Abstract
Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-by-sequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodness-of-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10–17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops.
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Acknowledgments
This work is partially supported by FAPESP/CAPES (São Paulo Research Foundation), grants 2014/20389-2 for L.F.V.F and A.A.F.G. Phenotypic evaluations and GBS data is supported by Fapes (Espírito Santo Research Foundation), grants 55207464/11 and 65192036/14. Additional support is provided by the Instituto Capixaba de Pesquisa, Assitência Tecnica e Extensão Rural (Incaper) and Embrapa Cafe. A.A.F.G, R.G.F, M.A.G.F and A.F have a fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). The author thank Livia Souza and Anete P. de Souza (CBMEG, Unicamp/Brazil) by the assistance in the DNA extraction step; and Paulo Volpi (Incaper/Brazil) by the support on the phenotypic evaluation.
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L.F.V.F, A.A.F.G, R.G.F, M.A.G.F and A.F conceived the study and designed the experiments. R.G.F, M.A.G.F and A.F installed the experimental design and collected the phenotypic data. L.F.V.F performed the DNA extraction. L.F.V.F and A.A.F.G performed the genomic prediction analysis and proposed the theoretical idea of the model. L.F.V.F wrote the paper.
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Data Archiving Statement
The genotypes in the study belong to the germplasm collection and breeding program of the Incaper institution (ES,Brazil). Phenotypic and genotypic data were submitted as Supplementary Material.
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Communicated by: J. Beaulieu
This article is part of the Topical Collection on Breeding
Key Message: First insights into the Genotyping-by-Sequencing (GBS) in Coffea canephora and a genomic prediction model considering the theory about multiplicative mixed model to accommodate the interaction effects across sites and harvests
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Ventorim Ferrão, L., Gava Ferrão, R., Ferrão, M. et al. A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora . Tree Genetics & Genomes 13, 95 (2017). https://doi.org/10.1007/s11295-017-1171-7
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DOI: https://doi.org/10.1007/s11295-017-1171-7