Contribution of model organism phenotypes to the computational identification of human disease genes
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 valub...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Company of Biologists
2022-07-01
|
| Series: | Disease Models & Mechanisms |
| Subjects: | |
| Online Access: | http://dmm.biologists.org/content/15/7/dmm049441 |
| _version_ | 1798037153274396672 |
|---|---|
| author | Sarah M. Alghamdi Paul N. Schofield Robert Hoehndorf |
| author_facet | Sarah M. Alghamdi Paul N. Schofield Robert Hoehndorf |
| author_sort | Sarah M. Alghamdi |
| collection | DOAJ |
| description | 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. This article has an associated First Person interview with the first author of the paper. |
| first_indexed | 2024-04-11T21:22:42Z |
| format | Article |
| id | doaj.art-0042250c76d9478faaffbb82df7cf580 |
| institution | Directory Open Access Journal |
| issn | 1754-8403 1754-8411 |
| language | English |
| last_indexed | 2024-04-11T21:22:42Z |
| publishDate | 2022-07-01 |
| publisher | The Company of Biologists |
| record_format | Article |
| series | Disease Models & Mechanisms |
| spelling | doaj.art-0042250c76d9478faaffbb82df7cf5802022-12-22T04:02:32ZengThe Company of BiologistsDisease Models & Mechanisms1754-84031754-84112022-07-0115710.1242/dmm.049441049441Contribution of model organism phenotypes to the computational identification of human disease genesSarah M. Alghamdi0Paul N. Schofield1Robert Hoehndorf2 Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia 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. This article has an associated First Person interview with the first author of the paper.http://dmm.biologists.org/content/15/7/dmm049441model organismphenotypedisease gene discoveryontologysemantic similaritymachine learning |
| spellingShingle | Sarah M. Alghamdi Paul N. Schofield Robert Hoehndorf Contribution of model organism phenotypes to the computational identification of human disease genes Disease Models & Mechanisms model organism phenotype disease gene discovery ontology semantic similarity machine learning |
| title | Contribution of model organism phenotypes to the computational identification of human disease genes |
| title_full | Contribution of model organism phenotypes to the computational identification of human disease genes |
| title_fullStr | Contribution of model organism phenotypes to the computational identification of human disease genes |
| title_full_unstemmed | Contribution of model organism phenotypes to the computational identification of human disease genes |
| title_short | Contribution of model organism phenotypes to the computational identification of human disease genes |
| title_sort | contribution of model organism phenotypes to the computational identification of human disease genes |
| topic | model organism phenotype disease gene discovery ontology semantic similarity machine learning |
| url | http://dmm.biologists.org/content/15/7/dmm049441 |
| work_keys_str_mv | AT sarahmalghamdi contributionofmodelorganismphenotypestothecomputationalidentificationofhumandiseasegenes AT paulnschofield contributionofmodelorganismphenotypestothecomputationalidentificationofhumandiseasegenes AT roberthoehndorf contributionofmodelorganismphenotypestothecomputationalidentificationofhumandiseasegenes |