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BGGE: A new package for genomic prediction incorporating genotype by environments models

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title BGGE: A new package for genomic prediction incorporating genotype by environments models
 
Identifier https://hdl.handle.net/11529/10548107
 
Creator Granato, Italo
Cuevas, Jaime
Luna, Francisco
Crossa, Jose
Burgueño, Juan
Fritsche-Neto, Roberto
 
Publisher CIMMYT Research Data & Software Repository Network
 
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.
 
Subject Agricultural Sciences
Maize
genotype by environment
Bayesian Genomic Genotype × Environment Interaction
Bayesian Genomic Linear Regression
GE interaction
BGGE
BGLR
 
Language English
 
Date 2018-07-19
 
Contributor Shrestha, Rosemary
CGIAR Research Program on Wheat (WHEAT)
CGIAR
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
 
Type Dataset