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MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning.

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Summary

A new R package, MTMEGPS, enhances genomic and phenomic selection using deep learning. It offers a user-friendly workflow for predicting complex traits across multiple traits and environments, improving breeding program efficiency.

Keywords:
deep learninggenomic selectionhyperparameters optimizationmolecular markersmulti-traits and multi-environmentsspectral information

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Area of Science:

  • Plant breeding and genetics
  • Computational biology
  • Machine learning applications in agriculture

Background:

  • Genomic and phenomic selection (GPS) are crucial for data-driven prediction of complex traits in modern breeding.
  • Deep learning (DL) offers enhanced predictive ability by modeling nonlinear patterns often missed by traditional methods.
  • Limited adoption of DL in breeding stems from computational costs and lack of accessible tools for non-programmers.

Purpose of the Study:

  • Introduce MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package designed for streamlined genomic and phenomic selection.
  • Provide an end-to-end workflow for Uni- and Multi-Trait (UT/MT) and Uni- and Multi-Environment (UE/ME) prediction using DL.
  • Facilitate DL adoption in breeding programs by offering accessible tools for users without extensive programming experience.

Main Methods:

  • Developed MTMEGPS, an R package encompassing data preparation, hyperparameter optimization, model training, and DL-based evaluation.
  • Applied MTMEGPS to Maize (genomic) and Eucalyptus (NIR phenomic) datasets, plus an independent multi-environment dataset.
  • Evaluated package performance against benchmark models across various UT/MT and UE/ME scenarios.

Main Results:

  • MTMEGPS demonstrated superior predictive ability compared to benchmark models in most scenarios, especially UT for internal datasets and MT for the independent multi-environment dataset.
  • Mean Squared Error (MSE) values were comparable across models and remained within a moderate range.
  • The package proved efficient for both genomic and phenomic selection, even with moderate prediction errors.

Conclusions:

  • MTMEGPS offers a practical and efficient solution for advanced genomic and phenomic selection using deep learning.
  • The R package democratizes the use of DL in breeding, supporting complex trait prediction across diverse conditions.
  • MTMEGPS enhances breeding program capabilities by providing accessible, data-driven predictive tools.