Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study
Year: 2026
Type: article
Abstract: Background Affordable genomics assays have enabled new study designs such as genome-wide association studies (GWAS), advancing scientific understanding of disease etiology, prevention, and treatment. Objective A standardized process for linking study participants’ residential histories with publicly available geospatial environmental data across existing and future epidemiologic cohorts could enable cost-effective geographic information systems (GIS)-based exposome-wide association studies (GIS-ExWAS), similarly expanding scientific understanding of disease. Methods To explore this approach, we used open-source GIS software to link data from 7531 Personalized Environment and Genes Study (PEGS) participants on 11 common disease phenotypes with multiple types of GIS data from various sources to develop 88 GIS-based exposure indicators. We then estimated cross-sectional associations between these exposure indicators and the selected phenotypes using covariate-adjusted logistic regression, inverse probability of treatment weighted (IPTW), and the least absolute shrinkage and selection operator (LASSO) models. Results The IPTW and LASSO models identified GIS-phenotype associations complementary to those found in a prior ExWAS in questionnaire-based data from the PEGS cohort, including associations between air pollution and cardiovascular disease, as well as novel suggestive associations between several phenotypes and environmental factors not previously assessed by PEGS. Significance Our results suggest GIS linkages can enhance exposome assessment but also highlight key methodological complexities that must be overcome to develop standardized GIS-based ExWAS that can be broadly applied. Developing standardized open-source tools for GIS data linkage and exposure assessment could enhance public health practice, precision medicine, and scientific understanding of the exposome’s impacts on health. Impact GIS linkages can enhance exposome assessment and offer benefits for public health and clinical research. Adding GIS-based exposome assessments to survey-based data from cohort studies can help detect exposure-phenotype associations not detectable in survey data alone. Developing a standardized exposome assay based on GIS data would enable low-cost baseline geospatial assessments of environmental quality using only a residential address. Open-source GIS data linkage tools leveraging geospatially linked publicly available data would improve research, clinical practice, and public health.
Institution: National Institute of Environmental Health Sciences
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,490
OpenAlex ID: W7225752323