Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

Abstract

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Keywords: Exposome-Wide Association Study (ExWAS); data science; epidemiology; exposome; false discovery rate; phenotype.

Figure 1.

Key considerations for Exposome-Wide Association Studies.

Figure 2.

A Manhattan plot illustrating the findings of an ExWAS for type 2 diabetes. The X-axis represents the exposures, while the Y-axis shows the corresponding probability values. Each point in the plot signifies the association test for a single exposure. The red horizontal line indicates the threshold for statistical significance. Reproduced from Patel et al., used under Creative Commons Attribution License.

Figure 3.

A correlation globe showing the associations among chemical biomarkers for females, males, and couples. The right half of the globe represents female biomarkers, while the left half represents male biomarkers. Only correlations greater than 0.25 or smaller than −0.25 are displayed as connections. A red line signifies a positive correlation, whereas a dark green line represents a negative correlation. Both color intensity and line width correspond to the magnitude of the correlation. Reproduced from Chung et al., used under CC-BY-NC-ND 4.0 license.