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The biological coherence of human phenome databases.

Martin Oti¹, Martijn A Huynen, Han G Brunner

¹Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands.

American Journal of Human Genetics

|December 17, 2009

Summary

Improving human phenome databases enhances genetic disease analysis. Fine-grained ontologies and feature frequency estimates boost accuracy, while adding data from POSSUM to OMIM improves representation.

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Area of Science:

Genomics and Bioinformatics
Human Genetics and Disease Research

Background:

Disease networks offer insights complementary to gene/protein interaction networks.
The accuracy of disease networks relies heavily on the quality of phenotype data in human genetic disease databases.

Purpose of the Study:

To determine which phenotype database architecture and content elements best represent disease biology.
To evaluate the biological coherence of phenotype similarity across major human phenotype databases.

Main Methods:

Utilized OMIM-based Human Phenotype Ontology (HPO), Orphanet, and POSSUM databases.
Developed a biological coherence score based on shared Gene Ontology annotations.
Assessed phenotype similarity reflecting pathobiology.

Main Results:

A fine-grained phenotype ontology significantly improves phenome representation accuracy.
The widely used OMIM database is notably underannotated.
Integrating POSSUM data into HPO enhances OMIM phenotype representation; feature frequency estimation (used in Orphanet) improves phenome quality.

Conclusions:

Enhancing human phenome databases through improved curation and systematic annotation can significantly advance genetic disease analysis.
Implementing measures like fine-grained ontologies, data integration, and feature frequency estimation offers substantial benefits.