The effect of bienniality on genomic prediction of yield in arabica coffee
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The effect of bienniality on genomic prediction of yield in arabica coffee Humberto Fanelli Carvalho . Giovanni Galli . Luı´s Felipe Ventorim Ferra˜o Juliana Vieira Almeida Nonato . Lilian Padilha . Mirian Perez Maluf . Ma´rcio Fernando Ribeiro de Resende Jr . Oliveiro Guerreiro Filho . Roberto Fritsche-Neto
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Received: 9 April 2020 / Accepted: 29 May 2020 Springer Nature B.V. 2020
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 Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10681-020-02641-7) contains supplementary material, which is available to authorized users. H. Fanelli Carvalho J. Vieira Almeida Nonato O. Guerreiro Filho Agronomic Institute of Campinas (IAC), Coffee Center, Campinas, Sa˜o Paulo, Brazil G. Galli R. Fritsche-Neto (&) Department of Genetics, Luiz de Queiroz College of Agriculture, University of Sa˜o Paulo, Piracicaba, Sa˜o Paulo, Brazil e-mail: [email protected] L. F. Ventorim Ferra˜o M. F. Ribeiro de Resende Jr Horticultural Sciences Department—IFAS, University of Florida, Gainesville, FL, USA
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 9 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. Keywords Coffea arabica Genomic selection Genotyping-by-sequencing Year prediction
L. Padilha M. Perez Maluf Brazilian Agriculture Research Corporation (EMBRAPA), Coffee, Campinas, Sa˜o Paulo, Brazil
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Introduction Coffee
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