Characterization of tomato generations according to a three-way data analysis
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=S0006-87052020000100008 |
Resumo: | ABSTRACT Availability of a three-way data analysis to characterize two consecutive tomato (Solanum lycopersicum) generations is necessary to continue a plant breeding program with less uncertainty. The aim of this work was to analyze tomato fruit quality from F2 and F3 populations by two three-way data analysis: multiple factorial analysis (MFA) and generalized procrustes analysis (GPA). These techniques have the same main objective, searching for a common structure, but they achieve it in different ways. This work evaluated 18 tomato genotypes, represented by individual plants in F2 and selfed families in F3. The same quantitative traits related to fruit quality were measured in both generations. The first two axes of the MFA represented 51.0% of the total variability. From the representation of the genotypes on these axes, traits differing from one generation to another were identified. The first two axes of the GPA represented 56.4% of the total variability. This analysis provided a table of analysis of variance (ANOVA), which corroborated the graphic and analytical interpretations of the MFA, a technique that provides the composition of the obtained axes. The comparison between the results obtained from these techniques indicated that both MFA and GPA allowed the detection of genotypes with discrepancies between the two generations. The MFA technique presented the advantage of studying graphically and analytically the nature and degree of phenotypic differences among genotypes in both generations, while the GPA complemented the analysis with an ANOVA, achieving the quantification of statistical significances for the discrepancies or similarities between them. |
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Characterization of tomato generations according to a three-way data analysisthree-way data analysismultiple factorial analysisgeneralized procrustes analysisplant breedingplant genetic resourcesABSTRACT Availability of a three-way data analysis to characterize two consecutive tomato (Solanum lycopersicum) generations is necessary to continue a plant breeding program with less uncertainty. The aim of this work was to analyze tomato fruit quality from F2 and F3 populations by two three-way data analysis: multiple factorial analysis (MFA) and generalized procrustes analysis (GPA). These techniques have the same main objective, searching for a common structure, but they achieve it in different ways. This work evaluated 18 tomato genotypes, represented by individual plants in F2 and selfed families in F3. The same quantitative traits related to fruit quality were measured in both generations. The first two axes of the MFA represented 51.0% of the total variability. From the representation of the genotypes on these axes, traits differing from one generation to another were identified. The first two axes of the GPA represented 56.4% of the total variability. This analysis provided a table of analysis of variance (ANOVA), which corroborated the graphic and analytical interpretations of the MFA, a technique that provides the composition of the obtained axes. The comparison between the results obtained from these techniques indicated that both MFA and GPA allowed the detection of genotypes with discrepancies between the two generations. The MFA technique presented the advantage of studying graphically and analytically the nature and degree of phenotypic differences among genotypes in both generations, while the GPA complemented the analysis with an ANOVA, achieving the quantification of statistical significances for the discrepancies or similarities between them.Instituto Agronômico de Campinas2020-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000100008Bragantia v.79 n.1 2020reponame:Bragantiainstname:Instituto Agronômico de Campinas (IAC)instacron:IAC10.1590/1678-4499.20190047info:eu-repo/semantics/openAccessMedico,Ana Paula DelCabodevila,Victoria GuadalupeVitelleschi,Maria SusanaPratta,Guillermo Raúleng2020-04-28T00:00:00Zoai:scielo:S0006-87052020000100008Revistahttps://www.scielo.br/j/brag/https://old.scielo.br/oai/scielo-oai.phpbragantia@iac.sp.gov.br||bragantia@iac.sp.gov.br1678-44990006-8705opendoar:2020-04-28T00:00Bragantia - Instituto Agronômico de Campinas (IAC)false |
dc.title.none.fl_str_mv |
Characterization of tomato generations according to a three-way data analysis |
title |
Characterization of tomato generations according to a three-way data analysis |
spellingShingle |
Characterization of tomato generations according to a three-way data analysis Medico,Ana Paula Del three-way data analysis multiple factorial analysis generalized procrustes analysis plant breeding plant genetic resources |
title_short |
Characterization of tomato generations according to a three-way data analysis |
title_full |
Characterization of tomato generations according to a three-way data analysis |
title_fullStr |
Characterization of tomato generations according to a three-way data analysis |
title_full_unstemmed |
Characterization of tomato generations according to a three-way data analysis |
title_sort |
Characterization of tomato generations according to a three-way data analysis |
author |
Medico,Ana Paula Del |
author_facet |
Medico,Ana Paula Del Cabodevila,Victoria Guadalupe Vitelleschi,Maria Susana Pratta,Guillermo Raúl |
author_role |
author |
author2 |
Cabodevila,Victoria Guadalupe Vitelleschi,Maria Susana Pratta,Guillermo Raúl |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Medico,Ana Paula Del Cabodevila,Victoria Guadalupe Vitelleschi,Maria Susana Pratta,Guillermo Raúl |
dc.subject.por.fl_str_mv |
three-way data analysis multiple factorial analysis generalized procrustes analysis plant breeding plant genetic resources |
topic |
three-way data analysis multiple factorial analysis generalized procrustes analysis plant breeding plant genetic resources |
description |
ABSTRACT Availability of a three-way data analysis to characterize two consecutive tomato (Solanum lycopersicum) generations is necessary to continue a plant breeding program with less uncertainty. The aim of this work was to analyze tomato fruit quality from F2 and F3 populations by two three-way data analysis: multiple factorial analysis (MFA) and generalized procrustes analysis (GPA). These techniques have the same main objective, searching for a common structure, but they achieve it in different ways. This work evaluated 18 tomato genotypes, represented by individual plants in F2 and selfed families in F3. The same quantitative traits related to fruit quality were measured in both generations. The first two axes of the MFA represented 51.0% of the total variability. From the representation of the genotypes on these axes, traits differing from one generation to another were identified. The first two axes of the GPA represented 56.4% of the total variability. This analysis provided a table of analysis of variance (ANOVA), which corroborated the graphic and analytical interpretations of the MFA, a technique that provides the composition of the obtained axes. The comparison between the results obtained from these techniques indicated that both MFA and GPA allowed the detection of genotypes with discrepancies between the two generations. The MFA technique presented the advantage of studying graphically and analytically the nature and degree of phenotypic differences among genotypes in both generations, while the GPA complemented the analysis with an ANOVA, achieving the quantification of statistical significances for the discrepancies or similarities between them. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-03-01 |
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=S0006-87052020000100008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052020000100008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4499.20190047 |
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.1 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_ |
1754193307305508864 |