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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
18/03/2014 |
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Data da última atualização: |
18/03/2014 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BAPTISTA, R. C.; FERNANDES, M. A. M.; GILAVERTE, S.; QUEIROZ, S. C. do N. de; FERRACINI, V. L.; ASSALIN, M. R.; MONTEIRO, A. L. G.; REYES, F. G. R. |
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Afiliação: |
SONIA CLAUDIA DO N DE QUEIROZ, CNPMA; VERA LUCIA FERRACINI, CNPMA; MARCIA REGINA ASSALIN, CNPMA. |
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Título: |
Pharmacokinetic parameters of moxidectin residue in the serum of lambs. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
In: ANNUAL MEETING BRAZILIAN SOCIETY OF ANIMAL SCIENCE, 50., 2013, Campinas. The integration of knowledge in animal production: abstracts. Campinas: SBZ, 2013. 1 CD ROM. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Anthelmintic; Persistence; Serium concentration; Subcutaneous administration. |
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Thesaurus Nal: |
sheep. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/99528/1/2013RA070.pdf
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Marc: |
LEADER 00854nam a2200241 a 4500
001 1982687
005 2014-03-18
008 2013 bl uuuu u00u1 u #d
100 1 $aBAPTISTA, R. C.
245 $aPharmacokinetic parameters of moxidectin residue in the serum of lambs.$h[electronic resource]
260 $aIn: ANNUAL MEETING BRAZILIAN SOCIETY OF ANIMAL SCIENCE, 50., 2013, Campinas. The integration of knowledge in animal production: abstracts. Campinas: SBZ, 2013. 1 CD ROM.$c2013
650 $asheep
653 $aAnthelmintic
653 $aPersistence
653 $aSerium concentration
653 $aSubcutaneous administration
700 1 $aFERNANDES, M. A. M.
700 1 $aGILAVERTE, S.
700 1 $aQUEIROZ, S. C. do N. de
700 1 $aFERRACINI, V. L.
700 1 $aASSALIN, M. R.
700 1 $aMONTEIRO, A. L. G.
700 1 $aREYES, F. G. R.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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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: |
06/08/2020 |
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Data da última atualização: |
07/05/2021 |
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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: |
OLIVEIRA, A. A. de; RESENDE JÚNIOR, M. F. R.; FERRÃO, L. F. V.; AMADEU, R. R.; GUIMARAES, L. J. M.; GUIMARÃES, C. T.; PASTINA, M. M.; MARGARIDO, G. R. A. |
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Afiliação: |
Amanda Avelar de Oliveira, Escola Superior de Agricultura "Luiz de Queiroz"; Marcio F. R. Resende Júnior, University of Florida; Luís Felipe Ventorim Ferrão, University of Florida; Rodrigo Rampazo Amadeu, University of Florida; LAURO JOSE MOREIRA GUIMARAES, CNPMS; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; Gabriel Rodrigues Alves Margarido, Escola Superior de Agricultura "Luiz de Queiroz'. |
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Título: |
Genomic prediction applied to multiple traits and environments in second season maize hybrids. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Heredity, v. 125, n. 1/2, p. 60-72, 2020. |
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DOI: |
https://doi.org/10.1038/s41437-020-0321-0 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary. MenosGenomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to pre... Mostrar Tudo |
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Thesagro: |
Genética; Milho; Seleção Genética. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02519naa a2200253 a 4500
001 2124209
005 2021-05-07
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1038/s41437-020-0321-0$2DOI
100 1 $aOLIVEIRA, A. A. de
245 $aGenomic prediction applied to multiple traits and environments in second season maize hybrids.$h[electronic resource]
260 $c2020
520 $aGenomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.
650 $aGenética
650 $aMilho
650 $aSeleção Genética
700 1 $aRESENDE JÚNIOR, M. F. R.
700 1 $aFERRÃO, L. F. V.
700 1 $aAMADEU, R. R.
700 1 $aGUIMARAES, L. J. M.
700 1 $aGUIMARÃES, C. T.
700 1 $aPASTINA, M. M.
700 1 $aMARGARIDO, G. R. A.
773 $tHeredity$gv. 125, n. 1/2, p. 60-72, 2020.
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