this specific scenario. Conversely, from a statistical viewpoint, when a data set is tested in multiple angles, the threshold for statistical significance should be adjusted to reduce the inflated type I error. To correct for multiple testing, as suggested by Feise [60], a prudently chosen balance needs to be reached for a study's statistical significance in consideration of multiple factors such as the magnitude of the genetic effect, biological function(s) of the marker(s) of interest, the study's quality, as well as the collective supportive evidence of the genetic locus from other independent studies [60]. Specifically, in this study, we applied the Bonferroni correction procedure for the number of SNPs analyzed within each sample for a given phenotype, but we did not correct for the number of smoking-related phenotypes (i.e., SI, SQ, and SC), the number of genetic models employed (i.e., recessive, additive, and dominant), or the number of study samples (i.e., total and male sample). The rationales underlying our decisions are that the smoking-related phenotypes are highly inter-related, the male smokers constitute the predominant fraction of all smokers in the total sample, and the three genetic models cannot be treated as totally independent. Therefore, given that the variants in CHRNA5/A3/B4
