A marker weighting approach for enhancing within-family accuracy in genomic prediction
Genomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain r...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Oxford University Press
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/137924 |
| _version_ | 1855533844683292672 |
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| author | Montesinos-Lopez, Osval A. Crespo Herrera, Leonardo A. Xavier, Alencar Gowda, Manje Beyene, Yoseph Saint Pierre, Carolina Rosa-Santamaria, Roberto de la Salinas-Ruiz, Josafhat Gerard, Guillermo S. Vitale, Paolo Dreisigacker, Susanne Lillemo, Morten Grignola, Fernando Sarinelli, Martin Pozzo, Ezequiel Quiroga, Marco Montesinos-Lopez, Abelardo Crossa, José |
| author_browse | Beyene, Yoseph Crespo Herrera, Leonardo A. Crossa, José Dreisigacker, Susanne Gerard, Guillermo S. Gowda, Manje Grignola, Fernando Lillemo, Morten Montesinos-Lopez, Abelardo Montesinos-Lopez, Osval A. Pozzo, Ezequiel Quiroga, Marco Rosa-Santamaria, Roberto de la Saint Pierre, Carolina Salinas-Ruiz, Josafhat Sarinelli, Martin Vitale, Paolo Xavier, Alencar |
| author_facet | Montesinos-Lopez, Osval A. Crespo Herrera, Leonardo A. Xavier, Alencar Gowda, Manje Beyene, Yoseph Saint Pierre, Carolina Rosa-Santamaria, Roberto de la Salinas-Ruiz, Josafhat Gerard, Guillermo S. Vitale, Paolo Dreisigacker, Susanne Lillemo, Morten Grignola, Fernando Sarinelli, Martin Pozzo, Ezequiel Quiroga, Marco Montesinos-Lopez, Abelardo Crossa, José |
| author_sort | Montesinos-Lopez, Osval A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain reasonable accuracies, which is of paramount importance to improve the selection process. For this reason, in this research, we propose the Adversaria-Boruta (AB) method, which combines the virtues of the adversarial validation (AV) method and the Boruta feature selection method. The AB method operates primarily by minimizing the disparity between training and testing distributions. This is accomplished by reducing the weight assigned to markers that display the most significant differences between the training and testing sets. Therefore, the AB method built a weighted genomic relationship matrix that is implemented with the genomic best linear unbiased predictor (GBLUP) model. The proposed AB method is compared using 12 real data sets with the GBLUP model that uses a nonweighted genomic relationship matrix. Our results show that the proposed AB method outperforms the GBLUP by 8.6, 19.7, and 9.8% in terms of Pearson’s correlation, mean square error, and normalized root mean square error, respectively. Our results support that the proposed AB method is a useful tool to improve the prediction accuracy of a complete family, however, we encourage other investigators to evaluate the AB method to increase the empirical evidence of its potential. |
| format | Journal Article |
| id | CGSpace137924 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1379242025-12-08T10:11:39Z A marker weighting approach for enhancing within-family accuracy in genomic prediction Montesinos-Lopez, Osval A. Crespo Herrera, Leonardo A. Xavier, Alencar Gowda, Manje Beyene, Yoseph Saint Pierre, Carolina Rosa-Santamaria, Roberto de la Salinas-Ruiz, Josafhat Gerard, Guillermo S. Vitale, Paolo Dreisigacker, Susanne Lillemo, Morten Grignola, Fernando Sarinelli, Martin Pozzo, Ezequiel Quiroga, Marco Montesinos-Lopez, Abelardo Crossa, José genomics selection methods data Genomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain reasonable accuracies, which is of paramount importance to improve the selection process. For this reason, in this research, we propose the Adversaria-Boruta (AB) method, which combines the virtues of the adversarial validation (AV) method and the Boruta feature selection method. The AB method operates primarily by minimizing the disparity between training and testing distributions. This is accomplished by reducing the weight assigned to markers that display the most significant differences between the training and testing sets. Therefore, the AB method built a weighted genomic relationship matrix that is implemented with the genomic best linear unbiased predictor (GBLUP) model. The proposed AB method is compared using 12 real data sets with the GBLUP model that uses a nonweighted genomic relationship matrix. Our results show that the proposed AB method outperforms the GBLUP by 8.6, 19.7, and 9.8% in terms of Pearson’s correlation, mean square error, and normalized root mean square error, respectively. Our results support that the proposed AB method is a useful tool to improve the prediction accuracy of a complete family, however, we encourage other investigators to evaluate the AB method to increase the empirical evidence of its potential. 2024-02-07 2024-01-17T21:49:03Z 2024-01-17T21:49:03Z Journal Article https://hdl.handle.net/10568/137924 en Open Access application/pdf Oxford University Press Montesinos-López, O. A., Crespo-Herrera, L., Xavier, A., Godwa, M., Beyene, Y., Pierre, C. S., de la Rosa-Santamaria, R., Salinas-Ruiz, J., Gerard, G., Vitale, P., Dreisigacker, S., Lillemo, M., Grignola, F., Sarinelli, M., Pozzo, E., Quiroga, M., Montesinos-López, A., & Crossa, J. (2023). A marker weighting approach for enhancing within-family accuracy in genomic prediction. G3: Genes, Genomes, Genetics, 14(2). https://doi.org/10.1093/g3journal/jkad278 |
| spellingShingle | genomics selection methods data Montesinos-Lopez, Osval A. Crespo Herrera, Leonardo A. Xavier, Alencar Gowda, Manje Beyene, Yoseph Saint Pierre, Carolina Rosa-Santamaria, Roberto de la Salinas-Ruiz, Josafhat Gerard, Guillermo S. Vitale, Paolo Dreisigacker, Susanne Lillemo, Morten Grignola, Fernando Sarinelli, Martin Pozzo, Ezequiel Quiroga, Marco Montesinos-Lopez, Abelardo Crossa, José A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title | A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title_full | A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title_fullStr | A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title_full_unstemmed | A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title_short | A marker weighting approach for enhancing within-family accuracy in genomic prediction |
| title_sort | marker weighting approach for enhancing within family accuracy in genomic prediction |
| topic | genomics selection methods data |
| url | https://hdl.handle.net/10568/137924 |
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