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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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

Diego Jarquín1, José Crossa, Xavier Lacaze

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New methods improve wheat grain yield prediction by modeling complex genetic and environmental interactions. This approach enhances accuracy by accounting for how genes and environmental factors influence crop performance.

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Area of Science:

  • Agricultural Science
  • Genetics
  • Biometrics

Background:

  • Genetic by environmental interaction (G × E) significantly modulates crop traits.
  • High-dimensional genotypic and environmental data are increasingly available.
  • Explicitly modeling all G × E interactions is computationally infeasible.

Purpose of the Study:

  • To develop a method for modeling interactions between high-dimensional markers and environmental covariates (ECs).
  • To improve prediction accuracy of grain yield in wheat by incorporating G × E effects.

Main Methods:

  • Utilized covariance functions to model interactions between high-dimensional marker sets and ECs.
  • Employed a reaction norm model where genetic and environmental gradients are linear functions of markers and ECs.
  • Analyzed wheat grain yield data with 2,395 SNPs and 68 ECs across multiple years and locations.

Main Results:

  • Interaction terms explained a significant portion (16%) of within-environment yield variance.
  • Models incorporating interaction terms showed substantially higher prediction accuracy (17-34%) compared to main effects only.
  • The proposed method effectively handles high-dimensional genomic and environmental data.

Conclusions:

  • The developed method enhances prediction accuracy for grain yield by effectively modeling G × E interactions.
  • This approach is crucial for breeding programs aiming to develop crops adapted to specific environmental conditions.
  • Capitalizing on genomic and environmental data is key for future crop improvement.