title: Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.
abstract: In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.
authors.forename: Jaime
authors.initials: J
authors.lastname: Cuevas
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.forename: Víctor
authors.initials: V
authors.lastname: Soberanis
authors.forename: Sergio
authors.initials: S
authors.lastname: Pérez-Elizalde
authors.forename: Paulino
authors.initials: P
authors.lastname: Pérez-Rodríguez
authors.forename: Gustavo de Los
authors.initials: GL
authors.lastname: Campos
authors.forename: O A
authors.initials: OA
authors.lastname: Montesinos-López
authors.forename: Juan
authors.initials: J
authors.lastname: Burgueño
mesh_terms: D001499:Bayes Theorem; D059647:Gene-Environment Interaction; D018745:Genome, Plant / Q000235:genetics*; D005838:Genotype; D008957:Models, Genetic; D014908:Triticum / Q000235:genetics*