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A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

Author: Montesinos-Lopez, O.A.
Author: Montesinos-López, A.
Author: Crossa, J.
Author: Toledo, F.H.
Author: Montesinos-López, J.C.
Author: Singh, P.K.
Author: Juliana, P.
Author: Salinas Ruiz. J.
Year: 2017
Abstract: When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with non informative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.
Format: PDF
Language: English
Publisher: Genetics Society of America
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Type: Article
Place of Publication: Bethesda, MD
Pages: 1595-1606
Issue: 5
Volume: 7
DOI: 10.1534/g3.117.039974
Keywords: Count Phenotype
Keywords: Multi-Trait Multi-Environment
Keywords: Bayesian Genomic Enabled Prediction
Keywords: Genomic Selection
Keywords: GenPred
Keywords: Shared Data Resources
Related Datasets:
Journal: G3: Genes, Genomes, Genetics

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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