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| Registros recuperados : 1.748 | |
| 1. |  | MARTINS, C. L.; FORNAZIER, M. J.; DE MUNER, L. H.; SARTORI, R.; MUSSI, M. B. Análise econômica do desempenho produtivo de clones do Conilon Vitória na região sul do estado do Espírito Santo : 2006-2011. In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 37., 2011, Poços de Caldas. Anais... Brasília, DF: Embrapa Café, 2011.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 3. | | OLIVEIRA, R. B. de.; SILVA, S. de A.; SOUZA, G. S. de.; PASSOS, R. R.; PREZOTTI, L. C.; LIMA, J. S. de S. Análise espacial de variáveis indicadoras de fertilidade do solo sob cultivo de cafeeiro conilon. In: ENCONTRO LATINO AMERICANO DE INICIAÇÃO CIENTÍFICA, 10.; ENCONTRO LATINO AMERICANO PÓS-GRADUAÇÃO, 7., 2006, São José dos Campos, SP.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 5. | | MARRACCINI, P.; SILVA, V. A. da.; ELBELT, S.; GUIMARÃES, B. L. S.; LOUREIRO, M. E.; DAMATTA, F. M.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; SILVA, F. R. da.; ANDRADE, A. C. Análise da expressão de genes candidatos para a tolerância à seca em folhas de clones de Coffea canephora var. Conillon, caracterizados fisiologicamente. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 5., 2007, Águas de Lindóia, SP. Anais... Brasília, DF: Embrapa Café, 2007. 5p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 6. | | BRUM, V. J.; AMARAL, J. A. T.; REIS, E. F.; JESUS JUNIOR, W. C.; MARQUES, P. C.; CAMPOS, L. P. de A.; BREGONCI, I. dos S. Análise foliar, caracterização química e granulométrica do solo num consórcio de café conilon com pupunha. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 5., 2007, Águas de Lindóia, SP. Anais... Brasília, DF: Embrapa Café, 2007 6p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 8. | | BARBOSA, W. M.; MEIRA, R. M. S. A.; CORDEIRO, A. T.; OTONI, W. C.; SAKIYAMA, N. S. S. Análise histólica comparativa da inundação de calos em genótipos de coffea. In: Simpósio de Pesquisa dos Cafés do Brasil, 5., 2007, Águas de Lindóia. Anais. Brasília, D.F. : Embrapa Café, 2007. 4p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 11. | | FONSECA, A. F. A. da.; SEDIYAMA, T.; CRUZ, C. D.; SAKAYAMA, N. S.; FERRÃO, R. G.; FERRÃO, M. A. G.; BRAGANÇA, S. M. Análise de repetibilidade em café conilon. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 3., 2003, Porto Seguro. Resumos... Brasília, DF: Embrapa Café, p. 214.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 12. | | GALEANO, E. A. V.; KROHLING, C. A. Análise de rico na cafeicultura de arábica no Estado do Espírito Santo considerando colheita manual e semimecanizada. In: CONGRESSO CAPIXABA DE PESQUISA AGROPECUÁRIA, 1., Vitória, ES. Anais 2021 : congresso capixaba de pesquisa agropecuária [recurso eletrônico]. Vitória, ES: Incaper, 2021. color. PDF ; 25,4 MB. E-book, no formato PDF. (Incaper, Documentos, 289). Pedro Luís Pereira Teixeira de Carvalho, Carlos Henrique Rodrigues de Oliveira, José Aires Ventura, Marcos Vinicius Winckler Caldeira e Romário Gava Ferrão, editores. 109p.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| Registros recuperados : 1.748 | |
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
04/07/2018 |
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Data da última atualização: |
04/07/2018 |
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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: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; CARBONETTO, P.; STEPHENS, M.; GARCIA, A. A. F. |
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Afiliação: |
Luis Felipe Ventorim Ferrão, ESALQ; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Peter Carbonetto, Research Computing Center, University of Chicago; Matthew Stephens, Research Computing Center, University of Chicago; Antonio Augusto Franco Garcia, ESALQ. |
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Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, june 2018. |
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Idioma: |
Português |
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Conteúdo: |
Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. MenosGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our... Mostrar Tudo |
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Palavras-Chave: |
Cafe conilon. |
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Thesaurus NAL: |
Coffea canephora; Genomic. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://www.nature.com/articles/s41437-018-0105-y
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Marc: |
LEADER 02393naa a2200229 a 4500
001 1020469
005 2018-07-04
008 2018 bl uuuu u00u1 u #d
100 1 $aFERRÃO, L. F. V.
245 $aAccurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.$h[electronic resource]
260 $c2018
520 $aGenomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
650 $aCoffea canephora
650 $aGenomic
653 $aCafe conilon
700 1 $aFERRÃO, R. G.
700 1 $aFERRÃO, M. A. G.
700 1 $aFONSECA, A. F. A. da.
700 1 $aCARBONETTO, P.
700 1 $aSTEPHENS, M.
700 1 $aGARCIA, A. A. F.
773 $tHeredity, june 2018.
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