Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

Table 4 Genomic variance (\(\sigma_{g}^{2}\)), marker variance explained (\(\sigma_{g}^{2} /\sigma_{a}^{2}\)) and genomic heritability (\(h_{g}^{2}\)) by fully corrected phenotype and medium-density SNP

Trait (unit)	Method^a	\(\sigma_{g}^{2} \left( {SE} \right)\) ^b	\(\sigma_{g}^{2} /\sigma_{a}^{2}\)	\(h_{g}^{2} = h^{2} \frac{{\sigma_{g}^{2} }}{{\sigma_{a}^{2} }}\)
BT (mm)	BayesC²	3.71 (0.75)	0.67	0.33
	BayesL	3.63 (0.75)	0.65	0.32
	GBLUP	3.62 (0.73)	0.65	0.32
CW (kg)	BayesC	330.73 (72.12)	1.05	0.33
	BayesL	299.73 (72.96)	0.95	0.30
	GBLUP	300.70 (69.013)	0.95	0.30
EMA (cm²)	BayesC	23.19 (4.04)	0.87	0.37
	BayesL	23.00 (4.16)	0.86	0.37
	GBLUP	22.84 (4.14)	0.85	0.37
lnMS (Score)	BayesC	0.055 (0.009)	0.69	0.42
	BayesL	0.054 (0.009)	0.68	0.41
	GBLUP	0.053 (0.009)	0.66	0.40

BT backfat thickness, CW carcass weight, EMA eye muscle area, MS marbling score
^aFor BayesC, \(\pi\) values of 0.97, 0.99, 0.97 and 0.91 (the highest accuracy) were considered for BT, CW, EMA and MS, respectively
^bSE in Bayesian methods were estimated as the standard deviation of the posterior distribution

ISSN: 1297-9686