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

dc.creatorMontesinos-Lopez, O.A.
dc.creatorMontesinos-López, A.
dc.creatorCrossa, J.
dc.creatorToledo, F.H.
dc.creatorMontesinos-López, J.C.
dc.creatorSingh, P.K.
dc.creatorJuliana, P.
dc.creatorSalinas Ruiz. J.
dc.date2017-08-16T16:17:38Z
dc.date2017-08-16T16:17:38Z
dc.date2017
dc.date.accessioned2023-07-17T20:01:30Z
dc.date.available2023-07-17T20:01:30Z
dc.identifierhttp://hdl.handle.net/10883/18833
dc.identifier10.1534/g3.117.039974
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7510897
dc.descriptionWhen 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.
dc.description1595-1606
dc.formatPDF
dc.languageEnglish
dc.publisherGenetics Society of America
dc.relationhttp://hdl.handle.net/11529/10866
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dc.rightsOpen Access
dc.source5
dc.source7
dc.sourceG3: Genes, Genomes, Genetics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectCount Phenotype
dc.subjectMulti-Trait Multi-Environment
dc.subjectBayesian Genomic Enabled Prediction
dc.subjectGenomic Selection
dc.subjectGenPred
dc.subjectShared Data Resources
dc.subjectBAYESIAN THEORY
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.subjectSTATISTICAL METHODS
dc.titleA bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
dc.typeArticle
dc.coverageBethesda, MD


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