Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models
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 kerne...
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Series: | The Plant Genome |
Online Access: | https://dl.sciencesocieties.org/publications/tpg/articles/9/3/plantgenome2016.03.0024 |
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doaj-881106f2d0424355a674238a0378b1712020-11-25T01:19:10ZengWileyThe Plant Genome1940-33722016-11-019310.3835/plantgenome2016.03.0024plantgenome2016.03.0024Genomic Prediction of Genotype × Environment Interaction Kernel Regression ModelsJaime CuevasJosé CrossaVíctor SoberanisSergio Pérez-ElizaldePaulino Pérez-RodríguezGustavo de los CamposO. A. Montesinos-LópezJuan BurgueñoIn 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.https://dl.sciencesocieties.org/publications/tpg/articles/9/3/plantgenome2016.03.0024 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jaime Cuevas José Crossa Víctor Soberanis Sergio Pérez-Elizalde Paulino Pérez-Rodríguez Gustavo de los Campos O. A. Montesinos-López Juan Burgueño |
spellingShingle |
Jaime Cuevas José Crossa Víctor Soberanis Sergio Pérez-Elizalde Paulino Pérez-Rodríguez Gustavo de los Campos O. A. Montesinos-López Juan Burgueño Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models The Plant Genome |
author_facet |
Jaime Cuevas José Crossa Víctor Soberanis Sergio Pérez-Elizalde Paulino Pérez-Rodríguez Gustavo de los Campos O. A. Montesinos-López Juan Burgueño |
author_sort |
Jaime Cuevas |
title |
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models |
title_short |
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models |
title_full |
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models |
title_fullStr |
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models |
title_full_unstemmed |
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models |
title_sort |
genomic prediction of genotype × environment interaction kernel regression models |
publisher |
Wiley |
series |
The Plant Genome |
issn |
1940-3372 |
publishDate |
2016-11-01 |
description |
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. |
url |
https://dl.sciencesocieties.org/publications/tpg/articles/9/3/plantgenome2016.03.0024 |
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