Bioinformatics Resources


Many human diseases are caused by single nucleotide polymorphisms (SNP). The SNP3D website aims to combine results of our prediction models of deleterious mutations with knowledge of protein-protein interactions, homology models of protein structures, and sequence conservation profiles

An Integrated eQTL Database

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.

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