Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | LOCUS Repositório Institucional da UFV |
Texto Completo: | http://locus.ufv.br/handle/123456789/1400 |
Resumo: | Safflower (Carthamus tinctorius L.) is an oilseed species with a large genetic potential available in genebanks. Source of relevant characters, safflower germplasm banks have shown limited use due to the large number of accessions available in collections. The present study aimed to explore the genetic diversity of safflower through the establishment of more expressive core collections using maximization strategy and the stratification of genotypes in genetic groups. The study also aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower accessions, using the focused identification of germplasm strategy to explore the association and increase the chances of finding safflower genotypes with high oil content. Core collections were established using phenotypic qualitative and quantitative traits data of 1640 safflower accessions from 48 countries. The accessions were stratified into genetic groups according to country s origin and sampled according to the maximization strategy (M strategy) . The established core collections were compared with the base collection using the following validation statistics: chi-square test, mean difference, difference of variances, coincidence rate, variable rate and Shannon index. Magnitude estimates of validation statistics indicated that base collection s genetic variability was preserved in the core collections based on safflower centers of similarity. Core collections stratified by genetic groups consisted in about 60 genotypes, with a mean difference of 7% over the base collection and coincidence rate above 94%. The combined use of the maximization strategy and stratification of genotypes in genetic groups maximized the capture of genetic variation and introduced more efficiency, establishing core collections with a fewer number of accessions. The core collections included approximately 3,75% accessions conserved in safflower base collection. To establish expressive core collections is necessary selecting accessions properly from base collection. The Focused Identification Germplasm Strategy (FIGS) is an efficient method to optimize the selection of useful accessions kept in collections. The FIGS makes use of predictive association between characteristics and environmental variables in the search for genotypes with high probability of containing the trait of interest. The present study aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower genotypes using the FIGS based on machine learning approaches. Random forests, support vector machines and artificial neural networks were used to model the association between oil content of 100 safflower genotypes and 56 ecogeographic parameters. The models accuracies indicated that the distribution of safflower genotypes with high oil content is not random but associated to environmental factors, even with some degree of overlap between the oil content in some environments. The final results suggest that exploring the predictive association between oil content and ecogeographic parameters of original collection site of safflower germplasm increases the chances of finding genotypes with high oil content. |
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Boldt, Alberto Souzahttp://lattes.cnpq.br/7023600988596207Sediyama, Tuneohttp://lattes.cnpq.br/4911178878735418Cruz, Cosme Damiãohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788274A6Motoike, Sérgio Yoshimitsuhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4728221T8Cecon, Paulo Robertohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788114T5Sá, Rogério Oliveira dehttp://lattes.cnpq.br/3925401164572173Dias, Luiz Antonio dos Santoshttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4763137P62015-03-26T12:45:43Z2015-01-262015-03-26T12:45:43Z2014-04-08BOLDT, Alberto Souza. Core collections and association between safflower oil and ecogeographic data by computational intelligence. 2014. 67 f. Tese (Doutorado em Genética animal; Genética molecular e de microrganismos; Genética quantitativa; Genética vegetal; Me) - Universidade Federal de Viçosa, Viçosa, 2014.http://locus.ufv.br/handle/123456789/1400Safflower (Carthamus tinctorius L.) is an oilseed species with a large genetic potential available in genebanks. Source of relevant characters, safflower germplasm banks have shown limited use due to the large number of accessions available in collections. The present study aimed to explore the genetic diversity of safflower through the establishment of more expressive core collections using maximization strategy and the stratification of genotypes in genetic groups. The study also aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower accessions, using the focused identification of