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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models

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Ventorim Ferrao, Luis Felipe [1] ; Ferrao, Romario Gava [2] ; Gava Ferrao, Maria Amelia [2, 3] ; Fonseca, Aymbire [2, 3] ; Carbonetto, Peter [4, 5] ; Stephens, Matthew [4, 6] ; Franco Garcia, Antonio Augusto [1]
Total Authors: 7
[1] Univ Sao Paulo, Dept Genet, Escola Super Agr Luiz de Queiroz, BR-13400970 Piracicaba, SP - Brazil
[2] Assistencia Tecn & Extensa Rural Incaper, Inst Capixaba Pesquisa, BR-29052010 Vitoria, ES - Brazil
[3] Empresa Brasileira Pesquisa Agr Embrapa Cafe, Brasilia, DF - Brazil
[4] Univ Chicago, Dept Human Genet, Chicago, IL 60637 - USA
[5] Univ Chicago, Res Comp Ctr, Chicago, IL 60637 - USA
[6] Univ Chicago, Dept Stat, Chicago, IL 60637 - USA
Total Affiliations: 6
Document type: Journal article
Source: HEREDITY; v. 122, n. 3, p. 261-275, MAR 2019.
Web of Science Citations: 0

Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee-production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. (AU)

FAPESP's process: 14/20389-2 - Development of statistic genetic models for genomic selection in Coffea canephora and other species
Grantee:Luís Felipe Ventorim Ferrão
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 16/05127-7 - Genomic prediction in Coffea canephora using polygenic modeling
Grantee:Luís Felipe Ventorim Ferrão
Support type: Scholarships abroad - Research Internship - Doctorate