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BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

Author: Granato, I.
Author: Cuevas, J.
Author: Luna-Vazquez, F.J.
Author: Crossa, J.
Author: Montesinos-Lopez, O.A.
Author: Burgueño, J.
Author: Fritsche-Neto, R.
Year: 2018
ISSN: 2160-1836
URI: https://hdl.handle.net/10883/19625
Abstract: 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.
Format: PDF
Language: English
Publisher: Genetics Society of America
Copyright: 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.
Type: Article
Place of Publication: Bethesda, Md., U.S.
Pages: 3039-3047
Issue: 9
Volume: 8
DOI: 10.1534/g3.118.200435
Keywords: BGGE
Keywords: Genomic Selection
Keywords: Bayesian Genomic Linear Regression
Keywords: GenPred
Keywords: Shared Data Resources
Keywords: BGLR
Agrovoc: BAYESIAN THEORY
Agrovoc: GENOMICS
Agrovoc: SELECTION
Agrovoc: REGRESSION ANALYSIS
Agrovoc: GENOTYPE ENVIRONMENT INTERACTION
Related Datasets: https://hdl.handle.net/11529/10548107
Journal: G3: Genes, Genomes, Genetics


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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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