To control the risk of false discoveries in GWAS studies, for each p-value, we calculated a q-value [47,48]. A q-value is an estimate of the proportion of false discoveries among all significant markers (i.e., q-values are FDRs) when the corresponding p-value is used as the threshold to declare significance. As argued previously [49], we preferred this approach to more traditional multiple testing methods that control the probability of producing one or more false discoveries for a set of tests [50]. Our approach was preferred because these q-values 1) provide a better balance between the competing goals of finding true positives versus controlling false discoveries, 2) allow the use of more similar standards in terms of the proportion of false discoveries produced across studies due to much less dependence on the number of tests performed, 3) are relatively robust against the effects of correlated tests [47,49,51-56], and 4) rather than an all-or-nothing conclusion about whether a study produces significant results, instead provide a more subtle picture about the possible relevance of the tested markers. The FDR procedure is performed in the R statistical package.
