A novel method for genomic-enabled prediction of cultivars in new environments.

Abstract

Genomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs. In this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments. 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. 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. 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%.