Several findings deserve comment. First, irrespective of the various parameters and genetic models tested, the absence of heterogeneity in samples allows for a power to detect association that would require a far larger sample size if a medium or high level of heterogeneity were present. A recent study examining the impact of diagnostic misclassification in genetic studies by Wray et al. [32] has reached similar results with a different method. In the Supplementary Information to their paper, Wray et al. [32] provide an analytical solution that allows the calculation of statistical power under misclassification without computer simulation. These observations are consistent with the findings of pharmacogenomic GWAS. Indeed, the robustness of the phenotypic measure of treatment response given by specific biomarkers (for example identifiable in serum) allowed the identification of significant association signals even with relatively small sample sizes [33]. In addition, pharmacogenetic traits have not been subject to selection and so larger effect sizes may exist compared to complex traits.
