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

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.

Figure 1

High-throughput data analyses in exposome-related research. (a) The exposome is a unified, multimodal, temporally dependent, and comprehensive digital representation of external and internal environmental exposures linked to humans. Individual exposome indicators are depicted in purple. Individual genomes are depicted in green (and are static, in contrast with the exposome). (b) Data analytics of the exposome can be used to systematically discover relationships between mixtures of exposures, the genome, and (c) the traits and diseases that make up the human phenome. Phenotypes of the phenome are depicted in blue. In a and c, diet and gut flora are linked with genomic markers to type 2 diabetes (T2D) and blood pressure (BP). Analytic methods to discover exposures (EWAS), genotypes (e.g., GWAS), and phenotypes (e.g., PheWAS) are depicted in purple, green, and blue, respectively.

Figure 2

High-level schematic of ExO integration within a broader biological context (adapted from Reference 32). CTD, Comparative Toxicogenomics Database; ExO, exposure ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MeSH, Medical Subject Headings; OMIM, Online Mendelian Inheritance in Man.