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A Bayesian optimization R package for multitrait parental selection
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DOI：10.1002/tpg2.20433.png)
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Abstract
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Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package-an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions-EvalMPS, FastMPS, and ApproxMPS-catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits. The Multitrait Parental Selection (MPS) R package aids plant and animal breeders in addressing multitrait parental selection in both phenotypic and genomic selection. MPS incorporates Bayesian optimization algorithms into genomic selection by employing various loss functions. The results generated by MPS can be utilized to identify the most promising parental candidates based on genetic gain and diversity. The MPS R package seamlessly integrates with the widely used Bayesian Generalized Linear Regression (BGLR) software. Selecting and mating parents in genomic selection are crucial in plant and animal breeding programs. During selection, researchers need to identify superior individuals to be parents considering multiple traits simultaneously, some of which act antagonistically; therefore, selection becomes complex. In this paper, we present an R package named MPS (Multitrait Parental Selection) to facilitate the selection process. MPS uses Bayesian optimization to identify superior individuals. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.
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