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Annu Rev Public Health. 2017 Mar 20;38:279-294. doi: 10.1146/annurev-publhealth-082516-012737. Epub 2016 Dec 23.

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health.

Author information

1
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115; email: chirag_patel@hms.harvard.edu.
2
National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709; email: balshaw@niehs.nih.gov.
3
Center for Systems Genomics, The Pennsylvania State University, College Station, Pennsylvania 16802.
4
Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
5
Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, North Carolina 27695.
6
Geisinger Health System, Danville, Pennsylvania 17821.
7
Renaissance Computing Institute, Chapel Hill, North Carolina 27517.
8
Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9011.
9
Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.
10
Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, Massachusetts 02114.

Abstract

The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

KEYWORDS:

bioinformatics; data standards; environment-wide association studies; exposures; genomics

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