Boosting genomic prediction transferability with sparse testing
Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluat...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/176261 |
| _version_ | 1855536752781950976 |
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| author | Montesinos-Lopez, Osval A. Crossa, Jose Vitale, Paolo Gerard, Guillermo S. Crespo Herrera, Leonardo A. Dreisigacker, Susanne Saint Pierre, Carolina Delgado-Enciso, Iván Montesinos-López, Abelardo Howard, Reka |
| author_browse | Crespo Herrera, Leonardo A. Crossa, Jose Delgado-Enciso, Iván Dreisigacker, Susanne Gerard, Guillermo S. Howard, Reka Montesinos-Lopez, Osval A. Montesinos-López, Abelardo Saint Pierre, Carolina Vitale, Paolo |
| author_facet | Montesinos-Lopez, Osval A. Crossa, Jose Vitale, Paolo Gerard, Guillermo S. Crespo Herrera, Leonardo A. Dreisigacker, Susanne Saint Pierre, Carolina Delgado-Enciso, Iván Montesinos-López, Abelardo Howard, Reka |
| author_sort | Montesinos-Lopez, Osval A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. |
| format | Journal Article |
| id | CGSpace176261 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1762612025-12-08T10:29:22Z Boosting genomic prediction transferability with sparse testing Montesinos-Lopez, Osval A. Crossa, Jose Vitale, Paolo Gerard, Guillermo S. Crespo Herrera, Leonardo A. Dreisigacker, Susanne Saint Pierre, Carolina Delgado-Enciso, Iván Montesinos-López, Abelardo Howard, Reka genomics forecasting testing marker-assisted selection environment Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. 2025 2025-08-29T16:46:10Z 2025-08-29T16:46:10Z Journal Article https://hdl.handle.net/10568/176261 en Open Access application/pdf MDPI Montesinos-López, O. A., Crossa, J., Vitale, P., Gerard, G., Crespo-Herrera, L., Dreisigacker, S., Pierre, C. S., Delgado-Enciso, I., Montesinos-López, A., & Howard, R. (2025). Boosting Genomic Prediction Transferability with Sparse Testing. Genes, 16(7), 827. https://doi.org/10.3390/genes16070827 |
| spellingShingle | genomics forecasting testing marker-assisted selection environment Montesinos-Lopez, Osval A. Crossa, Jose Vitale, Paolo Gerard, Guillermo S. Crespo Herrera, Leonardo A. Dreisigacker, Susanne Saint Pierre, Carolina Delgado-Enciso, Iván Montesinos-López, Abelardo Howard, Reka Boosting genomic prediction transferability with sparse testing |
| title | Boosting genomic prediction transferability with sparse testing |
| title_full | Boosting genomic prediction transferability with sparse testing |
| title_fullStr | Boosting genomic prediction transferability with sparse testing |
| title_full_unstemmed | Boosting genomic prediction transferability with sparse testing |
| title_short | Boosting genomic prediction transferability with sparse testing |
| title_sort | boosting genomic prediction transferability with sparse testing |
| topic | genomics forecasting testing marker-assisted selection environment |
| url | https://hdl.handle.net/10568/176261 |
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