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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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Esconder MarcMostrar Marc Completo |
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Biblioteca |
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| Registros recuperados : 1.096 | |
| 262. |  | BENASSI, V. L. R. M.; BUSOLI, A. C. Biologia de Cephalonomia stephanoderis Betrem (Hymenoptera: bethylidae), parasitóide da broca-do-café, em temperaturas constantes. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 5., 2007, Águas de Lindóia, SP. Anais... Brasília, DF: Embrapa Café, 2007. 5p.| Tipo: Publicação em Anais de Congresso |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 266. | | RODRIGUES, W. R.; TOMAZ, M. A.; FERRÃO, M. A. G.; MARTINS, L. D.; COLODETTI, T.V.; BRINATE, S. V. B.; AMARAL, J. F. T.; SOBREIRA, F. M.; APOSTÓLICO, M. A. Biometry and diversity of Arabica coffee genotypes cultivated in a high density plant system. Genetics and Molecular Research, v. 15, n. 1, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 267. | | FERRÃO, M. A. G.; FERRÃO, L. F. V.; MOTTA, L. B.; FONSECA, A. F. A. da.; FERRÃO, R. G.; RIVA-SOUZA, E. M. Biotechnology applied to Coffea canephora. In: FERRÃO, R. G.; FONSECA, A. F. A. da.; FERRÃO, M. A. G.; DE MUNER, L. H. (Ed.). Conilon Coffee. 3 edition updated and expanded Vitória, ES : Incaper, 2019. Cap. 8, p. 223-253. Translated from: Café Conilon, 2017 - Incaper. English translation: Marcele Gualda Pasolini.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 268. | | FERRÃO, M. A. G.; FERRÃO, L. F. V.; MOTTA, L. B.; FONSECA, A. F. A. da.; FERRÃO, R. G.; RIVA-SOUZA, E. M. Biotecnologia aplicada a Coffea canephora. In: FERRÃO, R. G.; FONSECA, A. F. A. da.; FERRÃO, M. A. G.; DE MUNER, L. H. (Ed.). Café Conilon. 2 ed. atual. ampli. Vitória, ES: Incaper, p. 193-217, 2017.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 269. | | TÚLER, A. C.; VALBON, W. R.; RODRIGUES, H. de S.; NOIA, L. R.; SANTOS, L. M. L.; FOGAÇA, I.; RONDELLI, V. M.; VERDIN FILHO, A. C. Black twig borer, Xylosandrus compactus (Eichhoff), a potential threat to coffee production. Revista de Ciências Agrícolas, v. 36, n. E, p. 5-16, 2019.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 270. | | BOAS Práticas na cultura do café: renovação das lavouras. Vitória, ES: Incaper/GTTC, 2020. Vídeo (1 h 37 m e 27 seg.) : son., color. Equipe técnica: César Abel Khroling e Maurício José Fornazier, pesquisadores do Incaper. Welington Braida Marré, extensionista do Incaper. Tatiana, jornalista. Webinar.| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 273. | | FORNAZIER, M. J.; VOLPI, P. S.; FERRÃO, M. A. G.; VERDIN FILHO, A. C.; FERRÃO, R. G.; FONSECA, A. F. A. da.; MIGUEL, G. S.; PEREIRA, A. A.; FAZUOLI, L. C. Broca dos ramos, Xylosandrus compactus, em cafés robusta no Espírito Santo. In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 35., 2009, Araxá. Trabalhos apresentados... Varginha, MG: Fundação PROCAFÉ, 2009.| Tipo: Publicação em Anais de Congresso |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 274. | | COLODETTI, T. V.; RODRIGUES, W. N.; MARQUES, R. M.; FERREIRA, D. S.; CÔGO, A. D.; APOSTÓLICO, M. A.; MARTINS, L. D.; VERDIN FILHO, A. C.; TOMAZ, M. A. Brotação em mudas arqueadas de diferentes genótipos de cafeeiro conilon em altitude de transição. In: CONGRESSO BRASILEIRO DE PESQUISAS CAFEEIRAS, 44., 2018, Franca, SP. Nosso café, melhorado desde o pé: anais... Brasília, DF: Embrapa Café, 2018.| Tipo: Publicação em Anais de Congresso |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 277. | | MORELI, A. P.; TAQUES, R. C.; GOMES, W. dos S.; SOARES, S. F.; DONZELES, S. M. L.; SILVA, J. de S. Classificação física de Coffea arabica de seis regiões produtoras no Estado do Espírito Santo. 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. p. 123-126| Tipo: Publicação em Anais de Congresso |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 278. | | GUARÇONI, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; FERRÃO, R. G.; VERDIN FILHO, A. C.; VOLPI, P. S.; MORELI, A. P. Classificação por tipo do café 'Conilon' em função do tempo entre a colheita e o início da secagem e do armazenamento. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 6., 2009, Vitória. Inovação científica, competitividade e mudanças climáticas: anais... Vitória: Consórcio Pesquisa Café, 2009 Não paginado.| Tipo: Publicação em Anais de Congresso |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| 279. | | FONSECA, A. F. A. da.; VERDIN FILHO, A. C.; VOLPI, P. S.; MAURI, A. L.; FERRÃO, R. G.; DOUSSEAU, S.; POSSE, S. C. P. Clonal gardens, seed production and Conilon coffee seedling. In: FERRÃO, R. G.; FONSECA, A. F. A. da.; FERRÃO, M. A. G.; DE MUNER, L. H. (Ed.). Conilon Coffee. 3 edition updated and expanded Vitória, ES : Incaper, 2019. Cap. 10, p. 289-325. Translated from: Café Conilon, 2017 - Incaper. English translation: Marcele Gualda Pasolini.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Biblioteca Rui Tendinha. |
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| Registros recuperados : 1.096 | |
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