Abstract
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 = 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 × 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.
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Fanelli Carvalho, Humberto; Vieira Almeida Nonato, Juliana; Padilha, Lilian; Perez Maluf, Mirian; Guerreiro Filho, Oliveiro (2020), “Arabica coffee—IAC/EMBRAPA—BRAZIL”, Mendeley Data, V2, https://doi.org/10.17632/whtnz2w7t9.2.
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Acknowledgements
The authors are grateful to the National Council for Scientific and Technological Development (CNPq) for research fellowship (OGF CNPq DT 308.634/2016-0), Coordination of Superior Level Staff Improvement (CAPES)—Finance Code 001, Agronomic Institute of Campinas (IAC), Brazilian Agricultural Research Corporation (EMBRAPA-Coffee), Secretariat of Agriculture and Supply of São Paulo State (SAASP), and Brazilian Consortium for Coffee Research and Development (Projects 02.13.02.023.00.02 and 02.13.02.034.00.02).
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HFC, RFN, and LFVF designed the study. OGF conducted the field experiment and collected the phenotypic data. HFC and JVAN performed DNA extraction. MPM and LP supported and supervised the GBS and molecular biology lab. HFC and MFRRJ performed the bioinformatics analysis. HFC and GG performed data analyses and interpretation. HFC wrote the first version of the paper. RFN, GG, MFRRJ, LFVF, and OGF provided analytical expertise and edited the manuscript. RFN and OGF supervised the whole study. All authors read and approved the final version of the manuscript for publication.
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Fanelli Carvalho, H., Galli, G., Ventorim Ferrão, L.F. et al. The effect of bienniality on genomic prediction of yield in arabica coffee. Euphytica 216, 101 (2020). https://doi.org/10.1007/s10681-020-02641-7
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DOI: https://doi.org/10.1007/s10681-020-02641-7