intervals of 2%, and SNPs from each bin were selected to match the distribution in the test set. Empirical significance for enrichment of eQTLs in mQTLs was ascertained by counting the number of simulations with at least as many SNPs overlapping the set of sentinelized subsignals from the aveALL analysis in Ramasamy et al (2014)30, as the true ‘clumped’ Bonferroni significant mQTL SNP set and dividing by the number of simulations performed. Fold change statistics were calculated as the true overlap divided by the mean overlap of these simulations, and 95% confidence intervals as the true overlap divided by the 2.5th and 97.5th quantiles of the distribution of overlaps.
