Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Alghamdi, Sarah M.; Sundberg, Beth A.; Sundberg, John P.; Schofield, Paul N.; Hoehndorf, Robert

doi:10.1038/s41598-019-40368-1

Cited by 14 publications

(8 citation statements)

References 54 publications

(74 reference statements)

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“…First, our results hold true across two cross-species phenotype ontologies, Pheno-e and uPheno . Both ontologies have similar content and goals but are based on different ontology design patterns (Gkoutos et al, 2017;Alghamdi et al, 2019). We compared the two ontologies in our analyses to test whether the underlying ontology design patterns have a significant impact but we did not consistently identify significant differences between both ontologies, indicating that our results hold true independent of the choice of phenotype ontology.…”

Section: Discussionmentioning

confidence: 90%

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

Alghamdi

Schofield

Hoehndorf

2021

Preprint

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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 identifying disease-associated genes using computational approaches 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 only mouse phenotypes can accurately predict human gene--disease associations. 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.

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Section: Discussionmentioning

confidence: 90%

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

Alghamdi

Schofield

Hoehndorf

2021

Preprint

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“…First, our results hold true across two cross-species phenotype ontologies, Pheno-e and uPheno ( Matentzoglu et al, 2019 ). Both ontologies have similar content and goals but are based on different ontology design patterns ( Gkoutos et al, 2017 ; Alghamdi et al, 2019 ). We compared the two ontologies in our analyses to test whether the underlying ontology design patterns have a significant impact, but we did not consistently identify significant differences between both ontologies, indicating that our results hold true independent of the choice of phenotype ontology.…”

Section: Discussionmentioning

confidence: 99%

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

Alghamdi

Schofield

Hoehndorf

2022

Disease Models &Amp; Mechanisms

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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.

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“…2,3 Vascular lesions were coded to the Mouse Pathology (MPATH) and Mouse Anatomy (MA) ontologies as previously described, 29,33 and anatomical location and pathological diagnoses were combined into the precomposed PAM ontology, which classifies lesions from MPATH by anatomical site using the MA ontology. 1 Overrepresentation was calculated using the Ontofunc and Func tools 14 as described previously. 1 We performed a hypergeometric test to establish the strains in which vascular lesions are overrepresented.…”

Section: Methodsmentioning

confidence: 99%

Hyaline Arteriolosclerosis in 30 Strains of Aged Inbred Mice

et al. 2019

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During a screen for vascular phenotypes in aged laboratory mice, a unique discrete phenotype of hyaline arteriolosclerosis of the intertubular arteries and arterioles of the testes was identified in several inbred strains. Lesions were limited to the testes, and did not occur as part of any renal, systemic, or pulmonary arteriopathy or vasculitis phenotype. There was no evidence of systemic or pulmonary hypertension, and lesions did not occur in female ovaries. Frequency was highest in males of the SM/J (27/30, 90%) and WSB/EiJ (19/26, 73%) strains, aged 383 to 847 days. Lesions were sporadically present in males from several other inbred strains at a much lower (<20%) frequency. The risk of testicular hyaline arteriolosclerosis is at least partially underpinned by a genetic predisposition which is not associated with other vascular lesions (including vasculitis), separating out the etiology of this form and site of arteriolosclerosis from other related conditions that often co-occur in other strains of mice and in humans. Because of their genetic uniformity and controlled dietary and environmental conditions, mice are an excellent model to dissect the pathogenesis of human disease conditions. In this study a discrete genetically driven phenotype of testicular hyaline arteriolosclerosis in aging mice was identified. These observations open the possibility of identifying the underlying genetic variant(s) associated with the predisposition and therefore allowing future interrogation of the pathogenesis of this condition. AbstractDuring a screen for vascular phenotypes in aged laboratory mice, a unique discrete phenotype of hyaline arteriolosclerosis of the intertubular arteries and arterioles of the testes was identified in several inbred strains. Lesions were limited to the testes, and did not occur as part of any renal, systemic, or pulmonary arteriopathy or vasculitis phenotype. There was no evidence of systemic or pulmonary hypertension, and lesions did not occur in female ovaries. Frequency was highest in males of the SM/J (27/30, 90%) and WSB/EiJ (19/26, 73%) strains, aged 383 to 847 days. Lesions were sporadically present in males from several other inbred strains at a much lower (<20%) frequency. The risk of testicular hyaline arteriolosclerosis is at least partially underpinned by a genetic predisposition which is not associated with other vascular lesions (including vasculitis), separating out the etiology of this form and site of arteriolosclerosis from other related conditions that often co-occur in other strains of mice and in humans. Because of their genetic uniformity and controlled dietary and environmental conditions, mice are an excellent model to dissect the pathogenesis of human disease conditions. In this study a discrete genetically driven phenotype of testicular hyaline arteriolosclerosis in aging mice was identified. These observations open the possibility of identifying the underlying genetic variant(s) associated with the predisposition and therefore allowing future interrogation of the...

show abstract

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Cited by 14 publications

References 54 publications

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

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

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

Hyaline Arteriolosclerosis in 30 Strains of Aged Inbred Mice

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