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Registro Completo |
Biblioteca(s): |
Embrapa Gado de Corte. |
Data corrente: |
27/12/2022 |
Data da última atualização: |
23/01/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
AONO, A. H.; FERREIRA, R. C. U.; MORAES, A. da C. L.; LARA, L. A. de C.; PIMENTA, R. J. G.; COSTA, E. A.; PINTO, L. R.; LANDELL, M. G. de A.; SANTOS, M. F.; JANK, L.; BARRIOS, S. C. L.; VALLE, C. B.; CHIARI, L.; GARCIA, A. A. F.; KUROSHU, R. M.; LORENA, A. C.; GORJANC, G.; SOUZA, A. P. de. |
Afiliação: |
ALEXANDRE HILD AONO, UNIVERSIDADE DE CAMPINAS, UNIVERSITY OF EDINBURGH; REBECCA CAROLINE ULBRICHT FERREIRA, UNIVERSDIDADE DE CAMPINAS; ALINE DA COSTA LIMA MORAES, UNIVERSIDADE DE CAMPINAS; LETÍCIA APARECIDA DE CASTRO LARA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"; RICARDO JOSÉ GONZAGA PIMENTA, UNIVERSIDADE DE CAMPINAS; ESTELAARAUJO COSTA, UNIVEDRSIDADE FEDERAL DE SÃO PAULO; LUCIANA ROSSINI PINTO, INSTITUTO AGRONÔMICO DE CAMPINAS; MARCOS GUIMARÃES DE ANDRADE LANDELL, INSTITUTO AGRONÔMICO DE CAMPINAS; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; SANZIO CARVALHO LIMA BARRIOS, CNPGC; CACILDA BORGES DO VALLE, CNPGC; LUCIMARA CHIARI, CNPGC; ANTONIO AUGUSTO FRANCO GARCIA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"; REGINALDO MASSANOBU KUROSHU, UNIVERSIDADE FERDERAL DE SÃO PAULO; ANA CAROLINA LORENA, INSTITUTO TECNOLÓGICO DE AERONÁUTICA; GREGOR GORJANC, UNIVERSITY OF EDINBURGH; ANETE PEREIRA DE SOUZA, UNIVERSIDADE DE CAMPINAS. |
Título: |
A joint learning approach for genomic prediction in polyploid grasses. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Scientific Reports, 12, article 12499, 2022. |
Páginas: |
17 p. |
ISSN: |
2045-2322 |
DOI: |
https://doi.org/10.1038/s41598-022-16417-7 |
Idioma: |
Inglês |
Conteúdo: |
Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in diferent cross-validation scenarios. By combining classifcation and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains. |
Thesagro: |
Cana de Açúcar; Gramínea Forrageira; Recurso Genético. |
Thesaurus Nal: |
Forage grasses; Genetic resources; Plant breeding; Poaceae; Polyploidy; Saccharum; Sugarcane. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150365/1/Joint-learning-approach-genomic-2022.pdf
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Marc: |
LEADER 02671naa a2200481 a 4500 001 2150365 005 2023-01-23 008 2022 bl uuuu u00u1 u #d 022 $a2045-2322 024 7 $ahttps://doi.org/10.1038/s41598-022-16417-7$2DOI 100 1 $aAONO, A. H. 245 $aA joint learning approach for genomic prediction in polyploid grasses.$h[electronic resource] 260 $c2022 300 $a17 p. 520 $aPoaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in diferent cross-validation scenarios. By combining classifcation and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains. 650 $aForage grasses 650 $aGenetic resources 650 $aPlant breeding 650 $aPoaceae 650 $aPolyploidy 650 $aSaccharum 650 $aSugarcane 650 $aCana de Açúcar 650 $aGramínea Forrageira 650 $aRecurso Genético 700 1 $aFERREIRA, R. C. U. 700 1 $aMORAES, A. da C. L. 700 1 $aLARA, L. A. de C. 700 1 $aPIMENTA, R. J. G. 700 1 $aCOSTA, E. A. 700 1 $aPINTO, L. R. 700 1 $aLANDELL, M. G. de A. 700 1 $aSANTOS, M. F. 700 1 $aJANK, L. 700 1 $aBARRIOS, S. C. L. 700 1 $aVALLE, C. B. 700 1 $aCHIARI, L. 700 1 $aGARCIA, A. A. F. 700 1 $aKUROSHU, R. M. 700 1 $aLORENA, A. C. 700 1 $aGORJANC, G. 700 1 $aSOUZA, A. P. de 773 $tScientific Reports, 12, article 12499, 2022.
