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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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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
001 2159395
005 2023-12-08
008 2023 bl uuuu u00u1 u #d
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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Registro original: |
Embrapa Café (CNPCa) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
25/02/2013 |
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Data da última atualização: |
28/02/2013 |
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Tipo da produção científica: |
Software |
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Autoria: |
YANO, I. H.; MORAES, F. R. de; SALIM, J. A.; NESHICH, G.; MAZONI, I.; JARDINE, J. G. |
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Afiliação: |
INÁCIO HENRIQUE YANO, CNPTIA; FÁBIO ROGÉRIO DE MORAES; JOSÉ AUGUSTO SALIM; GORAN NESHICH, CNPTIA; IVAN MAZONI, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA. |
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Título: |
RNDS: Rede Neural para Classificar Descritores do Sting. Versão 1.0. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Campinas: Embrapa Informática Agropecuária, 2012. |
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Descrição Física: |
1 CD-ROM. |
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Idioma: |
Português |
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Conteúdo: |
Rede Neural para Classificar Descritores do Sting, utilizado para levantar os descritores do Sting mais significativos na identificação de interfaces proteína-proteína. Trata-se de uma versão modificada da Rede Neural em Java de Phil Brierley (http://www.philbrierley.com/) disponibilizado para livre utilização e modificação, cuja versão original funcionava para redes com somente quatro padrões, e que nesta versão funciona para um número variável de padrões, cujos valores são previamente normalizados para atender às características dos descritores do Sting. Outras modificações foram: a separação entre treino e validação; testes com validação cruzada; e classificação das entradas, por meio de novos treinos recursivos da rede, após a retirada das entradas menos significativas. |
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Palavras-Chave: |
Rede neural. |
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Thesaurus NAL: |
Computer software; Neural networks. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01402nam a2200217 a 4500
001 1950847
005 2013-02-28
008 2012 bl uuuu u0uu1 u #d
100 1 $aYANO, I. H.
245 $aRNDS$bRede Neural para Classificar Descritores do Sting. Versão 1.0.
260 $aCampinas: Embrapa Informática Agropecuária$c2012
300 $c1 CD-ROM.
520 $aRede Neural para Classificar Descritores do Sting, utilizado para levantar os descritores do Sting mais significativos na identificação de interfaces proteína-proteína. Trata-se de uma versão modificada da Rede Neural em Java de Phil Brierley (http://www.philbrierley.com/) disponibilizado para livre utilização e modificação, cuja versão original funcionava para redes com somente quatro padrões, e que nesta versão funciona para um número variável de padrões, cujos valores são previamente normalizados para atender às características dos descritores do Sting. Outras modificações foram: a separação entre treino e validação; testes com validação cruzada; e classificação das entradas, por meio de novos treinos recursivos da rede, após a retirada das entradas menos significativas.
650 $aComputer software
650 $aNeural networks
653 $aRede neural
700 1 $aMORAES, F. R. de
700 1 $aSALIM, J. A.
700 1 $aNESHICH, G.
700 1 $aMAZONI, I.
700 1 $aJARDINE, J. G.
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Embrapa Agricultura Digital (CNPTIA) |
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