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Updated: May 14, 2026

Cigarette Smoke Exposure in Mice using a Whole-Body Inhalation System
Published on: October 22, 2020
Cigarette experimentation and the population attributable fraction for associated genetic and non-genetic risk
Anna V Wilkinson1, Michael D Swartz, Xiaoying Yu
1University of Texas School of Public Health, Austin Regional Campus, Austin, Texas, United States of America. Anna.V.Wilkinson@uth.tmc.edu
Background:
We, and others, have shown that experimenting with cigarettes is a function of both non-genetic and genetic factors. In this analysis we ask: how much of the total risk of experimenting with cigarettes, among those who had not experimented with cigarettes when they enrolled in a prospective cohort, is attributable to genetic factors and to non-genetic factors?
Methods:
Participants (N = 1,118 Mexican origin youth), recruited from a large population-based cohort study in Houston, Texas, provided prospective data on cigarette experimentation over three years. Non-genetic data were elicited twice - baseline and follow-up. Participants were genotyped for 672 functional and tagging variants in the dopamine, serotonin and opioid pathways.
Results:
In the overall model, the adjusted combined non-genetic PAF was 71.2% and the adjusted combined genetic PAF was 58.5%. Among committed never smokers the adjusted combined non-genetic PAF was 67.0% and the adjusted combined genetic PAF was 53.5%. However, among cognitively susceptible youth, the adjusted combined non-genetic PAF was 52.0% and the adjusted combined genetic PAF was 68.4%.
Conclusions:
Our results suggest there may be differences in genotypes between youth who think they will try cigarettes in the future compared to their peers who think they will not and underscore the possibility that the relative influence of genetic vs. non-genetic factors on the uptake of smoking may vary between these two groups of youth.
Impact:
A clearer understanding of the relative role of genetic vs. non-genetic factors in the uptake of smoking may have implications for the design of prevention programs.
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