germplasm strategy to explore the association and increase the chances of finding safflower genotypes with high oil content. Core collections were established using phenotypic qualitative and quantitative traits data of 1640 safflower accessions from 48 countries. The accessions were stratified into genetic groups according to country s origin and sampled according to the maximization strategy (M strategy) . The established core collections were compared with the base collection using the following validation statistics: chi-square test, mean difference, difference of variances, coincidence rate, variable rate and Shannon index. Magnitude estimates of validation statistics indicated that base collection s genetic variability was preserved in the core collections based on safflower centers of similarity. Core collections stratified by genetic groups consisted in about 60 genotypes, with a mean difference of 7% over the base collection and coincidence rate above 94%. The combined use of the maximization strategy and stratification of genotypes in genetic groups maximized the capture of genetic variation and introduced more efficiency, establishing core collections with a fewer number of accessions. The core collections included approximately 3,75% accessions conserved in safflower base collection. To establish expressive core collections is necessary selecting accessions properly from base collection. The Focused Identification Germplasm Strategy (FIGS) is an efficient method to optimize the selection of useful accessions kept in collections. The FIGS makes use of predictive association between characteristics and environmental variables in the search for genotypes with high probability of containing the trait of interest. The present study aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower genotypes using the FIGS based on machine learning approaches. Random forests, support vector machines and artificial neural networks were used to model the association between oil content of 100 safflower genotypes and 56 ecogeographic parameters. The models accuracies indicated that the distribution of safflower genotypes with high oil content is not random but associated to environmental factors, even with some degree of overlap between the oil content in some environments. The final results suggest that exploring the predictive association between oil content and ecogeographic parameters of original collection site of safflower germplasm increases the chances of finding genotypes with high oil content.Cártamo (Carthamus tinctorius L.) é uma espécie oleaginosa com um grande potencial genético confinado nos bancos de germoplasma. Fonte de características relevantes, os bancos de germoplasma de cártamo tem apresentado uso limitado devido ao grande número de acessos disponíveis nas coleções. O presente trabalho objetivou explorar a diversidade genética de cártamo por meio do estabelecimento de coleções nucleares mais expressivas utilizando as estratégias de maximização e estratificação de genótipos em grupos genéticos conhecidos. O trabalho também objetivou investigar a existência de associação preditiva entre teor de óleo e variáveis ecogeográficas da origem de acessos de cártamo, utilizando a estratégia de identificação focada de germoplasma para explorar a associação e aumentar as chances de encontrar genótipos de cártamo com alto teor óleo. No estabelecimento das coleções nucleares foram utilizados caracteres fenotípicos, qualitativos e quantitativos, de 1640 acessos de cártamo provenientes de 48 países. Os acessos foram estratificados nos grupos genéticos de acordo com país de origem e amostrados segundo a estratégia de maximização. As coleções nucleares estabelecidas foram comparadas com a coleção base utilizando estatísticas de validação adequadas. As magnitudes das estimativas das estatísticas de validação indicaram que a variabilidade genética dos acessos da coleção base foi preservada nas coleções nucleares estabelecidas. As coleções nucleares estratificadas por grupos genéticos apresentaram aproximadamente 60 genótipos, com diferença média de apenas 7% em relação a coleção base e com taxa de coincidência de superior a 94%. O uso conjunto da estratégia de maximização e da estratificação dos genótipos em grupos genéticos maximizou a captação da variabilidade genética e introduziu maior eficiência no estabelecimento das coleções nucleares ao selecionar uma quantidade reduzida de acessos. As coleções nucleares estabelecidas incluíram aproximadamente 3.75% dos acessos conservados na coleção base. Para estabelecer coleções nucleares expressivas é necessário que os acessos sejam selecionados da coleção base de maneira apropriada. A estratégia de identificação focada de germoplasma é um método eficiente de otimizar a seleção de acessos presentes nos bancos de germoplasma. A FIGS faz uso da associação