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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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 Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

From genotype × environment interaction to gene × environment interaction.

Jose Crossa1

  • 1Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico, D.F., Mexico.

Current Genomics
|November 2, 2012
PubMed
Summary
This summary is machine-generated.

Plant breeders use statistical models to understand genotype × environment interactions for trait stability and performance prediction. Advances in genomic data and high-density markers improve prediction accuracy for complex traits.

Keywords:
Genomics-enable prediction and selection.Genotype × environment interaction (GE)Quantitative Trait Loci (QTL)environmental and genotypic covariablesgene × environment interactionmolecular markers (MM)

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Area of Science:

  • Plant genetics and breeding
  • Statistical modeling
  • Genomic prediction

Background:

  • Genotype × environment interaction (G×E) models are crucial for plant breeding to assess trait stability and predict genotype performance across diverse environments.
  • Quantitative Trait Loci (QTL) mapping has enabled the study of G×E interactions at specific chromosome regions, but is less effective for complex traits influenced by many genes.
  • High-density marker data now allows for genomic prediction of breeding values, surpassing traditional pedigree-based methods in precision.

Purpose of the Study:

  • To review statistical models for analyzing genotype × environment interaction (G×E), QTL × environment interaction (QTL×E), and marker effect × environment interaction.
  • To highlight the evolution of statistical approaches in plant breeding, from traditional G×E models to modern genomic-based methods.
  • To emphasize the challenges and necessity of exploring diverse statistical models for understanding complex genetic effects and their environmental interactions.

Main Methods:

  • Review of historical and contemporary statistical models for G×E interaction.
  • Discussion of QTL mapping and its application to QTL × environment interactions.
  • Exploration of genomic prediction models utilizing high-density marker data.
  • Analysis of marker effect × environment interactions.

Main Results:

  • Traditional G×E models aid in assessing trait stability and predicting genotype performance.
  • QTL × environment interaction studies reveal differential responses of chromosome regions across environments.
  • Genomic prediction with high-density markers enhances prediction accuracy for complex traits compared to pedigree information.
  • Genomic data facilitates the assessment of marker effects and their environmental covariability.

Conclusions:

  • Integrating diverse statistical models is essential for advancing the understanding of genetic effects and their interactions with the environment.
  • High-density genomic data offers powerful tools for precise genetic value prediction and dissecting complex trait architecture.
  • Continued statistical innovation is critical for addressing the complexities of genotype x environment interactions in plant breeding.