Título

Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models

Autor

Jaime Cuevas

Osval Antonio Montesinos-Lopez

Juan Burgueño

Paulino Pérez-Rodríguez

Gustavo de los Campos

Nivel de Acceso

Acceso Abierto

Licencia

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

Materias

Bayesian theory - (AGROVOC) Genotype environment interaction - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)

Resumen o descripción

The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G · E interaction are extensions of a single environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u: We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G · E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u.

Fecha de publicación

2017

Tipo de publicación

Artículo

Recurso de información

http://hdl.handle.net/10883/18616

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

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