preditiva entre características e variáveis ambientais na busca de genótipos com maior probabilidade de conter a característica de interesse. Florestas aleatórias, máquinas de vetor de suporte e redes neurais artificias foram utilizadas para modelar a associação entre teor de óleo de 100 genótipos cártamo e 56 variáveis ecogeográficas. As acurácias dos modelos utilizados mostraram que a distribuição de genótipos de cártamo com alto teor de óleo não é aleatória mas ligada a fatores ambientais, mesmo com certo grau de sobreposição entre os teores de óleo em alguns ambientes. Os resultados finais sugerem que explorar a associação preditiva entre o teor de óleo e as características ecogeográficas do local de origem do germoplasma aumenta as chances de encontrar genótipos com alto teor óleo.application/pdfporUniversidade Federal de ViçosaDoutorado em Genética e MelhoramentoUFVBRGenética animal; Genética molecular e de microrganismos; Genética quantitativa; Genética vegetal; MePlantas oleoginosasCarthamus tinctoriusAçafrãoGermoplasma vegetalColeção nuclearOleaginous plantsCarthamus tinctoriusSaffronPlant germplasmCore collectionCNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETALColeções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacionalCore collections and association between safflower oil and ecogeographic data by computational intelligenceinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALtexto completo.pdfapplication/pdf1017397https://locus.ufv.br//bitstream/123456789/1400/1/texto%20completo.pdf51b8f66863f7eab41a9c5596e5bd5367MD51TEXTtexto completo.pdf.txttexto completo.pdf.txtExtracted texttext/plain134535https://locus.ufv.br//bitstream/123456789/1400/2/texto%20completo.pdf.txt6aca5d173edd4b0ae9ca8b33d477941fMD52THUMBNAILtexto completo.pdf.jpgtexto completo.pdf.jpgIM Thumbnailimage/jpeg3732https://locus.ufv.br//bitstream/123456789/1400/3/texto%20completo.pdf.jpg3360f42d6cc4306b4e228dab82564690MD53123456789/14002016-04-07 23:07:39.439oai:locus.ufv.br:123456789/1400Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452016-04-08T02:07:39LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false |
dc.title.por.fl_str_mv |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
dc.title.alternative.eng.fl_str_mv |
Core collections and association between safflower oil and ecogeographic data by computational intelligence |
title |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
spellingShingle |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional Boldt, Alberto Souza Plantas oleoginosas Carthamus tinctorius Açafrão Germoplasma vegetal Coleção nuclear Oleaginous plants Carthamus tinctorius Saffron Plant germplasm Core collection CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
title_short |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
title_full |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
title_fullStr |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
title_full_unstemmed |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
title_sort |
Coleções nucleares e associação do teor de óleo de cártamo com variáveis ecogeográficas por inteligência computacional |
author |
Boldt, Alberto Souza |
author_facet |
Boldt, Alberto Souza |
author_role |
author |
dc.contributor.authorLattes.por.fl_str_mv |
http://lattes.cnpq.br/7023600988596207 |
dc.contributor.author.fl_str_mv |
Boldt, Alberto Souza |
dc.contributor.advisor-co1.fl_str_mv |
Sediyama, Tuneo |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/4911178878735418 |
dc.contributor.advisor-co2.fl_str_mv |
Cruz, Cosme Damião |
dc.contributor.advisor-co2Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788274A6 |
dc.contributor.advisor1.fl_str_mv |
Motoike, Sérgio Yoshimitsu |
dc.contributor.advisor1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4728221T8 |
dc.contributor.referee1.fl_str_mv |
Cecon, Paulo Roberto |
dc.contributor.referee1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788114T5 |
dc.contributor.referee2.fl_str_mv |
Sá, Rogério Oliveira de |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/3925401164572173 |
dc.contributor.referee3.fl_str_mv |
Dias, Luiz Antonio dos Santos |
dc.contributor.referee3Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4763137P6 |
contributor_str_mv |
Sediyama, Tuneo Cruz, Cosme Damião Motoike, Sérgio Yoshimitsu Cecon, Paulo Roberto Sá, Rogério Oliveira de Dias, Luiz Antonio dos Santos |
dc.subject.por.fl_str_mv |
Plantas oleoginosas Carthamus tinctorius Açafrão Germoplasma vegetal Coleção nuclear |
topic |
Plantas oleoginosas Carthamus tinctorius Açafrão Germoplasma vegetal Coleção nuclear Oleaginous plants Carthamus tinctorius Saffron Plant germplasm Core collection CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
dc.subject.eng.fl_str_mv |
Oleaginous plants Carthamus tinctorius Saffron Plant germplasm Core collection |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
