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How much do model organism phenotypes contribute to the computational identification of human disease genes?

cam.depositDate2022-06-13
cam.issuedOnline2022-06-27
cam.orpheus.counter2
cam.orpheus.successMon Jul 04 07:23:45 BST 2022 - Embargo updated
dc.contributor.authorAlghamdi, Sarah
dc.contributor.authorSchofield, Paul
dc.contributor.authorHoehndorf, Robert
dc.contributor.orcidSchofield, Paul [0000-0002-5111-7263]
dc.date.accessioned2022-06-13T23:30:47Z
dc.date.available2022-06-13T23:30:47Z
dc.date.issued2022-06-27
dc.date.updated2022-06-13T15:06:14Z
dc.description.abstractComputing 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.
dc.description.sponsorshipKing 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.
dc.identifier.doi10.17863/CAM.85454
dc.identifier.eissn1754-8411
dc.identifier.issn1754-8403
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338045
dc.language.isoeng
dc.publisherCompany of Biologists
dc.publisher.departmentDepartment of Physiology, Development And Neuroscience
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleHow much do model organism phenotypes contribute to the computational identification of human disease genes?
dc.typeArticle
dcterms.dateAccepted2022-06-13
prism.publicationDate
prism.publicationNameDisease Models and Mechanisms
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1242/dmm.049441

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