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Published on: August 15, 2019
WormQTLHD--a web database for linking human disease to natural variation data in C. elegans
K Joeri van der Velde1, Mark de Haan, Konrad Zych
1Genomics Coordination Center, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands, Groningen Bioinformatics Center, University of Groningen, P.O. Box 11103, 9700 CC Groningen, The Netherlands, Department of Genetics, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands, Department of Bioinformatics, Hanze University of Applied Sciences, Groningen, Zernikeplein 11, 9747 AS, The Netherlands and Laboratory of Nematology, Wageningen University, 6708 PB Wageningen, The Netherlands.
WormQTL(HD) links C. elegans expression quantitative trait loci (eQTL) data to human diseases. This database aids in discovering conserved gene networks and predicting disease-associated genes using model organism research.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Protein interactions are evolutionarily conserved, enabling disease study in model organisms.
- Caenorhabditis elegans is a valuable model for molecular quantitative genetics and systems biology.
- Existing data links C. elegans quantitative trait loci (QTL) to human gene-disease associations.
Purpose of the Study:
- To develop WormQTL(HD), a database connecting C. elegans expression QTL (eQTL) data with human disease associations.
- To provide tools for identifying functionally coherent, evolutionarily conserved gene networks.
- To facilitate prediction of novel gene-gene interactions and functions for disease-related genes.
Main Methods:
- Integrated C. elegans eQTL datasets with human disease associations (OMIM, DGA, GWAS Central, NHGRI GWAS Catalogue).
- Utilized orthologous genes between C. elegans and humans to link phenotypes.
- Leveraged QTL results, molecular and classical phenotypes, and genotype data from the WormQTL database.
- Built the database using MOLGENIS and xQTL workbench, offering open-source software.
Main Results:
- Created a comprehensive database linking C. elegans eQTL data to 34,337 human gene-disease associations.
- Developed user-friendly tools for exploring conserved gene networks.
- Enabled prediction of novel gene functions and associations relevant to human diseases.
Conclusions:
- WormQTL(HD) serves as a valuable resource for comparative genomics and disease gene discovery.
- The database facilitates the use of C. elegans as a model system for understanding human disease mechanisms.
- It supports the identification of evolutionary conserved gene networks underlying diseases.
