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.

The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

New Bedford Harbor (NBH), MA, is a federal Superfund site that is heavily contaminated with polychlorinated biphenyls (PCBs) and other halogenated aromatic hydrocarbons (HAHs), including some potent aryl hydrocarbon receptor (AhR) agonists. A population of Atlantic killifish (Fundulus heteroclitus) continues to inhabit this site, despite accumulating extraordinarily high concentrations of PCBs (272 microg/g dry weight). To determine if NBH killifish have developed resistance to HAHs that act through the AhR, we examined the inducibility of cytochrome P4501A1 (CYP1A1), UDP glucuronosyl transferase (UGT), and glutathione S-transferase (GST) in fish from NBH and a reference site, Scorton Creek (SC, Cape Cod, MA; PCB concentrations 0.177 microg/g dry weight). 2,3,7,8-Tetrachlorodibenzofuran (TCDF) induced CYP1A1 mRNA, protein, and activity in SC fish in all tissues examined (liver, heart, gut, gill, kidney, spleen, and gonad). In contrast, NBH fish expressed low levels of CYP1A1 and showed no induction of CYP1A1 mRNA, protein, or activity by TCDF, or induction that was lower in magnitude or required higher doses of inducer. p-Nitrophenol UGT activity was not induced by TCDF in either population, while GST activity with 1-chloro-2,4-dinitrobenzene as substrate was induced only in NBH fish in one experiment. Inducibility of CYP1A1 by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or beta-naphthoflavone (BNF) was measured in primary hepatocyte cultures prepared from SC and NBH fish. TCDD induced CYP1A1 activity (ethoxyresorufin O-deethylase) to the same degree in hepatocytes from both populations, demonstrating the functionality of the AhR signaling pathway in NBH fish. However, hepatocytes from NBH fish were 14-fold less sensitive to TCDD than were those from SC fish. The nonhalogenated AhR agonist BNF also induced CYP1A1 in cells from both populations, although with only a 3-fold difference in sensitivity (NBH < SC). These results indicate that chronic exposure to high levels of HAHs has led to a reduction in the sensitivity of NBH killifish to AhR agonists. The resistance is systemic and pretranslational, and exhibits compound-specific differences in magnitude. These findings suggest an alteration in the AhR signal transduction pathway in NBH fish.

The mouse Gene Expression Database (GXD) is an extensive, well-curated community resource freely available at www.informatics.jax.org/expression.shtml. Covering all developmental stages, GXD includes data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments in wild-type and mutant mice. GXD’s gene expression information is integrated with the other data in Mouse Genome Informatics and interconnected with other databases, placing these data in the larger biological and biomedical context. Since the last report, the ability of GXD to provide insights into the molecular mechanisms of development and disease has been greatly enhanced by the addition of new data and by the implementation of new web features. These include: improvements to the Differential Gene Expression Data Search, facilitating searches for genes that have been shown to be exclusively expressed in a specified structure and/or developmental stage; an enhanced anatomy browser that now provides access to expression data and phenotype data for a given anatomical structure; direct access to the wild-type gene expression data for the tissues affected in a specific mutant; and a comparison matrix that juxtaposes tissues where a gene is normally expressed against tissues, where mutations in that gene cause abnormalities.