Limits...
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.

Show MeSH

Related in: MedlinePlus

Highlights the applied transformation in our method.Our mappings are not symmetrical. Therefore, we can “translate” phenotype concepts in two directions: we can translate all mouse models into an HPO-based representation (using either the lexical, ontology-based or merged mapping approach), and we can translate all human diseases into an MP-based representation (using either of the mappings). When both mouse phenotypes and human diseases are represented using the same ontology, their similarity can be computed to suggest candidate disease genes. The original data obtained from OMIM (disease annotations in HPO) is illustrated with a brown color whilst the data obtained from MGI is illustrated with a light blue color. The purple arrows show the “translation” process using either the lexical, the ontological or the combined mapping. Once diseases and mouse models are represented using the same ontology, the prioritization based on a phenotype similarity will be calculated.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3375301&req=5

pone-0038937-g003: Highlights the applied transformation in our method.Our mappings are not symmetrical. Therefore, we can “translate” phenotype concepts in two directions: we can translate all mouse models into an HPO-based representation (using either the lexical, ontology-based or merged mapping approach), and we can translate all human diseases into an MP-based representation (using either of the mappings). When both mouse phenotypes and human diseases are represented using the same ontology, their similarity can be computed to suggest candidate disease genes. The original data obtained from OMIM (disease annotations in HPO) is illustrated with a brown color whilst the data obtained from MGI is illustrated with a light blue color. The purple arrows show the “translation” process using either the lexical, the ontological or the combined mapping. Once diseases and mouse models are represented using the same ontology, the prioritization based on a phenotype similarity will be calculated.

Mentions: Based on the ontological mappings between the MP and HPO, we applied a measure of semantic similarity to compare experimentally derived phenotype descriptions of mice with the phenotypes that are associated with human diseases. Figure 3 provides an overview of the experimental setup of our approach. We used the phenotype annotations of mouse models available from the MGI database [22] and compared those to the phenotypes associated with diseases described in OMIM. To automatically compare the similarity between mouse and disease phenotypes, we converted either the mouse phenotypes into an HPO-based representation or the disease phenotypes into an MP-based representation. This transformation allowed us to perform a similarity-based comparison between phenotypes using either HPO or MP (also illustrated in Figure 3).


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)

Highlights the applied transformation in our method.Our mappings are not symmetrical. Therefore, we can “translate” phenotype concepts in two directions: we can translate all mouse models into an HPO-based representation (using either the lexical, ontology-based or merged mapping approach), and we can translate all human diseases into an MP-based representation (using either of the mappings). When both mouse phenotypes and human diseases are represented using the same ontology, their similarity can be computed to suggest candidate disease genes. The original data obtained from OMIM (disease annotations in HPO) is illustrated with a brown color whilst the data obtained from MGI is illustrated with a light blue color. The purple arrows show the “translation” process using either the lexical, the ontological or the combined mapping. Once diseases and mouse models are represented using the same ontology, the prioritization based on a phenotype similarity will be calculated.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0038937-g003: Highlights the applied transformation in our method.Our mappings are not symmetrical. Therefore, we can “translate” phenotype concepts in two directions: we can translate all mouse models into an HPO-based representation (using either the lexical, ontology-based or merged mapping approach), and we can translate all human diseases into an MP-based representation (using either of the mappings). When both mouse phenotypes and human diseases are represented using the same ontology, their similarity can be computed to suggest candidate disease genes. The original data obtained from OMIM (disease annotations in HPO) is illustrated with a brown color whilst the data obtained from MGI is illustrated with a light blue color. The purple arrows show the “translation” process using either the lexical, the ontological or the combined mapping. Once diseases and mouse models are represented using the same ontology, the prioritization based on a phenotype similarity will be calculated.
Mentions: Based on the ontological mappings between the MP and HPO, we applied a measure of semantic similarity to compare experimentally derived phenotype descriptions of mice with the phenotypes that are associated with human diseases. Figure 3 provides an overview of the experimental setup of our approach. We used the phenotype annotations of mouse models available from the MGI database [22] and compared those to the phenotypes associated with diseases described in OMIM. To automatically compare the similarity between mouse and disease phenotypes, we converted either the mouse phenotypes into an HPO-based representation or the disease phenotypes into an MP-based representation. This transformation allowed us to perform a similarity-based comparison between phenotypes using either HPO or MP (also illustrated in Figure 3).

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