TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Crossa,J.
AU  - Vitale,P.
AU  - Gerard,G.S.
AU  - Crespo-Herrera,L.A.
AU  - Dreisigacker,S.
AU  - Saint Pierre,C.
AU  - Posadas,L.G.
AU  - Agbona,A.
AU  - Buenrostro-Mariscal,R.
AU  - Montesinos-Lopez,A.
AU  - Chawade,A.
TI  - GBLUP outperforms quantile mapping and outlier detection for enhanced genomic prediction
SN  - 1422-0067
PY  - 2025///
CY  - Switzerland
PB  - MDPI, 
KW  - Marker-assisted selection
KW  - AGROVOC
KW  - Plant breeding
KW  - Forecasting
KW  - Wheat
N1  - Peer review; Open Access
N2  - 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
UR  - https://hdl.handle.net/10883/35640
DO  - https://doi.org/10.3390/ijms26083620
T2  - International Journal of Molecular Sciences
ER  -