Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle

BMC Genomics

Table 5 Prediction ability (Acc) and regression coefficient (b) for RFI differentially (SNPs and PFV)

Validation for functional mutations	Adrenal		Pituitary		Hypothalamus		Muscle		Liver
Validation for functional mutations	Acc	b (SE)	Acc	b (SE)	Acc	b (SE)	Acc	b (SE)	Acc	b (SE)
ssGBLUP + wG + QTN:1-fold	0.16	0.74 (0.03)	0.16	0.723 (0.03)	0.15	0.73 (0.03)	0.15	0.85 (0.03)	0.15	0.82 (0.03)
ssGBLUP + wG + QTN:2-fold	0.18	0.67 (0.03)	0.18	0.65 (0.03)	0.18	0.66 (0.03)	0.18	0.75 (0.03)	0.18	0.79 (0.03)
ssGBLUP + wG + QTN:3-fold	0.20	0.62 (0.03)	0.19	0.60 (0.03)	0.19	0.61 (0.03)	0.20	0.71 (0.03)	0.20	0.77 (0.03)
ssGBLUPrecords + wG + QTN:1-fold	0.31	1.02 (0.02)	0.31	1.00 (0.02)	0.31	1.02 (0.02)	0.29	1.11 (0.2)	0.30	1.08 (0.02)

Prediction ability (Acc) and regression coefficient (b) for weighted single-step GBLUP (ssGBLUP+wG) including selected variants (PFV) in the model and applying different weighting approaches for PFV (1-fold, 2-fold and 3-fold the maximum weighted obtained in the ssGWAS)

ISSN: 1471-2164