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MouseFinder: Candidate disease genes from mouse phenotype data

Chao-Kung Chen1, Christopher J Mungall, Georgios V Gkoutos

  • 1Vertebrate Genomics Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

Human Mutation
|February 15, 2012
PubMed

Insights

Mouse phenotype data aids disease gene discovery. A semantic approach and the MouseFinder web application improve mapping between human and mouse traits, identifying candidate genes like ARTN for hereditary ptosis.

Area of Science:

  • Genetics
  • Bioinformatics
  • Genomic Medicine

Background:

  • Mouse phenotype data is crucial for identifying disease-associated genes, particularly when the molecular basis is unknown.
  • Systematic use of this data has been hindered by challenges in mapping human clinical features to mouse phenotype annotations.
  • Bridging this gap is essential for advancing genetic research and understanding disease mechanisms.

Purpose of the Study:

  • To develop a semantic approach for effectively mapping human clinical features to mouse phenotype data.
  • To create a user-friendly web application for accessing and utilizing this integrated data.
  • To demonstrate the utility of this approach in identifying novel disease-gene associations.

Main Methods:

  • Development of a semantic framework to standardize and compare human and mouse phenotype descriptions.
  • Creation of the MouseFinder web application (www.mousemodels.org) for querying whole-phenome comparisons.
  • Validation of the approach by assessing recall of known disease-gene associations and orthology relationships.

Main Results:

  • The semantic approach significantly improved the mapping accuracy between human and mouse phenotypes.
  • The MouseFinder application provides a searchable interface for exploring cross-species phenotype data.
  • The study successfully identified ARTN as a strong candidate gene for a hereditary form of ptosis located at the 1p34.1-p32 locus.

Conclusions:

  • A semantic approach effectively overcomes challenges in integrating human and mouse phenotype data for gene discovery.
  • The MouseFinder web application is a valuable tool for researchers investigating genetic basis of diseases.
  • This methodology enhances the identification of candidate genes and understanding of orthology relationships in human disease.

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