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Prediction-based breeding benefits from diverse Genome-to-Phenome (G2P) models, especially when using ensembles. This approach enhances genetic gain by accounting for complex trait architecture and breeding context.

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Area of Science:

  • Genetics and Plant Breeding
  • Computational Biology
  • Agricultural Science

Background:

  • Whole Genome Prediction (WGP) methods are influenced by trait Genome-to-Phenome (G2P) dimensionality and breeding context.
  • The "No Free Lunch" theory suggests no single WGP method is universally optimal.
  • Understanding complex trait genetic architecture is crucial for advancing breeding methodologies and accelerating genetic gain.

Purpose of the Study:

  • To investigate the application of diverse G2P models within an ensemble framework for WGP.
  • To explore how Artificial Intelligence and Machine Learning (AI-ML) can enhance G2P model diversity for prediction-based breeding.
  • To demonstrate how hybrid Crop Growth Model-G2P (CGM-G2P) models can improve ensemble-based prediction and selection trajectories.

Main Methods:

  • Utilizing ensembles of diverse G2P models to capture trait genetic architecture.
  • Integrating AI-ML algorithms to introduce novel G2P model diversity.
  • Developing and applying hybrid CGM-G2P models that combine crop growth simulation with G2P predictions.
  • Designing multi-environment trials to expose trait by environment interactions.

Main Results:

  • Ensembles of diverse G2P models provide a framework to investigate trait genetic architecture for WGP.
  • AI-ML algorithms contribute valuable diversity to ensemble-based WGP.
  • Hybrid CGM-G2P models offer potential for enhanced ensemble-based prediction and understanding of yield performance.
  • Multi-environment trials can reveal influential CGM-G2P dimensions for selection.

Conclusions:

  • Ensemble-based prediction using diverse G2P models, including AI-ML and hybrid CGM-G2P approaches, offers new avenues for genetic gain in prediction-based breeding.
  • A deeper understanding of trait genetic architecture through these methods can accelerate crop improvement.
  • The maize TeoNAM experiment serves as a model for leveraging G2P ensembles to improve prediction-based breeding strategies.