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Cohort Effects in the Genetic Influence on Smoking

Abstract

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.

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Notes

  1. 1.

    Specifically we use the RAND Fat Files (Clair et al. 2011).

  2. 2.

    Clumping takes place in two steps. The first pass is done in fairly narrow windows (250 kb) for all SNPs (the p value significance thresholds for both index and secondary SNPs is set to 1) with a liberal LD threshold (r2 = 0.5). In a second pass, SNPs remaining after the first prune are again pruned in broader windows (5000 kb) but with a more conservative LD threshold (r2 = 0.2).

  3. 3.

    Available at http://spark.rstudio.com/ctgg/gctaPower/. The method is described in Visscher et al. (2014).

  4. 4.

    We used linear regression models instead of logistic regression models so as to ease interpretation of the relevant coefficients.

  5. 5.

    Estimates for PGS and its interaction with birth year and being female were 0.010 (p = 0.43), 0.001 (p = 0.06), and 0.026 (p = 0.01) respectively.

  6. 6.

    Estimates for PGS and its interaction with birth year and being female were 0.010 (p = 0.50), 0.001 (p = 0.05), and 0.026 (p = 0.01) respectively.

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Acknowledgments

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.

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Correspondence to Benjamin W. Domingue.

Appendix: GWDF validation

Appendix: GWDF validation

We conducted a simulation to demonstrate a correspondence in relatively simple settings between GREML heritability estimates and the b 3 estimate from Eq. 2. This simulation was based on a random sample of 5000 respondents from the full set of respondents and random sample of 200,000 SNPs. Based on this set of respondents and SNPs, we simulated three sets of phenotypes using GCTA. The sets differed only in the true heritability of the phenotypes. The true heritabilites for the three sets were 0.25, 0.5, and 0.75. For each level of heritability, we simulated ten phenotypes. Thus, we have 30 simulated phenotypes in total.

Genetic similarities from the full set of markers (not the reduced set of 200,000 used to simulate the phenotypes) were then used to compute GREML heritability estimates as well as the b 3 GWDF coefficient. Results are shown in Fig. 6. Figure 6a and b shows that the GREML heritability and GWDF coefficient estimates both increase along with the true heritability. The GREML heritability estimates and GWDF b3 estimates are correlated with the true heritabilities at 0.91 and 0.86 respectively. More importantly, the heritability estimates were strongly correlated (0.92) with the GWDF b3 estimates. The convergence of these results using two very different statistical techniques enhances our confidence in the validity of the GWDF approach and the empirical results that we present in the paper.

Fig. 6
figure6

Comparison of true heritability (which is known since phenotypes are simulated), GREML estimates, and b3 estimates from GWDF models

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Domingue, B.W., Conley, D., Fletcher, J. et al. Cohort Effects in the Genetic Influence on Smoking. Behav Genet 46, 31–42 (2016). https://doi.org/10.1007/s10519-015-9731-9

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Keywords

  • Smoking
  • GCTA
  • GREML
  • Genome-wide
  • Polygenic score