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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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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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Background and Environment Affect Phenotype02:27

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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Related Experiment Video

Updated: Sep 9, 2025

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

Jingmao Li1, Yaqing Xu2, Shuangge Ma1

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, United States.

Bioinformatics (Oxford, England)
|August 29, 2025
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Summary

This study introduces a novel pathological imaging-assisted neural additive model (GE-IA-NAM) to enhance gene-environment interaction analysis in cancer research. The method integrates imaging data to improve accuracy and interpretability for complex genetic and environmental influences.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene-environment (G-E) interaction analysis is vital for understanding cancer etiology.
  • Traditional regression-based G-E methods may lack flexibility for complex patterns.
  • Deep learning models for G-E analysis can be limited by small sample sizes and high dimensionality.

Purpose of the Study:

  • To develop a novel method for gene-environment interaction analysis that incorporates pathological imaging data.
  • To improve the flexibility and interpretability of G-E interaction models.
  • To leverage pathological images to overcome information deficiencies in existing G-E models.

Main Methods:

  • Proposed the pathological imaging-assisted neural additive model (GE-IA-NAM).
  • Utilized a flexible and interpretable additive network architecture for individualized effects.
  • Implemented an assisted learning strategy integrating pathological image information via joint analysis.

Main Results:

  • The GE-IA-NAM demonstrated competitive performance in simulations.
  • Analysis of lung and skin cancer datasets from The Cancer Genome Atlas confirmed the method's efficacy.
  • The model effectively accounts for individualized genetic, environmental, and interaction effects.

Conclusions:

  • Pathological imaging can significantly enhance gene-environment interaction analysis in cancer research.
  • The proposed GE-IA-NAM offers a flexible and interpretable approach to model complex G-E interactions.
  • This method holds promise for improving our understanding of cancer development and progression.