Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun 1;46(3):894-904.
doi: 10.1093/ije/dyw318.

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function

Affiliations

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function

Hugues Aschard et al. Int J Epidemiol. .

Abstract

Background: Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV 1 (forced expiratory volume in 1 second) or FEV 1 /FVC (FEV 1 /forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking.

Methods: We evaluated the interaction between smoking exposure, considered as either ever vs never or pack-years, and a 26-single nucleotide polymorphisms (SNPs) genetic risk score in relation to FEV 1 or FEV 1 /FVC in 50 047 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta consortia.

Results: We identified an interaction ( βint = -0.036, 95% confidence interval, -0.040 to -0.032, P = 0.00057) between an unweighted 26 SNP genetic risk score and smoking status (ever/never) on the FEV 1 /FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV /FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. A replication analysis in two independent datasets, although not statistically significant, showed a similar trend in the interaction effect.

Conclusions: This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs and shows, for the first time, that persons with the highest genetic risk for low FEV 1 /FVC may be more susceptible to the deleterious effects of smoking.

Keywords: FEV1/FVC; genetic risk score; gene–environment interaction; smoking.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Distribution of interaction effects on FEV1/FVC. Single SNP risk allele-by-smoking status (ever/never) interaction effect estimates (βint) and 95% confidence intervals are plotted by increasing values. The unweighted genetic risk score-by-smoking status interaction is plotted at the bottom.
Figure 2.
Figure 2.
Overview of the unweighted genetic risk score-by-smoking interaction effect on FEV1/FVC. Upper panel (A) presents the distribution of the unweighted genetic risk score (GRS, grey density plot) and the relationship between the unweighted GRS and standardized FEV1/FVC in ever smokers (dashed line) and never smokers (solid line). Lower panel (B) shows the excess relative risk (RR) of having FEV1/FVC in the lowest 1%, 5% and 20% of the population for ever smokers compared with never smokers, as stratified by GRS quintiles.
Figure 3.
Figure 3.
Underlying causal model. Potential causal diagrams underlying the gene and smoking interaction effects on FEV1/FVC. Panel (A) presents a scenario where each genetic variant influences the outcome through a SNP-specific pathway, and interactions with the environmental exposure take place along these pathways. Panel (B) presents an alternative (and simpler) model where multiple genetic variants influence an unmeasured intermediate biomarker U, which effect on FEV1/FVC depends on smoking. In scenario (A), the single SNP-by-smoking interaction test is the optimal approach, whereas, in scenario (B), the single SNP-by-smoking interaction test can become inefficient, and interaction would be easier to detect using a genetic risk score-by-smoking interaction test, because it summarizes all interaction effects in a single test.

References

    1. Rabe KF, Hurd S, Anzueto A. et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2007;176:532–55. - PubMed
    1. Lange P, Celli B, Agusti A. et al. Lung-function trajectories leading to chronic obstructive pulmonary disease. N Engl J Med;373:111–22. - PubMed
    1. Vestbo J, Hurd SS, Agusti AG. et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med;187:347–65. - PubMed
    1. Soler Artigas M, Loth DW, Wain LV. et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat Genet 2011;43:1082–90. - PMC - PubMed
    1. Aschard H, Lutz S, Maus B. et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 2012;131:1591–1613. - PMC - PubMed

Publication types