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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
30/08/2024 |
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Data da última atualização: |
27/05/2025 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SOUSA, I. C. de; BARRETO, C. A. V.; CAIXETA, E. T.; NASCIMENTO, A. C. C; AZEVEDO, C. F.; ALKIMIM, E. R.; NASCIMENTO, M. |
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Afiliação: |
ITHALO COELHO DE SOUSA, UNIVERSIDADE FEDERAL DE RONDÔNIA; CYNTHIA APARECIDA VALIATI BARRETO, UNIVERSIDADE FEDERAL DE VIÇOSA; EVELINE TEIXEIRA CAIXETA MOURA, CNPCA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; EMILLY RUAS ALKIMIM, UNIVERSIDADE FEDERAL DO TRIANGULO MINEIRO; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
The trade‑of between density marker panels size and predictive ability of genomic prediction for agronomic traits in Cofea canephora. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Euphytica, v. 220, n. 46, 2024. |
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Páginas: |
11 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic prediction in Coffee breeding has shown good potential in predictive ability (PA), genetic gains and reduction of the selection cycle time. It is known that the cost of genotyping was prohibitive for many species, and their value is associated with the density markers panel used. The use of optimize marker density panel may reduce the genotyping cost and improve the PA. We aimed to evaluate the trade-off between density marker panels size and the PA for eight agronomic traits in Coffea canephora using machine learning algorithms. These approaches were compared with BLASSO method. The used data consisted of 165 genotypes of C. canephora genotyped with 14,387 SNP markers. The plants were phenotyped for vegetative vigor (Vig), rust (Rus) and cercosporiose incidence (Cer), fruit maturation time (Mat), fruit size (FS), plant height (PH), diameter of the canopy projection (DC) and yield (Y). Twelve different density marker panels were used. The common trend observed in the analysis shows an increase of the PA as the number of markers decreases, having a peak when used between 500 and 1,000 markers. Comparing the best and the worse results (full SNP panel density) for each trait, some had an improvement around of 100% (PH: 0.35–0.77; Cer: 0.40–0.84; DC: 0.39–0.82; Rus: 0.39–0.83, Vig: 0.40–0.77), the other showed an improvement more than 340% (Mat: 0.12–0.60; Y: 0.14–0.61; FS: 0.07–0.60). The results of the current study indicate that the reduction of the number of markers can improve the selection of individuals at a lower cost. MenosGenomic prediction in Coffee breeding has shown good potential in predictive ability (PA), genetic gains and reduction of the selection cycle time. It is known that the cost of genotyping was prohibitive for many species, and their value is associated with the density markers panel used. The use of optimize marker density panel may reduce the genotyping cost and improve the PA. We aimed to evaluate the trade-off between density marker panels size and the PA for eight agronomic traits in Coffea canephora using machine learning algorithms. These approaches were compared with BLASSO method. The used data consisted of 165 genotypes of C. canephora genotyped with 14,387 SNP markers. The plants were phenotyped for vegetative vigor (Vig), rust (Rus) and cercosporiose incidence (Cer), fruit maturation time (Mat), fruit size (FS), plant height (PH), diameter of the canopy projection (DC) and yield (Y). Twelve different density marker panels were used. The common trend observed in the analysis shows an increase of the PA as the number of markers decreases, having a peak when used between 500 and 1,000 markers. Comparing the best and the worse results (full SNP panel density) for each trait, some had an improvement around of 100% (PH: 0.35–0.77; Cer: 0.40–0.84; DC: 0.39–0.82; Rus: 0.39–0.83, Vig: 0.40–0.77), the other showed an improvement more than 340% (Mat: 0.12–0.60; Y: 0.14–0.61; FS: 0.07–0.60). The results of the current study indicate that the reduction of the number of markers ... Mostrar Tudo |
