Linking Place-Based Exposures and Genomics to understand Human Phenotypes
Rattachement africain : gb, us, nl. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Evidence that the environment has a greater impact on health than genomics alone is driving a need to understand exposure impacts and their interactions at scale. Geospatially tagged exposure data, such as for weather, climate and pollution, along with social factors associated with neighbourhoods such as distance to healthcare or community-wide socioeconomic status, can be used to build a picture of an individual's exposure conditions over time. Referred to as “place-based” exposures, these data, often collected by governmental agencies, provide opportunities to retrospectively explore environmental exposure impacts on human cohorts. This requires longitudinal location data from cohort participants (commonly residential address history) to build a clear picture of exposure histories and associations with phenotypes at the relevant latency. However, this raises concerns regarding confidentiality as a person's residential location is often not shareable as it is personal data and the linked, place-based data can potentially identify individuals within research datasets. This creates challenges in linking genomics and health data to relevant place-based exposure information. In addition, a lack of standards for representing exposures linked to cohort participants prevents cross-comparison and reproducible data analysis. This results in missed opportunities to understand exposure-genome impacts accurately and at scale. We call for the development of community-led standards to enable accurate exposure-genome-phenome analysis.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
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European Bioinformatics Institute pays non établi dans la noticeStructure de recherche
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University of Bristol Bristol Medical School pays non établi dans la noticeUniversité ou école supérieure
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Swansea University Population Data Science pays non établi dans la noticeUniversité ou école supérieure
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Health Data Research UK pays non établi dans la noticeOrganisation à but non lucratif
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London School of Hygiene & Tropical Medicine Environment & Health Modelling (EHM) Lab pays non établi dans la noticeUniversité ou école supérieure
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National Institute of Environmental Health Sciences Office of Data Science pays non établi dans la noticeStructure de recherche
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University of North Carolina at Chapel Hill pays non établi dans la noticeUniversité ou école supérieure
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Utrecht University Institute for Risk Assessment Sciences pays non établi dans la noticeUniversité ou école supérieure
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Manchester Academic Health Science Centre pays non établi dans la noticeÉtablissement de santé
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University of Manchester pays non établi dans la noticeUniversité ou école supérieure
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Manchester University NHS Foundation Trust Endocrinology and Metabolism Centre pays non établi dans la noticeÉtablissement de santé
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UK Biobank pays non établi dans la noticeOrganisation à but non lucratif
European Bioinformatics Institute, Bristol Medical School — University of Bristol et Population Data Science — Swansea University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.