Skip to main content
Log in

Implementation of genome-wide selection in wheat

  • Published:
Russian Journal of Genetics: Applied Research

Abstract

With the expected development of thousands of molecular markers in most crops, the marker-assisted selection theory has recently shifted from the use of a few markers targeted in QTL regions (or derived from candidate or validated genes) to the use of many more markers covering the whole genome. These genome-wide markers are already used for association analysis between polymorphisms for anonymous markers and qualitative or quantitative traits. The condition for success is that a sufficient level of linkage disequilibrium (LD) exists between the adjacent markers used for genotyping and the true genes or QTLs. This LD is known to vary among species and type of genetic material. In selfing species, particularly among breeding lines, LD has been reported to range up to 1 cM or more. In such conditions, uncharacterized markers can be used to predict the breeding value of a trait without referring to actual QTLs. We present an example applying DArT markers to the INRA wheat breeding material in an attempt to implement whole genome selection as an alternative to phenotypic selection. This study assesses different models (GBLUP, Ridge Regression BLUP, Bayesian Ridge Regression and Lasso) and their ability to predict the yields of genotypes evaluated in a multi-site network from 2000 to 2009 in a highly unbalanced design. The prediction coefficients obtained by cross-validation techniques are encouraging, given the small size of the training population.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Balding, D.J., Bishop, M., and Cannings, C., Handbook of Statistical Genetics, Chichester: UK: Wiley, 2007, vol. 2, pp. 919–921.

    Book  Google Scholar 

  • Bernardo, R., Moreau, L., and Charcosset, A., Number and Fitness of Selected Individuals in Marker-Assisted and Phenotypic Recurrent Selection, Crop Sci., 2006, vol. 46, pp. 1972–1980.

    Article  Google Scholar 

  • Bernardo, R. and Yu, J.M., Prospects for Genome-Wide Selection for Quantitative Traits in Maize, Crop Sci., 2007, vol. 47, pp. 1082–1090.

    Article  Google Scholar 

  • Blanc, G., Charcosset, A., Veyrieras, J.B., et al., Marker-Assisted Selection Efficiency in Multiple Connected Populations: A Simulation Study Based on the Results of a QTL Detection Experiment in Maize, Euphytica, 2008, vol. 161, pp. 71–84.

    Article  Google Scholar 

  • de los Campos, G. and Perez, P., The Bayesian Linear Regression Package V.1.2, 2010. http://cran.r-project.org/web/packages/BLR/index.html

  • Complementary Strategies to Raise Wheat Yield Potential, in Proc. Symp. Complementary Strategies to Raise Wheat Yield Potential, CIMMYT headquarters, Reynolds, M. and Eaton, D. Eds. November 10–13, 2009. Mexico: CIMMYT, 2009.

  • Coster, A., http://cran.r-project.org/web/packages/pedigree/pedigree.pdf

  • Crossa, J., Campos, G., Perez, P., et al., Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers, Genetics, 2010, vol. 186, pp. 713–724.

    Article  PubMed  CAS  Google Scholar 

  • Eathington, S.R., Crosbie, T.M., Edwards, M.D., et al., Molecular Markers in a Commercial Breeding Program, Crop Sci., 2007, vol. 47, pp. 154–163.

    Article  Google Scholar 

  • FAO. World Agriculture: Towards 2015/2030. Roma: FAO, 2002. 77 p.

    Google Scholar 

  • Gimelfarb, A. and Lande, R., Simulation of Marker Assisted Selection in Hybrid Populations, Genet. Res., 1994, vol. 63, pp. 39–47.

    Article  PubMed  CAS  Google Scholar 

  • Goddard, M.E. and Hayes, B.J., Genomic Selection, J. Anim. Breed. Genet., 2007, vol. 124, pp. 323–330.

    Article  PubMed  CAS  Google Scholar 

  • Hayes, B.J., Bowman, P.J., Chamberlain, A.J., and Goddard, M.E., Genomic Selection in Dairy Cattle: Progress and Challenges: Invited Review, J. Dairy Sci., 2009, vol. 433–443.

