title: A Genomic Bayesian Multi-trait and Multi-environment Model.
abstract: When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype × environment interaction (G × E) is usually employed. Comprehensive models that simultaneously take into account the correlated traits and trait × genotype × environment interaction (T × G × E) are lacking. In this research, we propose a Bayesian model for analyzing multiple traits and multiple environments for whole-genome prediction (WGP) model. For this model, we used Half-[Formula: see text] priors on each standard deviation term and uniform priors on each correlation of the covariance matrix. These priors were not informative and led to posterior inferences that were insensitive to the choice of hyper-parameters. We also developed a computationally efficient Markov Chain Monte Carlo (MCMC) under the above priors, which allowed us to obtain all required full conditional distributions of the parameters leading to an exact Gibbs sampling for the posterior distribution. We used two real data sets to implement and evaluate the proposed Bayesian method and found that when the correlation between traits was high (>0.5), the proposed model (with unstructured variance-covariance) improved prediction accuracy compared to the model with diagonal and standard variance-covariance structures. The R-software package Bayesian Multi-Trait and Multi-Environment (BMTME) offers optimized C++ routines to efficiently perform the analyses.
authors.affiliation: Biometrics and Statistics Unit and Global Wheat Program of the International Maize and Wheat Improvement Center (CIMMYT), México, D.F. 06600, México.
authors.forename: Osval A
authors.initials: OA
authors.lastname: Montesinos-López
authors.affiliation: Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Guanajuato 36240, México.
authors.forename: Abelardo
authors.initials: A
authors.lastname: Montesinos-López
authors.affiliation: Biometrics and Statistics Unit and Global Wheat Program of the International Maize and Wheat Improvement Center (CIMMYT), México, D.F. 06600, México j.crossa@cgiar.org.
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.affiliation: Biometrics and Statistics Unit and Global Wheat Program of the International Maize and Wheat Improvement Center (CIMMYT), México, D.F. 06600, México.
authors.forename: Fernando H
authors.initials: FH
authors.lastname: Toledo
authors.affiliation: Department of Biology and Agriculture, University of Central Missouri, Warrensburg, Missouri 64093.
authors.forename: Oscar
authors.initials: O
authors.lastname: Pérez-Hernández
authors.affiliation: Statistics Department, University of Nebraska, Lincoln, Nebraska, 68583.
authors.forename: Kent M
authors.initials: KM
authors.lastname: Eskridge
authors.affiliation: Biometrics and Statistics Unit and Global Wheat Program of the International Maize and Wheat Improvement Center (CIMMYT), México, D.F. 06600, México.
authors.forename: Jessica
authors.initials: J
authors.lastname: Rutkoski
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D059647:Gene-Environment Interaction; D016678:Genome; D023281:Genomics; D005838:Genotype; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D019655:Quantitative Trait, Heritable
keywords: Bayesian estimation; GenPred; genome-enabled prediction; genomic selection; multi-environment; multi-trait; shared data resource