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Accounting for Correlation Between Traits in Genomic Prediction.

Osval Antonio Montesinos-López1, Abelardo Montesinos-López2, Brandon A Mosqueda-Gonzalez3

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Summary

Genomic selection models benefit from multitrait analysis, which leverages correlations between traits to improve prediction accuracy. This review covers statistical and deep learning approaches, including R code for practical application in breeding.

Keywords:
Bayesian methodsDeep learning methodsGenomic selectionMultitraitPlant breeding

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Area of Science:

  • Genetics and Breeding
  • Statistical Genomics
  • Machine Learning in Biology

Background:

  • Genomic selection (GS) is crucial for breeding, but model performance varies according to the No Free Lunch Theorem.
  • Multitrait models enhance genomic prediction accuracy by utilizing correlations between phenotypic traits.

Purpose of the Study:

  • To review multitrait models for genome-enabled prediction.
  • To illustrate the application and benefits of these models with real-world examples.
  • To provide R code for user implementation in plant and animal breeding.

Main Methods:

  • Review of conventional statistical models (Bayesian Ridge regression, BLUP) for multitrait genomic prediction.
  • Implementation of a deep learning framework for multitrait prediction accommodating diverse outcome types.
  • Development of detailed R code examples for practical application.

Main Results:

  • Multitrait models, particularly deep learning frameworks, offer advantages in handling complex, mixed-outcome data.
  • Demonstrated increased prediction accuracy through the use of trait correlations in genomic prediction.
  • Provided accessible R code to facilitate the adoption of these advanced methods.

Conclusions:

  • Multitrait genomic prediction models, especially those leveraging deep learning, are powerful tools for enhancing breeding programs.
  • The availability of user-friendly software (R code) promotes wider adoption and application in plant and animal breeding.
  • Advanced modeling approaches are essential for maximizing the potential of genomic selection.