Habier et al. Genetics Selection Evolution 2010, 42:5 http://www.gsejournal.org/content/42/1/5 structure) in real populations, which might explain the lower accuracy due to LD estimated in this study. To analyze the causes of the different results between simu- lations and real experiments in more detail, simulations should be conducted using the real pedigree, as done by [26,36]. The decay of accuracy with amax, especially for BayesB, was similar to that observed in simulations over generations without further phenotyping after training [1,2,14]. In simulations, the additive-genetic relationship with training individuals is halved each generation and therefore amax values of the first four simulated genera- tions after training correspond to those specified in this study. Thus, the decay of accuracy with amax might point to the decay of accuracy in generations after train- ing when phenotyping is stopped. Note, however, that the number of relatives in training at a certain amax is different from simulations. The differences between BayesB and G-BLUP in accu- racy due to LD confirm simulation results [2], but they tended to be higher in this study than in [2]. The reason may be that 40,588 SNPs were utilized here to calculate the genomic relationship matrix used in G-BLUP, whereas only 1,000 SNPs were used in [2]. This indi- cates that too many SNPs dilute LD information as shown by Fernando et al. [37]. Thus, as SNP density increases in the future, the genomic relationship matrix may be less valuable than using the current density unless the training data size increases largely (see also Goddard [33]) and/or SNPs are pre-selected based on other methods such as QTL fine mapping approaches that exploit both LD and cosegregation [38]. Comparison with other GS studies GEBVs were combined in other GS studies analyzing real data with pedigree-based EBVs by using selection index theory [12], which increases the proportion of additive-genetic relationship information in GEBVs. In this study only direct GEBVs were considered to deter- mine the impact of additive-genetic relationships cap- tured by SNPs. Accuracies of combined GEBVs in those studies should be higher, but conversely the decay of accuracy with amax is also expected to be larger. Further difficul- ties for meaningful comparisons are different numbers of training bulls and that no information about the addi- tive-genetic relationships between training and valida- tion bulls was provided by the other authors. However, VanRaden et al. [10] also presented squared correlations between GEBVs and DYDs using 3,500 training bulls. For the traits milk yield, fat yield, protein yield and somatic cell score correlations obtained by G-BLUP were 0.68, 0.65, 0.68 and 0.61, respectively. Correlations found for amax = 0.6 and 0.49 (Figure 3) were somewhat Page 10 of 12 lower, which may be due to a smaller training size of 2,096 bulls. However, Hayes et al. [12] reported an accu- racy of 0.67 for protein yield using G-BLUP and only 798 training bulls. Because the accuracy for protein yield with G-BLUP and 1,048 training bulls was 0.6 in this study, the relatively high accuracy estimated in [12] might indicate the contribution of additive-genetic rela- tionships either captured by SNPs or from pedigree- based EBVs. In contrast to this study, VanRaden et al. [10] found a lower correlation for somatic cell score with G-BLUP than with a non-linear method similar to BayesB. The fact that SNPs capture additive-genetic relation- ships has to be taken into account when genomic breed- ing values are combined with pedigree-based EBVs in practice. Otherwise the advantages of GS with respect to inbreeding and effective selection intensity may be lower. Future performance testing, training intervals and methods The acceptance of GS by breeders depends to a large extent on the level of accuracy of GEBVs. Until now, breeders mainly use progeny tested bulls with a high accuracy above 0.9, which is not yet achieved with GEBVs without information from relatives. The most realistic scenario at this moment is to use GEBVs for pre-selection of young calves in combination with a sub- sequent progeny testing. The latter will continuously pro- vide relatives for training and thereby ensure the highest accuracy of GEBVs. This also means that SNP effects should be re-estimated in short time intervals to always include the latest phenotypic data. The combination of GEBVs with pedigree-based EBVs might not be the only criterion for selection as deviations from expected rela- tionships provide additional and specific information. However, the accuracy of future cohorts can be lower than for the current ones because if bulls are selected on GEBVs and mated to the breeding population as soon as they are sexually mature, the progeny test results will not be available before the next generation is ready to be selected on GEBVs (Kay-Uwe Götz, personal communi- cation). Consider the following situation of a possible breeding program: Suppose sons of a progeny tested bull are just born. After 1.5 years, these sons can be selected on GEBVs and then mated to the population to produce both the next breeding generation and test progeny. The accuracy of their GEBVs is expected to be as high as for amax = 0.6 or 0.49. Another 2.5 years later, the grand- sons become selection candidates, but the accuracy of their GEBVs should be at least as low as for amax = 0.249, because progeny testing lasts four years in cattle and therefore no half and full sib information will be available for these grand-sons. Consequently, the GEBV accuracy