Data/hora: 23/09/2024 12:21:37

Biblioteca(s): 	Embrapa Café.	
Data corrente: 	14/07/2020	
Data da última atualização: 	14/07/2020	
Autoria: 	CARVALHO, H. F.; GALLI, G.; FERRÃO, L. F. V.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR, M. F. R. de; GUERREIRO FILHO, O.; FRITSCHE-NETO, R.	
Afiliação: 	HUMBERTO FANELLI CARVALHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; GIOVANNI GALLI, UNIVERSIDADE DE SÃO PAULO; LUÍS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORIDA; JULIANA VIEIRA ALMEIDA NONATO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; LILIAN PADILHA, CNPCa; MIRIAN PEREZ MALUF, CNPCa; MÁRCIO FERNANDO RIBEIRO DE RESENDE JR, UNIVERSITY OF FLORIDA; OLIVEIRO GUERREIRO FILHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; ROBERTO FRITSCHE-NETO, UNIVERSIDADE DE SÃO PAULO.	
Título: 	The effect of bienniality on genomic prediction of yield in arabica coffee.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Euphytica, v. 216, n. 101, p. 100-111, 2020.	
DOI: 	https://doi.org/10.1007/s10681-020-02641-7	
Idioma: 	Inglês	
Conteúdo: 	The most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n&#8201;=&#8201;586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype&#8201;×&#8201;year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased selection response using genomic selection, mainly due to the reduced time per breeding cycle. These results can shed light on the implementation of a genome-based selection of arabica coffee and lead to more efficient breeding strategies.	
Palavras-Chave: 	Sequenciamento genético.	
Thesagro: 	Coffea Arábica; Genoma; Seleção Genética.	
Thesaurus Nal: 	Genome; Plant selection guides.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Café (CNPCa)	
Exemplares: 	CNPCa - (1382-1) - UPC - AP - DD -  -  -  - DIS