The Human Phenotype Ontology in 2017

Abstract

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Comprehensive screening shows that mutations in the known syndromic genes are rare in individuals presenting with hyperinsulinaemic hypoglycaemia

“…We utilised the Phenomizer bowser to identify disease entries annotated for hyperinsulinaemic hypoglycaemia (HPO id: 0000825) [14]. A survey of the literature was also undertaken to identify further genes in which mutations have been reported to cause HH as part of a syndrome.…”

Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

“…The relative ease of using pre-existing cohorts and registries to inexpensively boost sample size has favored "phenotype-light" sample collection. This balance could be shifted by the adoption of consistent phenotyping schema 51,52 , identification of reliable neuropsychiatric biomarkers, or utilization of electronic medical records. Several large-scale initiatives are already working in .…”

A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine

“…One important component required for data integration is the careful curation and mapping of data to controlled vocabularies or ontologies. For genomic data integration with clinical information, data from primary care, hospitals, outcomes, registries, and social care records should be first recorded using controlled clinical terminologies, such as SNOMED Clinical Terms and the Human Phenotype Ontology [21]. Ontologies as such are not ever complete and end-users such as clinicians will need to work with ontology developers to continuously improve the precision and accuracy of terminologies [22].…”