Thus far, most phenotype ontologies have focused on abnormal phenotypes, often refined with qualitative assertions about whether an abnormality involves higher or lower levels of some phenotypic quality (e.g. 'enlarged ventricle' or 'high blood pressure'). Using this concept, we successfully converted IMPC quantitative phenotypes (e.g. body length) to qualitative semantic phenotypes and revealed new disease genes.[60] An emergent ontology, the Ontology of Biological Attributes (OBA) aims to represent traits such as ‘ventricle size’ or ‘blood pressure’ that would be at the attribute level rather than the value level for continuous variable measures and would include “normal” phenotypes. We will work with stakeholders to document the quantitative and normal phenotypes that would support variant interpretation. We will develop templates for specifying traits (ventricle size, blood pressure) aligned with uPheno, to be used in recording quantitative traits. For example, these can be used to provide a standard representation of measurements recorded in Common Data Elements (CDEs) in research data warehouses, typically encoded using systems like PhenX (RTI LoS).
sagehrke
commented
2 years ago
Thus far, most phenotype ontologies have focused on abnormal phenotypes, often refined with qualitative assertions about whether an abnormality involves higher or lower levels of some phenotypic quality (e.g. 'enlarged ventricle' or 'high blood pressure'). Using this concept, we successfully converted IMPC quantitative phenotypes (e.g. body length) to qualitative semantic phenotypes and revealed new disease genes.[60] An emergent ontology, the Ontology of Biological Attributes (OBA) aims to represent traits such as ‘ventricle size’ or ‘blood pressure’ that would be at the attribute level rather than the value level for continuous variable measures and would include “normal” phenotypes. We will work with stakeholders to document the quantitative and normal phenotypes that would support variant interpretation. We will develop templates for specifying traits (ventricle size, blood pressure) aligned with uPheno, to be used in recording quantitative traits. For example, these can be used to provide a standard representation of measurements recorded in Common Data Elements (CDEs) in research data warehouses, typically encoded using systems like PhenX (RTI LoS).