Abstract
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
Highlights
The Human Phenotype Ontology (HPO) provides comprehensive bioinformatic resources for the analysis of human diseases and phenotypes, offering a computational bridge between genome biology and clinical medicine
The RD-Connect platform contains ∼2000 exome, genome and panel sequencing datasets linked with HPO-coded phenotypic profiles from a range of rare diseases
D870 Nucleic Acids Research, 2017, Vol 45, Database issue the use of HPO terms in patients with epilepsy is worthwhile, given that the identified epilepsy phenotype was both homogeneous and clinically meaningful
Summary
The Human Phenotype Ontology (HPO) provides comprehensive bioinformatic resources for the analysis of human diseases and phenotypes, offering a computational bridge between genome biology and clinical medicine.
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