Título
Genomic prediction of genotype x environment interaction kernel regression models
Autor
Jaime Cuevas
Jose Crossa
Sergio Pérez-Elizalde
Paulino Pérez-Rodríguez
Gustavo de los Campos
Osval Antonio Montesinos-Lopez
Juan Burgueño
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
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 (triticum aestivum l.) and maize (zea mays 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
Fecha de publicación
2016
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional de Publicaciones Multimedia del CIMMYT
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