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Hum Genomics. 2018 Jan 26;12(1):4. doi: 10.1186/s40246-018-0134-x.

Beyond genomics: understanding exposotypes through metabolomics.

Author information

1
Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.
2
Collaboration for Research Integrity and Transparency (CRIT), Yale Law School, New Haven, CT, USA.
3
Center for Outcomes Research and Evaluation (CORE), Yale-New Haven Health System, New Haven, CT, USA.
4
Department of Surgery, Section of Surgical Oncology, Yale University School of Medicine, New Haven, CT, USA.
5
Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA.
6
Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA.
7
Department of Health Research and Policy, Stanford University, Stanford, CA, USA.
8
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
9
Department of Statistics, Stanford University, Stanford, CA, USA.
10
Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA.
11
Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA. caroline.johnson@yale.edu.
12
Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA. caroline.johnson@yale.edu.

Abstract

BACKGROUND:

Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

MAIN TEXT:

Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.

CONCLUSIONS:

Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

KEYWORDS:

Chemometrics; Exposome; Exposotype; Genetic epidemiology; Genomics; Metabolomics

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