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Genomic prediction of sugar content and cane yield in sugar cane clones in different stages of selection in a breeding program, with and without pedigree information

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Abstract

High cane yield and commercially extractable sucrose (CCS) content are two of the key sugarcane commercial traits selected in sugarcane breeding programs. Advancements in genomic prediction may provide opportunities to speed up gains for these traits in breeding programs by combining accurate prediction of breeding values in candidate parent clones shortening generation intervals. Selection trials in commercial breeding programs may provide training populations for developing genomic predictions. In this study, three different populations of clones in early and advanced stage selection trials in an established commercial sugarcane breeding program were used to assess genomic prediction accuracy. The clones (genotypes) were evaluated for cane yield and sugar content in field trials and genotyped using a SNP array developed for sugarcane cultivars and parents. Five models (Bayes A, Bayes B, Bayesian LASSO, Bayesian GBLUP and RKHS) were tested using pedigree and/or marker data. Prediction models that included marker information had higher prediction accuracies than models with pedigree data only. For CCS, the prediction accuracies for genotypes in advanced stage trials using DNA markers were superior compared with prediction accuracies for early-stage trials, suggesting that prior intensive selection for CCS did not diminish accuracy of genomic prediction. However, by contrast, for cane yield, the prediction accuracies were much less for the population in the advanced stages of selection. The levels of prediction accuracy obtained in most datasets (0.25–0.45) are encouraging for developing applications of genomic prediction to predict breeding values of yield and sugar content in sugarcane breeding programs.

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Author contribution statement

XW and PJ initiated the study and coordinated collection of phenotypic data. XW conducted analyses of phenotypic data. KA and RK coordinated development of the SNP array and collation of DNA marker data. ED conducted all analyses related to genomic prediction. PPR assisted in the development of R scripts for fitting Bayesian models. ED, PJ and XW wrote the manuscript, and all authors read the manuscript.

Funding

Financial support for the research reported on here, including the collection of the large datasets, was provided by Sugar Research Australia, Syngenta and Commonwealth Scientific and Industrial Research Organisation. A large number of staff from breeding programs operated Sugar Research Australia and Wilmar Sugar Australia contributed to collecting phenotypic data from field trials, and we are grateful for their competent conduct of these trials.

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Authors and Affiliations

  1. Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia

    Emily Deomano

  2. CSIRO Agriculture and Food, Australian Tropical Science Innovation Precinct, James Cook University, Townsville, QLD, 4811, Australia

    Phillip Jackson

  3. Sugar Research Australia, 26135 Peak Downs Highway, Te Kowai, QLD, 4741, Australia

    Xianming Wei

  4. CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD, 4067, Australia

    Karen Aitken

  5. Syngenta Crop Protection LLC, Research Triangle Park, NC, USA

    Raja Kota

  6. Colegio de Postgraduados, Montecillos, Edo. de México, 56230, México

    Paulino Pérez-Rodríguez

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  1. Emily Deomano
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Correspondence to Emily Deomano.

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Key message

• Genomic prediction of cane yield and sugar content in populations of sugarcane in early and advanced stages of selection using a range of models provide encouraging levels of accuracies for developing practical applications in sugarcane breeding.

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Deomano, E., Jackson, P., Wei, X. et al. Genomic prediction of sugar content and cane yield in sugar cane clones in different stages of selection in a breeding program, with and without pedigree information. Mol Breeding 40, 38 (2020). https://doi.org/10.1007/s11032-020-01120-0

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