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Theoretical and Applied Genetics

, Volume 126, Issue 10, pp 2575–2586 | Cite as

Experimental assessment of the accuracy of genomic selection in sugarcane

  • M. Gouy
  • Y. Rousselle
  • D. Bastianelli
  • P. Lecomte
  • L. Bonnal
  • D. Roques
  • J.-C. Efile
  • S. Rocher
  • J. Daugrois
  • L. Toubi
  • S. Nabeneza
  • C. Hervouet
  • H. Telismart
  • M. Denis
  • A. Thong-Chane
  • J. C. Glaszmann
  • J.-Y Hoarau
  • S. Nibouche
  • L. CostetEmail author
Original Paper

Abstract

Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker-assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1,499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology, and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space, and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within-panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane.

Keywords

Sugarcane Partial Less Square Regression Genomic Selection Neutral Detergent Fiber Ridge Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors wish to thank T. Dumont, C. Lallemand, I. Promi, R. Tibère, M. Carbel, J. M. Coupan, O. Calvados and N. Lubin for field work, M. Hoarau for lab work. This study was funded by the eRcane company, by CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement) ATP-SEPANG project grant, by the Conseil Régional de la Réunion, by the European Union (European regional development fund—ERDF), by ANR (Agence Nationale de la Recherche) Delicas project grant ANR-08-GENM-001, ANR Grass biofuel project grant ANR-07-GPLA-018-005 and by the ANRT (Association Nationale de la Recherche et de la Technologie) through the CIFRE Ph.D grant N°600/2012 of M. Gouy.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The authors declare that the experiments presented in this publication comply with current French laws.

Supplementary material

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Supplementary material 1 (DOCX 23 kb)
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Supplementary material 2 (DOCX 615 kb)
122_2013_2156_MOESM3_ESM.docx (24 kb)
Supplementary material 3 (DOCX 23 kb)
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Supplementary material 4 (DOCX 23 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • M. Gouy
    • 1
    • 2
    • 3
  • Y. Rousselle
    • 2
  • D. Bastianelli
    • 8
  • P. Lecomte
    • 8
  • L. Bonnal
    • 8
  • D. Roques
    • 4
  • J.-C. Efile
    • 4
  • S. Rocher
    • 4
    • 7
  • J. Daugrois
    • 6
  • L. Toubi
    • 4
  • S. Nabeneza
    • 9
  • C. Hervouet
    • 5
  • H. Telismart
    • 2
  • M. Denis
    • 5
  • A. Thong-Chane
    • 1
  • J. C. Glaszmann
    • 5
  • J.-Y Hoarau
    • 4
  • S. Nibouche
    • 2
  • L. Costet
    • 2
    Email author
  1. 1.eRcaneSainte-ClotildeLa Réunion, France
  2. 2.CiradUMR PVBMTSaint-PierreLa Réunion, France
  3. 3.Université de La RéunionUMR PVBMTSaint-PierreLa Réunion, France
  4. 4.CiradUMR AGAPPetit BourgGuadeloupe, France
  5. 5.CiradUMR AGAPMontpellierFrance
  6. 6.CiradUMR BGPIPetit BourgGuadeloupe, France
  7. 7.Université des Antilles et de la GuyanePointe-à-PitreGuadeloupe, France
  8. 8.CiradUMR SELMETMontpellierFrance
  9. 9.CiradUMR SELMETSaint-PierreLa Réunion, France

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