  • Heffner, E.L., Sorrells, M.E., and Jannink, J.L., Genomic Selection for Crop Improvement, Crop Sci., 2009, vol. 49, pp. 1–12.

    Article  CAS  Google Scholar 

  • Heffner, E.L., Lorenz, A.J., Jannink, J.-L., and Sorrells, M.E., Plant Breeding with Genomic Selection: Gain per Unit Time and Cost, Crop Sci., 2010, vol. 50, pp. 1681–1690.

    Article  Google Scholar 

  • Henderson, C.R., Best Linear Unbiased Estimation and Prediction under a Selection Model, Biometrics, 1975, vol. 31, pp. 423–444.

    Article  PubMed  CAS  Google Scholar 

  • Hospital, F., Moreau, L., Lacoudre, F., et al., More on the Efficiency of Marker-Assisted Selection, Theor. Appl, Genet., 1997, vol. 95, pp. 1181–1189.

    Article  Google Scholar 

  • Iwata, H. and Jannink, J.L., Accuracy of Genomic Selection Prediction in Barley Breeding Programs: A Simulation Study Based on the Real Single Nucleotide Polymorphism Data of Barley Breeding Lines, Crop Sci., 2011, vol. 51, pp. 1915–1927.

    Article  Google Scholar 

  • Jannink, J.L., Lorenz, A.J., and Iwata, H., Genomic Selection in Plant Breeding: from Theory To Practice, Brief, Funct. Genom. Proteom., 2010, vol. 9, pp. 166–177.

    CAS  Google Scholar 

  • Lande, R. and Thompson, R., Efficiency of Marker-Assisted Selection in the Improvement of Quantitative Traits, Genetics, 1990, vol. 124, pp. 743–756.

    PubMed  CAS  Google Scholar 

  • Lorenzana, R.E. and Bernardo, R., Accuracy of Genotypic Value Predictions for Marker-Based Selection in Biparental Plant Populations, Theor. Appl. Genet., 2009, vol. 120, pp. 151–161.

    Article  PubMed  Google Scholar 

  • Meuwissen, T.H.E., Hayes, B., and Goddard, M.E., Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps, Genetics, 2001, vol. 157, pp. 1819–1829.

    PubMed  CAS  Google Scholar 

  • Moreau, L., Charcosset, A., Hospital, F., and Gallais, A., Marker-Assisted Selection Efficiency in Populations of Finite Size, Genetics, 1998, vol. 148, pp. 1353–1365.

    PubMed  CAS  Google Scholar 

  • Perez, P., Campos, G., Crossa, J., and Gianola, D., Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the BLR Package in R, Plant Genome, 2010, vol. 3, pp. 106–116.

    Article  PubMed  Google Scholar 

  • Piepho, H.P., Ridge Regression and Extensions for Genomewide Selection in Maize, Crop Sci., 2009, vol. 49, pp. 1165–1175.

    Article  Google Scholar 

  • R Development Core Team. R: A Language and Environment for Statistical Computing, Vienna: R Foundation for Statistical Computing, 2011. http://www.R-project.org

    Google Scholar 

  • Wheat Facts and Futures. Dixon, J., Braun, H.J., Kosina, P., and Crouch, J. Eds. Mexico, D.F.: CIMMYT, 2009.

    Google Scholar 

  • Whittaker, J.C., Thompson, R., and Denham, M.C., Marker-Assisted Selection Using Ridge Regression, Genet. Res., 2000, vol. 75, pp. 249–252.

    Article  PubMed  CAS  Google Scholar 

  • Zhong, S.Q., Dekkers, J.C.M., Fernando, R.L., and Janink, J.L., Factors Affecting Accuracy from Genomic Selection in Populations Derived from Multiple Inbred Lines: A Barley Case Study, Genetics, 2009, vol. 182, pp. 355–364.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Charmet.

Additional information

The article is published in the original.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Charmet, G., Storlie, E. Implementation of genome-wide selection in wheat. Russ J Genet Appl Res 2, 298–303 (2012). https://doi.org/10.1134/S207905971204003X

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S207905971204003X

Keywords

Navigation