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Montesinos-López, O. A., Ramos-Pulido, S., Hernandez-Suarez, C., Mosqueda-Gonzalez, B. A., Valladares-Anguiano, F. A., Vitale, P., Montesinos-López, A., & Crossa, J. (2023). A novel method for genomic-enabled prediction of cultivars in new environments. Frontiers in Plant Science, 14, 1218151. https://doi.org/10.3389/fpls.2023.1218151
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Introduction: Genomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs. Objectives of the research: In this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments. Method-1: The new method trained the model with a modified response variable (a difference of response variables) that decreases the lack of a non-stationary distribution between the training and testing and improved the prediction accuracy. Comparing new and conventional method: We compared the prediction accuracy of the conventional genomic best linear unbiased prediction (GBLUP) model (M1) including (or not) genotype × environment interaction (GE) (M1_GE; M1_NO_GE) versus the proposed method (M2) on several data sets. Results and discussion: The gain in prediction accuracy of M2, versus M1_GE, M1_NO_GE in terms of Pearson´s correlation was of at least 4.3%, while in terms of percentage of top-yielding lines captured when was selected the 10% (Best10) and 20% (Best20) of lines was at least of 19.5%, while in terms of Normalized Root Mean Squared Error (NRMSE) was of at least of 42.29%.
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Journal
Frontiers in Plant Science
Journal volume
14
Journal issue
Article number
1218151
Place of Publication
Switzerland
Publisher
Frontiers Media S.A.