Genetics Selection Evolution 2009, 41:35 http://www.gsejournal.org/content/41/1/35 Table 2: Effect of doubling simultaneously the genome size and the size of the training data set (N) on the accuracy of predictions of genetic values using a marker density of 20 Ne/M Genome size (M) 2.5 5 10 N 500 1000 2000 BayesB G-BLUP 0.890 0.882 0.875 0.831 0.817 0.803 BayesB has the advantage of using an informative priori distribution, which agrees well with the simulated distri- bution of QTL effects. Therefore, an alternative distribu- tion for the QTL effects was also investigated, namely the normal distribution, which makes it harder for BayesB to detect and give extra weight to large QTL (since there are fewer). With N = 2000 and density 20 Ne/M, the accuracy of selection reduced to 0.914, 0.916 and 0.879 for BayesB, MIXTURE and G-BLUP, respectively (result not shown elsewhere). Thus, the effect of normal vs. exponentially distributed QTL effects was small, but larger for BayesB than for G-BLUP as might be expected. Although the dif- ference is small and may well be due to sampling, the MIXTURE model seems to yield the highest accuracy when QTL effects are normally distributed, which may be expected since it attempts to estimate the prior distribu- tion from the data, and the normally distributed QTL effects may be more in accordance with the assumptions underlying the MIXTURE model. Requirements for high accuracy The results presented here imply that the accurate predic- tion of breeding values of unrelated individuals requires a set of ~10*Ne*L SNP markers and ~2*Ne*M genotyped and phenotypes training individuals for the estimation of SNP effects. The former requirement is likely to be achieved in species where the genome sequence is availa- ble, but the latter will be challenging. If we accept accura- cies of 0.7 – 0.8, ~1*Ne*L training individuals is sufficient. For humans, this still implies ~350,000 train- Table 3: Errors of predicting accuracies by Equation (1) (rEqn(1)) relative to simulation results (rsim), when G1t was used as training data set and the genetic values of G1e individuals were predicted Marker density (Ne/M) BayesB G-BLUP N 2000 1000 500 20 1 20 1 20 1 rsim-rEqn (1) -0.024 -0.027 0.005 0.014 -0.012 -0.023 0.005 -0.025 -0.006 0.005 -0.014 0.019 ing records, which makes the risk prediction for truly polygenic diseases and for unrelated individuals probably impossible unless genotyping and phenotyping for such diseases becomes very common in the future. For cattle, 1NeL implies N = 30,000. Using Holstein dairy bulls, VanRaden et al. [23] found accuracies of 0.7–0.8 using N = 3,576, but in this situation the training and evaluation bulls were often highly related, and genomic EBVs were combined with T-BLUP EBVs, which were based on a much larger data set. Thus, the aforementioned requirements can be substantially reduced if the training and evaluation individuals are related, and Equation (2) can be used to predict by how much they can be reduced. Conclusion 1. Accuracies of ~0.9 for unrelated individuals require 10*Ne*L SNPs and 2*Ne*L training records. For related individuals these requirements can be substan- tially lowered. 2. Accuracy increases approximately linearly with marker density, when expressed as |R| between adja- cent markers. 3. The superiority of BayesB over G-BLUP increases with marker density. 4. BayesB yielded more persistent estimates of SNP effects over generations. 5. As the size of the training data set increases, the dif- ference between G-BLUP and BayesB decreases. 6. To take advantage of high marker densities, large training data sets are needed. 7. The regression of the inverse of the prediction error variance (PEV-1) on the number of training records (N) is linear, and the regression coefficients can be used to predict the accuracy for different N. 8. The scaling arguments predicted from Equation (1) hold approximately, but they over-predicted the accu- racies found here, perhaps because the training and evaluation individuals were less related than expected. Competing interests The author declares that they have no competing interests. Authors' contributions THEM performed the computer simulations and wrote the manuscript. Page 8 of 9 (page number not for citation purposes)