pescosolido BA, Bucholz K, Edenberg H, Kramer J, Kuperman S et al (2013) Gender-specific gene–environment interaction in alcohol dependence: the impact of daily life events and GABRA2. Behav Genet 43(5):402–414PubMedPubMedCentralCrossRefGoogle Scholar
  27. Plomin R, Haworth CM, Meaburn EL, Price TS, Davis OS (2013) Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychol Sci 24(4):562–568PubMedPubMedCentralCrossRefGoogle Scholar
  28. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460(7256):748–752PubMedGoogle Scholar
  29. Raine A (2002) Biosocial studies of antisocial and violent behavior in children and adults: a review. J Abnorm Child Psychol 30(4):311–326PubMedCrossRefGoogle Scholar
  30. Rehkopf DH (2014) Understanding the role of social and economic factors in GCTA heritability estimates. Presented at the 5th Annual IGSS Conference. Boulder, COGoogle Scholar
  31. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW et al (2013) GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340(6139):1467–1471PubMedPubMedCentralCrossRefGoogle Scholar
  32. Speed D, Balding DJ (2015) Relatedness in the post-genomic era: is it still useful? Nat Rev Genet 16(1):33–44PubMedCrossRefGoogle Scholar
  33. Spitz MR, Amos CI, Dong Q, Lin J, Wu X (2008) The CHRNA5-A3 region on chromosome 15q24-25.1 is a risk factor both for nicotine dependence and for lung cancer. J Natl Cancer Inst 100(21):1552–1556PubMedPubMedCentralCrossRefGoogle Scholar
  34. Tobacco and Genetics Consortium (2010) Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 42(5):441–447CrossRefGoogle Scholar
  35. Vink JM, Boomsma DI (2011) Interplay between heritability of smoking and environmental conditions? A comparison of two birth cohorts. BMC Public Health 11(1):316PubMedPubMedCentralCrossRefGoogle Scholar
  36. Visscher PM, Hemani G, Vinkhuyzen AA, Chen GB, Lee SH, Wray NR et al (2014) Statistical power to detect genetic (co) variance of complex traits using SNP data in unrelated samples. PLoS Genet 10(4):e1004269PubMedPubMedCentralCrossRefGoogle Scholar
  37. Vrieze SI, McGue M, Miller MB, Hicks BM, Iacono WG (2013) Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: twin biometry, GCTA, and genome-wide scoring. Behav Genet 43(2):97–107PubMedPubMedCentralCrossRefGoogle Scholar
  38. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42(D1):D1001–D1006PubMedPubMedCentralCrossRefGoogle Scholar
  39. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7):565–569PubMedPubMedCentralCrossRefGoogle Scholar
  40. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82PubMedPubMedCentralCrossRefGoogle Scholar
  41. Zajacova A, Burgard SA (2013) Healthier, wealthier, and wiser: a demonstration of compositional changes in aging cohorts due to selective mortality. Popul Res Policy Rev 32(3):311–324PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA
  2. 2.Department of Sociology & Center for Genomics and Systems BiologyNew York UniversityNew YorkUSA
  3. 3.La Follette School of Public Affairs, Department of Sociology, & Center for Demography and EcologyUniversity of Wisconsin-MadisonMadisonUSA
  4. 4.Department of Sociology & Institute of Behavioral ScienceUniversity of ColoradoBoulderUSA

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