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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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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.
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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Sep 11, 2025

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable

Tingxi Yu1,2, Hao Zhang1,2, Shoukun Chen1,2

  • 1State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, International Maize and Wheat Improvement Center (CIMMYT)-China Office, No. 12 Zhongguancun South Street, Haidian District, Beijing 100081, China.

Briefings in Bioinformatics
|August 15, 2025
PubMed
Summary

We developed EXGEP, an explainable machine learning framework, to predict crop grain yield by analyzing genotype-by-environment interactions. EXGEP significantly improves prediction accuracy and provides insights into genetic and environmental factors influencing crop traits.

Keywords:
explainable artificial intelligencegenotype-by-environment interactionsmachine learningmaizephenotypic prediction

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Area of Science:

  • Agricultural Science
  • Genetics
  • Machine Learning

Background:

  • Phenotypic variation in crops arises from genotype, environment, and their interactions.
  • Quantifying genetic and environmental contributions is crucial for developing adaptable crop varieties.
  • Accurate prediction of complex traits across diverse environments is a key challenge in crop breeding.

Purpose of the Study:

  • To develop and evaluate an explainable machine learning framework (EXGEP) for predicting crop grain yield.
  • To assess the performance of EXGEP compared to traditional models.
  • To enhance understanding of genotype-by-environment interactions through explainable AI.

Main Methods:

  • Developed an explainable machine learning framework named explainable genotype-by-environment interactions prediction (EXGEP).
  • Applied EXGEP to a large dataset of 70,693 phenotypic records for grain yield across 3,793 hybrids, including genotype and environmental data.
  • Utilized SHapley Additive exPlanations (SHAP) values for model interpretability.

Main Results:

  • EXGEP outperformed the Bayesian ridge regression model in yield prediction by 17.37%-42.35% across different data combinations.
  • EXGEP successfully identified key genetic and environmental features and their interactions driving yield predictions.
  • The framework demonstrated superior prediction accuracy and enhanced explainability.

Conclusions:

  • EXGEP offers a powerful and interpretable approach for predicting complex crop traits in varied environments.
  • The framework provides valuable insights into genotype-by-environment interactions, aiding crop breeding strategies.
  • EXGEP represents a significant advancement in applying machine learning for agricultural applications.