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Area of Science:

  • Agricultural Science
  • Genetics
  • Plant Breeding

Background:

  • Genomic prediction models integrate genetic and environmental data for genotype selection.
  • Multi-environmental genomic prediction is crucial for adapting crops to varied conditions.
  • Application in apple breeding is limited by complex datasets and models.

Purpose of the Study:

  • To apply advanced statistical and deep learning models for multi-environmental genomic prediction in apples.
  • To evaluate the integration of genomic and enviromic data for predicting eleven apple traits.
  • To assess the impact of genotype-by-environment interaction and nonadditive effects.

Main Methods:

  • Utilized statistical models incorporating genotype-by-environment interaction (GxE).
  • Employed deep learning approaches for genomic prediction.
  • Integrated genomic (additive, nonadditive) and enviromic data.
  • Compared models against a benchmark (G-BLUP).

Main Results:

  • Statistical models with GxE improved predictive ability by up to 0.08 for nine traits.
  • Alternative kernels (Gaussian, Deep) effectively substituted G-BLUP.
  • Deep learning models achieved the highest predictive ability (up to 0.10 improvement) for three oligogenic traits.
  • Nonadditive and enviromic effects showed minimal impact compared to the benchmark.

Conclusions:

  • Statistical models effectively capture genotype-by-environment interactions in apples.
  • Deep learning models efficiently integrate diverse genomic and enviromic data.
  • This study promotes multi-environmental genomic prediction for climate-resilient apple cultivar selection.