Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions

Table 4 Accuracy and bias of within- and multi-breed genomic predictions for milk production traits

Ref dataset	Prediction method	Validation dataset	FY		MY		PY		F%		P%		Avg.
Ref dataset	Prediction method	Validation dataset	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias	Acc.	Bias
Prediction of Holstein
Holstein	GBLUP	Holstein	0.60	1.18	0.58	0.89	0.59	1.06	0.71	0.91	0.83	1.01	0.66	1.01
Holstein	BayesR	Holstein	0.63	1.22	0.62	0.89	0.58	1.02	0.81	1.01	0.83	1.02	0.69	1.03
Hol/Jer	GBLUP	Holstein	0.61	1.20	0.59	0.90	0.59	1.05	0.72	0.92	0.82	1.01	0.67	1.01
Hol/Jer	BayesR	Holstein	0.65	1.25	0.63	0.89	0.58	0.99	0.81	0.98	0.83	1.00	0.70	1.02
Prediction of Jersey
Jersey	GBLUP	Jersey	0.56	0.88	0.62	0.93	0.67	1.20	0.63	0.83	0.75	0.88	0.65	0.95
Jersey	BayesR	Jersey	0.56	0.89	0.70	0.98	0.72	1.24	0.77	0.89	0.79	0.92	0.71	0.98
Hol/Jer	GBLUP	Jersey	0.58	0.88	0.64	0.91	0.69	1.17	0.66	0.82	0.77	0.90	0.67	0.94
Hol/Jer	BayesR	Jersey	0.56	0.93	0.69	0.95	0.71	1.18	0.76	0.92	0.79	0.87	0.70	0.97
Avg.	GBLUP		0.59	1.04	0.61	0.91	0.63	1.12	0.68	0.87	0.79	0.95	0.66	0.98
	BayesR		0.60	1.07	0.66	0.93	0.65	1.11	0.79	0.95	0.81	0.95	0.70	1.00

FY = fat yield (kg/lactation), MY = milk yield (L/lactation), PY = protein yield (kg/lactation), F% = fat percentage (%) and P% = protein percentage in milk (%); Acc. = accuracy, measured as r(ŷ, y), where ŷ is the prediction of genetic merit; Bias = bias of the prediction, measured as the regression coefficient, b (ŷ, y); standard errors are approximately \( \frac{1}{\sqrt{262}}=0.062 \) for the Holstein predictions, \( \frac{1}{\sqrt{105}}=0.098 \) for the Jersey predictions.

ISSN: 1297-9686