Title

A genomic bayesian multi-trait and multi-environment model

Author

Osval Antonio Montesinos-Lopez

Jessica Rutkoski

Jose Crossa

Access level

Open Access

Summary or description

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 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.

Publish date

2016

Publication type

Article

Information Resource

Format

application/pdf

Language

English

Audience

Researchers

Source repository

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

Downloads

0

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