Damian Smedley

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

A Genomewide Scan for Loci Predisposing to Type 2 Diabetes in a U.K. Population (The Diabetes UK Warren 2 Repository): Analysis of 573 Pedigrees Provides Independent Replication of a Susceptibility Locus on Chromosome 1q

Improved molecular understanding of the pathogenesis of type 2 diabetes is essential if current therapeutic and preventative options are to be extended. To identify diabetes-susceptibility genes, we have completed a primary (418-marker, 9-cM) autosomal-genome scan of 743 sib pairs (573 pedigrees) with type 2 diabetes who are from the Diabetes UK Warren 2 repository. Nonparametric linkage analysis of the entire data set identified seven regions showing evidence for linkage, with allele-sharing LOD scores > or =1.18 (P< or =.01). The strongest evidence was seen on chromosomes 8p21-22 (near D8S258 [LOD score 2.55]) and 10q23.3 (near D10S1765 [LOD score 1.99]), both coinciding with regions identified in previous scans in European subjects. This was also true of two lesser regions identified, on chromosomes 5q13 (D5S647 [LOD score 1.22] and 5q32 (D5S436 [LOD score 1.22]). Loci on 7p15.3 (LOD score 1.31) and 8q24.2 (LOD score 1.41) are novel. The final region showing evidence for linkage, on chromosome 1q24-25 (near D1S218 [LOD score 1.50]), colocalizes with evidence for linkage to diabetes found in Utah, French, and Pima families and in the GK rat. After dense-map genotyping (mean marker spacing 4.4 cM), evidence for linkage to this region increased to a LOD score of 1.98. Conditional analyses revealed nominally significant interactions between this locus and the regions on chromosomes 10q23.3 (P=.01) and 5q32 (P=.02). These data, derived from one of the largest genome scans undertaken in this condition, confirm that individual susceptibility-gene effects for type 2 diabetes are likely to be modest in size. Taken with genome scans in other populations, they provide both replication of previous evidence indicating the presence of a diabetes-susceptibility locus on chromosome 1q24-25 and support for the existence of additional loci on chromosomes 5, 8, and 10. These data should accelerate positional cloning efforts in these regions of interest.

BioMart Central Portal—unified access to biological data

BioMart Central Portal (www.biomart.org) offers a one-stop shop solution to access a wide array of biological databases. These include major biomolecular sequence, pathway and annotation databases such as Ensembl, Uniprot, Reactome, HGNC, Wormbase and PRIDE; for a complete list, visit, http://www.biomart.org/biomart/martview. Moreover, the web server features seamless data federation making cross querying of these data sources in a user friendly and unified way. The web server not only provides access through a web interface (MartView), it also supports programmatic access through a Perl API as well as RESTful and SOAP oriented web services. The website is free and open to all users and there is no login requirement.

Ensembl’s 10th year

Ensembl (http://www.ensembl.org) integrates genomic information for a comprehensive set of chordate genomes with a particular focus on resources for human, mouse, rat, zebrafish and other high-value sequenced genomes. We provide complete gene annotations for all supported species in addition to specific resources that target genome variation, function and evolution. Ensembl data is accessible in a variety of formats including via our genome browser, API and BioMart. This year marks the tenth anniversary of Ensembl and in that time the project has grown with advances in genome technology. As of release 56 (September 2009), Ensembl supports 51 species including marmoset, pig, zebra finch, lizard, gorilla and wallaby, which were added in the past year. Major additions and improvements to Ensembl since our previous report include the incorporation of the human GRCh37 assembly, enhanced visualisation and data-mining options for the Ensembl regulatory features and continued development of our software infrastructure.

Fusion of splicing factor genes PSF and NonO (p54nrb) to the TFE3 gene in papillary renal cell carcinoma

We demonstrate that the cytogenetically defined translocation t(X;1)(p11.2;p34) observed in papillary renal cell carcinomas results in the fusion of the splicing factor gene PSF located at 1p34 to the TFE3 helix – loop – helix transcription factor gene at Xp11.2. In addition we define an X chromosome inversion inv(X)(p11.2;q12) that results in the fusion of the NonO (p54nrb) gene to TFE3. NonO (p54nrb), the human homologue of the Drosophila gene NonAdiss which controls the male courtship song, is closely related to PSF and also believed to be involved in RNA splicing. In each case the rearrangement results in the fusion of almost the entire splicing factor protein to the TFE3 DNA-binding domain. These observations suggest the possibility of intriguing links between the processes of RNA splicing, DNA transcription and oncogenesis.

