Enhancing wheat genomic prediction by a hybrid kernel approach
This study integrates genomic and pedigree data by leveraging advanced modeling techniques, aiming to enhance the predictive performance of genomic selection models by capturing complex genetic relationships through the interaction of both matrices and exploring the utility of non-linear methods, su...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/177386 |
| _version_ | 1855513982755930112 |
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| author | Cuevas, Jaime Crossa, Jose Montesinos-Lopez, Abelardo Martini, Johannes W.R. Gerard, Guillermo Sebastián Ortegón, Jaime Dreisigacker, Susanne Velu, Govindan Perez-Rodriguez, Paulino Saint Pierre, Carolina Crespo-Herrera, Leonardo Abdiel Montesinos-Lopez, Osval A. Vitale, Paolo |
| author_browse | Crespo-Herrera, Leonardo Abdiel Crossa, Jose Cuevas, Jaime Dreisigacker, Susanne Gerard, Guillermo Sebastián Martini, Johannes W.R. Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval A. Ortegón, Jaime Perez-Rodriguez, Paulino Saint Pierre, Carolina Velu, Govindan Vitale, Paolo |
| author_facet | Cuevas, Jaime Crossa, Jose Montesinos-Lopez, Abelardo Martini, Johannes W.R. Gerard, Guillermo Sebastián Ortegón, Jaime Dreisigacker, Susanne Velu, Govindan Perez-Rodriguez, Paulino Saint Pierre, Carolina Crespo-Herrera, Leonardo Abdiel Montesinos-Lopez, Osval A. Vitale, Paolo |
| author_sort | Cuevas, Jaime |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study integrates genomic and pedigree data by leveraging advanced modeling techniques, aiming to enhance the predictive performance of genomic selection models by capturing complex genetic relationships through the interaction of both matrices and exploring the utility of non-linear methods, such as kernel matrices. Our goal was to improve genomic prediction accuracy by combining the pedigree-based or genetic similarity matrix ( A ) with the genomic similarity matrix ( G ). Using various wheat datasets, we performed five single-environment models and five multi-environment models that incorporated genotype-by-environment (G x E) interactions. The proposed models S5 and M5 significantly enhanced prediction accuracy by incorporating two novel symmetric kernels, C and P , derived from the interaction of genomic and pedigree matrices. These hybrid kernels captured additional, independent genetic variation not explained by conventional matrices. The proposed prediction model outperformed the standard conventional models in most single-environment and multi-environment models. The genomic models with non-linear kernels were better predictors than the linear prediction models. |
| format | Journal Article |
| id | CGSpace177386 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1773862025-10-29T02:00:42Z Enhancing wheat genomic prediction by a hybrid kernel approach Cuevas, Jaime Crossa, Jose Montesinos-Lopez, Abelardo Martini, Johannes W.R. Gerard, Guillermo Sebastián Ortegón, Jaime Dreisigacker, Susanne Velu, Govindan Perez-Rodriguez, Paulino Saint Pierre, Carolina Crespo-Herrera, Leonardo Abdiel Montesinos-Lopez, Osval A. Vitale, Paolo genomics pedigrees environment genotype environment interaction This study integrates genomic and pedigree data by leveraging advanced modeling techniques, aiming to enhance the predictive performance of genomic selection models by capturing complex genetic relationships through the interaction of both matrices and exploring the utility of non-linear methods, such as kernel matrices. Our goal was to improve genomic prediction accuracy by combining the pedigree-based or genetic similarity matrix ( A ) with the genomic similarity matrix ( G ). Using various wheat datasets, we performed five single-environment models and five multi-environment models that incorporated genotype-by-environment (G x E) interactions. The proposed models S5 and M5 significantly enhanced prediction accuracy by incorporating two novel symmetric kernels, C and P , derived from the interaction of genomic and pedigree matrices. These hybrid kernels captured additional, independent genetic variation not explained by conventional matrices. The proposed prediction model outperformed the standard conventional models in most single-environment and multi-environment models. The genomic models with non-linear kernels were better predictors than the linear prediction models. 2025-08-01 2025-10-28T18:47:17Z 2025-10-28T18:47:17Z Journal Article https://hdl.handle.net/10568/177386 en Open Access application/pdf Frontiers Media Cuevas, J., Crossa, J., Montesinos-López, A., Martini, J. W. R., Gerard, G. S., Ortegón, J., Dreisigacker, S., Govindan, V., Pérez-Rodríguez, P., Saint Pierre, C., Herrera, L. A. C., Montesinos-López, O. A., & Vitale, P. (2025). Enhancing wheat genomic prediction by a hybrid kernel approach. Frontiers in Plant Science, 16, 1605202. https://doi.org/10.3389/fpls.2025.1605202 |
| spellingShingle | genomics pedigrees environment genotype environment interaction Cuevas, Jaime Crossa, Jose Montesinos-Lopez, Abelardo Martini, Johannes W.R. Gerard, Guillermo Sebastián Ortegón, Jaime Dreisigacker, Susanne Velu, Govindan Perez-Rodriguez, Paulino Saint Pierre, Carolina Crespo-Herrera, Leonardo Abdiel Montesinos-Lopez, Osval A. Vitale, Paolo Enhancing wheat genomic prediction by a hybrid kernel approach |
| title | Enhancing wheat genomic prediction by a hybrid kernel approach |
| title_full | Enhancing wheat genomic prediction by a hybrid kernel approach |
| title_fullStr | Enhancing wheat genomic prediction by a hybrid kernel approach |
| title_full_unstemmed | Enhancing wheat genomic prediction by a hybrid kernel approach |
| title_short | Enhancing wheat genomic prediction by a hybrid kernel approach |
| title_sort | enhancing wheat genomic prediction by a hybrid kernel approach |
| topic | genomics pedigrees environment genotype environment interaction |
| url | https://hdl.handle.net/10568/177386 |
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