Linking Complex Disease and Exposure Data to Established Data Standards

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PROJECT SUMMARY/ABSTRACT The goals of this proposed study, “Linking Complex Disease and Exposure Data to Established Data Standards,” are to use the community-based data standard consensus measures for Phenotypes and eXposures (PhenX) to link existing studies and, through that linkage, to extend semantic standards to environmental contributors to complex diseases and conditions. Our approach will address a documented standards gap, perform a pilot analysis, increase the reusability of seminal studies, and serve as a model for future data-driven standards efforts. To accomplish this, we offer a unique cross-disciplinary team of standards developers and researchers. The proposed 3-year research plan will meet the evolving needs of the environmental and human health research communities and address the need for Big Data to Knowledge (BD2K) Community-Based Data and Metadata Standards. We propose extending the application of existing standards to identify multifaceted interactions between environmental exposures and complex diseases through the following specific aims. 1: Use PhenX measures to link variables and identify gaps within our collaborative analysis of the four seminal studies: the Gulf Resilience on Women's Health Study, Gulf Long-term Follow-up Study, Multi-Ethnic Study of Atherosclerosis, and Columbia Center for Children's Environmental Health cohort. We will (a) identify study variable linkages to the PhenX data standard, common variables among the four studies, and potential cross-cutting research questions and (b) perform a pilot cross-study analysis to demonstrate that data harmonization revealed new results. 2: Extend existing formal ontologies in exposures and complex diseases to further support variable linkage and data harmonization efforts and to begin modeling exposure-disease interactions. (a) Use identified linkages to evaluate and extend the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) to include concepts relevant to environmental contributors to complex disease, such as route of exposure and health outcomes. (b) Use the complex hierarchical structure provided by the ontology to facilitate further harmonization and evaluation of mappings to standard data elements. (c) Build and make available a community-based ontology of concepts relevant to environmental exposures and complex diseases. 3: Create Logical Observation Identifiers Names and Codes (LOINC) to simplify the electronic exchange of results. LOINC, a widely adopted standard in laboratory and clinical observation systems, will facilitate the integration of identified, linked data elements into existing clinical informatics pipelines. Through these aims, the proposed work has the potential to have a dramatic impact on the scientific inquiries by advancing tools for data integration in an area of current public health concern and active research: environmental contributors to complex disease occurrences and outcomes.