BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is 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 genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices 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 for creating genomic GE kernels and making genomic predictions.
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Genetics Society of America
2018
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, BGGE, Genomic Selection, Bayesian Genomic Linear Regression, GenPred, Shared Data Resources, BGLR, BAYESIAN THEORY, GENOMICS, SELECTION, REGRESSION ANALYSIS, GENOTYPE ENVIRONMENT INTERACTION, |
Online Access: | https://hdl.handle.net/10883/19625 |
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dig-cimmyt-10883-196252021-02-09T18:24:50Z BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models Granato, I. Cuevas, J. Luna-Vazquez, F.J. Crossa, J. Montesinos-Lopez, O.A. Burgueño, J. Fritsche-Neto, R. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is 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 genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices 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 for creating genomic GE kernels and making genomic predictions. 3039-3047 2018-10-02T17:41:57Z 2018-10-02T17:41:57Z 2018 Article 2160-1836 https://hdl.handle.net/10883/19625 10.1534/g3.118.200435 English https://hdl.handle.net/11529/10548107 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF Bethesda, Md., U.S. Genetics Society of America 9 8 G3: Genes, Genomes, Genetics |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION Granato, I. Cuevas, J. Luna-Vazquez, F.J. Crossa, J. Montesinos-Lopez, O.A. Burgueño, J. Fritsche-Neto, R. BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
description |
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is 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 genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices 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 for creating genomic GE kernels and making genomic predictions. |
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Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY BGGE Genomic Selection Bayesian Genomic Linear Regression GenPred Shared Data Resources BGLR BAYESIAN THEORY GENOMICS SELECTION REGRESSION ANALYSIS GENOTYPE ENVIRONMENT INTERACTION |
author |
Granato, I. Cuevas, J. Luna-Vazquez, F.J. Crossa, J. Montesinos-Lopez, O.A. Burgueño, J. Fritsche-Neto, R. |
author_facet |
Granato, I. Cuevas, J. Luna-Vazquez, F.J. Crossa, J. Montesinos-Lopez, O.A. Burgueño, J. Fritsche-Neto, R. |
author_sort |
Granato, I. |
title |
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
title_short |
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
title_full |
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
title_fullStr |
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
title_full_unstemmed |
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
title_sort |
bgge: a new package for genomic-enabled prediction incorporating genotype × environment interaction models |
publisher |
Genetics Society of America |
publishDate |
2018 |
url |
https://hdl.handle.net/10883/19625 |
work_keys_str_mv |
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1756086756671750144 |