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

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), beca...

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Autores: Montesinos-Lopez, O.A., Montesinos-López, A., Crossa, J., Toledo, F.H., Montesinos-Lopez, J.C., Singh, P.K., Juliana, P., Salinas Ruiz, J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
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:10883/18833
Acceso en línea:http://hdl.handle.net/10883/18833
Access Level:acceso abierto
Palabra clave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Count Phenotype
Multi-Trait Multi-Environment
Bayesian Genomic Enabled Prediction
Genomic Selection
GenPred
Shared Data Resources
BAYESIAN THEORY
GENOTYPE ENVIRONMENT INTERACTION
STATISTICAL METHODS
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spelling A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled predictionMontesinos-Lopez, O.A.Montesinos-López, A.Crossa, J.Toledo, F.H.Montesinos-Lopez, J.C.Singh, P.K.Juliana, P.Salinas Ruiz, J.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYCount PhenotypeMulti-Trait Multi-EnvironmentBayesian Genomic Enabled PredictionGenomic SelectionGenPredShared Data ResourcesBAYESIAN THEORYGENOTYPE ENVIRONMENT INTERACTIONSTATISTICAL METHODSWhen 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.1595-1606Genetics Society of America2017-08-16T16:17:38Z2017-08-16T16:17:38Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePDFapplication/pdfhttp://hdl.handle.net/10883/1883310.1534/g3.117.03997457G3: Genes, Genomes, Geneticsreponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttp://hdl.handle.net/11529/10866Bethesda, MDCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.Open Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/188332024-10-11T19:59:25Z
dc.title.none.fl_str_mv A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
title A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
spellingShingle A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
Montesinos-Lopez, O.A.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Count Phenotype
Multi-Trait Multi-Environment
Bayesian Genomic Enabled Prediction
Genomic Selection
GenPred
Shared Data Resources
BAYESIAN THEORY
GENOTYPE ENVIRONMENT INTERACTION
STATISTICAL METHODS
title_short A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
title_full A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
title_fullStr A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
title_full_unstemmed A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
title_sort A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
dc.creator.none.fl_str_mv Montesinos-Lopez, O.A.
Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Montesinos-Lopez, J.C.
Singh, P.K.
Juliana, P.
Salinas Ruiz, J.
author Montesinos-Lopez, O.A.
author_facet Montesinos-Lopez, O.A.
Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Montesinos-Lopez, J.C.
Singh, P.K.
Juliana, P.
Salinas Ruiz, J.
author_role author
author2 Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Montesinos-Lopez, J.C.
Singh, P.K.
Juliana, P.
Salinas Ruiz, J.
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Count Phenotype
Multi-Trait Multi-Environment
Bayesian Genomic Enabled Prediction
Genomic Selection
GenPred
Shared Data Resources
BAYESIAN THEORY
GENOTYPE ENVIRONMENT INTERACTION
STATISTICAL METHODS
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Count Phenotype
Multi-Trait Multi-Environment
Bayesian Genomic Enabled Prediction
Genomic Selection
GenPred
Shared Data Resources
BAYESIAN THEORY
GENOTYPE ENVIRONMENT INTERACTION
STATISTICAL METHODS
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-16T16:17:38Z
2017-08-16T16:17:38Z
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10883/18833
10.1534/g3.117.039974
url http://hdl.handle.net/10883/18833
identifier_str_mv 10.1534/g3.117.039974
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv http://hdl.handle.net/11529/10866
dc.rights.none.fl_str_mv Open Access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Open Access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv PDF
application/pdf
dc.coverage.none.fl_str_mv Bethesda, MD
dc.publisher.none.fl_str_mv Genetics Society of America
publisher.none.fl_str_mv Genetics Society of America
dc.source.none.fl_str_mv 5
7
G3: Genes, Genomes, Genetics
reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
instname:Centro Internacional de Mejoramiento de Maíz y Trigo
instacron:CIMMYT
instname_str Centro Internacional de Mejoramiento de Maíz y Trigo
instacron_str CIMMYT
institution CIMMYT
reponame_str Repositorio Institucional de Publicaciones Multimedia del CIMMYT
collection Repositorio Institucional de Publicaciones Multimedia del CIMMYT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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