description |
Safflower (Carthamus tinctorius L.) is an oilseed species with a large genetic potential available in genebanks. Source of relevant characters, safflower germplasm banks have shown limited use due to the large number of accessions available in collections. The present study aimed to explore the genetic diversity of safflower through the establishment of more expressive core collections using maximization strategy and the stratification of genotypes in genetic groups. The study also aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower accessions, using the focused identification of germplasm strategy to explore the association and increase the chances of finding safflower genotypes with high oil content. Core collections were established using phenotypic qualitative and quantitative traits data of 1640 safflower accessions from 48 countries. The accessions were stratified into genetic groups according to country s origin and sampled according to the maximization strategy (M strategy) . The established core collections were compared with the base collection using the following validation statistics: chi-square test, mean difference, difference of variances, coincidence rate, variable rate and Shannon index. Magnitude estimates of validation statistics indicated that base collection s genetic variability was preserved in the core collections based on safflower centers of similarity. Core collections stratified by genetic groups consisted in about 60 genotypes, with a mean difference of 7% over the base collection and coincidence rate above 94%. The combined use of the maximization strategy and stratification of genotypes in genetic groups maximized the capture of genetic variation and introduced more efficiency, establishing core collections with a fewer number of accessions. The core collections included approximately 3,75% accessions conserved in safflower base collection. To establish expressive core collections is necessary selecting accessions properly from base collection. The Focused Identification Germplasm Strategy (FIGS) is an efficient method to optimize the selection of useful accessions kept in collections. The FIGS makes use of predictive association between characteristics and environmental variables in the search for genotypes with high probability of containing the trait of interest. The present study aimed to investigate the existence of predictive association between oil content and ecogeographic parameters of the original site of safflower genotypes using the FIGS based on machine learning approaches. Random forests, support vector machines and artificial neural networks were used to model the association between oil content of 100 safflower genotypes and 56 ecogeographic parameters. The models accuracies indicated that the distribution of safflower genotypes with high oil content is not random but associated to environmental factors, even with some degree of overlap between the oil content in some environments. The final results suggest that exploring the predictive association between oil content and ecogeographic parameters of original collection site of safflower germplasm increases the chances of finding genotypes with high oil content. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-04-08 |
dc.date.accessioned.fl_str_mv |
2015-03-26T12:45:43Z |
dc.date.available.fl_str_mv |
2015-01-26 2015-03-26T12:45:43Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
BOLDT, Alberto Souza. Core collections and association between safflower oil and ecogeographic data by computational intelligence. 2014. 67 f. Tese (Doutorado em Genética animal; Genética molecular e de microrganismos; Genética quantitativa; Genética vegetal; Me) - Universidade Federal de Viçosa, Viçosa, 2014. |
dc.identifier.uri.fl_str_mv |
http://locus.ufv.br/handle/123456789/1400 |
identifier_str_mv |
BOLDT, Alberto Souza. Core collections and association between safflower oil and ecogeographic data by computational intelligence. 2014. 67 f. Tese (Doutorado em Genética animal; Genética molecular e de microrganismos; Genética quantitativa; Genética vegetal; Me) - Universidade Federal de Viçosa, Viçosa, 2014. |
url |
http://locus.ufv.br/handle/123456789/1400 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Universidade Federal de Viçosa |
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Doutorado em Genética e Melhoramento |
dc.publisher.initials.fl_str_mv |
UFV |
dc.publisher.country.fl_str_mv |
BR |
dc.publisher.department.fl_str_mv |
Genética animal; Genética molecular e de microrganismos; Genética quantitativa; Genética vegetal; Me |
publisher.none.fl_str_mv |
Universidade Federal de Viçosa |
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LOCUS Repositório Institucional da UFV |
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