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| Registros recuperados : 26 | |
| 21. |  | OLIVEIRA, A. A. de; RESENDE JÚNIOR, M. F. R.; FERRÃO, L. F. V.; AMADEU, R. R.; GUIMARAES, L. J. M.; GUIMARÃES, C. T.; PASTINA, M. M.; MARGARIDO, G. R. A. Genomic prediction applied to multiple traits and environments in second season maize hybrids. Heredity, v. 125, n. 1/2, p. 60-72, 2020.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 22. | | ADUNOLA, P.; FERRÃO, M. A. G.; FERRÃO, R. G.; FONSECA, A. F. A. da; VOLPI, P. S.; COMÉRIO, M.; VERDIN FILHO, A. C.; MUNOZ, P. R.; FERRÃO, L. F. V. Genomic selection for genotype performance and environmental stability in Coffea canéfora. G3: Genes, Genomes, Genetics, v. 13, n. 6, p. 1-13, 2023.| Biblioteca(s): Embrapa Café. |
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| 23. | | FERRAO, M. A. G.; FONSECA, A. F. A. da; VOLPI, P. S.; SOUZA, L. C. de; COMÉRIO, M.; VERDIN FILHO, A. C.; RIVA-SOUZA, E. M.; MUNOZ, P. R.; FERRÃO, R. G.; FERRÃO, L. F. V. Genomic-assisted breeding for climate-smart coffee. The Plant Genome, e20321, 2023. 19 p.| Biblioteca(s): Embrapa Café. |
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| 24. | | CARVALHO, H. F.; GALLI, G.; FERRÃO, L. F. V.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR, M. F. R. de; GUERREIRO FILHO, O.; FRITSCHE-NETO, R. The effect of bienniality on genomic prediction of yield in arabica coffee. Euphytica, v. 216, n. 101, p. 100-111, 2020.| Biblioteca(s): Embrapa Café. |
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| 25. | | DIAS, K. O. das G.; PASTINA, M. M.; GUIMARAES, P. E. de O.; SANTOS, J. R. P. dos; KRAUSE, M. D.; FERRÃO, L. F. V.; GARCIA, A. A. F. Application of multi-environment bayesian models to study genotype-by-environment interaction in maize. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 9., 2017, Foz do Iguaçu. Melhoramento de plantas: projetando o futuro. Foz do Iguaçu: SBMP, 2017. p. 149.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 26. | | VERDIN FILHO, A. C.; FERRÃO, R. G.; FERRAO, M. A. G.; SILVEIRA, J. S. M.; VOLPI, P. S.; FONSECA, A. F. A. da; LANI, J. A.; MARTINS, A. G.; FERRÃO, L. F. V.; SILVEIRA, T. B. Poda programada de ciclo para o café conilon. In: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 6., 2009, Vitória. Inovação científica, competitividade e mudanças climáticas: anais... Vitória: Consórcio Pesquisa Café, 2009.| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 26 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
08/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 4 |
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Autoria: |
ADUNOLA, P.; FERRÃO, M. A. G.; FERRÃO, R. G.; FONSECA, A. F. A. da; VOLPI, P. S.; COMÉRIO, M.; VERDIN FILHO, A. C.; MUNOZ, P. R.; FERRÃO, L. F. V. |
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Afiliação: |
PAUL ADUNOLA, UNIVERSITY OF FLORIDA; MARIA AMÉLIA G FERRÃO, CNPCa; ROMÁRIO G FERRÃO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; AYMBIRE F A DA FONSECA, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; PAULO S VOLPI, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; MARCONE COMÉRIO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; ABRAÃO C VERDIN FILHO, INSTITUTO CAPIXABA DE PESQUISA, ASSISTÊNCIA TÉCNICA E EXTENSÃO RURAL; PATRICIO R MUNOZ, UNIVERSITY OF FLORIDA; LUÍS FELIPE V FERRÃO, UNIVERSITY OF FLORIDA. |
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Título: |
Genomic selection for genotype performance and environmental stability in Coffea canéfora. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
G3: Genes, Genomes, Genetics, v. 13, n. 6, p. 1-13, 2023. |
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DOI: |
https://doi.org/10.1093/g3journal/jkad062 |
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Idioma: |
Inglês |
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Conteúdo: |
Coffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated. MenosCoffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted an... Mostrar Tudo |
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Thesagro: |
Coffea Canephora. |
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Thesaurus NAL: |
Genomics; Genotype; Plant breeding; Prediction. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159395/1/Genomic-selection-for-genotype.pdf
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Marc: |
LEADER 02692naa a2200289 a 4500
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024 7 $ahttps://doi.org/10.1093/g3journal/jkad062$2DOI
100 1 $aADUNOLA, P.
245 $aGenomic selection for genotype performance and environmental stability in Coffea canéfora.$h[electronic resource]
260 $c2023
520 $aCoffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated.
650 $aGenomics
650 $aGenotype
650 $aPlant breeding
650 $aPrediction
650 $aCoffea Canephora
700 1 $aFERRÃO, M. A. G.
700 1 $aFERRÃO, R. G.
700 1 $aFONSECA, A. F. A. da
700 1 $aVOLPI, P. S.
700 1 $aCOMÉRIO, M.
700 1 $aVERDIN FILHO, A. C.
700 1 $aMUNOZ, P. R.
700 1 $aFERRÃO, L. F. V.
773 $tG3: Genes, Genomes, Genetics$gv. 13, n. 6, p. 1-13, 2023.
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Embrapa Café (CNPCa) |
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