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| Registros recuperados : 23 | |
| 12. |  | BRITO, F. C. de; ÁVILA, S.; PACHECO, S. D. G.; SILVEIRA, A. C. da; VARGAS, V. M. F.; LAZZAROTTO, M. Avaliação do desenvolvimento dos frutos de Ilex paraguariensis St.Hil. In: SEMANA DE APERFEIÇOAMENTO EM ENGENHARIA FLORESTAL DA UFPR, 2., 2018, Curitiba. Anais. Curitiba: CIFLOMA, 2018. 4 p.| Biblioteca(s): Embrapa Florestas. |
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| 13. | | LAZZAROTTO, S. R. da S.; SCHERRUTH, M. S. de F.; CALIXTO, P. S.; CARRARO, M. de M.; SILVEIRA, A. C. da; LAZZAROTTO, M. Método de Folin Ciocalteau adaptado para quantificar polifenóis em extratos de erva-mate. Movimenta, v. 13, n. 3, p. 419-426, 2020.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | SIQUEIRA, G. L. de A. de; LAZZAROTTO, S. R. da S.; HORNUNG, P. S.; LOURENÇON, T. V.; SILVEIRA, A. C. da; SCHNITZLER, E.; LAZZAROTTO, M. Effect of acid alcoholic solution in waxy maize starch treatment. In: CONGRESSO BRASILEIRO DE ANÁLISE TÉRMICA E CALORIMETRIA, 10.; CONGRESSO PAN AMERICANO DE ANÁLISE TÉRMICA E CALORIMETRIA, 4., 2016, São Paulo. Trabalhos. [S.l.]: ABRATEC, 2016. 4 p. Disponível online.| Biblioteca(s): Embrapa Florestas. |
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| 15. | | LAZZAROTTO, S. R. da S.; HORNUNG, P. S.; SIQUEIRA, G. L. de A. de; SILVEIRA, A. C. da; SCHNITZLER, E.; LAZZAROTTO, M. Effect of the KMnO4 treatment on the "pinhão" starch. In: CONGRESSO BRASILEIRO DE ANÁLISE TÉRMICA E CALORIMETRIA, 10.; CONGRESSO PAN AMERICANO DE ANÁLISE TÉRMICA E CALORIMETRIA, 4., 2016, São Paulo. Trabalhos. [S.l.]: ABRATEC, 2016. 4 p. Disponível online.| Biblioteca(s): Embrapa Florestas. |
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| 16. | | SILVEIRA, A. C. da; SIQUEIRA, G. L. de A. de; MAYER, F. M.; MIGUEL, O. G.; ZINI, C. A.; LAZZAROTTO, M. Estudo da Composição dos Óleos Essenciais de três Genótipos de Eucalyptus spp. In: SEMANA DE APERFEIÇOAMENTO EM ENGENHARIA FLORESTAL DA UFPR, 2., 2018, Curitiba. Anais. Curitiba: CIFLOMA, 2018. 4 p.| Biblioteca(s): Embrapa Florestas. |
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| 17. | | SIQUEIRA, G. L. de A. de; SILVEIRA, A. C. da; LAZZAROTTO, S. R. da S.; GODOY, R. C. B. de; SCHNITZLER, E.; LAZZAROTTO, M. Hydrolysis of the low gelatinization temperature Araucaria angustifolia pine seed starch. Journal of Thermal Analysis and Calorimetry, v. 138, n. 2, p. 1269-1278, Oct. 2019.| Biblioteca(s): Embrapa Florestas. |
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| 18. | | SIQUEIRA, G. L. de A. de; HORNUNG, P. S.; SILVEIRA, A. C. da; LAZZAROTTO, S. R. da S.; CORDOBA, L. do P.; SCHNITZLER, E.; LAZZAROTTO, M. Impact of treatment with HCL/alcoholic in the modification of corn starch. Journal of Thermal Analysis and Calorimetry, v. 129, n. 3, p. 1705-1713, Dec. 2017.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | SIQUEIRA, G. L. de A. de; LAZZAROTTU, S. R. da S.; HORNUNG, P. S.; SILVEIRA, A. C. da; BANNACH, G.; SCHNITZLER, E.; LAZZAROTTO, M. Evaluation of double treated starches using thermal tools. In: CONGRESSO BRASILEIRO DE ANÁLISE TÉRMICA E CALORIMETRIA, 10.; CONGRESSO PAN AMERICANO DE ANÁLISE TÉRMICA E CALORIMETRIA, 4., 2016, São Paulo. Trabalhos. [S.l.]: ABRATEC, 2016. 4 p. Disponível online.| Biblioteca(s): Embrapa Florestas. |
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| 20. | | ZANONI, P. R. S.; SILVEIRA, A. C. da; LIMA, E. A. de; MATTOS, P. P. de; CURTO, R. de A.; MAGALHAES, W. L. E.; LAZZAROTTO, M. Uso dos produtos da araucária: madeira e coprodutos. In: SOUSA, V. A. de; FRITZSONS, E.; PINTO JUNIOR, J. E.; AGUIAR, A. V. de (ed.). Araucária: pesquisa e desenvolvimento no Brasil. Brasília, DF: Embrapa, 2021. cap. 17, p. 337-360.| Biblioteca(s): Embrapa Florestas. |
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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: |
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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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