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.

https://orbilu.uni.lu/bitstream/10993/28940/1/Nucl.%20Acids%20Res.-2016-Ko%cc%88hler-nar-gkw1039.pdf

The Human Phenotype Ontology in 2017

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.

/pdf/the-human-phenotype-ontology-in-2021-1tdig2slrx.pdf

The Human Phenotype Ontology in 2021

Since the publication of the human reference genome, the identities of specific genes associated with human diseases are being discovered at a rapid rate. A central problem is that the biological activity of these genes is often unclear. Detailed investigations in model vertebrate organisms, typically mice, have been essential for understanding the activities of many orthologues of these disease-associated genes. Although gene-targeting approaches and phenotype analysis have led to a detailed understanding of nearly 6,000 protein-coding genes, this number falls considerably short of the more than 22,000 mouse protein-coding genes. Similarly, in zebrafish genetics, one-by-one gene studies using positional cloning, insertional mutagenesis, antisense morpholino oligonucleotides, targeted re-sequencing, and zinc finger and TAL endonucleases have made substantial contributions to our understanding of the biological activity of vertebrate genes, but again the number of genes studied falls well short of the more than 26,000 zebrafish protein-coding genes. Importantly, for both mice and zebrafish, none of these strategies are particularly suited to the rapid generation of knockouts in thousands of genes and the assessment of their biological activity. Here we describe an active project that aims to identify and phenotype the disruptive mutations in every zebrafish protein-coding gene, using a well-annotated zebrafish reference genome sequence, high-throughput sequencing and efficient chemical mutagenesis. So far we have identified potentially disruptive mutations in more than 38% of all known zebrafish protein-coding genes. We have developed a multi-allelic phenotyping scheme to efficiently assess the effects of each allele during embryogenesis and have analysed the phenotypic consequences of over 1,000 alleles. All mutant alleles and data are available to the community and our phenotyping scheme is adaptable to phenotypic analysis beyond embryogenesis.

A Systematic Genome-Wide Analysis of Zebrafish Protein-Coding Gene Function

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.22858

The Matchmaker Exchange: a platform for rare disease gene discovery

The majority of rare diseases affect children, most of whom have an underlying genetic cause for their condition However, making a molecular diagnosis with current technologies and knowledge is often still a challenge Paediatric genomics is an immature but rapidly evolving field that tackles this issue by incorporating next-generation sequencing technologies, especially whole-exome sequencing and whole-genome sequencing, into research and clinical workflows This complex multidisciplinary approach, coupled with the increasing availability of population genetic variation data, has already resulted in an increased discovery rate of causative genes and in improved diagnosis of rare paediatric disease Importantly, for affected families, a better understanding of the genetic basis of rare disease translates to more accurate prognosis, management, surveillance and genetic advice; stimulates research into new therapies; and enables provision of better support

Paediatric genomics: diagnosing rare disease in children.

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.22851

PhenomeCentral: A Portal for Phenotypic and Genotypic Matchmaking of Patients with Rare Genetic Diseases

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency

Importance We report the first case to date of maternally transmitted infantile spinocerebellar ataxia type 7 (SCA7), in which a tract of (CAG)45 expands to lengths as large as (CAG)92-250. Observations A 38-year-old woman with classic SCA7 (and a son, who died at age 3 years) had pronounced cerebellar atrophy and a renal biopsy specimen that showed focal segmental glomerulosclerosis with abnormal podocytes containing cytoplasmic inclusions. Polymerase chain reaction amplification across the SCA7 repeat tract assessed expansion levels in tissues of the affected son. High levels of somatic CAG instability were observed in blood, kidney, and skeletal muscle. This transmitted expansion is considerably larger than previously reported maternal transmission expansions of 5 to 10 gained repeats. Conclusions and Relevance We document the first intertissue CAG instability reported to date in patients with SCA7, similar to SCA7 mouse models. Infantile SCA7, which is often paternally transmitted, can rarely arise by maternal transmission, which has implications for diagnosis and counseling among families of patients with SCA7.

https://jamanetwork.com/journals/jamaneurology/articlepdf/2022384/ncr140025.pdf

Heather Trang

Papers

PhenomeCentral: A Portal for Phenotypic and Genotypic Matchmaking of Patients with Rare Genetic Diseases

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency

Massive CAG repeat expansion and somatic instability in maternally transmitted infantile spinocerebellar ataxia type 7.