Given the 105-107 statistical comparisons in a GWAS, small p-values are expected by chance. To control the risk of false discoveries, q-values (45, 46) were computed for all p-values for single-marker tests of association. A q-value is an estimate of the proportion of false discoveries among all significant markers, or the false discovery rate (FDR) for the corresponding p-value. The use of q-values is preferable to more traditional multiple testing controls because q-values provide a better balance between the competing goals of finding true positives versus controlling false discoveries, allow more similar comparisons across studies because proportions of false discoveries are much less dependent on the number of tests conducted and are relatively robust against the effects of correlated tests (45, 47-54). The q-value threshold for declaring significance was 0.10 (i.e., the top 10% of the significant findings are, on average, allowed to be false discoveries) (50, 55). FDR thresholds <0.10 result in a disproportionate drop in power to detect true effects.
