Título

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction

Autor

Massaine e Sousa

Jaime Cuevas

Evellyn Couto

Paulino Pérez-Rodríguez

DIEGO JARQUIN

Roberto Fritsche-Neto

Juan Burgueño

Jose Crossa

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by/4.0

Materias

Forecasting - (AGROVOC) Genomics - (AGROVOC) Maize - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)

Resumen o descripción

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, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

Fecha de publicación

2017

Tipo de publicación

Artículo

Recurso de información

https://hdl.handle.net/10883/19328

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

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