From a lay perspective, ontologies are essentially terminology lists in a hierarchical/tree structure, often with annotations and links out to other information. They are a way to express concepts in standardized terminology, which means they are a valuable mechanism to enable data that is stored in different systems to be linked up and cross-compared. The Orphanet Rare Disease Ontology (ORDO) and the Human Phenotype Ontology (HPO) are considered the most relevant ontologies to be used in the rare disease research field and, more specifically, in RD-Connect. ORDO is used for “naming” diseases e.g. “autosomal recessive limb girdle muscular dystrophy type 21”, while HPO is used for describing the clinical phenotype observed in a patient e.g. “muscle weakness”.
Watch RD-Connect scientific advisory board member, Peter Robinson, talk about ontologies:
Why are ontologies important?
It is increasingly recognized that advances in sequencing technology do not replace the need for detailed clinical analysis of patients with rare diseases. On the contrary, deep phenotyping is more important than ever in order to interpret whole exome and genome sequencing results. However, where clinical notes are on paper systems in hospitals, or where doctors enter free text in electronic systems, the power of computers cannot be leveraged to support analysis. Phenotype ontologies are an attempt to standardize the collection of phenotypic data in order to make it accessible to computer analysis. Phenotype definition is one of the most important and, at the same time, difficult activities in clinical practice. Many phenotypic findings are described in imprecise ways in medical publications. Accurate standardized descriptions of phenotypic features, clinical course, laboratory findings as well as molecular genetic findings are needed in order to enable the community to harness the already existing data as well as information yet to come. Phenotype ontologies provide an important mechanism for the standardization of signs, symptoms, classifications and complete clinical phenotypes. They are also very helpful resources for checking associations of symptoms and laboratory data in an interactive way. In addition, phenotype ontologies allow interoperability between registries and other resources, such as biobanks or repositories for –omics data. For undiagnosed cases, standardization of phenotypic classification of patients through phenotype ontologies is also a mechanism for enabling computerized “matches” between patients with similar phenotypes, which can assist with diagnosis.