A genomic bayesian multi-trait and multi-environment model
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 · gen...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | México |
| Institución: | Centro Internacional de Mejoramiento de Maíz y Trigo |
| Repositorio: | Repositorio Institucional de Publicaciones Multimedia del CIMMYT |
| OAI Identifier: | oai:repository.cimmyt.org:10883/18870 |
| Acceso en línea: | http://hdl.handle.net/10883/18870 |
| Access Level: | acceso abierto |
| Palabra clave: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Multi-Trait Multi-Environment Bayesian Estimation Genome-Enabled Prediction Genomic Selection GenPred Shared Data Resources BAYESIAN THEORY STATISTICAL METHODS GENOMICS FORECASTING DATA PROCESSING |
| Sumario: | 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. |
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