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Tipo: Artigo
Título: Early selection enabled by the implementation of genomic selection in Coffea arabica breeding
Autor(es): Sousa, Tiago Vieira
Caixeta, Eveline Teixeira
Alkimim, Emilly Ruas
Oliveira, Antonio Carlos Baião
Pereira, Antonio Alves
Sakiyama, Ney Sussumu
Zambolim, Laércio
Resende, Marcos Deon Vilela
Abstract: 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-BLUP method, 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: Genetic gains
Selective efficiency
Genomic-enabled prediction accuracy
Plant breeding
SNP molecular marker
Complex traits
Accelerating improvement
Editor: Frontiers in Plant Science
Tipo de Acesso: Open Access
Data do documento: Jan-2019
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