An integrative, translational approach to understanding rare and orphan genetically based diseases

Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos

Research output: Contribution to journalArticlepeer-review

15 Citations (SciVal)

Abstract

PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to 'guilt-by-association' approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown. In addition to disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database, we have now integrated the clinical signs from Orphanet into PhenomeNet. We demonstrate that our approach can efficiently identify known candidate genes for genetic diseases in Orphanet and OMIM. Furthermore, we find evidence that mutations in the HIP1 gene might cause Bassoe syndrome, a rare disorder with unknown genetic aetiology. Our results demonstrate that integration and computational analysis of human disease and animal model phenotypes using PhenomeNet has the potential to reveal novel insights into the pathobiology underlying genetic diseases.
Original languageEnglish
JournalInterface Focus
Volume3
Issue number2
Early online date06 Apr 2013
Publication statusPublished - 01 May 2013

Keywords

  • phenotype
  • animal model
  • rare disease
  • orphan disease
  • orphanet
  • biomedical informatics

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