Contribution of model organism phenotypes to the computational identification of human disease genes

Sarah M. Alghamdi, Paul N. Schofield, Robert Hoehndorf*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms’ data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

Original languageEnglish (US)
Article numberdmm049441
JournalDMM Disease Models and Mechanisms
Volume15
Issue number7
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022. Published by The Company of Biologists Ltd.

Keywords

  • Disease gene discovery
  • Machine learning
  • Model organism
  • Ontology
  • Phenotype
  • Semantic similarity

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Medicine (miscellaneous)
  • Immunology and Microbiology (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology

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