Session 4 Chair: Helen Pan
Extension and Application of Exposure Ontologies in Linking Complex Disease and Exposure Data to Established Data Standards.
Data standards are essential to enable broad data sharing and reuse. The use of data and metadata standards help making data more Findable, Accessible, Interoperable and Reusable (FAIR). In the “Linking Complex Disease and Exposure Data to Established Data Standards” study, we use a community-based data standard to link existing studies and to assess environmental contributions to complex diseases and conditions. We have identified linkages between existing studies: the Gulf Resilience on Women’s Health (GROWH) Study, the Multi-Ethnic Study of Atherosclerosis (MESA) main and ancillary studies, and the National Health and Nutrition Examination Survey (NHANES) study. We used the consensus measures for Phenotypes and Exposures (PhenX) as the community-based standards to link these studies. We are evaluating relevant ontologies such as the Children's Health Exposure Analysis Resource (CHEAR) Ontology and Environmental conditions, treatments and exposures ontology (ECTO) based on these study concepts. Use of Standards and Ontology helps identify some indirect and complex linkage not apparent during direct one-to-one mapping. We will use the hierarchical structure provided by the ontology to facilitate data integration. In turn, we will also extend and build a community-based ontology of concepts relevant to environmental exposures and complex diseases.
Today, predictive toxicology relies heavily on a diverse array of datatypes spanning in vitro, laboratory animal toxicology, epidemiology, and ecology. Integration of these data requires formal mechanisms for describing the data and the biological objects and processes that the data represent. Ontologies provide a formal mechanism for describing biology and biological measurement and have been used successfully for this purpose when defining disease in the biomedical field. Ongoing efforts to organize toxicological data via formal ontologies are also showing promise, particularly in the field of nanotechnology (http://enanomapper.net/wp/2-ontology-development). However, individual, disconnected ontology development efforts create silos and prevent the integration of data and information across the field of toxicology that is needed to support predictive toxicology. Efforts such as the OBO Foundry (http://www.obofoundry.org/) and the Monarch initiative (https://monarchinitiative.org/) have shown the value of having a coordinated ontology development effort across a biological domain. In addition, the information assembled regarding the causes and mechanisms of disease for the biomedical community can be readily used in understanding the mechanisms underlying toxicology. This session will explore existing ontology development and application of environmental exposures and health effects and drug targets.