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 correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

Little information exists about the loss of all one’s teeth (edentulism) among older adults in low- and middle-income countries. This study examines the prevalence of edentulism and associated factors among older adults in a cross-sectional study across six such countries. Data from the World Health Organization (WHO’s) Study on global AGEing and adult health (SAGE) Wave 1 was used for this study with adults aged 50-plus from China (N = 13,367), Ghana (N = 4724), India (N = 7150), Mexico (N = 2315), Russian Federation (N = 3938) and South Africa (N = 3840). Multivariate regression was used to assess predictors of edentulism. The overall prevalence of edentulism was 11.7% in the six countries, with India, Mexico, and Russia has higher prevalence rates (16.3%–21.7%) than China, Ghana, and South Africa (3.0%–9.0%). In multivariate logistic analysis sociodemographic factors (older age, lower education), chronic conditions (arthritis, asthma), health risk behaviour (former daily tobacco use, inadequate fruits and vegetable consumption) and other health related variables (functional disability and low social cohesion) were associated with edentulism. The national estimates and identified factors associated with edentulism among older adults across the six countries helps to identify areas for further exploration and targets for intervention.

The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease.

The correlation of phenotypic outcomes with genetic