Abstract
Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism—Caenorhabditis elegans—has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTLHD (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene–disease associations in man. WormQTLHD, available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene–disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.
Highlights
Many exciting data sets have been collected in recent years for Caenorhabditis elegans, a free-living, non-parasitic soilrelated nematode that feeds on the bacteria of decaying organic matter
Recent genetical genomics studies in C. elegans have revealed thousands of genomic regions that are associated to the quantitative variation in a diverse range of phenotypes, such as gene expression [expression Quantitative Trait Loci] [3,4,5,6,7,8,9], lifespan [10], development [11,12,13], stress resistance [14,15], behaviour [16,17], dauer formation [15,18] and sensitivity to RNAi treatments [19]
To demonstrate the added value of WormQTLHD, we have reproduced findings from known studies and have shown that novel insights and hypotheses can be achieved with little time and effort
Summary
Many exciting data sets have been collected in recent years for Caenorhabditis elegans, a free-living, non-parasitic soilrelated nematode that feeds on the bacteria of decaying organic matter. To facilitate the exploitation of the worm eQTL data for human disease research we developed a new database, WormQTLHD, which quantitatively and systematically links many eQTLs findings in C. elegans to gene–disease associations in human. This is the first online database for the systematic investigation of C. elegans phenotype equivalents of human diseases by integrating known disease–gene associations, gene orthologue data, molecular phenotypes and QTL results.
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