. 2014 Mar 24;5:56. doi: 10.3389/fgene.2014.00056

Table 2.

Predictive correlation for ketosis (KET), displaced abomasum (DA), retained placenta (RP), lameness (LAME), metritis (METR), and clinical mastitis (CM) using various kernels and the average of five 10-fold cross-validation.

Traits	Types	Kernels
		G	GK_A	GK_D	GK_ALL	ALL
KET	PCP	0.16	0.18	0.16	0.19	0.18
	EBV	0.85	0.86	0.84	0.87	0.86
DA	PCP	0.07	0.08	0.07	0.08	0.07
	EBV	0.59	0.61	0.53	0.59	0.60
RP	PCP	0.03	0.05	0.05	0.06	0.05
	EBV	0.65	0.67	0.60	0.66	0.65
LAME	PCP	0.07	0.08	0.04	0.07	0.05
	EBV	0.64	0.66	0.58	0.65	0.64
METR	PCP	0.05	0.07	0.04	0.05	0.05
	EBV	0.48	0.52	0.43	0.50	0.49
CM	PCP	0.07	0.08	0.05	0.07	0.07
	EBV	0.72	0.74	0.68	0.73	0.73

Pre-corrected phenotype (PCP) and estimated breeding value (EBV) were target phenotypes. Kernels were: additive genomic relationship kernel (G), Gaussian additive kernel (GK_A), Gaussian dominance kernel (GK_D), multiple kernel learning using Gaussian additive, Gaussian dominance, and Gaussian additive by dominance kernels (GK_ALL), and fitting three parametric kernels (G, D, and G#D) simultaneously (ALL). The best prediction within trait and type of phenotype is italicized.