Habier et al. Genetics Selection Evolution 2010, 42:5 http://www.gsejournal.org/content/42/1/5 Page 6 of 12 Figure 2 Box plots of additive-genetic relationships between bulls in training and validation for a maximum additive- genetic relationship, amax, of 0.6, 0.49, 0.249 and 0.1249. Figure 2. The scenarios amax = 0.6, 0.49 and 0.249 only differed in the upper parts of their distributions, whereas mean (not shown), median and quartiles were nearly identical. The training data for amax = 0.49 contained half sibs which were not in the training data sets for amax = 0.249 and 0.1249, and the scenario with amax = 0.6 had also full sibs and fathers of bulls in validation which were not in the scenario 0.49. A validation bull had on average 10 half sibs in training in both scenarios with amax = 0.6 and 0.49, but numbers varied largely between 1 and 58. Only a few validation bulls had a full sib or father in training in the scenario with amax = 0.6. Accuracy of GEBVs Figure 3 depicts the accuracy of EBVs depending on amax for milk, fat and protein yield as well as somatic cell score obtained by BayesB, G-BLUP and P-BLUP utilizing 2,096 training bulls. For amax = 1, accuracies were close to unity for G-BLUP and P-BLUP, but somewhat lower for BayesB. The reason is that accuracies for amax = 1 describe goodness of fit rather than prediction ability and it is well known that the coefficient of determination, which is related to this accuracy, increases with the num- ber of explanatory variables. G-BLUP used all available SNPs, whereas BayesB fitted only 400 in each round of the MCMC-algorithm. Accuracy of P-BLUP decreased with amax as expected, where the overall level for milk and protein yield was higher than for fat yield and somatic cell score. P-BLUP was outperformed by both GS methods, where the absolute difference between the latter and P-BLUP was higher for fat yield and somatic Figure 3 Accuracy of EBVs, r, obtained by BayesB, G-BLUP and P-BLUP depending on the maximum additive-genetic relationship between bulls in training and validation, amax, for the traits milk yield, fat yield, protein yield and somatic cell score, based on 2,096 training bulls in each amax scenario.