Habier et al. Genetics Selection Evolution 2010, 42:5 http://www.gsejournal.org/content/42/1/5 cell score compared to milk and protein yield. The high- est accuracies of GEBVs were found for amax = 0.6 and 0.49 and equal to 0.68, 0.65 and 0.60 for milk, fat and protein yield, respectively, and 0.58 for somatic cell score. BayesB and G-BLUP gave similar results in all traits except for milk yield for which BayesB performed notably better. Interestingly, the accuracy of GEBVs from both GS methods was very similar for amax values = 0.6 and 0.49, although a decay was found from 0.6 to 0.49 for P- BLUP in most traits. No plausible reason could be found for that, especially as the decay of accuracy of the GS methods resembled that of P-BLUP quite well otherwise. Accuracy of GEBVs clearly decreased from amax = 0.49 to 0.1249 in all four traits and for both BayesB and G-BLUP (Figure 3). The decay of accuracy was similar for both GS methods in somatic cell score, but smaller with BayesB for the yield traits. As a result, the accuracy of the yield traits at amax = 0.1249 was higher with BayesB than with G-BLUP. Furthermore, the smallest decay of accuracy was found for fat yield, followed by somatic cell score. Accuracy with half the training data With a training size of only 1,048 bulls, the accuracy level of all methods decreased (Figure 3 and 4). Because the number of fathers, half and full sibs of validation bulls was identical for both training sizes analyzed, Page 7 of 12 accuracy of GEBVs for the yield traits decreased by only 0.03 to 0.05 for amax values = 0.6 and 0.49. The loss in accuracy with decreasing amax was similar for both training sizes from amax = 0.49 to 0.249, but consider- ably larger from 0.249 to 0.1249 with only 1,048 training bulls. The differences between BayesB and G-BLUP were comparable for the two training sizes, except for amax = 0.1249 where differences tend to decrease with the smaller training data set. Accuracy due to LD Table 4 shows the accuracy due to LD estimated by equation (2) for the two sizes of training data sets and the four traits analyzed. With 2,096 training bulls, the accuracy due to LD is always higher for BayesB than for G-BLUP, where the largest difference of 0.2 and 0.12 between methods was obtained for milk and protein yield, respectively, and smallest for somatic cell score. With only half the training size, both the accuracies due to LD and the differences between the two GS methods decreased considerably. The absolute decay of accuracies was similarly high for milk yield, fat yield and somatic cell score, but notably smaller for protein yield, which had the smallest accuracy of all traits with 2,096 training bulls. Furthermore, BayesB and G-BLUP gave very simi- lar accuracies for fat yield and somatic cell score using 1,048 training bulls, whereas BayesB was consistently Figure 4 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 1,048 training bulls in each amax scenario.