Montesinos-López Osval A, Ramos-Pulido Sofia, Hernández-Suárez Carlos Moisés, Mosqueda González Brandon Alejandro, Valladares-Anguiano Felícitas Alejandra, Vitale Paolo, Montesinos-López Abelardo, Crossa José
Facultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
Front Plant Sci. 2023 Jul 25;14:1218151. doi: 10.3389/fpls.2023.1218151. eCollection 2023.
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.
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.
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%.
基因组选择（GS）因其具有加速遗传进展和提高育种计划效率的潜力而在全球范围内变得至关重要。
在本研究中，我们提出了一种方法来提高新的（未测试的）环境中测试品系的预测准确性。
方法1：新方法使用修正后的响应变量（响应变量之差）训练模型，该变量减少了训练和测试之间非平稳分布的不足，提高了预测准确性。
我们在几个数据集上比较了传统基因组最佳线性无偏预测（GBLUP）模型（M1）（包括或不包括基因型×环境互作（GE））（M1_GE；M1_NO_GE）与所提出方法（M2）的预测准确性。
就皮尔逊相关性而言，M2相对于M1_GE、M1_NO_GE的预测准确性提高至少4.3%，而就选择10%（Best10）和20%（Best20）品系时捕获的高产品系百分比而言至少为19.5%，而就归一化均方根误差（NRMSE）而言至少为42.29%。
