IMPLEMENTATION OF GENOME-WIDE SELECTION IN WHEAT
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.
About the Authors
G. CharmetFrance
E. Storlie
France
References
1. Balding D.J., Bishop M., Cannings C. Handbook of Statistical Genetics. Chichester, UK: John Wiley and Sons Eds, 2007. V. 2. P. 919–921.
2. Bernardo R., Moreau L., Charcosset A. Number and fi tness of selected individuals in marker-assisted and phenotypic recurrent selection // Crop Sci. 2006. V. 46. P. 1972–1980.
3. Bernardo R., Yu J.M. Prospects for genomewide selection for quantitative traits in maize // Crop Sci. 2007. V. 47. P. 1082–1090.
4. Blanc G., Charcosset A., Veyrieras J.B. et al. Marker-assisted selection effi ciency in multiple connected populations: a simulation study based on the results of a QTL detection experiment in maize // Euphytica. 2008. V. 161. P. 71–84.
5. Complementary strategies to raise wheat yield potential // Proc. Symp. Complementary strategies to raise wheat yield potential, CIMMYT headquarters / Eds M. Reynolds, D. Eaton. 10–13 November 2009. Mexico: CIMMYT, 2009. 33 p.
6. Coster A. Package ‘pedigree’. 2010. Available at http://cran.r-project.org/web/packages/pedigree/pedigree.pdf
7. Crossa J., de los Campos G., Pérez P. et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers // Genetics. 2010. V. 186. P. 713–724.
8. De los Campos G., Pérez P. The Bayesian Linear Regression Package V.1.2. 2010. Available at http://cran.r-project. org/web/packages/BLR/index.html.
9. Eathington S.R., Crosbie T.M., Edwards M.D. et al. Molecular markers in a commercial breeding program // Crop Sci. 2007. V. 47. P. 154–163.
10. FAO. World Agriculture: Towards 2015/2030. Roma: FAO, 2002. 77 p.
11. Gimelfarb A., Lande R. Simulation of marker assisted selection in hybrid populations // Genet. Res. 1994. V. 63. P. 39–47.
12. Goddard M.E., Hayes B.J. Genomic selection // J. Anim. Breed. Genet. 2007. V. 124. P. 323–330.
13. Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. Genomic selection in dairy cattle: Progress and challenges: Invited review // J. Dairy Sci. 2009. V. 92. V. 433–443.
14. Heffner E.L., Sorrells M.E., Jannink J.L. Genomic selection for crop improvement // Crop Sci. 2009. V. 49. P. 1–12.
15. Heffner E.L., Lorenz A.J., Jannink J.-L. Sorrells M.E. Plant breeding with genomic selection: gain per unit time and cost // Crop Sci. 2010. V. 50. P. 1681–1690.
16. Henderson C.R. Best linear unbiased estimation and prediction under a selection model // Biometrics. 1975. V. 31. P. 423–444.
17. Hospital F., Moreau L., Lacoudre F. et al. More on the effi ciency of marker-assisted selection // Theor. Appl. Genet. 1997. V. 95. P. 1181–1189.
18. Iwata H., 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. V. 51. P. 1915–1927.
19. Jannink J.L., Lorenz A.J., Iwata H. Genomic selection in plant breeding: from theory to practice // Brief. Funct. Genom. Proteom. 2010. V. 9. P. 166–177.
20. Lande R., Thompson R. Effi ciency of marker-assisted selection in the improvement of quantitative traits // Genetics. 1990. V. 124. P. 743–756.
21. Lorenzana R.E., Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations // Theor. Appl. Genet. 2009. V. 120. P. 151–161.
22. Meuwissen T.H.E., Hayes B., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps // Genetics. 2001. V. 157. P. 1819–1829.
23. Moreau L., Charcosset A., Hospital F., Gallais A. Marker-assisted selection effi ciency in populations of fi nite size // Genetics. 1998. V. 148. P. 1353–1365.
24. Pérez P., de los Campos G., Crossa J., Gianola D. Genomicenabled prediction based on molecular markers and pedigree using the BLR package in R // Plant Genome. 2010. V. 3. P. 106–116.
25. Piepho H.P. Ridge regression and extensions for genomewide selection in maize // Crop Sci. 2009. V. 49. P. 1165–1175.
26. R Development Core Team. R: a language and environment for statistical computing. Vienna: R foundation for statistical computing, 2011. Available at http://www.R-project.org.
27. Wheat Facts and Futures / Eds J. Dixon, H.J. Braun, P. Kosina, J. Crouch. Mexico, D.F.: CIMMYT, 2009. 95 p.
28. Whittaker J.C., Thompson R., Denham M.C. Marker-assisted selection using ridge regression // Genet. Res. 2000. V. 75. P. 249–252.
29. Zhong S.Q., Dekkers J.C.M., Fernando R.L., Janink J.L. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study // Genetics. 2009. V. 182. P. 355–364.