ORIGINAL PAPER Accuracy of genomic selection in European maize elite breeding populations Yusheng Zhao • Manje Gowda • Wenxin Liu • Tobias Wu ¨ rschum • Hans P. Maurer • Friedrich H. Longin • Nicolas Ranc • Jochen C. Reif Received: 24 February 2011 / Accepted: 28 October 2011 / Published online: 11 November 2011 Ó Springer-Verlag 2011 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 vali- dation. 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 mark- ers. We used random regression best linear unbiased pre- diction 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. Introduction Genomic selection was suggested as a novel approach in the context of animal breeding with the potential to lead to a paradigm shift in the design and implementation of livestock and crop breeding programs (Meuwissen et al. 2001). Genomic selection differs from previous strategies such as linkage and association mapping in that it aban- dons the objective to map the effect of individual genes and instead focuses on an efficient estimation of breeding values on the basis of a large number of molecular markers, ideally covering the full genome (Jannink et al. 2010). As a first step in genomic selection, marker effects are estimated on the basis of a training set of genotypes, which are phenotyped and fingerprinted with dense mar- ker data. In the second step, individuals related to the training population that have been genotyped but not phenotyped are selected based on the estimated marker effects. Several statistical approaches have been suggested to estimate marker effects such as random regression best linear unbiased prediction (RR-BLUP; Whittaker et al. 2000; Meuwissen et al. 2001) and Bayesian shrinkage regression methods (Meuwissen et al. 2001; Xu 2003; Ter Braak et al. 2005; Calus et al. 2008). Relationships among statistical models have been theoretically investigated (e.g., Piepho 2009). Statistical models have also been compared based on simulation studies, which revealed that the accuracy depends on the genetic architecture of the trait (e.g., Daetwyler et al. 2010), the underlying population structure (e.g., Habier et al. 2007; Zhong et al. 2009), and the applied marker density (Meuwissen and Goddard 2010). Recently, statistical approaches have been com- pared using empirical data of cattle (Luan et al. 2009), maize, barley, wheat, and Arabidopsis (Lorenzana and Communicated by A. Charcosset. Y. Zhao  M. Gowda  W. Liu  T. Wu ¨rschum  H. P. Maurer  F. H. Longin  J. C. Reif (&) State Plant Breeding Institute, University of Hohenheim, 70599 Stuttgart, Germany e-mail: jochreif@uni-hohenheim.de N. Ranc Syngenta Seeds SAS, 12, chemin de l’Hobit, B.P. 27, 31790 Saint-Sauveur, France 123 Theor Appl Genet (2012) 124:769–776 DOI 10.1007/s00122-011-1745-y