Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement

Abstract

Abilities to successfully quit smoking display substantial evidence for heritability in classic and molecular genetic studies. Genome-wide association (GWA) studies have demonstrated single-nucleotide polymorphisms (SNPs) and haplotypes that distinguish successful quitters from individuals who were unable to quit smoking in clinical trial participants and in community samples. Many of the subjects in these clinical trial samples were aided by nicotine replacement therapy (NRT). We now report novel GWA results from participants in a clinical trial that sought dose/response relationships for "precessation" NRT. In this trial, 369 European-American smokers were randomized to 21 or 42 mg NRT, initiated 2 wks before target quit dates. Ten-week continuous smoking abstinence was assessed on the basis of self-reports and carbon monoxide levels. SNP genotyping used Affymetrix 6.0 arrays. GWA results for smoking cessation success provided no P value that reached "genome-wide" significance. Compared with chance, these results do identify (a) more clustering of nominally positive results within small genomic regions, (b) more overlap between these genomic regions and those identified in six prior successful smoking cessation GWA studies and (c) sets of genes that fall into gene ontology categories that appear to be biologically relevant. The 1,000 SNPs with the strongest associations form a plausible Bayesian network; no such network is formed by randomly selected sets of SNPs. The data provide independent support, based on individual genotyping, for many loci previously nominated on the basis of data from genotyping in pooled DNA samples. These results provide further support for the idea that aid for smoking cessation may be personalized on the basis of genetic predictors of outcome.

Trial registration: ClinicalTrials.gov NCT00734617.

Figure 1

Generation of Bayesian network for prediction of abstinence. SNPs are first sorted based on nominal P value, and SNPs with the 5 to 1,000 lowest P values are used. Networks are generated from real data using the Markov Chain Monte Carlo methods using the BayesWare factor.

Figure 2

Bayesian network including the 1,000 SNPs with the strongest P values in the current study. The SNPs in the inner circle (Markov Blanket of the outcome node) provide the most direct, strongest relationship to abstinence at week 10 of this trial. Many of the relationships between SNPs in the first ring with those in the second, third and fourth rings can be explained by linkage disequilibrium.