ANALYSIS OF CONSENSUS GENOME-WIDE EXPRESSION-QTLS AND THEIR RELATIONSHIPS TO HUMAN COMPLEX TRAIT DISEASES

Genome-wide association studies of human complex disease have identified a large number of disease associated genetic loci. However, most of these risk loci do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent genome-wide expression quantitative trait loci (eQTLs) association studies have provided information on genetic factors, especially SNPs, associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. Our first major goal was to establish a list of consensus eQTLs by integrating publicly available data for specific human populations and cell types. We used linkage disequilibrium data from Hapmap and the 1000 Genomes Project to integrate the results of eQTL studies. Overall, we find over 4000 genes that are involved in high confidence eQTL relationships. We also assessed the possible underlying mechanisms of tissue dependent eQTLs by mapping these to known genome sites of functional elements. Results of comparison of eQTLs across studies on the same cell type versus those on different cell types suggest that tissue specific eQTLs are less common than pan-tissue eQTLs. Our second major goal was to use these results to elucidate the role eQTLs play in human common diseases. For this purpose, we matched the high confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium (WTCCC1) genome-wide association study and follow-up studies for seven human common diseases. Our results show that the data are consistent with approximately 50% of these disease loci arising from an underlying expression change mechanism. In many cases, the results provide a proposed expression mechanism for genes previously suggested as disease relevant, in others, new disease relevant genes are identified. A web-based database, ExSNP, was designed to provide comprehensive access to the eQTL data and results from our analysis, including original eQTLs, high-confidence eQTLs, cell type dependent eQTLs, population dependent eQTLs, disease associated eQTLs, and functionally annotated eQTLs. The website also incorporates a genome browser that allows visualization of the relative positions of eQTL SNPs to their associated genes and other neighboring genes, as well as the relationship to functional elements and disease associations