Multivariate diallel analysis by factor analysis for establish mega-traits
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , , |
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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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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1754302868540620800 |