Background:


Genome-wide association studies (GWAS) of human complex disease have identified a large number of disease associated genetic loci, distinguished by an altered frequency of specific single nucleotide polymorphisms (SNPs) among individuals with a particular disease, compared to controls. 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 likely contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. Our primary goal is to establish a gold-standard list of consensus eQTLs by integrating publicly available data for specific human populations and cell types, so as to efficiently prioritize functional SNPs. We used linkage disequilibrium data from Hapmap and the 1000 Genome Project to integrate the results of eQTL studies. Separate gold-standard sets for various populations allowed us to investigate eQTLs which contribute to population-specific expression variation. Additionally, tissue-specific eQTL associations were identified by comparing eQTL data from six cell types: LCLs, B cells, Monocytes, Brain, Liver, and Skin. Moreover, to discover the role of these eQTLs play in human common diseases, we have integrated the current gold standard data with SNPs in disease risk loci from GWA studies of seven common human diseases.



Uses of the exSNP database:

  1. Query - eQTL datasets:
    • To query if the SNPs of interest are involved in eQTLs in tissues from 16 publicly available human eQTL studies.

    • To query if the genes of interest are associated with eQTLs in tissues from 16 publicly available human eQTL studies.

    (Query can be specified by chromosomes)

    * eQTL datasets sources

  2. Query - LD-eQTL query:
    • To query if the SNPs of interest are in a LD relationship with eQTLs in tissues from 16 publicly available human eQTL studies.

    (Query can be specified by various LD thresholds (r2))


  3. Query - High-Confidence eQTLs:
    • To query if the SNPs of interest are High-Confidence eQTLs in a specific set of eQTL studies.

    • To query if the genes of interest are associated with High-Confidence eQTLs in a specific set of eQTL studies.

    (Query could be specified by sets of eQTL studies, chromosomes, and LD thresholds (r2))


  4. Query - Disease associated eQTLs:
    • To query if the SNPs of interest are in disease-associated eQTLs in a specific set of eQTL studies.

    • To query if the genes of interest are involved in disease associated eQTLs in a specific set of eQTL studies.

    (Query can be specified by diseases, sets of eQTL studies, High-Confidence eQTLs, chromosomes, and LD thresholds (r2))


  5. Query - Tissue-specific eQTLs:
    • To query if the SNPs of interest are in tissue-specific eQTLs in a specific tissue set of eQTL studies.

    • To query if the genes of interest are in tissue-specific eQTLs in a specific tissue set of eQTL studies.

    (Query can be specified by cell types, High-Confidence eQTLs, and LD thresholds (r2))


  6. Query - Population-specific eQTLs:
    • To query if the SNPs of interest are in population-specific eQTLs in a specific tissue set of eQTL studies.

    • To query if the genes of interest are in population-specific eQTLs in a specific tissue set of eQTL studies.

    (Query can be specified by populations, High-Confidence eQTLs, and LD thresholds (r2))


  7. Browser - eQTL genome browser:
    • To browse eQTLs of interest on the human genome.

    • To browse exGenes of interest on the human genome.

    (Browsing can be specified by populations, cell types, eQTL studies, and LD thresholds (r2))