Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

Autor:	Chung MK; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China., House JS; Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA., Akhtari FS; Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA., Makris KC; Cyprus International Institute for Environmental and Public Health, School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus., Langston MA; Department of Electrical Engineering and Computer Science, University of TN, Knoxville, TN, USA., Islam KT; Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern CA, Los Angeles, CA, USA., Holmes P; Department of Physics, Villanova University, Villanova, Philadelphia, USA., Chadeau-Hyam M; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., Smirnov AI; Department of Chemistry, NC State University, Raleigh, NC, USA., Du X; Department of Bioinformatics and Genomics, College of Computing and Informatics, University of NC at Charlotte, Charlotte, NC, USA., Thessen AE; Department of Biomedical Informatics, University of CO Anschutz Medical Campus, Aurora, CO, USA., Cui Y; Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA., Zhang K; Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of NY, Rensselaer, NY, USA., Manrai AK; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Motsinger-Reif A; Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA., Patel CJ; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	Exposome [Exposome] 2024 Jan 17; Vol. 4 (1), pp. osae001. Date of Electronic Publication: 2024 Jan 17 (Print Publication: 2024).
DOI:	10.1093/exposome/osae001
Abstrakt:	This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health. Competing Interests: The authors declare that they have no conflicts of interest. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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