Table 2. Accuracy of GEBVs estimated from the simulated population-based datasets (Simulation 1) under different heritabilities.

Case	Marker density	Method	h2 = 0.1	h2 = 0.2	h2 = 0.3	h2 = 0.4	h2 = 0.5
G1		BLUP	0.29	0.47	0.53	0.68	0.70
	HD	LASSO	0.50	0.54	0.65	0.79	0.78
		RR-BLUP	0.74	0.82	0.89	0.92	0.92
		BL	0.29	0.42	0.47	0.63	0.65
		G-BLUP	0.74	0.81	0.88	0.94	0.94
		BayesA	0.74	0.82	0.88	0.94	0.94
		BayesB	0.73	0.82	0.89	0.94	0.93
	MD	LASSO	0.39	0.56	0.60	0.75	0.78
		RR-BLUP	0.69	0.86	0.87	0.92	0.92
		BL	0.23	0.39	0.54	0.63	0.65
		G-BLUP	0.70	0.86	0.87	0.92	0.92
		BayesA	0.70	0.86	0.87	0.92	0.92
		BayesB	0.70	0.86	0.87	0.92	0.92
	LD	LASSO	0.44	0.50	0.64	0.77	0.83
		RR-BLUP	0.47	0.72	0.88	0.90	0.92
		BL	0.23	0.33	0.43	0.55	0.66
		G-BLUP	0.69	0.82	0.84	0.90	0.92
		BayesA	0.72	0.82	0.86	0.90	0.92
		BayesB	0.76	0.82	0.90	0.90	0.92
G1- > G2		BLUP	0.24	0.37	0.57	0.59	0.76
	HD	LASSO	0.39	0.62	0.61	0.68	0.81
		RR-BLUP	0.77	0.90	0.91	0.91	0.94
		BL	0.59	0.76	0.79	0.82	0.89
		G-BLUP	0.74	0.87	0.92	0.93	0.94
		BayesA	0.73	0.86	0.91	0.93	0.93
		BayesB	0.73	0.87	0.91	0.93	0.95
	MD	LASSO	0.31	0.48	0.63	0.77	0.78
		RR-BLUP	0.73	0.85	0.91	0.94	0.94
		BL	0.26	0.39	0.48	0.59	0.65
		G-BLUP	0.72	0.85	0.91	0.93	0.93
		BayesA	0.73	0.85	0.91	0.93	0.93
		BayesB	0.73	0.86	0.91	0.93	0.93
	LD	LASSO	0.44	0.67	0.72	0.83	0.87
		RR-BLUP	0.27	0.89	0.89	0.92	0.93
		BL	0.31	0.38	0.51	0.56	0.65
		G-BLUP	0.76	0.88	0.88	0.92	0.94
		BayesA	0.80	0.85	0.89	0.92	0.94
		BayesB	0.81	0.89	0.89	0.92	0.93