Data/hora: 23/06/2024 18:43:02

Biblioteca(s): 	Embrapa Café; Embrapa Florestas.	
Data corrente: 	19/02/2019	
Data da última atualização: 	17/10/2019	
Autoria: 	SOUSA, T. V.; CAIXETA, E. T.; ALKIMIM, E. R.; OLIVEIRA, A. C. B. de; PEREIRA, A. A.; SAKIYAMA, N. S.; ZAMBOLIM, L.; RESENDE, M. D. V. de.	
Afiliação: 	TIAGO VIEIRA SOUSA, BIOAGRO/BioCafé/Universidade Federal de Viçosa - UFV; EVELINE TEIXEIRA CAIXETA, CNPCa; EMILLY RUAS ALKIMIM, Universidade Federal do Triângulo Mineiro - UFTM; ANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCa; ANTONIO ALVES PEREIRA, Empresa de Pesquisa Agropecuária de Minas Gerais – Epamig; NEY SUSSUMU SAKIYAMA, Universidade Federal de Viçosa - UFV/Departamento de Fitotecnia; LAÉRCIO ZAMBOLIM, Universidade Federal de Viçosa - UFV/Departamento de Fitopatologia; MARCOS DEON VILELA DE RESENDE, CNPF.	
Título: 	Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.	
Ano de publicação: 	2019	
Fonte/Imprenta: 	Frontiers in Plant Science, v. 9, January 2019.	
Idioma: 	Inglês	
Conteúdo: 	Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUPmethod, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.	
Palavras-Chave: 	Accelerating improvement; Complex traits; Ganho genético; Genetic gains; Genomic-enabled prediction accuracy; Selective efficiency; SNP molecular marker.	
Thesagro: 	Café; Coffea Arábica.	
Thesaurus Nal: 	Plant breeding.	
Categoria do assunto: 	--
G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/193820/1/Early-selection-enabled-by-the-implementation.pdf
http://ainfo.cnptia.embrapa.br/digital/bitstream/item/197479/1/2019-M.Deon-FPS-Early.pdf	
Registro original: 	Embrapa Café (CNPCa)	
Exemplares: 	CNPF - (56757-1) - UPC - AP - DD -  -  -  - DIS
CNPCa - (1282-1) - UPC - AP - DD -  -  -  - DIS