Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.

Detalhes bibliográficos
Autor(a) principal: ALVES, R. S.
Data de Publicação: 2020
Outros Autores: RESENDE, M. D. V. de, ROCHA, J. R. do A. S. de C., PEIXOTO, M. A., TEODORO, P. E., SILVA, F. F. e, BHERING, L. L., SANTOS, G. A. dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1129653
https://doi.org/10.1590/1678-4499.20200125
Resumo: An accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies ( r^gg >=0.90 ) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.
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spelling Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.Método EstatísticoMelhoramento Genético VegetalInteração GenéticaEucaliptoStatistical modelsGenotype-environment interactionForest treesPlant breedingEucalyptusAn accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies ( r^gg >=0.90 ) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.RODRIGO SILVA ALVES, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; JOÃO ROMERO DO AMARAL SANTOS DE CARVALHO ROCHA, UFV; MARCO ANTÔNIO PEIXOTO, UFV; PAULO EDUARDO TEODORO, UFMS; FABYANO FONSECA E SILVA, UFV; LEONARDO LOPES BHERING, UFV; GLEISON AUGUSTO DOS SANTOS, UFV.ALVES, R. S.RESENDE, M. D. V. deROCHA, J. R. do A. S. de C.PEIXOTO, M. A.TEODORO, P. E.SILVA, F. F. eBHERING, L. L.SANTOS, G. A. dos2021-01-29T00:53:54Z2021-01-29T00:53:54Z2021-01-282020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBragantia, v. 79, n. 4, 2020. p. 360-376.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1129653https://doi.org/10.1590/1678-4499.20200125enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2021-01-29T00:54:02Zoai:www.alice.cnptia.embrapa.br:doc/1129653Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542021-01-29T00:54:02falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542021-01-29T00:54:02Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
title Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
spellingShingle Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
ALVES, R. S.
Método Estatístico
Melhoramento Genético Vegetal
Interação Genética
Eucalipto
Statistical models
Genotype-environment interaction
Forest trees
Plant breeding
Eucalyptus
title_short Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
title_full Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
title_fullStr Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
title_full_unstemmed Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
title_sort Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
author ALVES, R. S.
author_facet ALVES, R. S.
RESENDE, M. D. V. de
ROCHA, J. R. do A. S. de C.
PEIXOTO, M. A.
TEODORO, P. E.
SILVA, F. F. e
BHERING, L. L.
SANTOS, G. A. dos
author_role author
author2 RESENDE, M. D. V. de
ROCHA, J. R. do A. S. de C.
PEIXOTO, M. A.
TEODORO, P. E.
SILVA, F. F. e
BHERING, L. L.
SANTOS, G. A. dos
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv RODRIGO SILVA ALVES, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; JOÃO ROMERO DO AMARAL SANTOS DE CARVALHO ROCHA, UFV; MARCO ANTÔNIO PEIXOTO, UFV; PAULO EDUARDO TEODORO, UFMS; FABYANO FONSECA E SILVA, UFV; LEONARDO LOPES BHERING, UFV; GLEISON AUGUSTO DOS SANTOS, UFV.
dc.contributor.author.fl_str_mv ALVES, R. S.
RESENDE, M. D. V. de
ROCHA, J. R. do A. S. de C.
PEIXOTO, M. A.
TEODORO, P. E.
SILVA, F. F. e
BHERING, L. L.
SANTOS, G. A. dos
dc.subject.por.fl_str_mv Método Estatístico
Melhoramento Genético Vegetal
Interação Genética
Eucalipto
Statistical models
Genotype-environment interaction
Forest trees
Plant breeding
Eucalyptus
topic Método Estatístico
Melhoramento Genético Vegetal
Interação Genética
Eucalipto
Statistical models
Genotype-environment interaction
Forest trees
Plant breeding
Eucalyptus
description An accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies ( r^gg >=0.90 ) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021-01-29T00:53:54Z
2021-01-29T00:53:54Z
2021-01-28
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Bragantia, v. 79, n. 4, 2020. p. 360-376.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1129653
https://doi.org/10.1590/1678-4499.20200125
identifier_str_mv Bragantia, v. 79, n. 4, 2020. p. 360-376.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1129653
https://doi.org/10.1590/1678-4499.20200125
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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