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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Gene-environment interactions within a precision environmental health framework.

Alison A Motsinger-Reif1, David M Reif2, Farida S Akhtari1

  • 1Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.

Cell Genomics
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

Gene-environment interactions (GEIs) are crucial for understanding disease development. Integrating exposome data into precision environmental health (PEH) can improve disease prediction and prevention strategies.

Keywords:
environmental exposuresenvironmental justiceepidemiologyepigeneticsgene-environment interactionsgeneticsprecision environmental health

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Area of Science:

  • Genetics
  • Environmental Health
  • Epidemiology

Background:

  • Understanding gene-environment interactions (GEIs) is vital for elucidating disease etiology.
  • GEIs play a significant role across human development stages.
  • Challenges exist in measuring environmental factors accurately.

Purpose of the Study:

  • To review the current state of GEI research.
  • To discuss the evolution of GEI study designs.
  • To highlight the translation of GEI findings into precision environmental health (PEH).

Main Methods:

  • Review of candidate gene-environment studies and genome-wide interaction studies (GWISs).
  • Exploration of multi-omics approaches in mediating GEI effects.
  • Discussion of advancements in GEI analysis methods and large-scale datasets.

Main Results:

  • GEI analysis offers advantages in understanding complex disease mechanisms.
  • Multi-omics data can reveal mediating effects in GEIs.
  • Large-scale datasets are essential for robust GEI findings.

Conclusions:

  • GEIs are significant for disease prediction and prevention.
  • Translating GEI findings into PEH applications is crucial for healthcare and public health.
  • Integrating the exposome into PEH omics studies is recommended.