The Sherlock method25 attempts to uncover disease-associated genes (“risk genes”) by using a Bayesian statistical framework to assess overlap between eQTL for a gene and GWA significant SNPs loci for a disease. Its underlying principle is that genetic-driven changes in expression levels of risk genes (discovered as eSNPs) should ultimately also manifest as genetic association of those same SNPs with disease (GWAS SNPs). Specifically, we expect that cis-eQTL and trans-eQTL for a risk gene should be associated with disease (if risk is mediated by expression changes of that gene); note that the converse need not be true (since not all associated SNPs need be related to the function of any single disease gene). Briefly, Sherlock uses a Bayesian model to integrate signal across all statistically independent eQTL loci for a gene, where an independent linkage block is defined as a genomic interval containing one or more eSNPs associated with a gene and having a within-eSNP interval of 500 kb or less. For each such independent block, a single Bayes factor is calculated as the mean of the SNP-level Bayes factors
