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Improving disease gene prioritization by comparing the semantic similarity of phenotypes in mice with those of human diseases.

Oellrich A, Hoehndorf R, Gkoutos GV, Rebholz-Schuhmann D - PLoS ONE (2012)

Bottom Line: Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease.Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates.Our method significantly outperforms previous phenotype-based approaches of prioritizing gene-disease associations.

View Article: PubMed Central - PubMed

Affiliation: European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom. anika@ebi.ac.uk

ABSTRACT
Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are "translated" into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are "translated" into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene-disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene-disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net.

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ROC curves resulting from our evaluation.The left panel includes all the results for “translating” mouse models from an MP representation to an HPO representation and performing the gene prediction in HPO. The right panel includes all the results for a “translation” of human diseases into an MP-based representation. Each plot shows the evaluation results using each of the three mappings: using lexical matching, using reasoning over ontologies, and the merged mappings. The two panels on the top are the results of the evaluation against OMIM and the two panels at the bottom are the results of the evaluation against MGI.
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pone-0038937-g004: ROC curves resulting from our evaluation.The left panel includes all the results for “translating” mouse models from an MP representation to an HPO representation and performing the gene prediction in HPO. The right panel includes all the results for a “translation” of human diseases into an MP-based representation. Each plot shows the evaluation results using each of the three mappings: using lexical matching, using reasoning over ontologies, and the merged mappings. The two panels on the top are the results of the evaluation against OMIM and the two panels at the bottom are the results of the evaluation against MGI.

Mentions: As a result, we obtained 12 ROC curves with their associated AUC values: we performed the similarity-based comparison based on HPO and based on MP for each of the three mapping approaches between MP and HPO and vice versa (based only on lexical matching, based only on reasoning over phenotype definitions, and based on the combination of both approaches), and evaluate the results against both MGI’s and MorbidMap’s gene–disease associations. Figure 4 illustrates the resulting ROC curves and table 2 shows the AUCs obtained for each.


Improving disease gene prioritization by comparing the semantic similarity of phenotypes in mice with those of human diseases.

Oellrich A, Hoehndorf R, Gkoutos GV, Rebholz-Schuhmann D - PLoS ONE (2012)

ROC curves resulting from our evaluation.The left panel includes all the results for “translating” mouse models from an MP representation to an HPO representation and performing the gene prediction in HPO. The right panel includes all the results for a “translation” of human diseases into an MP-based representation. Each plot shows the evaluation results using each of the three mappings: using lexical matching, using reasoning over ontologies, and the merged mappings. The two panels on the top are the results of the evaluation against OMIM and the two panels at the bottom are the results of the evaluation against MGI.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375301&req=5

pone-0038937-g004: ROC curves resulting from our evaluation.The left panel includes all the results for “translating” mouse models from an MP representation to an HPO representation and performing the gene prediction in HPO. The right panel includes all the results for a “translation” of human diseases into an MP-based representation. Each plot shows the evaluation results using each of the three mappings: using lexical matching, using reasoning over ontologies, and the merged mappings. The two panels on the top are the results of the evaluation against OMIM and the two panels at the bottom are the results of the evaluation against MGI.
Mentions: As a result, we obtained 12 ROC curves with their associated AUC values: we performed the similarity-based comparison based on HPO and based on MP for each of the three mapping approaches between MP and HPO and vice versa (based only on lexical matching, based only on reasoning over phenotype definitions, and based on the combination of both approaches), and evaluate the results against both MGI’s and MorbidMap’s gene–disease associations. Figure 4 illustrates the resulting ROC curves and table 2 shows the AUCs obtained for each.

Bottom Line: Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease.Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates.Our method significantly outperforms previous phenotype-based approaches of prioritizing gene-disease associations.

View Article: PubMed Central - PubMed

Affiliation: European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom. anika@ebi.ac.uk

ABSTRACT
Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are "translated" into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are "translated" into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene-disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene-disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net.

Show MeSH
Related in: MedlinePlus