1 0.9 0.8 0.7 cy 0.6 a rcu0.5 cA0.4 0.3 0.2 0.1 0 n=2000 n=1000 n=500 GBLUP(N=2000) Genetics Selection Evolution 2009, 41:35 http://www.gsejournal.org/content/41/1/35 0.35 0.45 0.65 0.55 |R| aAFsicgacuufuraencc2ytiofntoheftphreemdicatrikoenr odfegnesniteyt,icwvhai cluheiss oefxGpr2eessiendivaisdtuhaelssfqouraBreayreosoBt(oefxRce2pbteftowretehne addajsahcedntlimnearwkheircsh indicates GBLUP) Accuracy of the prediction of genetic values of G2e individuals for BayesB (except for the dashed line which indicates GBLUP) as a function of the marker density, which is expressed as the square root of R2 between adjacent markers. The markers shown from left to right are at densities of 1, 2, 5, and 10 Ne/M. chromosome, if the markers on chromosome 1 are inde- pendent to those of chromosome 2 (i.e. a balanced design). The markers on the two chromosomes are inde- pendent, but the number of markers is so large that some confounding between the markers of the two chromo- somes will still occur by chance. The latter probably resulted in the somewhat reduced accuracy when dou- bling the genome size and the number of phenotypes. Accounting for relationships Table 3 shows the errors of the predictions from Equation (2), when G1t was used as a training and G1e as an esti- mation data set, using BayesB and G-BLUP and the extremes of the marker densities. The accuracy of T-BLUP increased to 0.342, 0.410, and 0.412, for N = 500, 1000 and 2000, respectively, for these data sets, which was used in the prediction Equation (2). The errors of the predicted accuracies were all smaller than 0.027, and may in part be due to sampling errors from the Monte Carlo simulations. In general, it seems that Equation (2) provides quite pre- cise predictions of the accuracies for different degrees of relationship between the evaluation and training individ- uals. The effect of even more distant relationships between training and evaluation individuals was investigated by continuing the breeding of the lines in Figure 1 for two more generations. This resulted in G4t and G4e individu- als, which were separated by four more generations than the G2t and G2e individuals. Using density 20 Ne/M and 2000 G4t individuals, the accuracy reduced to 0.920 and 0.868 for BayesB and G-BLUP respectively (result not shown elsewhere). These accuracies compare to those in Table 1, i.e. 0.928 and 0.881, respectively. Thus, the four additional generations of genetic drift, and thus change of LD, did not reduce the accuracies much, especially not for BayesB, which seemed to yield more persistent estimates of SNP effects over generations. Table 1: A comparison of the accuracy of genetic value prediction of G2e individuals between BayesB and MIXTURE for extreme sizes of the training data set (N) and the marker density N 2000 1000 500 Marker density (Ne/M) 20 1 20 1 20 1 BayesB 0.928 0.773 0.882 0.732 0.829 0.697 MIXTURE 0.925 0.772 0.880 0.735 0.825 0.700 G-BLUP 0.881 0.758 0.817 0.717 0.727 0.657 Page 6 of 9 (page number not for citation purposes)