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

Detalhes bibliográficos
Autor(a) principal: Alves,Rodrigo Silva
Data de Publicação: 2020
Outros Autores: 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
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.
id IAC-1_a5d3c86b541285e1d421f5e6b5056c76
oai_identifier_str oai:scielo:S0006-87052020000400485
network_acronym_str IAC-1
network_name_str Bragantia
repository_id_str
spelling 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