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Thesagro: |
Coffea Canephora. |
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Thesaurus Nal: |
Agronomic traits; Genomics; Plant breeding. |
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Categoria do assunto: |
-- |
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Marc: |
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Registro original: |
Embrapa Café (CNPCa) |
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| Registros recuperados : 1 | |
| 1. |  | KUHN, J.; ADKINS, S.; AGWANDA, B. R.; KUBRUSLI, R. A.; ALKHOVSKY, S. V.; AMARASINGHE, G. K.; TATJANA, A.-Z.; AYLLÓN, M. A.; BAHL, J.; BALKEMA BUSCHMANN, A.; BALLINGER, M. J.; BASLER, C. F.; BAVARI, S.; BEER, M.; BEJERMAN, N.; BENNETT, A. J.; DBENTE, D. A.; BERGERON, É.; BIRD, B. H.; BLAIR, C. D.; BLASDELL, K. R.; BLYSTAD, D. R.; BOJKO, J.; BORTH, W. B.; BRADFUTE, S.; BREYTA, R.; BRIESE, T.; BROWN, P. A.; BROWN, J. K.; BUCHHOLZ, U. J.; BUCHMEIER, M. J.; ABUKREYEV, A.; BURT, F.; BÜTTNER, C.; CALISHER, C. H.; CAO, M.; CASAS, I.; CHANDRAN, K.; CHARREL, R. N.; CHENG, Q.; CHIAKI, Y.; CHIAPELLO, M.; CHOI, I. R.; CIUFO, M.; CLEGG, J. C. S.; CROZIER, I.; BÓ, E. D.; DE LA TORRE, J. C.; LAMBALLERIE, X. de; SWART, R. L. de; DEBAT, H.; DHEILLY, N. M.; DI CICCO, E.; DI PAOLA, N.; DI SERIO, F.; DIETZGEN, R. G.; DIGIARO, M.; DOLNIK, O.; DREBOT, M. A.; DREXLER, J. F.; DUNDON, W. G.; DUPREX, W. P.; DÜRRWALD, R.; DYE, J. M.; EASTON, A. J.; EBIHARA, H.; ELBEAINO, T.; ERGÜNAY, K.; FERGUSON, H. W.; FOOKS, A. R.; FORGIA, M.; FORMENTY, P. B. H.; FRÁNOVÁ, J.; ASTUA, J. de F.; FU, J.; FÜRL, S.; ZACHERT, S. G.; GĀO, G. F.; GARCÍA, M. L.; SASTRE, A. G.; GARRISON, A. R.; GASKIN, T.; GONZALE, J. P. J.; GRIFTHS, A.; GOLDBERG, T. L.; GROSCHUP, M. H.; GÜNTHER, S.; HALL, R. A.; HAMMOND, J.; HAN, O.; HEPOJOKI, J.; HEWSON, R.; HONG, J.; HONG, N.; HORIE, M.; HU, J. S.; HU, T.; HUGHES, H. R.; HÜTTNER, F.; HYNDMAN, T. H.; ILYAS, M.; JALKANEN, R.; JIĀNG, D.; JONSON, G. J.; JUNGLEN, S.; KADONO, F.; KAUKINEN, K. H.; KAWATE, M.; KLEMPA, B.; KLINGSTRÖM, J.; KOBINGER, G.; KOLONIUK, I.; KONDŌ, H.; KOONIN, V.; KRUPOVIC, M.; KUBOTA, K.; KURATH, G.; LAENEN, L.; LAMBERT, A. J.; LANGEVIN, S. L.; LEE, B.; LEFKOWITZ, E. J.; LI, L.; LEROY, E. M.; LI, S.; LÍ, J.; LIU, H.; LUKASHEVICH, I. S.; MAES, P.; SOUZA, W. M. de; MARKLEWITZ, M.; MARSHALL, S. H.; MARZANO, S. L.; MASSART, S.; MCCAULEY, J. W.; MELZER, M.; MILLER, K. M.; MING, T. J.; MIRAZIMI, A.; MORDECAI, G. J.; MÜHLBACH, H. P.; MÜHLBERGER, E.; NAIDU, R.; NATSUAKI, T.; NAVARRO, J. A.; NETESOV, S. Y V.; NEUMANN, G.; NOWOTNY, N.; NUNES, M. R. T.; VELARDE, A. O.; PALACIOS, G.; PALLÁS, V.; PÁLYI, B.; PAPA, A.; PARASKEVOPOULOU, S.; PARK, A. C.; PARRISH, C. R.; PATTERSON, D. A.; CORRÊA, A. P.; PAWESKA, J. T.; PAYNE, S.; PERACCHIO, C.; PÉREZ, D. R.; POSTLER, T. S.; QI, L.; RADOSHITZKY, S. R.; RESENDE, R. O.; REYES, C. A.; RIMA, B. K.; LUNA, G. R.; ROMANOWSKI, V.; ROTA, P.; RUBBENSTROTH, D.; RUBINO, L.; RUNSTADLER, J. A.; SABANADZOVIC, S.; SALL, A. A.; SALVATO, M. S.; SANG, R.; SASAYA, T.; SCHULZE, A. D.; SCHWEMMLE, M.; SHI, M.; SHÍ, X.; SHIMOMOTO, Y.; SHIRAKO, Y.; SIDDELL, S. G.; SIMMONDS, P.; SIRONI, M.; SMAGGHE, G.; SMITHER, S.; SONG, J. W.; SPANN, K.; SPENGLER, J. R.; STENGLEIN, M. D.; STONE, D. M.; SUGANO, J.; TABATA, A.; TAKADA, A.; TAKEUCHI, S.; TCHOUASSI, D. P.; TEFER, A.; TESH, R. B.; THORNBURG, N. J.; TOMITAKA, Y.; TOMONAGA, K.; TORDO, N.; TORTO, B.; TOWNER, J. S.; TSUDA, S.; TU, C.; TURINA, M.; TZANETAKIS, I. E.; UCHIDA, J.; USUGI, T.; VAIRA, A. M.; VALLINO, M.; VAN DEN HOOGEN, B.; VARSANI, A.; VASILAKIS, N.; VERBEEK, M.; BARGEN, S. V.; WADA, J.; WAHL, V.; WALKER, P. J.; WANG, L. F.; WANG, G.; WANG, Y.; WANG, Y.; WAQAS, M.; WÈI, T.; WEN, S.; WHITFELD, A. E.; WILLIAMS, J. V.; WOLF, Y. I.; WU, J.; XU, L.; YANAGISAWA, H.; YANG, C.; YANG, Z.; ZERBINI, F. M.; ZHAI, L.; ZHANG, Y. Z.; ZHANG, O.; ZHANG, J.; ZHANG, Z.; ZHOU, X. 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirale. Archives of Virology, August, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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