Table 2.

Accuracy of genomic prediction of three traits in Germany cattle population r(EBVs, GEBVs)

Traits	N	GBLUP	BayesB	BayesBπ	BayesCπ
MY	200	0.438 ± 0.010	0.385 ± 0.018	0.382 ± 0.016	0.128 ± 0.016
	500	0.547 ± 0.007	0.547 ± 0.012	0.574 ± 0.009	0.324 ± 0.010
	1000	0.620 ± 0.005	0.663 ± 0.005	0.663 ± 0.004	0.560 ± 0.006
	2000	0.693 ± 0.003	0.722 ± 0.002	0.716 ± 0.002	0.718 ± 0.002
	Mean	0.574 ± 0.006	0.579 ± 0.009	0.584 ± 0.008	0.432 ± 0.008
MFP	200	0.353 ± 0.012	0.558 ± 0.018	0.544 ± 0.018	0.112 ± 0.012
	500	0.467 ± 0.008	0.629 ± 0.011	0.670 ± 0.010	0.332 ± 0.005
	1000	0.594 ± 0.004	0.709 ± 0.007	0.763 ± 0.003	0.709 ± 0.007
	2000	0.698 ± 0.003	0.815 ± 0.002	0.799 ± 0.002	0.799 ± 0.001
	Mean	0.528 ± 0.007	0.678 ± 0.010	0.694 ± 0.008	0.488 ± 0.006
SCS	200	0.347 ± 0.017	0.292 ± 0.015	0.290 ± 0.018	0.161 ± 0.017
	500	0.469 ± 0.008	0.440 ± 0.011	0.465 ± 0.009	0.265 ± 0.006
	1000	0.568 ± 0.004	0.570 ± 0.006	0.572 ± 0.006	0.535 ± 0.005
	2000	0.650 ± 0.007	0.647 ± 0.002	0.647 ± 0.002	0.646 ± 0.002
	Mean	0.508 ± 0.009	0.487 ± 0.008	0.494 ± 0.009	0.402 ± 0.008

The highest accuracies (Mean ± SE) among methods in different scenarios (subpopulations for different traits) are in bold faces. For each trait, accuracies among subpopulations are averaged to test the overall performances (i.e., the “Mean” accuracies here) of methods. For example, the overall performance of GBLUP in MY is the mean of its prediction accuracies for this trait among subpopulation 200, 500, 1000, and 2000