Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
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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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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1794503501833830400 |