The problem of multiple hypothesis testing in biomedical studies is an important yet complex issue that needs to be considered carefully [58]. For complex traits such as smoking behavior, variants in genes of multiple biological pathways are likely to be involved in producing the phenotype through their mutual interactions and interactions with environmental factors. Therefore, genetic effects of common variants for smoking behavior are often weak secondary to genetic heterogeneity, variable expressivity, and low penetrance; and exactly how to correct for multiple testing remains a debatable and profound topic. For example, for gene-based studies such as this study focusing on the CHRNA5/A3/B4 cluster, Neale and Sham [59] have emphasized the importance of replication rather than the sole interest of detecting association signals with very low P values, and the authors have challenged the need for correcting for multiple testing in 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
