Optimizing genomic parental selection for categorical and continuous-categorical multi-trait mixtures
This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distri...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | México |
| Institución: | Centro Internacional de Mejoramiento de Maíz y Trigo |
| Repositorio: | Repositorio Institucional de Publicaciones Multimedia del CIMMYT |
| OAI Identifier: | oai:repository.cimmyt.org:10883/34681 |
| Acceso en línea: | https://hdl.handle.net/10883/34681 |
| Access Level: | acceso abierto |
| Palabra clave: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Bayesian Decision Theory Genomic Prediction Continuous Traits Categorical Traits Genomic Parental Selection Mixture Traits BAYESIAN THEORY GENOMICS BREEDING PROGRAMMES EXPERIMENTAL DATA MARKER-ASSISTED SELECTION Wheat |
| Sumario: | This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks. |
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