Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Accuracy of genomic selection in European maize elite breeding populations

Abstract

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3–4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.

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

Fig. 1
Fig. 2
Fig. 3

References

  1. Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090

  2. Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224

  3. Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF (2008) Accuracy of genomic selection using different methods to define haplotypes. Genetics 178:553–561

  4. Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625

  5. Crossa J, Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun H-J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

  6. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–1031

  7. de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183:1545–1553

  8. Dekkers JCM (2007) Prediction of response to marker-assisted and genomic selection using selection index theory. J Anim Breed Genet 124:331–341

  9. Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397

  10. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

  11. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

  12. Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

  13. Henderson CR (1984) Application of linear models in animal breeding. University of Guelph, Guelph

  14. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

  15. Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177

  16. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756

  17. Liu W, Gowda M, Steinhoff J, Maurer HP, Würschum T, Longin CFH, Cossic F, Reif JC (2011) Association mapping in an elite maize breeding population. Theor Appl Genet 123:847–858

  18. Lorenzana R, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161

  19. Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen TH (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183:1119–1126

  20. Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and larger bias in estimates of QTL effects. Genetics 149:383–403

  21. Melchinger AE, Utz HF, Piepho HP, Zeng ZB, Schön CC (2007) The role of epistasis in the manifestation of heterosis: a systems-oriented approach. Genetics 177:1815–1825

  22. Meuwissen THE, Goddard ME (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631

  23. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

  24. Piepho HP (2009) Ridge regression and extensions for genome-wide selection in maize. Crop Sci 49:1165–1176

  25. Reif JC, Liu W, Gowda M, Maurer HP, Möhring J, Fischer S, Schechert A, Würschum T (2010) Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping. Theor Appl Genet 121:1489–1499

  26. Steinhoff J, Liu W, Maurer HP, Würschum T, Longin CFH, Reif JC (2011) Variation in allele substitution effects determined with multiple-line QTL-mapping in maize. Crop Sci. doi:10.2135/cropsci2011.03.0181

  27. Ter Braak CJF, Boer M, Bink M (2005) Extending Xu’s Bayesian model for estimating polygenic effects using markers of the entire genome. Genetics 170:1435–1438

  28. Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE (2011) Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet 123:11–20

  29. Wegenast T, Longin CFH, Utz HF, Melchinger AE, Maurer HP, Reif JC (2008) Hybrid maize breeding with doubled haploids IV. Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance. Theor Appl Genet 117:251–260

  30. Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75:249–252

  31. Xu S (2003) Estimating polygenic effects using markers of the entire genome. Genetics 163:789–801

  32. Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364

Download references

Acknowledgments

This research was conducted within the Biometric and Bioinformatic Tools for Genomics based Plant Breeding project supported by the German Federal Ministry of Education and Research (BMBF) within the framework of GABI–FUTURE initiative. We greatly appreciate the helpful comments and suggestions of two anonymous reviewers.

Author information

Correspondence to Jochen C. Reif.

Additional information

Communicated by A. Charcosset.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zhao, Y., Gowda, M., Liu, W. et al. Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124, 769–776 (2012). https://doi.org/10.1007/s00122-011-1745-y

Download citation

Keywords

  • Prediction Accuracy
  • Genomic Selection
  • Training Population
  • Genomic Breeding
  • Biparental Population