A Bayesian optimization R package for multitrait parental selection
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, selec...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/162514 |
| _version_ | 1855522068301348864 |
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| author | Villar-Hernandez, Bartolo de J. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Perez-Rodriguez, Paulino Perez-Elizalde, Sergio Toledo, Fernando H. Crossa, José |
| author_browse | Crespo-Herrera, Leonardo A. Crossa, José Dreisigacker, Susanne Perez-Elizalde, Sergio Perez-Rodriguez, Paulino Toledo, Fernando H. Villar-Hernandez, Bartolo de J. |
| author_facet | Villar-Hernandez, Bartolo de J. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Perez-Rodriguez, Paulino Perez-Elizalde, Sergio Toledo, Fernando H. Crossa, José |
| author_sort | Villar-Hernandez, Bartolo de J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace162514 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1625142025-12-08T10:11:39Z A Bayesian optimization R package for multitrait parental selection Villar-Hernandez, Bartolo de J. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Perez-Rodriguez, Paulino Perez-Elizalde, Sergio Toledo, Fernando H. Crossa, José bayesian theory marker-assisted selection breeding programmes databases 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. 2024-06 2024-11-21T14:18:21Z 2024-11-21T14:18:21Z Journal Article https://hdl.handle.net/10568/162514 en Open Access application/pdf Wiley Villar-Hernández, B. J., Dreisigacker, S., Crespo Herrera, L. A., Pérez‐Rodríguez, P., Pérez‐Elizalde, S., Toledo, F., & Crossa, J. (2024). A Bayesian optimization R package for multitrait parental selection. The Plant Genome, e20433. https://doi.org/10.1002/tpg2.20433 |
| spellingShingle | bayesian theory marker-assisted selection breeding programmes databases Villar-Hernandez, Bartolo de J. Dreisigacker, Susanne Crespo-Herrera, Leonardo A. Perez-Rodriguez, Paulino Perez-Elizalde, Sergio Toledo, Fernando H. Crossa, José A Bayesian optimization R package for multitrait parental selection |
| title | A Bayesian optimization R package for multitrait parental selection |
| title_full | A Bayesian optimization R package for multitrait parental selection |
| title_fullStr | A Bayesian optimization R package for multitrait parental selection |
| title_full_unstemmed | A Bayesian optimization R package for multitrait parental selection |
| title_short | A Bayesian optimization R package for multitrait parental selection |
| title_sort | bayesian optimization r package for multitrait parental selection |
| topic | bayesian theory marker-assisted selection breeding programmes databases |
| url | https://hdl.handle.net/10568/162514 |
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