GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction
Genomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)—an adjustment to GBLUP predictions—and four outlier detection methods. Using 14 real datasets, predictive accu...
| 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/10568/175487 |
| _version_ | 1855532005298536448 |
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| author | Montesinos-Lopez, O.A. Crossa, J. Vitale, P. Gerard, G. Crespo-Herrera, L. Dreisigacker, S. Pierre, C.S. Posadas, L.G. Agbona, A. Buenrostro-Mariscal, R. Montesinos-Lopez, A. Chawade, A. |
| author_browse | Agbona, A. Buenrostro-Mariscal, R. Chawade, A. Crespo-Herrera, L. Crossa, J. Dreisigacker, S. Gerard, G. Montesinos-Lopez, A. Montesinos-Lopez, O.A. Pierre, C.S. Posadas, L.G. Vitale, P. |
| author_facet | Montesinos-Lopez, O.A. Crossa, J. Vitale, P. Gerard, G. Crespo-Herrera, L. Dreisigacker, S. Pierre, C.S. Posadas, L.G. Agbona, A. Buenrostro-Mariscal, R. Montesinos-Lopez, A. Chawade, A. |
| author_sort | Montesinos-Lopez, O.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)—an adjustment to GBLUP predictions—and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson’s correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (<7%), and their removal had minimal impact on GBLUP’s predictive performance. QM provided slight improvements in datasets with skewed distributions but showed no significant advantage in well-distributed data. These findings confirm GBLUP’s robustness and reliability, suggesting limited utility for QM when data deviations are minimal. |
| format | Journal Article |
| id | CGSpace175487 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1754872025-12-08T10:29:22Z GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction Montesinos-Lopez, O.A. Crossa, J. Vitale, P. Gerard, G. Crespo-Herrera, L. Dreisigacker, S. Pierre, C.S. Posadas, L.G. Agbona, A. Buenrostro-Mariscal, R. Montesinos-Lopez, A. Chawade, A. mapping plant breeding genomics forecasting Genomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)—an adjustment to GBLUP predictions—and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson’s correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (<7%), and their removal had minimal impact on GBLUP’s predictive performance. QM provided slight improvements in datasets with skewed distributions but showed no significant advantage in well-distributed data. These findings confirm GBLUP’s robustness and reliability, suggesting limited utility for QM when data deviations are minimal. 2025 2025-07-04T12:08:48Z 2025-07-04T12:08:48Z Journal Article https://hdl.handle.net/10568/175487 en Open Access application/pdf MDPI Montesinos-López, O.A., Crossa, J., Vitale, P., Gerard, G., Crespo-Herrera, L., Dreisigacker, S., ... & Chawade, A. (2025). GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction. International Journal of Molecular Sciences, 26(8): 3620, 1-34. |
| spellingShingle | mapping plant breeding genomics forecasting Montesinos-Lopez, O.A. Crossa, J. Vitale, P. Gerard, G. Crespo-Herrera, L. Dreisigacker, S. Pierre, C.S. Posadas, L.G. Agbona, A. Buenrostro-Mariscal, R. Montesinos-Lopez, A. Chawade, A. GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title | GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title_full | GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title_fullStr | GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title_full_unstemmed | GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title_short | GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| title_sort | gblup outperforms quantile mapping and outlier detection for enhanced genomic prediction |
| topic | mapping plant breeding genomics forecasting |
| url | https://hdl.handle.net/10568/175487 |
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