BGGE: A new package for genomic prediction incorporating genotype by environments models
One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.
Main Authors: | , , , , , |
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Format: | Experimental data biblioteca |
Language: | English |
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CIMMYT Research Data & Software Repository Network
2018
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Subjects: | Agricultural Sciences, Maize, genotype by environment, Bayesian Genomic Genotype × Environment Interaction, Bayesian Genomic Linear Regression, GE interaction, BGGE, BGLR, |
Online Access: | https://hdl.handle.net/11529/10548107 |
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dat-cimmyt-11529105481072020-08-02T01:00:12ZBGGE: A new package for genomic prediction incorporating genotype by environments modelshttps://hdl.handle.net/11529/10548107Granato, ItaloCuevas, JaimeLuna, FranciscoCrossa, JoseBurgueño, JuanFritsche-Neto, RobertoCIMMYT Research Data & Software Repository NetworkOne of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.Agricultural SciencesMaizegenotype by environmentBayesian Genomic Genotype × Environment InteractionBayesian Genomic Linear RegressionGE interactionBGGEBGLREnglish2018Shrestha, Rosemary CGIAR Research Program on Wheat (WHEAT)CGIARGlobal Wheat Program (GWP)Genetic Resources Program (GRP)Experimental data |
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México |
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biblioteca |
region |
America del Norte |
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Centro Internacional de Mejoramiento de Maíz y Trigo |
language |
English |
topic |
Agricultural Sciences Maize genotype by environment Bayesian Genomic Genotype × Environment Interaction Bayesian Genomic Linear Regression GE interaction BGGE BGLR Agricultural Sciences Maize genotype by environment Bayesian Genomic Genotype × Environment Interaction Bayesian Genomic Linear Regression GE interaction BGGE BGLR |
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Agricultural Sciences Maize genotype by environment Bayesian Genomic Genotype × Environment Interaction Bayesian Genomic Linear Regression GE interaction BGGE BGLR Agricultural Sciences Maize genotype by environment Bayesian Genomic Genotype × Environment Interaction Bayesian Genomic Linear Regression GE interaction BGGE BGLR Granato, Italo Cuevas, Jaime Luna, Francisco Crossa, Jose Burgueño, Juan Fritsche-Neto, Roberto BGGE: A new package for genomic prediction incorporating genotype by environments models |
description |
One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions. |
author2 |
Shrestha, Rosemary |
author_facet |
Shrestha, Rosemary Granato, Italo Cuevas, Jaime Luna, Francisco Crossa, Jose Burgueño, Juan Fritsche-Neto, Roberto |
format |
Experimental data |
topic_facet |
Agricultural Sciences Maize genotype by environment Bayesian Genomic Genotype × Environment Interaction Bayesian Genomic Linear Regression GE interaction BGGE BGLR |
author |
Granato, Italo Cuevas, Jaime Luna, Francisco Crossa, Jose Burgueño, Juan Fritsche-Neto, Roberto |
author_sort |
Granato, Italo |
title |
BGGE: A new package for genomic prediction incorporating genotype by environments models |
title_short |
BGGE: A new package for genomic prediction incorporating genotype by environments models |
title_full |
BGGE: A new package for genomic prediction incorporating genotype by environments models |
title_fullStr |
BGGE: A new package for genomic prediction incorporating genotype by environments models |
title_full_unstemmed |
BGGE: A new package for genomic prediction incorporating genotype by environments models |
title_sort |
bgge: a new package for genomic prediction incorporating genotype by environments models |
publisher |
CIMMYT Research Data & Software Repository Network |
publishDate |
2018 |
url |
https://hdl.handle.net/11529/10548107 |
work_keys_str_mv |
AT granatoitalo bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels AT cuevasjaime bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels AT lunafrancisco bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels AT crossajose bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels AT burguenojuan bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels AT fritschenetoroberto bggeanewpackageforgenomicpredictionincorporatinggenotypebyenvironmentsmodels |
_version_ |
1778656918873571328 |