The Human Phenotype Ontology in 2017

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.

Improved exome prioritization of disease genes through cross-species phenotype comparison.

Numerous new disease-gene associations have been identified by whole-exome sequencing studies in the last few years. However, many cases remain unsolved due to the sheer number of candidate variants remaining after common filtering strategies such as removing low quality and common variants and those deemed unlikely to be pathogenic. The observation that each of our genomes contains about 100 genuine loss-of-function variants makes identification of the causative mutation problematic when using these strategies alone. We propose using the wealth of genotype to phenotype data that already exists from model organism studies to assess the potential impact of these exome variants. Here, we introduce PHenotypic Interpretation of Variants in Exomes (PHIVE), an algorithm that integrates the calculation of phenotype similarity between human diseases and genetically modified mouse models with evaluation of the variants according to allele frequency, pathogenicity, and mode of inheritance approaches in our Exomiser tool. Large-scale validation of PHIVE analysis using 100,000 exomes containing known mutations demonstrated a substantial improvement (up to 54.1-fold) over purely variant-based (frequency and pathogenicity) methods with the correct gene recalled as the top hit in up to 83% of samples, corresponding to an area under the ROC curve of >95%. We conclude that incorporation of phenotype data can play a vital role in translational bioinformatics and propose that exome sequencing projects should systematically capture clinical phenotypes to take advantage of the strategy presented here.

BioMart Central Portal: an open database network for the biological community

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities. Database URL: http://central.biomart.org.

The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data

The International Mouse Phenotyping Consortium (IMPC) web portal (http://www.mousephenotype.org) provides the biomedical community with a unified point of access to mutant mice and rich collection of related emerging and existing mouse phenotype data. IMPC mouse clinics worldwide follow rigorous highly structured and standardized protocols for the experimentation, collection and dissemination of data. Dedicated ‘data wranglers’ work with each phenotyping center to collate data and perform quality control of data. An automated statistical analysis pipeline has been developed to identify knockout strains with a significant change in the phenotype parameters. Annotation with biomedical ontologies allows biologists and clinicians to easily find mouse strains with phenotypic traits relevant to their research. Data integration with other resources will provide insights into mammalian gene function and human disease. As phenotype data become available for every gene in the mouse, the IMPC web portal will become an invaluable tool for researchers studying the genetic contributions of genes to human diseases.

Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome

Patients with genetic disease of unknown causes can be rapidly diagnosed by bioinformatic analysis of disease-associated DNA sequences and phenotype. Efficient Diagnosis of Genetic Disease We know which genes are mutated in almost 3000 inherited human diseases and have good descriptions of how these mutations affect the human phenotype. Now, Zemojtel et al. have coupled this knowledge with rapid sequencing of these genes in a group of 40 patients with undiagnosed genetic diseases. Bioinformatic matching of the patients’ clinical characteristics and their disease gene sequences to databases of current genetic and phenotype knowledge enabled the authors to successfully diagnose almost 30% of the patients. The process required only about 2 hours of a geneticists’ time. Zemojtel et al. have made their tools available to the community, enabling a fast straightforward process by which clinicians and patients can easily identify the genetic basis of inherited disease in certain people. Less than half of patients with suspected genetic disease receive a molecular diagnosis. We have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical data into an effective diagnostic workflow. We used variants in the 2741 established Mendelian disease genes [the disease-associated genome (DAG)] to develop a targeted enrichment DAG panel (7.1 Mb), which achieves a coverage of 20-fold or better for 98% of bases. Furthermore, we established a computational method [Phenotypic Interpretation of eXomes (PhenIX)] that evaluated and ranked variants based on pathogenicity and semantic similarity of patients’ phenotype described by Human Phenotype Ontology (HPO) terms to those of 3991 Mendelian diseases. In computer simulations, ranking genes based on the variant score put the true gene in first place less than 5% of the time; PhenIX placed the correct gene in first place more than 86% of the time. In a retrospective test of PhenIX on 52 patients with previously identified mutations and known diagnoses, the correct gene achieved a mean rank of 2.1. In a prospective study on 40 individuals without a diagnosis, PhenIX analysis enabled a diagnosis in 11 cases (28%, at a mean rank of 2.4). Thus, the NGS of the DAG followed by phenotype-driven bioinformatic analysis allows quick and effective differential diagnostics in medical genetics.