Habier et al. Genetics Selection Evolution 2010, 42:5 http://www.gsejournal.org/content/42/1/5 here as the dependency between the allele states at dif- ferent loci of all individuals in the available data set. In case of linkage equilibrium, the accuracy of GEBVs is not necessarily zero but will approach the accuracy of pedigree-based BLUP-EBVs as the number of SNPs fitted in the model increases. The reason is that SNPs capture additive-genetic relationships irrespective of the amount of LD in the population as demonstrated by Habier et al. [2] and Gianola et al. [3]. In those studies as well as here, additive-genetic relationships are defined as twice the coefficient of coancestry given by Malécot [4]. Note that this does not require that the training individuals are related, but only that individuals for which GEBVs are estimated are related to the training individuals. This is demonstrated in detail in additional file 1 in this paper. In practice, LD exists in cattle popu- lations [5-7] and thus two types of information are uti- lized to estimate GEBVs: LD and additive-genetic relationships. If cosegregation is modeled, then a third type of information can be utilized. However, cosegrega- tion was not modeled in this study. The persistence of the accuracy of GEBVs over generations, and therefore the potential of GS to reduce future phenotyping [8,9], depends largely on the amount of LD, which originates in outbred populations from historic mutations and drift, cosegregation, migration, selection and recent drift. In simulations, Habier et al. [2] estimated the accuracy of GEBVs that is only due to LD (in short, accuracy due to LD), which was considerably smaller than the GEBV accuracy resulting from both LD and additive-genetic relationships in the offspring of the training individuals, but it was fairly persistent over generations. Further- more, the ability to exploit LD information by the statis- tical methods used to estimate SNP effects varies. Meuwissen et al. [1] proposed a Bayesian model aver- aging approach termed BayesB, which fits only a small proportion of the available SNPs in each round of a Markov-Chain Monte Carlo (MCMC) algorithm and models SNP effects with a t-distributed prior. They further used Ridge-Regression BLUP (RR-BLUP), which fits all SNP effects with a normal prior. Habier et al. [2] showed that BayesB was more able to exploit LD infor- mation and less affected by additive-genetic relation- ships than RR-BLUP. Accuracy of GEBVs from real cattle data has been reported for Holstein Friesian popu- lations from North America [10], Australia, the Nether- lands and New Zealand [11,12]. In those studies, accuracies of GEBVs for milk performance, fertility and functional traits ranged from 0.63 to 0.84, and depended on the size of the training data, heritability and SNP density. These accuracies confirmed those found in simulations [1,2,13,14] quite well, but RR-BLUP was only slightly inferior compared to methods that fit only a fraction of the available SNPs such as BayesB. Page 2 of 12 VanRaden et al. [10] and Hayes et al. [12] concluded that, unlike in most simulations, only a few QTL with a large effect and many with a small effect contribute to genetic variation. These studies, however, did not show the dependency of the GEBV accuracy on additive- genetic relationships, which is a function of the number of relatives in training, the degree of relationship with training individuals [2] and heritability. Thus, a lower accuracy with decreasing training size [10] could be the result of a lower number of relatives in training, mean- ing that the more persistent accuracy due to LD and the GS method that exploits LD information best remains to be evaluated for real cattle data. More important, the dependency of GEBV accuracy on additive-genetic rela- tionships as well as the accuracy due to LD must be known to develop future breeding programs, because close relatives that were progeny tested for quantitative traits may not be available when GEBVs are applied to select animals early in lifetime. The objectives of this study were to analyze the impact of additive-genetic relationships between training and validation data sets on the accuracy of GEBVs and to estimate the accuracy due to LD in the German Holstein Friesian population. Thereby, the accuracy of GEBVs for current and future selection candidates as well as for individuals that are unrelated to the population were estimated. Further- more, the comparison of BayesB and RR-BLUP based on the accuracy due to LD will show which statistical model has the potential to reduce future phenotyping. Materials and methods Genotyped bulls A total of 3,863 German Holstein Friesian bulls, progeny tested with at least 30 daughters in the first lactation and genotyped for 54,001 SNPs distributed over the whole genome, were available. The proportion of miss- ing genotypes for any bull was lower than 5%, at an average of 1%. The distribution of genotyped bulls by birth year and the average number of daughters per bull are shown in Table 1. The family structure of these bulls, consisting of paternal half and full sib families as well as genotyped fathers and sons, are summarized in Table 2. SNP data DNA was extracted either from frozen semen, leukocyte pellets or fullblood samples. The BovineSNP50 Bead- Chip (Illumina, San Diego, CA) was used to obtain SNP genotypes for all bulls. A detailed description of the SNP content was given by [15]. Only SNPs with less than 5% missing genotypes and minor allele frequency greater than 3% were used, resulting in 40,588 SNPs. Minor allele frequencies of the selected SNPs were nearly uniformly distributed with a mean = 0.27.