For our multiple testing procedure, we used the meta-analyzed permutations to calculate the overall FDR, as previously described5. In short, we reasoned that the large numbers of correlated SNPs and genes present in the cis-eQTL results might cause inflated estimates (i.e. highly correlated SNPs associated with a specific gene would result in equal permuted P-values for that particular gene). To circumvent this issue, we first selected the lowest association P-value per gene in both the permuted and non-permuted meta-analyses. The resulting lists of P-values were sorted and, per given P-value in the non-permuted data, we determined the proportion of P-values equal to or below this value in both the permuted and non-permuted data. We then determined our FDR estimate as the proportion of permuted P-values over the proportion of non-permuted P-values. If a specific eQTL from the full set was not among the set of per-gene lowest association P-values, this eQTL was assigned the higher FDR value corresponding to the next eQTL available among the set of lead variants per gene. We refer to this procedure as ‘gene-level’ FDR, but
