Table 2.

Variance components (standard error in brackets) for two $\mathbf{G}$ matrices estimated for the carcass traits in Hanwoo cattle (n = 10,717)

	h2	${\mathit{\sigma }}_{\mathit{a}}^2$	${\mathit{\sigma }}_{\mathit{e}}^2$
BFT
${\mathbf{G}}_1$	0.15 (0.02)	2.85 (0.34)	15.15 (0.29)
${\mathbf{G}}_2$	0.07 (0.01)	1.30 (0.20)
EMA
${\mathbf{G}}_1$	0.16 (0.02)	18.42 (2.00)	88.13 (1.66)
${\mathbf{G}}_2$	0.05 (0.01)	5.73 (1.21)
CWT
${\mathbf{G}}_1$	0.12 (0.02)	251.95 (34.72)	173.76 (32.08)
${\mathbf{G}}_2$	0.08 (0.01)	177.46 (25.15)
MS
${\mathbf{G}}_1$	0.18 (0.02)	0.43 (0.04)	1.83 (0.04)
${\mathbf{G}}_2$	0.06 (0.01)	0.14 (0.03)

h²: estimated heritability; ${\sigma }_a^2$: additive genetic variance; ${\sigma }_e^2$: residual variance; ${\mathbf{G}}_1$: adjusted $\mathbf{G}$ matrix with 50 k SNP array (top SNPs removed); ${\mathbf{G}}_2$: $\mathbf{G}$ matrix with the top-SNPs from the 3000 discovery dataset

BFT back fat thickness, EMA eye muscle area, CWT carcass weight, MS marbling score