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...

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Detalles Bibliográficos
Autores: Montesinos-Lopez, O.A., Montesinos-López, A., Crossa, J., Toledo, F.H., Pérez-Hernández, O., Eskridge, K., Rutkoski, J.
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
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spelling A genomic bayesian multi-trait and multi-environment modelMontesinos-Lopez, O.A.Montesinos-López, A.Crossa, J.Toledo, F.H.Pérez-Hernández, O.Eskridge, K.Rutkoski, J.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYMulti-Trait Multi-EnvironmentBayesian EstimationGenome-Enabled PredictionGenomic SelectionGenPredShared Data ResourcesBAYESIAN THEORYSTATISTICAL METHODSGENOMICSFORECASTINGDATA PROCESSINGWhen 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.2725-2744Genetics Society of America2017-08-23T15:41:23Z2017-08-23T15:41:23Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePDFapplication/pdfhttp://hdl.handle.net/10883/1887010.1534/g3.116.03235996G3: Genes, Genomes, Geneticsreponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttp://hdl.handle.net/11529/10646https://www.g3journal.org/content/6/9/2725.figures-onlyhttp://hdl.handle.net/11529/10646Bethesda, 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:10883/188702024-10-11T19:59:28Z
dc.title.none.fl_str_mv A genomic bayesian multi-trait and multi-environment model
title A genomic bayesian multi-trait and multi-environment model
spellingShingle A genomic bayesian multi-trait and multi-environment model
Montesinos-Lopez, O.A.
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
title_short A genomic bayesian multi-trait and multi-environment model
title_full A genomic bayesian multi-trait and multi-environment model
title_fullStr A genomic bayesian multi-trait and multi-environment model
title_full_unstemmed A genomic bayesian multi-trait and multi-environment model
title_sort A genomic bayesian multi-trait and multi-environment model
dc.creator.none.fl_str_mv Montesinos-Lopez, O.A.
Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Pérez-Hernández, O.
Eskridge, K.
Rutkoski, J.
author Montesinos-Lopez, O.A.
author_facet Montesinos-Lopez, O.A.
Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Pérez-Hernández, O.
Eskridge, K.
Rutkoski, J.
author_role author
author2 Montesinos-López, A.
Crossa, J.
Toledo, F.H.
Pérez-Hernández, O.
Eskridge, K.
Rutkoski, J.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv 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
topic 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
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.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017-08-23T15:41:23Z
2017-08-23T15:41:23Z
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/10883/18870
10.1534/g3.116.032359
url http://hdl.handle.net/10883/18870
identifier_str_mv 10.1534/g3.116.032359
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv http://hdl.handle.net/11529/10646
https://www.g3journal.org/content/6/9/2725.figures-only
http://hdl.handle.net/11529/10646
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 9
6
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
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