Table 3.

Prediction reliability from univariate and bivariate BayesAS models

Traita	BayesAS-1SNP		BayesAS-100SNP		BayesAS-Genome
	MT	ST	MT	ST	MT	ST
$\mathrm{\alpha }$ S1-CN	0.10	0.09	0.13	0.09	0.10	0.09
$\mathrm{\alpha }$ S1-CN-8P	0.04	0.02	0.06	0.03	0.03	0.03
$\mathrm{\alpha }$ S2-CN	0.03	0.03	0.18	0.16	0.03	0.03
$\mathrm{\kappa }$-CN	0.38	0.37	0.68	0.63	0.16	0.16
G-$\mathrm{\kappa }$-CN	0.41	0.39	0.76	0.70	0.13	0.14
$\mathrm{\alpha }$-LA	0.11	0.09	0.14	0.14	0.11	0.11
$\mathrm{\beta }$-LG	0.39	0.39	0.52	0.50	0.21	0.19
Protein %	0.14	0.14	0.18	0.17	0.12	0.11

^aProtein composition expressed as a fraction of the total milk protein percentage by weight wt (wt/wt), protein % expressed as percentage of the total milk yield; individual proteins comprise only the peaks identified as intact proteins and isoforms,i.e., $\mathrm{\alpha }$ _S1-CN (comprises $\mathrm{\alpha }$ _S1-CN 8P + 9P), $\mathrm{\alpha }$ _S2-CN (comprises $\mathrm{\alpha }$ _S2-CN 11P + 12P), $\mathrm{\kappa }$-CN (comprises $\mathrm{\kappa }$-CN G 1P + unglycosylated $\mathrm{\kappa }$-CN 1P), where P = phosphorylated serine group. G-$\mathrm{\kappa }$-CN = glycosylated-$\mathrm{\kappa }$-CN; $\mathrm{\alpha }$ _S1-CN-8P = $\mathrm{\alpha }$ _S1-CN with 8 phosphorylated serine groups