Habier et al. Genetics Selection Evolution 2010, 42:5 http://www.gsejournal.org/content/42/1/5 of future candidates may be lower than reported in pre- vious studies. Most likely breeders would not accept these low accuracies, meaning that the generation inter- val cannot be decreased at this stage of the GS develop- ments. However, as statistical methods improve and both SNP density and training size increase, the currently expected accuracy of future candidates may also be higher. The better approach to predict GEBVs in the future is probably BayesB rather than G-BLUP, because as SNP density and data size increase, BayesB may be able to address more QTL such that the accuracy due to LD is higher and additive-genetic relationship information becomes less important. This was demonstrated here at least for training data size (Table 4). Furthermore, there were many adjacent SNPs with r2 close to zero and thus QTL in between might not be picked up by SNPs. Van- Raden et al. [10] compared several SNP densities by removing SNPs from the 54K panel and found an increase in accuracy with density. Another option to increase both the level of accuracy and the persistence with decreasing additive-genetic relationships might be to model cosegregation in addition to LD as proposed by [39-41]. For an average r2 of 0.225 between adjacent SNPs, which is identical to this study, Calus et al. [42] found no clear differences in the accuracy of GEBVs between a model similar to BayesB and the approach of [39] using simulated data. The question remains, how- ever, whether this simulation result holds in practice, because the real genetic model seems to be different from the simulated one. This can be suspected from the high accuracy of 0.8 for the offspring of 1,000 training individuals in the simulations by [42] compared to the accuracies of this study. Therefore additional informa- tion from cosegregation should be useful in practice. Conclusions Additive-genetic relationships between the training indi- viduals and a selection candidate captured by SNPs affect the GEBV accuracy of that candidate. Thus, accu- racy of current candidates can vary in practice. These additive-genetic relationships must be known to provide the accuracy along with GEBVs, and SNP effects should be re-estimated in short time intervals to include the most recent phenotypic data from relatives. The accu- racy of future selection candidates can be smaller than reported in previous studies because information from relatives might not be available when selection on GEBVs is possible and they can be used for breeding. The decay of accuracy with decreasing additive-genetic relationships is higher with a smaller number of training individuals. Differences in accuracy of GEBVs between G-BLUP and BayesB are small, but BayesB is much more able to exploit LD information than G-BLUP. Page 11 of 12 Therefore, as SNP density and training data size increase a Bayesian model averaging approach is more suitable for GS than G-BLUP. Further studies are needed to analyze the source of LD, its possible persis- tence with selection and the accuracy to predict Mende- lian sampling terms. Cross-validations that do not take into account the structure of the data, and additive- genetic relationships in particular, are not meaningful enough for problems in plant and animal breeding. Additional file 1: Accuracy of GEBVs in case of linkage equilibrium and unrelated training individuals. This is a pdf file used to demonstrate that the accuracy of GEBVs approaches the accuracy of pedigree-based BLUP in case of linkage equilibrium. Click here for file [ http://www.biomedcentral.com/content/supplementary/1297-9686-42-5- S1.PDF ] Acknowledgements This study is part of the FUGATO-plus project GenoTrack and was financially supported by the German Ministry of Education and Research, BMBF, and the Förderverein Biotechnologieforschung e.V. (FBF). Part of the SNP- genotypes of bulls were gratefully contributed by the German Holstein Association (DHV). Author details 1Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, Olshausenstrasse 40, 24098 Kiel, Germany. 2Vereinigte Informationssysteme Tierhaltung w.V., Heideweg 1, 27283 Verden/Aller, Germany. 3Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. Authors’ contributions DH raised the initial questions, coded the statistical methods, conducted the analyses and wrote the manuscript; JT conducted DNA extraction and organized SNP genotyping, FS calculated daughter yield deviations, and PL helped genotyping SNPs. GT was project coordinator, added valuable suggestions and discussed the manuscript with DH. All authors read and approved the manuscript. Competing interests The authors declare that they have no competing interests. Received: 23 August 2009 Accepted: 19 February 2010 Published: 19 February 2010 References 1. Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157(4):1819-1829. 2. Habier D, Fernando RL, Dekkers JCM: The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values. Genetics 2007, 177(4):2389-2397. 3. Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R: Additive Genetic Variability and the Bayesian Alphabet. 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