Principal Investigator: Dr Dana Hancock
RTI International, USATags: 24603, addiction, fev1, gender, interaction, nicotine, smoking
We aim to identify genetic associations with nicotine dependence by conducting GWAS meta-analyses and expand our findings to smoking-related health outcomes, e.g., pulmonary function. We have assembled 16+ GWAS study samples with Fagerstrom Test for Nicotine Dependence data, totaling N>45,000 of European and African ancestry. We propose using results from GWAS of UK Biobank data to replicate genetic associations with heavy vs. never smoking and translate our associations to pulmonary function. We also propose using the data to perform GWAS analyses that condition on known SNPs and incorporate SNP-by-SNP and SNP-by-gender interactions. Nicotine dependence is one of the strongest predictors of failing to quit cigarette smoking. Results of this genome-wide study may be used to better understand the genetic risk factors for nicotine dependence, smoking cessation, and other smoking behaviors and outcomes and ultimately reduce the burden of smoking’s health consequences including lung cancer and chronic obstructive pulmonary disease. Our study aims to identify and replicate new genetic variants associated with nicotine dependence. Using the largest collection of nicotine dependence samples with genome-wide data assembled to date, we are identifying new genetic variants by using the standard genome-wide association study approach and further by testing interactions with gender and with nicotine receptor and metabolism genes that are already known to influence a person’s risk of becoming nicotine dependent. We propose using the UK Biobank data to take the same analytic approaches in assessing genetic variant associations with heavy vs. never smoking and pulmonary function (e.g., FEV1), an important smoking-related trait. We propose using the participants with GWAS, smoking, and pulmonary function measures available (e.g., FEV1). GWAS of smoking and pulmonary function on these participants (N~50,000) were published in 2015 by other researchers using UK Biobank data (Wain et al. Lancet Respir Med, PubMed ID 26423011).
Project extension – January 2020
When modeling pulmonary function and its decline over time, we propose a comprehensive set of GWAS models, including a standard model (i.e., no interactions) and interaction models that embed SNP interactions with smoking, gender, other SNPs, and/or nutrition.
Last updated Jan 28, 2020