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.

Keywords: GCTA; GREML; Genome-wide; Polygenic score; Smoking.

Figures

Figure A1

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

Figure 1

(A) Sample size, (B) % ever smokers, and (C) % female in our sample as a function of birth year cohort in HRS.

Figure 2

Changes in means by gender (along with fitted trends) for various variables as a function of birth year.

Figure 3

(A) Estimated heritability, adjusted for gender and birth year, of having ever been a smoker in HRS in overlapping birth windows centered at years show on x-axis. (B) Genome-wide DeFries-Fulker coefficients (b3 from Eqn 2) includes adjustments for multiple entry of individual outcomes and with controls for the birth year and gender of each individual as well as within-individual interactions between birth year and gender.

Figure 4

(A) Bivariate correlation between genetic risk of smoking and ever smoking. (B) Mean genetic risk for smoking as a function of birth year.

Figure 5

Estimated probability of ever smoking as a function of PGS, gender, and birth year. (A) includes controls for PGS, birth year, gender, interaction of PGS and gender, and the interaction of PGS and birth year. (B) includes controls PGS, main effects for three splines based on birth year, gender, interaction of PGS and gender, and interaction of PGS and birth year. Estimates for females are indicated via the darker gray confidence intervals.