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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
04/07/2018 |
Data da última atualização: |
12/04/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
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. |
Título: |
Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Heredity, june 2018. |
Idioma: |
Português |
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 |
Palavras-Chave: |
Cafe conilon. |
Thesaurus NAL: |
Coffea canephora; Genomic. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/item/4674/1/s41437-018-0105-y.pdf
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Marc: |
LEADER 02393naa a2200229 a 4500 001 1020469 005 2024-04-12 008 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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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Registro Completo |
Biblioteca(s): |
Biblioteca Rui Tendinha. |
Data corrente: |
17/08/2015 |
Data da última atualização: |
10/05/2016 |
Tipo da produção científica: |
Livro |
Autoria: |
SCHIMIDT, H. C.; DE MUNER, L. H.; FORNAZIER, M. J. |
Afiliação: |
Lucio Herzog De Muner, Incaper; Mauricio José Fornazier, Incaper. |
Título: |
Cadeia produtiva do café arábica da agricultura familiar no Espírito Santo. |
Ano de publicação: |
2004 |
Fonte/Imprenta: |
Vitória, ES : Incaper, 2004. |
Páginas: |
52 p. |
ISBN: |
85-89274-06-3 |
Idioma: |
Português |
Conteúdo: |
O café no Espírito Santo; Realidade atual da produção cafeeira; Os insumos na produção; Comercialização (a visão do produtor); O cenário desejável; A agricultura familiar; Colheita e pós-colheita; O parque cafeeiro; O elo comercial; Homenagem - Guarino Bissoli, um exemplo de agricultor familiar. |
Palavras-Chave: |
Cadeia produtiva; Café arábica; Coffea arabica; Espírito Santo (Estado). |
Categoria do assunto: |
-- |
URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/961/1/CadeiaprodutivadocafearabicadaagriculturafamiliarnoEspiritoSanto-ResearchGate.compressed.pdf
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Marc: |
LEADER 00872nam a2200205 a 4500 001 1007513 005 2016-05-10 008 2004 bl uuuu 00u1 u #d 020 $a85-89274-06-3 100 1 $aSCHIMIDT, H. C. 245 $aCadeia produtiva do café arábica da agricultura familiar no Espírito Santo. 260 $aVitória, ES : Incaper$c2004 300 $a52 p. 520 $aO café no Espírito Santo; Realidade atual da produção cafeeira; Os insumos na produção; Comercialização (a visão do produtor); O cenário desejável; A agricultura familiar; Colheita e pós-colheita; O parque cafeeiro; O elo comercial; Homenagem - Guarino Bissoli, um exemplo de agricultor familiar. 653 $aCadeia produtiva 653 $aCafé arábica 653 $aCoffea arabica 653 $aEspírito Santo (Estado) 700 1 $aDE MUNER, L. H. 700 1 $aFORNAZIER, M. J.
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