Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without GxE interactions, the multi-environment single variance GxE deviation model (MDs), and the multi-environment environment-specific variance GxE deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best unbiased predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment GxE interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex GxE that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex GxE wheat data sets, the prediction accuracy of the model-method combination with GxE, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the GxE interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.

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