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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: |
12/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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://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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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
13/09/2013 |
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Data da última atualização: |
02/01/2017 |
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Tipo da produção científica: |
Circular Técnica |
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Autoria: |
FERRÃO, M. A. G.; FERRÃO, R. G.; FORNAZIER, M. J.; PREZOTTI, L. C.; FONSECA, A. F. A. da.; ALIXANDRE, F. T.; COSTA, H.; ROCHA, A. C. da.; MORELI, A. P.; GUARÇONI M., A.; RIVA-SOUZA, E. M.; ARAÚJO, J. B. S.; VENTURA, J. A.; CASTRO, L. L. F. de.; GUARÇONI, R. G. |
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Afiliação: |
Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Romário Gava Ferrão, Incaper; Mauricio José Fornazier, Incaper; Luiz Carlos Prezotti, Incaper; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Fabiano Tristao Alixandre, Incaper; Helcio Costa, Incaper; Aledir Cassiano da Rocha, Incaper; Aldemar Polonini Moreli, Incaper; Andre Guarçoni Martins, Incaper; Elaine Manelli Riva-Souza, Incaper; João Batista Silva Araújo, Incaper; Jose Aires Ventura, Incaper; Lucio Livio Froes de Castro, Incaper; Rogerio Carvalho Guarçoni, Incaper. |
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Título: |
Técnicas de produção de café arábica: renovação e revigoramento das lavouras no Estado do Espírito Santo. |
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Edição: |
2 ed. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Vitória: INCAPER, 2008. |
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Páginas: |
56 p. |
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Série: |
(Incaper. Circular Técnica, 05-I). |
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ISSN: |
1519-2059 |
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Idioma: |
Português |
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Conteúdo: |
Principais tecnologias para renovação, revigoramento e produção de café arábica no Espírito Santo com sustentabilidade, abordando os seguintes aspectos: escolha de área; variedades indicadas pelo Incaper; mudas; espaçamento e plantio; calagem e adubação; conservação de solo; controle de plantas daninhas; poda de produção e de revigoramento; pragas e doenças; colheita, secagem; processamento, beneficiamento e qualidade; além de algumas considerações sobre irrigação, café orgânico, cafeicultura sustentável e produção de sementes. |
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Palavras-Chave: |
Brasil; Café arábica; Espírito Santo; Técnica de produção. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01607nam a2200373 a 4500
001 1000552
005 2017-01-02
008 2008 bl uuuu 00u1 u #d
022 $a1519-2059
100 1 $aFERRÃO, M. A. G.
245 $aTécnicas de produção de café arábica$brenovação e revigoramento das lavouras no Estado do Espírito Santo.
250 $a2 ed.
260 $aVitória: INCAPER$c2008
300 $a56 p.
490 $a(Incaper. Circular Técnica, 05-I).
520 $aPrincipais tecnologias para renovação, revigoramento e produção de café arábica no Espírito Santo com sustentabilidade, abordando os seguintes aspectos: escolha de área; variedades indicadas pelo Incaper; mudas; espaçamento e plantio; calagem e adubação; conservação de solo; controle de plantas daninhas; poda de produção e de revigoramento; pragas e doenças; colheita, secagem; processamento, beneficiamento e qualidade; além de algumas considerações sobre irrigação, café orgânico, cafeicultura sustentável e produção de sementes.
653 $aBrasil
653 $aCafé arábica
653 $aEspírito Santo
653 $aTécnica de produção
700 1 $aFERRÃO, R. G.
700 1 $aFORNAZIER, M. J.
700 1 $aPREZOTTI, L. C.
700 1 $aFONSECA, A. F. A. da.
700 1 $aALIXANDRE, F. T.
700 1 $aCOSTA, H.
700 1 $aROCHA, A. C. da.
700 1 $aMORELI, A. P.
700 1 $aGUARÇONI M., A.
700 1 $aRIVA-SOUZA, E. M.
700 1 $aARAÚJO, J. B. S.
700 1 $aVENTURA, J. A.
700 1 $aCASTRO, L. L. F. de.
700 1 $aGUARÇONI, R. G.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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