Evidence map›Paper›PMID 43009536›Full record

ArticleJournal of exposure science & environmental epidemiology2026

Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study.

Nathaniel MacNell, Jonathan V Lewis, Steve B Ampah, Farida S Akhtari, Kyle P Messier, John S House, Dillon Lloyd, Charles P Schmitt, David C Fargo, Lawrence S Kirschner and 5 more

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In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Nathaniel MacNellDLH, LLC, Bethesda, MD, USA.
Jonathan V LewisDLH, LLC, Bethesda, MD, USA.
Steve B AmpahDLH, LLC, Bethesda, MD, USA.
Farida S AkhtariBiostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
Kyle P MessierBiostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
John S HouseBiostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
Dillon LloydBiostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
Charles P SchmittOffice of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA.
David C FargoOffice of Environmental Science Cyberinfrastructure, National Institute of Environmental Health Science, Durham, NC, USA.
Lawrence S KirschnerClinical Research Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
Elizabeth H SchollSciome, LLC, Durham, NC, USA.
Rebecca C ElmoreSciome, LLC, Durham, NC, USA.
Deepak MavSciome, LLC, Durham, NC, USA.
Janet E HallClinical Research Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
Alison A Motsinger-ReifBiostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA. motsingerreifaa@nih.gov.ORCID http://orcid.org/0000-0003-1346-2493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAffordable genomics assays have enabled new study designs such as genome-wide association studies (GWAS), advancing scientific understanding of disease etiology, prevention, and treatment.

objectiveA 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.

methodsTo 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.

resultsThe 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.

Indexed as

Exposome dataExposome-wide association study (ExWAS)Geospatial dataPersonalized Environment and Genes Study (PEGS)

Identifiers

PMID43009536

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.