Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123866
Research center of Embrapa/Collection: Embrapa Café - Artigo em periódico indexado (ALICE)
Date Issued: 2018
Type of Material: Artigo em periódico indexado (ALICE)
Authors: FERRÃO, L. F. V.
FERRÃO, R. G.
FERRAO, M. A. G.
FONSECA, A. F. A. da
CARBONETTO, P.
SLEPHENS, M.
FRANCO, A. A.
Additional Information: LUÍS FELIPE VENTORIM FERRÃO, ESALQ; ROMÁRIO GAVA FERRÃO, INCAPER; MARIA AMELIA GAVA FERRAO, CNPCa; AYMBIRÉ FRANCISCO ALMEIDA DA FONSECA, CNPCa; PETER CARBONETTO, UNIVERSIDADE DE CHICAGO; MATTHEW SLEPHENS, UNIVERSIDADE DE CHICAGO; ANTONIO AUGUSTO FRANCO, ESALQ.
Title: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.
Publisher: Heredity, v. 122, p. 261-275, 2018.
Language: Ingles
Description: 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.
Thesagro: Seleção Genética
Seleção Genótipa
Coffea Canephora
Data Created: 2020-07-15
Appears in Collections:Artigo em periódico indexado (SAPC)

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