Multivariate diallel analysis by factor analysis for establish mega-traits

Detalhes bibliográficos
Autor(a) principal: NARDINO,MAICON
Data de Publicação: 2020
Outros Autores: BARROS,WILLIAN S., OLIVOTO,TIAGO, CRUZ,COSME DAMIÃO, SILVA,FABYANO F. E, PELEGRIN,ALAN J. DE, SOUZA,VELCI Q. DE, CARVALHO,IVAN R., SZARESKI,VINICIUS J., OLIVEIRA,ANTONIO C. DE, MAIA,LUCIANO C. DA, KONFLANZ,VALMOR A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652020000201013
Resumo: Abstract In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1’s of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1’s with lower plant stature, tassel size, and higher grain yield.
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spelling Multivariate diallel analysis by factor analysis for establish mega-traitsZea mays L.multivariate analysisselection multivariatemulti-enviromentsAbstract In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1’s of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1’s with lower plant stature, tassel size, and higher grain yield.Academia Brasileira de Ciências2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652020000201013Anais da Academia Brasileira de Ciências v.92 suppl.1 2020reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202020180874info:eu-repo/semantics/openAccessNARDINO,MAICONBARROS,WILLIAN S.OLIVOTO,TIAGOCRUZ,COSME DAMIÃOSILVA,FABYANO F. EPELEGRIN,ALAN J. DESOUZA,VELCI Q. DECARVALHO,IVAN R.SZARESKI,VINICIUS J.OLIVEIRA,ANTONIO C. DEMAIA,LUCIANO C. DAKONFLANZ,VALMOR A.eng2020-05-28T00:00:00Zoai:scielo:S0001-37652020000201013Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2020-05-28T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Multivariate diallel analysis by factor analysis for establish mega-traits
title Multivariate diallel analysis by factor analysis for establish mega-traits
spellingShingle Multivariate diallel analysis by factor analysis for establish mega-traits
NARDINO,MAICON
Zea mays L.
multivariate analysis
selection multivariate
multi-enviroments
title_short Multivariate diallel analysis by factor analysis for establish mega-traits
title_full Multivariate diallel analysis by factor analysis for establish mega-traits
title_fullStr Multivariate diallel analysis by factor analysis for establish mega-traits
title_full_unstemmed Multivariate diallel analysis by factor analysis for establish mega-traits
title_sort Multivariate diallel analysis by factor analysis for establish mega-traits
author NARDINO,MAICON
author_facet NARDINO,MAICON
BARROS,WILLIAN S.
OLIVOTO,TIAGO
CRUZ,COSME DAMIÃO
SILVA,FABYANO F. E
PELEGRIN,ALAN J. DE
SOUZA,VELCI Q. DE
CARVALHO,IVAN R.
SZARESKI,VINICIUS J.
OLIVEIRA,ANTONIO C. DE
MAIA,LUCIANO C. DA
KONFLANZ,VALMOR A.
author_role author
author2 BARROS,WILLIAN S.
OLIVOTO,TIAGO
CRUZ,COSME DAMIÃO
SILVA,FABYANO F. E
PELEGRIN,ALAN J. DE
SOUZA,VELCI Q. DE
CARVALHO,IVAN R.
SZARESKI,VINICIUS J.
OLIVEIRA,ANTONIO C. DE
MAIA,LUCIANO C. DA
KONFLANZ,VALMOR A.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv NARDINO,MAICON
BARROS,WILLIAN S.
OLIVOTO,TIAGO
CRUZ,COSME DAMIÃO
SILVA,FABYANO F. E
PELEGRIN,ALAN J. DE
SOUZA,VELCI Q. DE
CARVALHO,IVAN R.
SZARESKI,VINICIUS J.
OLIVEIRA,ANTONIO C. DE
MAIA,LUCIANO C. DA
KONFLANZ,VALMOR A.
dc.subject.por.fl_str_mv Zea mays L.
multivariate analysis
selection multivariate
multi-enviroments
topic Zea mays L.
multivariate analysis
selection multivariate
multi-enviroments
description Abstract In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1’s of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1’s with lower plant stature, tassel size, and higher grain yield.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
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=S0001-37652020000201013
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652020000201013
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765202020180874
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 Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.92 suppl.1 2020
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
instacron_str ABC
institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
repository.mail.fl_str_mv ||aabc@abc.org.br
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