Gene-environment interactions within a precision environmental health framework

Abstract

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Keywords: environmental exposures; environmental justice; epidemiology; epigenetics; gene-environment interactions; genetics; precision environmental health.

Graphical abstract

Figure 1

PEH PEH uses available biological and exposure data to assess individual risk and tailor interventions. These data consist of omic data (blue box); data on internal exposures from diet, lifestyle factors, stress, and pharmacological agents; as well as data on prototypical external exposures from home, air, water, and social determinates of health (yellow boxes). Rigorous statistical machine-learning methods are used to winnow down the features that contribute to explaining disease likelihood (green boxes). This in turn enables differential treatment based on individual omic and exposure differences.

Figure 2

Gene-environment contributions to trait variance Given a genetic locus of interest (x axis) and an exposure of interest (exposed = blue), the ways in which they can be related to a trait of interest (y axis) are illustrated. (A) Neither the genetic region of interest nor the exposure explain variance in the trait of interest. (B) The genotype contributes to trait variance while the exposure does not. (C) The exposure contributes to trait variance while the genotype does not. (D) In the absence of exposure information, the genotype does not contribute to trait variance, but there is a gene-by-exposure interaction. (E) Both the genotype and exposure contribute to trait variance as well as their interaction. (F) Both the genotype and exposure contribute to trait variance but without a gene-environment interaction.