Correct identification of the most promising single-nucleotide polymorphisms (SNPs) for follow-up is one of the greatest challenges of conducting genome-wide association studies (GWAS). Hundreds of thousands of simultaneous association tests are typically conducted across the genome, and the resulting p-values or test statistics are compared and often ranked to determine the most promising SNPs. This procedure is conducted regardless of the specific adjustment chosen for multiple testing correction. For example, the conservative Bonferroni correction to adjust the critical significance threshold simply lowers the threshold of α to account for the number of tests conducted; any SNP with a p-value below that adjusted threshold will be considered significant. False-discovery rate (FDR) procedures explicitly rank p-values before selecting the SNPs below the FDR threshold for further consideration. Genome-wide permutation testing methods involve randomly permuting case and control status and examining the distribution of the best resulting test statistic, thus creating an empirical distribution of extreme test statistics against which to compare observed results. With each of these methods however, if multiple SNPs reach this threshold, the investigator must choose which SNPs to follow for replication.
