Improving wheat grain yield genomic prediction accuracy using historical data
Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2025
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| Acceso en línea: | https://hdl.handle.net/10568/179135 |
| _version_ | 1855536461262094336 |
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| author | Vitale, Paolo Montesinos-Lopez, Osval Antonio Gerard, Guillermo Sebastián Velu, Govindan Tarekegn, Zerihun Tadesse Montesinos-Lopez, Abelardo Dreisigacker, Susanne Pacheco Gil, Rosa Angela Toledo, Fernando Henrique Saint Pierre, Carolina Pérez-Rodríguez, Paulino Gardner, Keith Crespo Herrera, Leonardo Abdiel Crossa, Jose |
| author_browse | Crespo Herrera, Leonardo Abdiel Crossa, Jose Dreisigacker, Susanne Gardner, Keith Gerard, Guillermo Sebastián Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval Antonio Pacheco Gil, Rosa Angela Pérez-Rodríguez, Paulino Saint Pierre, Carolina Tarekegn, Zerihun Tadesse Toledo, Fernando Henrique Velu, Govindan Vitale, Paolo |
| author_facet | Vitale, Paolo Montesinos-Lopez, Osval Antonio Gerard, Guillermo Sebastián Velu, Govindan Tarekegn, Zerihun Tadesse Montesinos-Lopez, Abelardo Dreisigacker, Susanne Pacheco Gil, Rosa Angela Toledo, Fernando Henrique Saint Pierre, Carolina Pérez-Rodríguez, Paulino Gardner, Keith Crespo Herrera, Leonardo Abdiel Crossa, Jose |
| author_sort | Vitale, Paolo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Similar trends were observed in the intermediate drought selection environment (beds with 2 irrigations), with prediction accuracy rising from 0.12 (1 year) to 0.21 (4 years) but minimal improvement beyond that. Conversely, some selection environments, such as flat 5 irrigations (flat optimal environment), did not significantly increase, with the prediction accuracy fluctuating around 0.09-0.14 regardless of the number of training years used. Additionally, average genetic diversity within the training population and the validation population influenced prediction accuracy. Indeed, a negative correlation between prediction accuracy and the genetic distance was observed. This highlights the need to balance genetic diversity to enhance the predictive power of genomic selection models. These findings exhibit the benefits of using an extended historical dataset while considering genetic diversity to maximize prediction accuracy in genomic selection strategies for wheat breeding, ultimately supporting the development of high-yielding varieties. |
| format | Journal Article |
| id | CGSpace179135 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1791352025-12-22T02:14:29Z Improving wheat grain yield genomic prediction accuracy using historical data Vitale, Paolo Montesinos-Lopez, Osval Antonio Gerard, Guillermo Sebastián Velu, Govindan Tarekegn, Zerihun Tadesse Montesinos-Lopez, Abelardo Dreisigacker, Susanne Pacheco Gil, Rosa Angela Toledo, Fernando Henrique Saint Pierre, Carolina Pérez-Rodríguez, Paulino Gardner, Keith Crespo Herrera, Leonardo Abdiel Crossa, Jose genomics forecasting plant breeding wheat data grain yields Genomic selection is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy for grain yield across various selection environments using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of grain yield data from 6 selection environments were analyzed, with the populations of 5 years (2018-2023) as the validation population and earlier years (back to 2013-2014) as the training population. Generally, we observed that as the number of training years increased, the prediction accuracy tended to improve or stabilize. For instance, in the late heat stress selection environment (beds late heat stress), prediction accuracy increased from 0.11 (1 training year) to 0.23 (5 years), stabilizing at 0.26. Similar trends were observed in the intermediate drought selection environment (beds with 2 irrigations), with prediction accuracy rising from 0.12 (1 year) to 0.21 (4 years) but minimal improvement beyond that. Conversely, some selection environments, such as flat 5 irrigations (flat optimal environment), did not significantly increase, with the prediction accuracy fluctuating around 0.09-0.14 regardless of the number of training years used. Additionally, average genetic diversity within the training population and the validation population influenced prediction accuracy. Indeed, a negative correlation between prediction accuracy and the genetic distance was observed. This highlights the need to balance genetic diversity to enhance the predictive power of genomic selection models. These findings exhibit the benefits of using an extended historical dataset while considering genetic diversity to maximize prediction accuracy in genomic selection strategies for wheat breeding, ultimately supporting the development of high-yielding varieties. 2025-04 2025-12-21T20:49:46Z 2025-12-21T20:49:46Z Journal Article https://hdl.handle.net/10568/179135 en Open Access application/pdf Oxford University Press Vitale, P., Montesinos-López, O., Gerard, G., Velu, G., Tarekegn, Z., Montesinos-López, A., Dreisigacker, S., Pacheco, A., Toledo, F., Pierre, C. S., Pérez-Rodríguez, P., Gardner, K., Crespo-Herrera, L., & Crossa, J. (2025). Improving wheat grain yield genomic prediction accuracy using historical data. G3 Genes Genomes Genetics, 15(4), jkaf038. https://doi.org/10.1093/g3journal/jkaf038 |
| spellingShingle | genomics forecasting plant breeding wheat data grain yields Vitale, Paolo Montesinos-Lopez, Osval Antonio Gerard, Guillermo Sebastián Velu, Govindan Tarekegn, Zerihun Tadesse Montesinos-Lopez, Abelardo Dreisigacker, Susanne Pacheco Gil, Rosa Angela Toledo, Fernando Henrique Saint Pierre, Carolina Pérez-Rodríguez, Paulino Gardner, Keith Crespo Herrera, Leonardo Abdiel Crossa, Jose Improving wheat grain yield genomic prediction accuracy using historical data |
| title | Improving wheat grain yield genomic prediction accuracy using historical data |
| title_full | Improving wheat grain yield genomic prediction accuracy using historical data |
| title_fullStr | Improving wheat grain yield genomic prediction accuracy using historical data |
| title_full_unstemmed | Improving wheat grain yield genomic prediction accuracy using historical data |
| title_short | Improving wheat grain yield genomic prediction accuracy using historical data |
| title_sort | improving wheat grain yield genomic prediction accuracy using historical data |
| topic | genomics forecasting plant breeding wheat data grain yields |
| url | https://hdl.handle.net/10568/179135 |
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