The recent successes of genome-wide genetic association studies have highlighted the need for large study populations in order to detect the small effect sizes often associated with common genetic polymorphisms. However, most pharmacogenomic studies conducted to date have involved relatively small sample populations (Holmes et al, 2009). Thus, the strengths of this analysis include: (a) its large sample population—the largest such study to date of bupropion for smoking cessation, the first (and therefore also largest) pharmacogenetic analysis of varenicline, and the first head-to-head analysis of these treatments; and (b) the availability of robust phenotypic data from three rigorously controlled clinical trials, providing a database of patient data, adverse events, and, importantly, carbon monoxide-confirmed smoking cessation outcomes. Limitations of this analysis include: (a) a lack of generalizability of the study findings to non-treatment-seeking smokers—although the highly motivated (to quit smoking) population recruited to these trials is the subset of the broader smoking population to which pharmacogenetic tailoring approaches would be provided, efforts to encourage non-motivated smokers to quit may have different pharmacogenetic characteristics; and (b) a lack of ethnic diversity among