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Embrapa Gado de Corte (CNPGC) |
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Registros recuperados : 5 | |
1. | | ROFL, M. M.; DECKER, J. E.; McKAY, S. D.; TIZIOTO, P. C.; BRANHAM, K. A.; WHITACRE, L. K.; HOFF, J. L.; REGITANO, L. C. de A.; TAYLOR, J. F. Genomics in the United States beef industry. Livestock Science, v.166, p. 84-93, aug. 2014.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
Biblioteca(s): Embrapa Pecuária Sudeste. |
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2. | | DECKER, J. E.; MCKAY, S. D.; ROLF, M. M.; ALCALA, A. M.; SONSTEGARD, T. S.; HANOTTE, O.; GOTHERSTROM, A.; SEABURY, C. M.; PRAHARANI, L.; BABAR, M. E.; REGITANO, L. C. de A.; YILDIZ, M. A.; HEATON, M. P.; LIU, W. S.; LEI, C. Z.; REECY, J. M.; SAIF-UR-REHMAN, M.; SCHNABEL, R. D.; TAYLOR, J. F. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. Plos Genetics, v. 10, n. 3, e1004254, 2014. 14 p.Tipo: Artigo em Periódico Indexado |
Biblioteca(s): Embrapa Pecuária Sudeste. |
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3. | | DECKER, J. E.; PIRES, J. C.; CONANT, G. C.; MCKAY, S. D.; HEATON, M. P.; VILKKI, J.; CHEN, K.; COOPER, A.; SEABURY, C. M.; CAETANO, A. R.; JOHNSON, G. S.; BRENNEMAN, R. A.; HANOTTE, O.; COUTINHO, L. L.; BABAR, M. E.; EGGERT, L. S.; WIENER, P.; KIM, J.-J.; KIM, K. S.; SONSTEGARD, T. S.; TASSELL, C. P. van; NEIBERGS, H. L.; SCHNABEL, R. D.; TAYLOR, J. F. Divergence times and signatures of selection from phylogenomic analysis of Pecoran species. In: INTERNATIONAL PLANT & ANIMAL GENOMES CONFERENCE, 17., 2009, San Diego, CA. [Proceedings...]. [S. l.: s.n.], 2009.Tipo: Resumo em Anais de Congresso |
Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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4. | | DECKER, J. E.; PIRES, J. C.; CONANT, G. C.; MCKAY, S. D.; HEATON, M. P.; CHEN, K.; COOPER, A.; VIKKI, J.; SEABURY, C. M.; CAETANO, A. R.; JOHNSON, G. D.; BRENNEMAN, R. A.; HANOTTE, O.; EGGERT, L. S.; WIENER, P.; KIM, J.-J.; KIM, K. S.; SONSTEGARD, T. S.; TASSELL, C. P. V.; NEIBERGS, H. L.; MCEWAN, J. C.; BRAUNING, R.; COUTINHO, L. L.; BABAR, M. E.; WILSON, G. A.; MCCLURE, M. C.; ROLF, M. M.; KIM, J. W.; SCHNABEL, R. D.; TAYLOR, J. F. Resolving the evolution of extant and extinct ruminants with hith-throughput phylogenomics. Proceedings of the National Academy of Sciences, v. 106, n. 44, p. 18644-18649, 2009Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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5. | | SNELLING, W. M.; CHIU, R.; SCHEIN, J. E.; HOBBS, M.; ABBEY, C. A.; ADELSON, D. L.; AERTS, J.; BENNETT, G. L.; BOSDET, I. E.; BOUSSAHA, M.; BRAUNING, R.; CAETANO, A. R.; COSTA, M. M.; CRAWFORD, A. M.; DALRYMPLE, B. P.; EGGEN, A.; WIND, A. E. van der; FLORIOT, S.; GAUTIER, M.; GILL, C. A.; GREEN, R. D.; HOST, R.; JANN, O.; JONES, S. J. M.; DAPPES, S. M.; KEELE, J. W.; JONG, P. J. de; LARKIN, D. M.; LEWIN, J. A.; MCEWAN, J. C.; MCKAY, S.; MARRA, M. A.; MATHEWSON, C. A.; MATUKUMALLI, L. K.; MOORE, S. S.; MURDOCH, B.; NICHOLAS, F. W.; OSOEGAWA, K.; ROY, A.; SALIH, H.; SCHIBLE, L.; SCHNAGEL, R. D.; SILVERI, L.; SKOW, L. C.; SMITH, T. P. L.; SONSTEGARD, T. S.; TAYLOR, J.; TELLAM, R.; TASSELL, C. P. van; WILLIAMS, J. L.; WOMACK, J. E.; WYE, N. H.; YANG, G.; ZHAO, S. A physical map of the bovine genome. Genome Biology, 8, p. R165, 2007.Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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Registros recuperados : 5 | |
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