Genetic variations in the CYP2A6 nicotine metabolic gene and the CHRNA5-CHRNA3-CHRNB4 (CHRNA5-A3-B4) nicotinic gene cluster have been independently associated with lung cancer. With genotype data from ever-smokers of European ancestry (417 lung cancer patients and 443 control subjects), we investigated the relative and combined associations of polymorphisms in these two genes with smoking behavior and lung cancer risk. Kruskal-Wallis tests were used to compare smoking variables among the different genotype groups, and odds ratios (ORs) for cancer risk were estimated using logistic regression analysis. All statistical tests were two-sided. Cigarette consumption (P < .001) and nicotine dependence (P = .036) were the highest in the combined CYP2A6 normal metabolizers and CHRNA5-A3-B4 AA (tag single-nucleotide polymorphism rs1051730 G>A) risk group. The combined risk group also exhibited the greatest lung cancer risk (OR = 2.03; 95% confidence interval [CI] = 1.21 to 3.40), which was even higher among those who smoked 20 or fewer cigarettes per day (OR = 3.03; 95% CI = 1.38 to 6.66). Variation in CYP2A6 and CHRNA5-A3-B4 was independently and additively associated with increased cigarette consumption, nicotine dependence, and lung cancer risk. CYP2A6 and CHRNA5-A3-B4 appear to be more strongly associated with smoking behaviors and lung cancer risk, respectively.

We investigated genetic variation in CYP2A6 in relation to lung cancer risk among African American smokers, a high-risk population. Previously, we found that CYP2A6, a nicotine/nitrosamine metabolism gene, was associated with lung cancer risk in European Americans, but smoking habits, lung cancer risk and CYP2A6 gene variants differ significantly between European and African ancestry populations. Herein, African American ever-smokers, drawn from two independent lung cancer case-control studies, were genotyped for reduced activity CYP2A6 alleles and grouped by predicted metabolic activity. Lung cancer risk in the Southern Community Cohort Study (n = 494) was lower among CYP2A6 reduced versus normal metabolizers, as estimated by multivariate conditional logistic regression [odds ratio (OR) = 0.44; 95% confidence interval (CI) = 0.26-0.73] and by unconditional logistic regression (OR = 0.62; 95% CI = 0.41-0.94). The association was replicated in an independent study from MD Anderson Cancer Center (n = 407) (OR = 0.64; 95% CI = 0.42-0.98), and pooling the studies yielded an OR of 0.64 (95% CI = 0.48-0.86). Exploratory analyses revealed a significant interaction between CYP2A6 genotype and sex on the risk for lung cancer (Southern Community Cohort Study: P = 0.04; MD Anderson: P = 0.03; Pooled studies: P = 0.002) with a CYP2A6 effect in men only. These findings support a contribution of genetic variation in CYP2A6 to lung cancer risk among African American smokers, particularly men, whereby CYP2A6 genotypes associated with reduced metabolic activity confer a lower risk of developing lung cancer.

Genome-wide association studies (GWAS) of complex behavioural phenotypes such as cigarette smoking typically employ self-report phenotypes. However, precise biomarker phenotypes may afford greater statistical power and identify novel variants. Here we report the results of a GWAS meta-analysis of levels of cotinine, the primary metabolite of nicotine, in 4,548 daily smokers of European ancestry. We identified a locus close to UGT2B10 at 4q13.2 (minimum p = 5.89 × 10−10 for rs114612145), which was consequently replicated. This variant is in high linkage disequilibrium with a known functional variant in the UGT2B10 gene which is associated with reduced nicotine and cotinine glucuronidation activity, but intriguingly is not associated with nicotine intake. Additionally, we observed association between multiple variants within the 15q25.1 region and cotinine levels, all located within the CHRNA5-A3-B4 gene cluster or adjacent genes, consistent with previous much larger GWAS using self-report measures of smoking quantity. These results clearly illustrate the increase in power afforded by using precise biomarker measures in GWAS. Perhaps more importantly however, they also highlight that biomarkers do not always mark the phenotype of interest. The use of metabolite data as a proxy for environmental exposures should be carefully considered in the context of individual differences in metabolic pathways.