Genomic prediction of breeding values using previously estimated SNP variances

Table 5 Coefficients of the regression of de-regressed EBV on GEBV

Trait	Scenario	BSSVS	BayesC	RR-BLUP	BLUP-SSVS	BLUP-C
Protein	FULL	0.926	0.907	0.753	0.894	0.876
	RAN50	0.779	0.747	0.633	0.905	0.896
	TOP50	1.045	1.035	0.786	1.001	0.994
	BOT50	1.001	1.021	0.599	0.991	0.979
UD	FULL	0.967	0.969	0.800	0.929	0.933
	RAN50	0.919	0.905	0.763	0.958	0.950
	TOP50	1.150	1.153	0.958	1.076	1.078
	BOT50	1.246	1.148	0.622	1.107	1.109
SCS	FULL	1.006	1.015	0.888	0.983	0.979
	RAN50	1.000	0.999	0.883	0.994	0.988
	TOP50	1.497	1.499	1.243	1.116	1.118
	BOT50	1.190	1.208	0.830	1.154	1.156
IFL	FULL	0.914	0.912	0.682	0.886	0.883
	RAN50	0.887	0.893	0.675	0.890	0.892
	TOP50	1.268	1.292	0.699	1.000	1.001
	BOT50	1.177	1.178	0.691	0.994	0.995
DLO	FULL	0.840	0.837	0.615	0.816	0.805
	RAN50	0.673	0.683	0.509	0.818	0.814
	TOP50	1.017	1.018	0.788	0.928	0.932
	BOT50	0.637	0.695	0.392	0.931	0.933
LON	FULL	0.850	0.847	0.649	0.825	0.813
	RAN50	0.721	0.718	0.543	0.836	0.836
	TOP50	1.032	1.029	0.821	0.935	0.937
	BOT50	0.543	0.708	0.417	0.941	0.945

Regressions are performed for six traits, five different models and four training scenarios using all (FULL), at random 50% (RAN50), the best 50% (TOP50), or the worst 50% (BOT50) of the training dataset.

ISSN: 1297-9686