Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian mu...

Full description

Bibliographic Details
Authors: Montesinos-Lopez, O.A., Montesinos-Lopez, J.C., Montesinos-López, A., Ramirez-Alcaraz, J.M., Poland, J., Singh, R.P., Dreisigacker, S., Crespo Herrera, L.A., Mondal, S., Velu, G., Juliana, P., Huerta-Espino, J., Shrestha, S., Varshney, R.K., Crossa, J.
Format: article
Status:Published version
Publication Date:2022
Country:México
Institution:Centro Internacional de Mejoramiento de Maíz y Trigo
Repository:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/21989
Online Access:https://hdl.handle.net/10883/21989
Access Level:Open access
Keyword:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Kernel Methods
Genomic Enabled Prediction
Genomic Prediction
GenPred
Shared Data Resources
PLANT BREEDING
GENOMICS
FORECASTING
BAYESIAN THEORY
id MX_7c4b11bdf55affb0d68e4bd72be255e2
oai_identifier_str oai:repository.cimmyt.org:10883/21989
network_acronym_str MX
network_name_str México
repository_id_str
spelling Bayesian multitrait kernel methods improve multienvironment genome-based predictionMontesinos-Lopez, O.A.Montesinos-Lopez, J.C.Montesinos-López, A.Ramirez-Alcaraz, J.M.Poland, J.Singh, R.P.Dreisigacker, S.Crespo Herrera, L.A.Mondal, S.Velu, G.Juliana, P.Huerta-Espino, J.Shrestha, S.Varshney, R.K.Crossa, J.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYKernel MethodsGenomic Enabled PredictionGenomic PredictionGenPredShared Data ResourcesPLANT BREEDINGGENOMICSFORECASTINGBAYESIAN THEORYWhen multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.Oxford University Press2022-02-22T01:10:17Z2022-02-22T01:10:17Z2022Published Versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10883/2198910.1093/g3journal/jkab4062122160-1836G3: Genes, Genomes, Geneticsjkab406reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttps://hdl.handle.net/11529/10548629Nutrition, health & food securityPoverty reduction, livelihoods & jobsAccelerated BreedingBreeding ResourcesGenetic InnovationBill & Melinda Gates Foundation (BMGF)United States Agency for International Development (USAID)CGIAR Research Program on WheatFoundation for Research Levy on Agricultural Products (FFL)Agricultural Agreement Research FundCGIAR Research Program on Maizehttps://hdl.handle.net/10568/126371Bethesda, MD (USA)CIMMYT 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 purposeOpen Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/219892024-10-11T19:57:25Z
dc.title.none.fl_str_mv Bayesian multitrait kernel methods improve multienvironment genome-based prediction
title Bayesian multitrait kernel methods improve multienvironment genome-based prediction
spellingShingle Bayesian multitrait kernel methods improve multienvironment genome-based prediction
Montesinos-Lopez, O.A.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Kernel Methods
Genomic Enabled Prediction
Genomic Prediction
GenPred
Shared Data Resources
PLANT BREEDING
GENOMICS
FORECASTING
BAYESIAN THEORY
title_short Bayesian multitrait kernel methods improve multienvironment genome-based prediction
title_full Bayesian multitrait kernel methods improve multienvironment genome-based prediction
title_fullStr Bayesian multitrait kernel methods improve multienvironment genome-based prediction
title_full_unstemmed Bayesian multitrait kernel methods improve multienvironment genome-based prediction
title_sort Bayesian multitrait kernel methods improve multienvironment genome-based prediction
dc.creator.none.fl_str_mv Montesinos-Lopez, O.A.
Montesinos-Lopez, J.C.
Montesinos-López, A.
Ramirez-Alcaraz, J.M.
Poland, J.
Singh, R.P.
Dreisigacker, S.
Crespo Herrera, L.A.
Mondal, S.
Velu, G.
Juliana, P.
Huerta-Espino, J.
Shrestha, S.
Varshney, R.K.
Crossa, J.
author Montesinos-Lopez, O.A.
author_facet Montesinos-Lopez, O.A.
Montesinos-Lopez, J.C.
Montesinos-López, A.
Ramirez-Alcaraz, J.M.
Poland, J.
Singh, R.P.
Dreisigacker, S.
Crespo Herrera, L.A.
Mondal, S.
Velu, G.
Juliana, P.
Huerta-Espino, J.
Shrestha, S.
Varshney, R.K.
Crossa, J.
author_role author
author2 Montesinos-Lopez, J.C.
Montesinos-López, A.
Ramirez-Alcaraz, J.M.
Poland, J.
Singh, R.P.
Dreisigacker, S.
Crespo Herrera, L.A.
Mondal, S.
Velu, G.
Juliana, P.
Huerta-Espino, J.
Shrestha, S.
Varshney, R.K.
Crossa, J.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Kernel Methods
Genomic Enabled Prediction
Genomic Prediction
GenPred
Shared Data Resources
PLANT BREEDING
GENOMICS
FORECASTING
BAYESIAN THEORY
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Kernel Methods
Genomic Enabled Prediction
Genomic Prediction
GenPred
Shared Data Resources
PLANT BREEDING
GENOMICS
FORECASTING
BAYESIAN THEORY
description When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-22T01:10:17Z
2022-02-22T01:10:17Z
2022
dc.type.none.fl_str_mv Published Version
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10883/21989
10.1093/g3journal/jkab406
url https://hdl.handle.net/10883/21989
identifier_str_mv 10.1093/g3journal/jkab406
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv https://hdl.handle.net/11529/10548629
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Accelerated Breeding
Breeding Resources
Genetic Innovation
Bill & Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
CGIAR Research Program on Maize
https://hdl.handle.net/10568/126371
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 application/pdf
dc.coverage.none.fl_str_mv Bethesda, MD (USA)
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv 2
12
2160-1836
G3: Genes, Genomes, Genetics
jkab406
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
_version_ 1858175837546741760
score 15,811543

Similar Items