Research Topic

Statistical Challenges and Advances in Exposome Research

About this Research Topic

The concept of the exposome in molecular epidemiology refers to the full set of external and internal exposures affecting individuals from conception onwards and having health consequences later in life. As such, the exposome complements the genome in the determination of health-related phenotypes and more generally in the characterization and understanding of the biological response to environmental stresses and in their contribution to the individual risk of chronic conditions.

While the exposome cannot directly be measured, high-throughput technologies such as metabolomics, transcriptomics and epigenomics have enabled the collection of a very large pool of molecular parameters providing ways into the characterization of the internal exposome. The development of cost-efficient personal sensors offers promising perspectives for the gathering of data at the individual level about contaminants and lifestyle factors such as diet and exercise providing tools to measure external exposome. The methods to integrate internal and external exposome data in order to fully unlock their potential is challenging due to the high dimensionality of the data, their intricate and unknown dependency structure and their potential underlying hierarchy.

This article collection on “Statistical challenges in Exposome research” will address the issues related to the analysis of exposomic data.
Topics of interests include:
• Methods to integrate exposomic data from the supervised learning perspective
• Methods to integrate multiple OMICs profiles
• Methods to explore population distribution of exposome
• Models handling multivariate predictors and responses
• Exploration of differential associations
• Causal inference and mediation models

For this Research Topic we invite high-quality papers with one of the following potentials:
• To advance current best statistical practice for the analysis of exposome data
• To propose and/or validate novel statistical approaches for exposome research: models and/or analysis tools
• To illustrate application of pertinent statistical methods to meaningful problems in exposome research
• To review the state-of-the-art in the field
• To introduce novel statistical approaches to explore the exposome
• Illustration of successful implementation of statistical methods to complex exposome data, including case studies


Keywords: OMICs profiling, OMICs integration, Mechanisms, Mixture models, Causal modelling, Big Data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The concept of the exposome in molecular epidemiology refers to the full set of external and internal exposures affecting individuals from conception onwards and having health consequences later in life. As such, the exposome complements the genome in the determination of health-related phenotypes and more generally in the characterization and understanding of the biological response to environmental stresses and in their contribution to the individual risk of chronic conditions.

While the exposome cannot directly be measured, high-throughput technologies such as metabolomics, transcriptomics and epigenomics have enabled the collection of a very large pool of molecular parameters providing ways into the characterization of the internal exposome. The development of cost-efficient personal sensors offers promising perspectives for the gathering of data at the individual level about contaminants and lifestyle factors such as diet and exercise providing tools to measure external exposome. The methods to integrate internal and external exposome data in order to fully unlock their potential is challenging due to the high dimensionality of the data, their intricate and unknown dependency structure and their potential underlying hierarchy.

This article collection on “Statistical challenges in Exposome research” will address the issues related to the analysis of exposomic data.
Topics of interests include:
• Methods to integrate exposomic data from the supervised learning perspective
• Methods to integrate multiple OMICs profiles
• Methods to explore population distribution of exposome
• Models handling multivariate predictors and responses
• Exploration of differential associations
• Causal inference and mediation models

For this Research Topic we invite high-quality papers with one of the following potentials:
• To advance current best statistical practice for the analysis of exposome data
• To propose and/or validate novel statistical approaches for exposome research: models and/or analysis tools
• To illustrate application of pertinent statistical methods to meaningful problems in exposome research
• To review the state-of-the-art in the field
• To introduce novel statistical approaches to explore the exposome
• Illustration of successful implementation of statistical methods to complex exposome data, including case studies


Keywords: OMICs profiling, OMICs integration, Mechanisms, Mixture models, Causal modelling, Big Data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

27 March 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

27 March 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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