Studies have shown that SNPs associated with eQTLs are more likely to influence complex traits and disease susceptibility6,10. Here, we provide further support for this observation for eQTLs, mQTLs, and haQTLs by performing an enrichment analysis on reported p-values of 16 GWAS datasets, including large-scale GWAS meta-analyses of AD36, Schizophrenia37, and type II Diabetes38 (Supplementary Information). Enrichment was assessed using stratified linkage disequilibrium (LD) score regression (LDSR)39. For all 12 GWAS studies (out of 16) with over 20,000 samples (Table S6, Figure 5A), significant enrichment was observed for the xQTL SNPs. We also repeated this analysis using a more stringent background model, where we considered enrichment of our xQTLs against a background set of SNPs falling in “generic” annotation categories as provided in the LDSR software39. Again, significant enrichment, albeit with lower effect size, was observed for many of the GWAS studies (Figure 5A, Table S6). Next, we hypothesized that SNPs shared between xQTL types, which affect multiple molecular phenotypes, are more likely to impact downstream processes and could constitute a list of “high confidence” functional SNPs. We therefore compared
