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Table 2 Prediction reliability from univariate and bivariate GBLUP models

From: Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

Traita

ST-GBLUP

MT-GBLUP

\({\upalpha}\) S1-CN

0.11

0.10

\({\upalpha}\) S1-CN-8P

0.03

0.03

\({\upalpha}\) S2-CN

0.03

0.06

\({{\upkappa}}\)-CN

0.16

0.16

G-\({{\upkappa}}\)-CN

0.14

0.14

\({\upalpha}\)-LA

0.12

0.11

\({{\upbeta}}\)-LG

0.21

0.21

Protein %

0.10

0.12

  1. 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., \({\upalpha}\) S1-CN (comprises \({\upalpha}\) S1-CN 8P + 9P), \({\upalpha}\) S2-CN (comprises \({\upalpha}\) S2-CN 11P + 12P), \({{\upkappa}}\)-CN (comprises \({{\upkappa}}\)-CN G 1P + unglycosylated \({{\upkappa}}\)-CN 1P), where P = phosphorylated serine group. G-\({{\upkappa}}\)-CN = glycosylated-\({{\upkappa}}\)-CN; \({\upalpha}\) S1-CN-8P = \({\upalpha}\) S1-CN with 8 phosphorylated serine groups