Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment...

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Bibliographic Details
Main Authors: Massaine Bandeira e Sousa, Jaime Cuevas, Evellyn Giselly de Oliveira Couto, Paulino Pérez-Rodríguez, Diego Jarquín, Roberto Fritsche-Neto, Juan Burgueño, Jose Crossa
Format: Article
Language:English
Published: Oxford University Press 2017-06-01
Series:G3: Genes, Genomes, Genetics
Subjects:
Genotype× Environment interaction (G×E)
Genomic Best Linear Unbiased Predictor (GBLUP) linear kernel
Gaussian nonlinear kernel
Genomic Selection
GenPred
Shared Data Resources
Online Access:http://g3journal.org/lookup/doi/10.1534/g3.117.042341

Internet

http://g3journal.org/lookup/doi/10.1534/g3.117.042341

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