A major application of eQTL data has been the functional annotation of loci identified in genome-wide association (GWA) studies. We investigated the regulatory impact of GWA variants by integrating cis-eQTLs (1% FDR, Supplementary Table 6) and disease SNPs (NHGRI database, accessed 21.12.10) with the RTC methodology as previously described 14. RTC scores≥0.9 indicate that overlapping eQTL and GWAS signals likely tag the same functional variant. In all three tissues we observed an overrepresentation of high RTC scoring candidates, suggestive of disease effects mediated through gene expression (Supplementary Fig. 9).Of the total interval-disease combinations tested in adipose (N = 765), LCL (N = 887) and skin (N = 639) we detect 181 (23.7%), 225 (25.4%) and 145 (22.7%) signals with RTC ≥0.9 respectively, more than twice the number expected by chance (adipose P = 0.0009, LCL P = 0.008, skin P = 0.0009). The disease eQTL candidates are largely tissue-dependent (~60%), in line with the estimated proportion of tissue-dependent cis-effects (52 of the total non-redundant 358 RTC signals were discovered across all three tissues - Supplementary Table 6).
