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

Table 4 Model reliability for bulls across the MT-BayesAS models

Trait^a	MT-BayesAS model reliability
Trait^a	1	50	100	200	Chromosome	Genome
\({\upalpha}\) _S1-CN	0.05	0.06	0.04	0.06	0.05	0.06
\({\upalpha}\) _S1-CN-8P	0.06	0.06	0.06	0.06	0.06	0.07
\({\upalpha}\) _S2-CN	0.12	0.32	0.32	0.26	0.21	0.14
\({{\upkappa}}\)-CN	0.56	0.71	0.71	0.68	0.56	0.21
G-\({{\upkappa}}\)-CN	0.42	0.56	0.56	0.54	0.39	0.15
\({\upalpha}\)-LA	0.07	0.07	0.08	0.08	0.08	0.06
\({{\upbeta}}\)-LG	0.37	0.50	0.51	0.49	0.27	0.19
Protein %	0.23	0.22	0.22	0.21	0.19	0.18

^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

ISSN: 1297-9686