An alternative to adjusting the false positive rate (alpha) is to determine the false discovery rate (FDR). The false discovery rate is an estimate of the proportion of significant results (usually at alpha = 0.05) that are false positives. Under the null hypothesis that there are no true associations in a GWAS dataset, p-values for association tests would follow a uniform distribution (evenly distributed from 0 to 1). Originally developed by Benjamini and Hochberg, FDR procedures essentially correct for this number of expected false discoveries, providing an estimate of the number of true results among those called significant [33]. These techniques have been widely applied to GWAS and extended in a variety of ways [34].
