Statistics, Department of



Kent M. Eskridge

Date of this Version



G3: Genes|Genomes|Genetics Early Online, published on June 24, 2016 as doi:10.1534/g3.116.032359


© The Author(s) 2013. Published by the Genetics Society of America.
Free via Creative Commons: CC


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-𝑡 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 hyperparameters. 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 BMTME offers optimized C++ routines to efficiently perform the analyses.