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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
03/07/2012 |
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Data da última atualização: |
13/03/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
SILVA, S. F. da; SANTOS, J. Y. B. dos; SOARES, E. B.; LEAO, P. C. de S. |
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Afiliação: |
SAMARA FERREIRA DA SILVA; JARDILINA YVES BEZERRA DOS SANTOS; EVELINE BARROS SOARES; PATRICIA COELHO DE SOUZA LEAO, CPATSA. |
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Título: |
Caracterização fenológica de acessos de uvas de mesa no Submédio São Francisco. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: ENCONTRO DE GENÉTICA DO NORDESTE, 19.; SIMPÓSIO DE GENÉTICA HUMANA E MÉDICA DO NORDESTE, 1.; GENÉTICA NA PRAÇA, 2012, Petrolina, Juazeiro. A genética, a natureza e o ser humano: mudando mentalidades e transformando vidas. Petrolina: Embrapa Semiárido: UNIVASF: SBG, 2012. |
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Descrição Física: |
1 CD-ROM. |
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Idioma: |
Português |
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Conteúdo: |
O presente trabalho teve o objetivo caracterizar a fenologia de 133 acessos, sendo 88 de uvas de mesa com sementes e 45 de uvas de mesa sem sementes, pertencentes ao Banco de Germoplasma da Embrapa Semiárido localizado no Campo Experimental de Mandacaru, Juazeiro-BA. |
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Palavras-Chave: |
Recursos genéticos; Sugraone; Thompson Seedless; Uva de mesa. |
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Thesagro: |
Fenologia; Melhoramento Genético Vegetal; Uva. |
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Thesaurus Nal: |
Grapes; Seedless varieties. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/61646/1/Patricia-2-2012.pdf
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Marc: |
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001 1927530
005 2024-03-13
008 2012 bl uuuu u00u1 u #d
100 1 $aSILVA, S. F. da
245 $aCaracterização fenológica de acessos de uvas de mesa no Submédio São Francisco.
260 $aIn: ENCONTRO DE GENÉTICA DO NORDESTE, 19.; SIMPÓSIO DE GENÉTICA HUMANA E MÉDICA DO NORDESTE, 1.; GENÉTICA NA PRAÇA, 2012, Petrolina, Juazeiro. A genética, a natureza e o ser humano: mudando mentalidades e transformando vidas. Petrolina: Embrapa Semiárido: UNIVASF: SBG$c2012
300 $c1 CD-ROM.
520 $aO presente trabalho teve o objetivo caracterizar a fenologia de 133 acessos, sendo 88 de uvas de mesa com sementes e 45 de uvas de mesa sem sementes, pertencentes ao Banco de Germoplasma da Embrapa Semiárido localizado no Campo Experimental de Mandacaru, Juazeiro-BA.
650 $aGrapes
650 $aSeedless varieties
650 $aFenologia
650 $aMelhoramento Genético Vegetal
650 $aUva
653 $aRecursos genéticos
653 $aSugraone
653 $aThompson Seedless
653 $aUva de mesa
700 1 $aSANTOS, J. Y. B. dos
700 1 $aSOARES, E. B.
700 1 $aLEAO, P. C. de S.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/07/2018 |
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Data da última atualização: |
05/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
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Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
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DOI: |
10.1038/s41437-018-0053-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
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Thesagro: |
Milho; Resistência a Seca. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03081naa a2200349 a 4500
001 2093500
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1038/s41437-018-0053-6$2DOI
100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
700 1 $aGUIMARÃES, C. T.
700 1 $aNAZARIAN, A.
700 1 $aSILVA, L. da C. e
700 1 $aPARENTONI, S. N.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aANONI, C. de O.
700 1 $aPÁDUA, J. M. V.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aRIBEIRO, C. A. G.
700 1 $aMAGALHAES, J. V. de
700 1 $aGARCIA, A. A. F.
700 1 $aSOUZA, J. C. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
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Embrapa Milho e Sorgo (CNPMS) |
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