Título
Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials
Autor
Jaime Cuevas
Italo Granato
Roberto Fritsche-Neto
Osval Antonio Montesinos-Lopez
Juan Burgueño
Massaine e Sousa
Jose Crossa
Nivel de Acceso
Acceso Abierto
Materias
Kernels - (AGROVOC) Maize - (AGROVOC) Genotype environment interaction - (AGROVOC) Genomic Enabled Prediction Accuracy - (AGROVOC) Main Genetic Effects - (AGROVOC) Deviations from Main Genetic Effects - (AGROVOC) Random Intercepts - (AGROVOC) Genomic Selection - (AGROVOC) Shared Data Resource - (AGROVOC) GenPred - (AGROVOC) GENOTYPE ENVIRONMENT INTERACTION - (AGROVOC) GENOMICS - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)
Resumen o descripción
In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multienvironment environment-specific variance G×E 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 Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E 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 G×E 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 G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.
Fecha de publicación
2018
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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