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| 10. |  | GALLEP, C. M.; AMARAL, E. M. do; ALONCO, G. C.; MALUF, M. P.; PADILHA, L. Germination vigour versus delayed luminescence of coffee seeds: preliminary series. In: INTERNATIONAL CONFERENCE ON PHOTONICS, OPTICS AND LASER TECHNOLOGY, 2., 2014, Lisboa. Photoptics 2014. Lisboa, 2014.| Biblioteca(s): Embrapa Café. |
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| 11. | | MALUF, M. P.; SILVESTRINI, M.; RUGGIERO, L. M. de C.; GUERREIRO FILHO, O.; COLOMBO, C. A. Genetic diversity of cultivated Coffea arabica inbred lines assessed by RAPD, AFLP and SSR marker systems. Scientia Agricola, v. 62, n. 4, p. 366-373, 2005. Título em português: Caracterização da diversidade genética de linhagens comerciais de Coffea arabica através de marcadores moleculares do tipo RAPD, AFLP e SSR.| Biblioteca(s): Embrapa Café. |
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| 15. | | ORTEGA, H. M.; PADILHA, L.; GUERREIRO FILHO, O.; GONÇALVES, W.; MALUF, M. P.; BRAGINI, M. T. Microssatellite for fingerprinting Coffea arabica clones. In: INTERNATIONAL CONFERENCE ON COFFEE SCIENCE, 24., 2012, Costa Rica. Programme & abstracts. [S.l.]: Association for Science and Information on Coffee, 2012. p. 202| Biblioteca(s): Embrapa Café. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
14/07/2020 |
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Data da última atualização: |
14/07/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
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. |
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Afiliação: |
HUMBERTO FANELLI CARVALHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; GIOVANNI GALLI, UNIVERSIDADE DE SÃO PAULO; LUÍS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORIDA; JULIANA VIEIRA ALMEIDA NONATO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; LILIAN PADILHA, CNPCa; MIRIAN PEREZ MALUF, CNPCa; MÁRCIO FERNANDO RIBEIRO DE RESENDE JR, UNIVERSITY OF FLORIDA; OLIVEIRO GUERREIRO FILHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; ROBERTO FRITSCHE-NETO, UNIVERSIDADE DE SÃO PAULO. |
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Título: |
The effect of bienniality on genomic prediction of yield in arabica coffee. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Euphytica, v. 216, n. 101, p. 100-111, 2020. |
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DOI: |
https://doi.org/10.1007/s10681-020-02641-7 |
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Idioma: |
Inglês |
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Conteúdo: |
The most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased selection response using genomic selection, mainly due to the reduced time per breeding cycle. These results can shed light on the implementation of a genome-based selection of arabica coffee and lead to more efficient breeding strategies. MenosThe most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased sel... Mostrar Tudo |
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Palavras-Chave: |
Sequenciamento genético. |
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Thesagro: |
Coffea Arábica; Genoma; Seleção Genética. |
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Thesaurus NAL: |
Genome; Plant selection guides. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $ahttps://doi.org/10.1007/s10681-020-02641-7$2DOI
100 1 $aCARVALHO, H. F.
245 $aThe effect of bienniality on genomic prediction of yield in arabica coffee.$h[electronic resource]
260 $c2020
520 $aThe most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased selection response using genomic selection, mainly due to the reduced time per breeding cycle. These results can shed light on the implementation of a genome-based selection of arabica coffee and lead to more efficient breeding strategies.
650 $aGenome
650 $aPlant selection guides
650 $aCoffea Arábica
650 $aGenoma
650 $aSeleção Genética
653 $aSequenciamento genético
700 1 $aGALLI, G.
700 1 $aFERRÃO, L. F. V.
700 1 $aNONATO, J. V. A.
700 1 $aPADILHA, L.
700 1 $aMALUF, M. P.
700 1 $aRESENDE JR, M. F. R. de
700 1 $aGUERREIRO FILHO, O.
700 1 $aFRITSCHE-NETO, R.
773 $tEuphytica$gv. 216, n. 101, p. 100-111, 2020.
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