How much do model organism phenotypes contribute to the computational identification of human disease genes?
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Publication Date
2022-06-27Journal Title
Disease Models and Mechanisms
ISSN
1754-8403
Publisher
Company of Biologists
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Alghamdi, S., Schofield, P., & Hoehndorf, R. (2022). How much do model organism phenotypes contribute to the computational identification of human disease genes?. Disease Models and Mechanisms https://doi.org/10.1242/dmm.049441
Abstract
Computing phenotypic similarity has been shown to be useful
in identification of new disease genes and for rare disease
diagnostic support. Genotype–phenotype data from orthologous
genes in model organisms can compensate for lack of
human data to greatly increase genome coverage. Work over
the past decade has demonstrated the power of cross-species
phenotype comparisons, and several cross-species phenotype
ontologies have been developed for this purpose. The relative
contribution of different model organisms to computational identification
of disease-associated genes is not yet fully explored.
We use methods based on phenotype ontologies to semantically
relate phenotypes resulting from loss-of-function mutations
in different model organisms to disease-associated phenotypes
in humans. Semantic machine learning methods are used to
measure how much different model organisms contribute to the
identification of known human gene–disease associations. We
find that mouse genotype-phenotype data is the most important
dataset in the identification of human disease genes by semantic
similarity and machine learning over phenotype ontologies.
Data from other model organisms does not improve identification
over that obtained by using the mouse alone, and therefore
does not contribute significantly to this task. Our work has
implications for the future development of integrated phenotype
ontologies, as well as for the use of model organism phenotypes
in human genetic variant interpretation.
Sponsorship
King Abdullah University of Science
and Technology (KAUST) Office of Sponsored Research (OSR) under Award No.URF/1/3790-01-01, URF/1/4355-01-01, and FCC/1/1976-34-01.
Identifiers
External DOI: https://doi.org/10.1242/dmm.049441
This record's URL: https://www.repository.cam.ac.uk/handle/1810/338045
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