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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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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
20/05/2019 |
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Data da última atualização: |
24/05/2019 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
SILVA, A. E. S. da.; MASO, L. J.; COSTA, E. B. da.; BASSANI, L. A.; GALEANO, E. A. V. |
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Afiliação: |
Antônio Elias Souza da Silva, Incaper; Ludovico José Maso, Incaper; Enio Bergoli da Costa, Incaper; Luiz Antonio Bassani, Incaper; Edileuza Aparecida Vital Galeano, Incaper. |
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Título: |
Economic and social importance of conilon coffee in the State of Espirito Santo. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
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. 2, p. 51-69. Translated from: Café Conilon, 2017 - Incaper. English translation: Marcele Gualda Pasolini. |
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Idioma: |
Inglês |
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Conteúdo: |
The beginning of the formation of the historical, economic, social and political idmy of Espirito Santo had as main pillar of the coffee farming structure from the middle of the nineteenth century on. This activity also integrated the state into the national economy and international market, in addition to helping to build its sociocultural base (CALIMAN, 2012). In 1850, the importance of coffee cultivation in the economy of Espirito Santo was already remarkable, and until the 19505, the state was always dependent on coffee. Although the economic base has diversif?ied, coffee production continues being the most important
activity in the state’s agricultural sector due to the economic and social density present in all municipalities in the state of Espirito Santo, with the exception of the capital Vitoria... |
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Palavras-Chave: |
Café conilon; Cafeicultura capixaba; Espírito Santo (Estado). |
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Thesaurus NAL: |
Coffee cultivation; Conilon coffee. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/3543/1/chapter-02-economic-social-importance.pdf
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Marc: |
LEADER 01727naa a2200229 a 4500
001 1021297
005 2019-05-24
008 2019 bl uuuu u00u1 u #d
100 1 $aSILVA, A. E. S. da.
245 $aEconomic and social importance of conilon coffee in the State of Espirito Santo.$h[electronic resource]
260 $c2019
520 $aThe beginning of the formation of the historical, economic, social and political idmy of Espirito Santo had as main pillar of the coffee farming structure from the middle of the nineteenth century on. This activity also integrated the state into the national economy and international market, in addition to helping to build its sociocultural base (CALIMAN, 2012). In 1850, the importance of coffee cultivation in the economy of Espirito Santo was already remarkable, and until the 19505, the state was always dependent on coffee. Although the economic base has diversif?ied, coffee production continues being the most important activity in the state’s agricultural sector due to the economic and social density present in all municipalities in the state of Espirito Santo, with the exception of the capital Vitoria...
650 $aCoffee cultivation
650 $aConilon coffee
653 $aCafé conilon
653 $aCafeicultura capixaba
653 $aEspírito Santo (Estado)
700 1 $aMASO, L. J.
700 1 $aCOSTA, E. B. da.
700 1 $aBASSANI, L. A.
700 1 $aGALEANO, E. A. V.
773 $tIn: 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. 2, p. 51-69. Translated from: Café Conilon, 2017 - Incaper. English translation: Marcele Gualda Pasolini.
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