Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping

BMC Genomics

Table 3 The multi-breed prediction accuracy and bias of GBLUP, BayesR, and HyB_BR on SEQ data related to Fat Yield, Milk Yield, Protein Yield, Fat%, Protein% and Fertility

		Holstein and Jersey reference to predict Holstein validation
		Fat Yield		Milk Yield		Protein Yield		Fat%		Protein%		Fertility
		Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias
GBLUP	+Poly^a	0.64	1.07	0.66	0.92	0.63	0.95	0.76	0.95	0.83	0.98	0.42	1.70
GBLUP	-Poly^b	0.62	1.32	0.60	0.83	0.58	1.15	0.75	1.01	0.81	1.09	0.42	1.70
BayesR	+Poly^a	0.65	1.27	0.69	0.91	0.68	1.04	0.81	1.01	0.83	0.99	0.42	1.32
BayesR	-Poly^b	0.63	1.17	0.67	0.85	0.65	0.91	0.80	1.01	0.82	0.96	0.42	1.32
HyB_BR	+Poly^a	0.66	1.04	0.69	0.89	0.68	0.96	0.81	0.99	0.83	0.96	0.42	1.32
HyB_BR	-Poly^b	0.63	0.96	0.69	0.89	0.66	0.88	0.81	0.99	0.81	0.94	0.42	1.32
		Holstein and Jersey reference to predict Jersey validation
		Fat Yield		Milk Yield		Protein Yield		Fat%		Protein%		Fertility
		Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias
GBLUP	+Poly^a	0.54	0.76	0.65	0.88	0.69	0.94	0.67	0.86	0.77	0.94	0.23	1.13
GBLUP	-Poly^b	0.52	0.93	0.65	1.03	0.68	1.24	0.66	0.93	0.75	1.02	0.23	1.13
BayesR	+Poly^a	0.57	0.88	0.70	0.96	0.72	1.22	0.77	0.97	0.77	0.89	0.23	1.03
BayesR	-Poly^b	0.52	0.73	0.68	0.87	0.67	1.02	0.76	0.95	0.77	0.87	0.23	1.02
HyB_BR	+Poly^a	0.58	0.87	0.69	0.95	0.73	0.91	0.77	0.93	0.79	0.87	0.23	0.97
HyB_BR	-Poly^b	0.57	0.74	0.69	0.85	0.73	0.91	0.76	0.93	0.78	0.85	0.23	0.97

The bulls and cows from two breeds of Holstein and Jersey are used as the reference set to predict Holstein bulls and Jersey bulls separately. ^aThe prediction accuracy when adding the polygenic term in the model; while^bis the prediction accuracy when leaving out the polygenic term from the model

ISSN: 1471-2164