title: Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era.
abstract: OBJECTIVE
  Mixtures, or combinations and interactions between multiple environmental exposures, are hypothesized to be causally linked with disease and health-related phenotypes. Established and emerging molecular measurement technologies to assay theexposome, the comprehensive battery of exposures encountered from birth to death, promise a new way of identifying mixtures in disease in the epidemiological setting. In this opinion, we describe the analytic complexity and challenges in identifying mixtures associated with phenotype and disease.
  
  
  RESULTS
  Existing and emerging machine-learning methods and data analytic approaches (e.g., "environment-wide association studies" [EWASs]), as well as large cohorts may enhance possibilities to identify mixtures of correlated exposures associated with phenotypes; however, the analytic complexity of identifying mixtures is immense.
  
  
  CONCLUSIONS
  If the exposome concept is realized, new analytical methods and large sample sizes will be required to ascertain how mixtures are associated with disease. The author recommends documenting prevalent correlated exposures and replicated main effects prior to identifying mixtures.
authors.affiliation: Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 USA.
authors.forename: Chirag J
authors.initials: CJ
authors.lastname: Patel
keywords: Combinations; Exposome; Machine learning; Mixtures