Data/hora: 16/06/2024 20:56:24

Biblioteca(s): 	Biblioteca Rui Tendinha.	
Data corrente: 	19/06/2019	
Data da última atualização: 	25/08/2022	
Autoria: 	FERRÃO, L. F. V.; FERRÃO, R. G.; FERRÃO, M. A. G.; FONSECA, A. F. A. da.; GARCIA, A. A. F.	
Afiliação: 	Luís Felipe Ventorim Ferrão., Escola Superior de Agricultura Luiz de Queiroz (ESALQ).; Romário Gava Ferrão, Incaper; Maria Amélia Gava Ferrão, Incaper/Embrapa Café; Aymbiré Francisco Almeida da Fonseca, Incaper/Embrapa Café; Antonio Augusto Franco Garcia., Universidade de São Paulo (USP).	
Título: 	A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora.	
Ano de publicação: 	2017	
Fonte/Imprenta: 	Tree Genetics & Genomes, Germany, v. 13, n. 95, p. 13, 2017.	
Páginas: 	13 p.	
Idioma: 	Inglês	
Conteúdo: 	Abstract Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-bysequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodnessof-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10?17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops.	
Palavras-Chave: 	Genomic selection.	
Thesaurus NAL: 	GBLUP; Gbs; Genomic selection; Genotyping by sequencing; MET; Multi environment trials; Perennial crops.	
Categoria do assunto: 	--	
Registro original: 	Biblioteca Rui Tendinha (BRT)	
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