Título:	Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
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.
Ano de publicação:	2019
Referência:	Frontiers in Plant Science, v. 9, January 2019.
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.
NAL Thesaurus:	Plant breeding
Palavras-chave:	Genetic gains Selective efficiency Genomic-enabled prediction accuracy SNP molecular marker
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (SAPC)