Data/hora: 25/06/2024 21:24:36

Biblioteca(s): 	Biblioteca Rui Tendinha.	
Data corrente: 	04/07/2018	
Data da última atualização: 	12/04/2024	
Autoria: 	FERRÃƒO, L. F. V.; FERRÃƒO, R. G.; FERRÃƒO, M. A. G.; FONSECA, A. F. A. da.; CARBONETTO, P.; STEPHENS, M.; GARCIA, A. A. F.	
Afiliação: 	Luis Felipe Ventorim FerrÃ£o, ESALQ; RomÃ¡rio Gava FerrÃ£o, Incaper; Maria AmÃ©lia Gava FerrÃ£o, Incaper/Embrapa CafÃ©; AymbirÃ© Francisco Almeida da Fonseca, Incaper/Embrapa CafÃ©; Peter Carbonetto, Research Computing Center, University of Chicago; Matthew Stephens, Research Computing Center, University of Chicago; Antonio Augusto Franco Garcia, ESALQ.	
Título: 	Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	Heredity, june 2018.	
Idioma: 	Português	
Conteúdo: 	Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee?production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.	
Palavras-Chave: 	Cafe conilon.	
Thesaurus NAL: 	Coffea canephora; Genomic.	
Categoria do assunto: 	G Melhoramento Genético	
URL: 	https://biblioteca.incaper.es.gov.br/digital/bitstream/item/4674/1/s41437-018-0105-y.pdf	
Registro original: 	Biblioteca Rui Tendinha (BRT)	
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