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: | Bragantia |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000400485 |
Resumo: | ABSTRACT 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 g ^ g ≥ 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 breedingmixed model methodologycovariance functionsgenotype × environment interactionsgenotypic plasticityforest tree breedingABSTRACT 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 g ^ g ≥ 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.Instituto Agronômico de Campinas2020-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000400485Bragantia v.79 n.4 2020reponame:Bragantiainstname:Instituto Agronômico de Campinas (IAC)instacron:IAC10.1590/1678-4499.20200125info:eu-repo/semantics/openAccessAlves,Rodrigo SilvaResende,Marcos Deon Vilela deRocha,João Romero do Amaral Santos de CarvalhoPeixoto,Marco AntônioTeodoro,Paulo EduardoSilva,Fabyano Fonseca eBhering,Leonardo LopesSantos,Gleison Augusto doseng2021-05-27T00:00:00Zoai:scielo:S0006-87052020000400485Revistahttps://www.scielo.br/j/brag/https://old.scielo.br/oai/scielo-oai.phpbragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br1678-44990006-8705opendoar:2021-05-27T00:00Bragantia - Instituto Agronômico de Campinas (IAC)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,Rodrigo Silva mixed model methodology covariance functions genotype × environment interactions genotypic plasticity forest tree breeding |
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,Rodrigo Silva |
author_facet |
Alves,Rodrigo Silva Resende,Marcos Deon Vilela de Rocha,João Romero do Amaral Santos de Carvalho Peixoto,Marco Antônio Teodoro,Paulo Eduardo Silva,Fabyano Fonseca e Bhering,Leonardo Lopes Santos,Gleison Augusto dos |
author_role |
author |
author2 |
Resende,Marcos Deon Vilela de Rocha,João Romero do Amaral Santos de Carvalho Peixoto,Marco Antônio Teodoro,Paulo Eduardo Silva,Fabyano Fonseca e Bhering,Leonardo Lopes Santos,Gleison Augusto dos |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Alves,Rodrigo Silva Resende,Marcos Deon Vilela de Rocha,João Romero do Amaral Santos de Carvalho Peixoto,Marco Antônio Teodoro,Paulo Eduardo Silva,Fabyano Fonseca e Bhering,Leonardo Lopes Santos,Gleison Augusto dos |
dc.subject.por.fl_str_mv |
mixed model methodology covariance functions genotype × environment interactions genotypic plasticity forest tree breeding |
topic |
mixed model methodology covariance functions genotype × environment interactions genotypic plasticity forest tree breeding |
description |
ABSTRACT 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 g ^ g ≥ 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 |
2020-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000400485 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000400485 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4499.20200125 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto Agronômico de Campinas |
publisher.none.fl_str_mv |
Instituto Agronômico de Campinas |
dc.source.none.fl_str_mv |
Bragantia v.79 n.4 2020 reponame:Bragantia instname:Instituto Agronômico de Campinas (IAC) instacron:IAC |
instname_str |
Instituto Agronômico de Campinas (IAC) |
instacron_str |
IAC |
institution |
IAC |
reponame_str |
Bragantia |
collection |
Bragantia |
repository.name.fl_str_mv |
Bragantia - Instituto Agronômico de Campinas (IAC) |
repository.mail.fl_str_mv |
bragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br |
_version_ |
1754193307896905728 |