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Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

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. Genomi...

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Autores principales: Cuevas, Jaime, Crossa, José, Montesinos-López, Osval A., Burgueño, Juan, Pérez-Rodríguez, Paulino, de los Campos, Gustavo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217122/
https://www.ncbi.nlm.nih.gov/pubmed/27793970
http://dx.doi.org/10.1534/g3.116.035584
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author Cuevas, Jaime
Crossa, José
Montesinos-López, Osval A.
Burgueño, Juan
Pérez-Rodríguez, Paulino
de los Campos, Gustavo
author_facet Cuevas, Jaime
Crossa, José
Montesinos-López, Osval A.
Burgueño, Juan
Pérez-Rodríguez, Paulino
de los Campos, Gustavo
author_sort Cuevas, Jaime
collection PubMed
description 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 [Formula: see text] 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 [Formula: see text] plus an extra component, f, that captures random effects between environments that were not captured by the random effects [Formula: see text] 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 [Formula: see text] 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 [Formula: see text]
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spelling pubmed-52171222017-01-09 Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models Cuevas, Jaime Crossa, José Montesinos-López, Osval A. Burgueño, Juan Pérez-Rodríguez, Paulino de los Campos, Gustavo G3 (Bethesda) Genomic Selection 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 [Formula: see text] 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 [Formula: see text] plus an extra component, f, that captures random effects between environments that were not captured by the random effects [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] Genetics Society of America 2016-10-28 /pmc/articles/PMC5217122/ /pubmed/27793970 http://dx.doi.org/10.1534/g3.116.035584 Text en Copyright © 2017 Cuevas et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Genomic Selection
Cuevas, Jaime
Crossa, José
Montesinos-López, Osval A.
Burgueño, Juan
Pérez-Rodríguez, Paulino
de los Campos, Gustavo
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title_full Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title_fullStr Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title_full_unstemmed Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title_short Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
title_sort bayesian genomic prediction with genotype × environment interaction kernel models
topic Genomic Selection
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217122/
https://www.ncbi.nlm.nih.gov/pubmed/27793970
http://dx.doi.org/10.1534/g3.116.035584
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