Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits

Detalhes bibliográficos
Autor(a) principal: Santana,Josefa Grasiela Silva
Data de Publicação: 2022
Outros Autores: Ramos,Helaine Christine Cancela, Santa-Catarina,Renato, Vettorazzi,Julio Cesar Fiorio, Miranda,Daniel Pereira, Pirovani,Adriana Azevedo Vimercati, Poltronieri,Tathianne Pastana de Sousa, Azevedo,Alinne Oliveira Nunes, Duarte,Rafaela Pereira, Bohry,Dieimes, Pereira,Messias Gonzaga
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Bragantia
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052022000100240
Resumo: ABSTRACT Selection indexes represent the real efforts of a breeding program to obtain genetic gains for various significant traits simultaneously. As such, this study selected superior F5 lines by combined selection for fruit quality using an index based on predicted genotypic values via residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology weighted by agronomic weights. To do so, 97 F5 papaya lines obtained by the single seed descent method, resulting from the biparental cross between the genotypes JS-12 and Sekati, were evaluated for the main traits related to fruit quality. Results of the analysis of deviance and the genetic parameters indicated that there was genetic variability, indicating possible success in the selection process. Based on the selection index, 29 lines were selected as superior for fruit quality with ‘Formosa’ pattern. The strategy of the selection index combining BLUPs of multiple traits of interest associated with agronomic weights allows maximizing genetic progress while discarding less-promising genotypes.
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spelling Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traitssegregating populationmixed modelsdigital phenotypingfruit marketgenetic breedingABSTRACT Selection indexes represent the real efforts of a breeding program to obtain genetic gains for various significant traits simultaneously. As such, this study selected superior F5 lines by combined selection for fruit quality using an index based on predicted genotypic values via residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology weighted by agronomic weights. To do so, 97 F5 papaya lines obtained by the single seed descent method, resulting from the biparental cross between the genotypes JS-12 and Sekati, were evaluated for the main traits related to fruit quality. Results of the analysis of deviance and the genetic parameters indicated that there was genetic variability, indicating possible success in the selection process. Based on the selection index, 29 lines were selected as superior for fruit quality with ‘Formosa’ pattern. The strategy of the selection index combining BLUPs of multiple traits of interest associated with agronomic weights allows maximizing genetic progress while discarding less-promising genotypes.Instituto Agronômico de Campinas2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052022000100240Bragantia v.81 2022reponame:Bragantiainstname:Instituto Agronômico de Campinas (IAC)instacron:IAC10.1590/1678-4499.20220040info:eu-repo/semantics/openAccessSantana,Josefa Grasiela SilvaRamos,Helaine Christine CancelaSanta-Catarina,RenatoVettorazzi,Julio Cesar FiorioMiranda,Daniel PereiraPirovani,Adriana Azevedo VimercatiPoltronieri,Tathianne Pastana de SousaAzevedo,Alinne Oliveira NunesDuarte,Rafaela PereiraBohry,DieimesPereira,Messias Gonzagaeng2022-11-24T00:00:00Zoai:scielo:S0006-87052022000100240Revistahttps://www.scielo.br/j/brag/https://old.scielo.br/oai/scielo-oai.phpbragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br1678-44990006-8705opendoar:2022-11-24T00:00Bragantia - Instituto Agronômico de Campinas (IAC)false
dc.title.none.fl_str_mv Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
title Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
spellingShingle Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
Santana,Josefa Grasiela Silva
segregating population
mixed models
digital phenotyping
fruit market
genetic breeding
title_short Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
title_full Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
title_fullStr Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
title_full_unstemmed Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
title_sort Use of an index based on best linear unbiased prediction value for the selection of superior papaya lines for multiple traits
author Santana,Josefa Grasiela Silva
author_facet Santana,Josefa Grasiela Silva
Ramos,Helaine Christine Cancela
Santa-Catarina,Renato
Vettorazzi,Julio Cesar Fiorio
Miranda,Daniel Pereira
Pirovani,Adriana Azevedo Vimercati
Poltronieri,Tathianne Pastana de Sousa
Azevedo,Alinne Oliveira Nunes
Duarte,Rafaela Pereira
Bohry,Dieimes
Pereira,Messias Gonzaga
author_role author
author2 Ramos,Helaine Christine Cancela
Santa-Catarina,Renato
Vettorazzi,Julio Cesar Fiorio
Miranda,Daniel Pereira
Pirovani,Adriana Azevedo Vimercati
Poltronieri,Tathianne Pastana de Sousa
Azevedo,Alinne Oliveira Nunes
Duarte,Rafaela Pereira
Bohry,Dieimes
Pereira,Messias Gonzaga
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Santana,Josefa Grasiela Silva
Ramos,Helaine Christine Cancela
Santa-Catarina,Renato
Vettorazzi,Julio Cesar Fiorio
Miranda,Daniel Pereira
Pirovani,Adriana Azevedo Vimercati
Poltronieri,Tathianne Pastana de Sousa
Azevedo,Alinne Oliveira Nunes
Duarte,Rafaela Pereira
Bohry,Dieimes
Pereira,Messias Gonzaga
dc.subject.por.fl_str_mv segregating population
mixed models
digital phenotyping
fruit market
genetic breeding
topic segregating population
mixed models
digital phenotyping
fruit market
genetic breeding
description ABSTRACT Selection indexes represent the real efforts of a breeding program to obtain genetic gains for various significant traits simultaneously. As such, this study selected superior F5 lines by combined selection for fruit quality using an index based on predicted genotypic values via residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology weighted by agronomic weights. To do so, 97 F5 papaya lines obtained by the single seed descent method, resulting from the biparental cross between the genotypes JS-12 and Sekati, were evaluated for the main traits related to fruit quality. Results of the analysis of deviance and the genetic parameters indicated that there was genetic variability, indicating possible success in the selection process. Based on the selection index, 29 lines were selected as superior for fruit quality with ‘Formosa’ pattern. The strategy of the selection index combining BLUPs of multiple traits of interest associated with agronomic weights allows maximizing genetic progress while discarding less-promising genotypes.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
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=S0006-87052022000100240
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052022000100240
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-4499.20220040
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.81 2022
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
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