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A Bayesian optimization R package for multitrait parental selection.

Bartolo de J Villar-Hernández1,2, Susanne Dreisigacker1, Leo Crespo1

  • 1International Maize and Wheat Improvement Center (CIMMYT), Estado de México, México.

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The Multitrait Parental Selection (MPS) R package aids plant breeders in selecting parents for improved crop traits. This tool enhances genetic improvement and precision breeding, even with complex trait correlations and missing data.

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Area of Science:

  • Plant breeding
  • Genetics
  • Bioinformatics

Background:

  • Selecting parents is crucial for crop improvement, especially when considering multiple traits simultaneously.
  • Challenges arise from negatively correlated traits and missing data, complicating conventional selection methods.

Purpose of the Study:

  • To introduce the Multitrait Parental Selection (MPS) R package for efficient multitrait parental selection.
  • To provide a tool for genetic improvement, precision breeding, and conservation genetics.

Main Methods:

  • The MPS R package utilizes Bayesian optimization algorithms.
  • It incorporates three distinct loss functions: Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss.
  • The package offers three functions (EvalMPS, FastMPS, ApproxMPS) for various data availability scenarios.

Main Results:

  • The MPS R package effectively identifies parental candidates with desirable multiple traits.
  • Application examples demonstrate its efficacy in multitrait genomic selection.
  • The tool enables informed decision-making for breeders.

Conclusions:

  • The MPS R package is a valuable tool for multitrait genomic selection in plant breeding.
  • It facilitates achieving strong performance across multiple traits, enhancing crop economic value.