Unfortunately, given the realities of epidemiological research and the desire to continue to use valuable existing studies (e.g., FHS), the required sample sizes are often not practical. Moreover, recent discussions have pointed out that corrections for multiple testing, such as the Bonferroni correction, are too conservative because they do not take into account correlations between the tests due to LD [Rice et al., 2008]. Rice et al. point out that the effect sizes of susceptibility alleles (and G×E interactions) will rarely reach the required level of significance in GWAS if a Bonferroni correction is used. Although the Bonferroni correction is easy and straightforward to calculate, less conservative methods, such as permutation testing, false-discovery rate, and sequential methods (splitting the data into a test set and replication set), may need to be applied to balance the type I and type II errors (false positives and false negatives, respectively). Alternately, Maenner et al. [2009] initially used a machine learning approach, which is not based on p-values so a Bonferroni correction is not applicable. Machine learning approaches can screen large amounts of data
