GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction

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dc.creatorMontesinos-Lopez, O.A.
dc.creatorCrossa, J.
dc.creatorVitale, P.
dc.creatorGerard, G.
dc.creatorCrespo-Herrera, L.
dc.creatorDreisigacker, S.
dc.creatorPierre, C.S.
dc.creatorPosadas, L.G.
dc.creatorAgbona, A.
dc.creatorBuenrostro-Mariscal, R.
dc.creatorMontesinos-Lopez, A.
dc.creatorChawade, A.
dc.date2025
dc.date2025-07-04T12:08:48Z
dc.date2025-07-04T12:08:48Z
dc.date.accessioned2026-06-27T16:02:01Z
dc.descriptionGenomic 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/175487
dc.identifier.urihttp://hdl.handle.net/123456789/120572
dc.languageen
dc.publisherMDPI
dc.rightsOpen Access
dc.sourceMontesinos-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.
dc.subjectmapping
dc.subjectplant breeding
dc.subjectgenomics
dc.subjectforecasting
dc.titleGBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction
dc.typeJournal Article

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Instituto Internacional de Agricultura Tropical (IITA)