Genomic selection accuracy using historical data generated in a wheat breeding program

Plant Genome

Volumn 6, Issue 1, 2013, Pages

  Storlie, Eric ^a   Charmet, Gilles ^b  

^a Department of Environmental and Radiological Health Sciences   (United States)

^b Diversity and Ecophysiology of Cereals   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84884332602     PISSN: None     EISSN: 19403372     Source Type: Journal    
DOI: 10.3835/plantgenome2013.01.0001     Document Type: Article

References (27)

1. Albrecht, T., V. Wimmer, H.-J. Auinger, M. Erbe, C. Knaak, M. Ouzunova, H. Simianer, and C.-C. Schön. 2011. Genome-based prediction of testcross values in maize. Theor. Appl. Genet. 123:339-350. doi:10.1007/s00122-011-1587-7.
2. Bates, D., M. Maechler, and B. Bolker. 2011. Welcome to lme4 -Mixed-effects models project. R Foundation for Statistical Computing, Vienna, Austria. http://lme4.r-forge.r-project.org/ (accessed 12.Sept. 2011).
3. Burgueño, J., G. de los Campos, K. Weigel, and J. Crossa. 2012. Genomic prediction of breeding values when modelling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 52:707-719. doi:10.2135/cropsci2011.06.0299.
4. Costner, A. 2010. Package 'pedigree.' R Foundation for Statistical Computing, Vienna, Austria. http://cran.r-project.org/web/packages/pedigree/pedigree.pdf (accessed 22. Feb, 2011).
5. Crossa, J., G. de los Campos, P. Pérez, D. Gianola, J. Burgueño, J.L. Araus, D. Makumbi, R.P. Singh, S. Dreisigacker, J. Yan, V. Arief, M. Banziger, and H.-J. Braun. 2010. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713-724. doi:10.1534/genetics.110.118521
6. de los Campos, G., and P. Pérez. 2010. BLR: Bayesian linear regression. R Foundation for Statistical Computing, Vienna, Austria. http://cran.rproject.org/web/packages/BLR/index.html (accessed 15. Sept. 2010).
7. Endelman, J. 2011. rrBLUP: Ridge regression and other kernels for genomic selection. R Foundation for Statistical Computing, Vienna, Austria. http://cran.r-project.org/web/packages/rrBLUP/index.html (accessed 2. Oct. 2011).
8. Goddard, M.E. 2009. Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica (The Hague) 136:245-257.
9. Hayes, B.J., P.J. Bowman, A.J. Chamberlain, and M.E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92:433-443. doi:10.3168/jds.2008-1646.
10. Heffner, E.L., J.-L. Jannink, and M.E. Sorrells. 2011. Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Gen. 4:65-75. doi:10.3835/plantgenome.2010.12.0029.
11. Heffner, E.L., A.J. Lorenz, J.-L. Jannink, and M.E. Sorrells. 2010. Plant breeding wiThgenomic selection: Gain per unit time and cost. Crop Sci. 50:1681-1690. doi:10.2135/cropsci2009.11.0662.
12. Heffner, E.L., M.E. Sorrells, and J.-L. Jannink. 2009. Genomic selection for crop improvement. Crop Sci. 49:1-12. doi:10.2135/ cropsci2008.08.0512.
13. Henderson, C.R. 1975. Best linear unbiased prediction under a selection model. Biometrics 31:423-447. doi:10.2307/2529430.
14. Heslot, N., H.-P. Yang, M.E. Sorrells, and J.-L. Jannink. 2012. Genomic selection in plant breeding: A comparison of models. Crop Sci. 52:146-160.
15. Iwata, H., and J.-L. Jannink. 2011. 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. 51:1915-1927. doi:10.2135/cropsci2010.12.0732.
16. Lorenzana, R.E., and R. Bernardo. 2009. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor. Appl. Genet. 120:151-161. doi:10.1007/s00122-009-1166-3.
17. Meuwissen, T.H.E., B.J. Hayes, and M.E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829.
18. Morales, M. 2010. Package 'sciplot.' R Foundation for Statistical Computing, Vienna, Austria. http://cran.univ-lyon1.fr/web/packages/sciplot/sciplot.pdf (accessed 20. July 2011).
19. Park, T., and G. Casella. 2008. The Bayesian LASSO. J. Am. Stat. Assoc. 103:681-686. doi:10.1198/016214508000000337.
20. Pérez, P., G. de los Campos, J. Crossa, and D. Gianola. 2010. Genomicenabled prediction based on molecular markers and pedigree using the Bayesian Linear Regression Package in R. Plant Gen. 3:106-116. doi:10.3835/plantgenome2010.04.0005.
21. Piepho, H.P. 2009. Ridge regression and extensions for genomewide selection in maize. Crop Sci. 49:1165-1175. doi:10.2135/ cropsci2008.10.0595.
22. Poland, J., J. Endelman, J. Dawson, J. Rutkoski, S. Wu, Y. Manes, S. Dreisigacker, J. Crossa, H. Sánchez-Villeda, M. Sorrells, and J.-L. Jannink. 2012. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Gen. 5:103-113. doi:10.3835/ plantgenome2012.06.0006.
23. R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
24. VanRaden, P.M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423. doi:10.3168/jds.2007-0980.
25. Whittaker, J.C., C. Haley, and R. Thompson. 1997. Optimal weighting of information in marker-assisted selection. Genet. Res. 69:137-144. doi:10.1017/S0016672397002711.
26. Zhao, Y., M. Gowda, W. Liu, T. Wurschum, H.P. Maurer, F.H. Longin, N. Ranc, and J.C. Reif. 2012. Accuracy of genomic selection in European maize elite breeding populations. Theor. Appl. Genet. 123: 10.1007/s00122-011-1745-y.
27. Zhong, S.Q., J.C.M. Dekkers, R.L. Fernando, and J.-L. Janink. 2009. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study. Genetics 182:355-364. doi:10.1534/genetics.108.098277