Table 5.

Number of selected SNPs, number of tagged QTL, percentage of genetic variance explained, and accuracies of genomic and phenotype prediction under different π values, sampling distribution for the QTL effects and density of the marker panel using BayesC method. Standard errors of accuracies are listed between parentheses

	(1-π) =0.90		(1-π) =0.95		(1-π) =0.98		(1-π) =0.99
	Gamma	Predefined	Gamma	Predefined	Gamma	Predefined	Gamma	Predefined
200 K marker density
# SNP	20 K	20 K	10 K	10 K	4 K	4 K	2 K	2 K
Tagged QTL3	76	97	61	96	53	94	46	91
% GV4	88.84	97.66	86.56	97.53	86.30	95.74	85.76	93.32
Acc_P5	0.453	0.451	0.467	0.459	0.484	0.477	0.496	0.493
	(0.019)	(0.009)	(0.019)	(0.009)	(0.018)	(0.008)	(0.018)	(0.008)
Acc_G6	0.769	0.751	0.791	0.766	0.821	0.794	0.842	0.821
	(0.017)	(0.009)	(0.018)	(0.008)	(0.018)	(0.009)	(0.018)	(0.006)
	400 K marker density
# SNP	40K	40K	20 K	20 K	8 K	8 K	4 K	4 K
Tagged QTL	85	99	68	98	53	97	48	95
% GV	92.05	98.97	91.59	98.37	90.98	96.95	90.16	95.81
Acc_P	0.444	0.441	0.456	0.447	0.472	0.459	0.485	0.472
	(0.013)	(0.017)	(0.013)	(0.017)	(0.014)	(0.017)	(0.014)	(0.018)
Acc_G	0.754	0.740	0.773	0.749	0.802	0.769	0.824	0.791
	(0.017)	(0.011)	(0.017)	(0.011)	(0.017)	(0.012)	(0.016)	(0.012)

¹QTL effects sampled from a Gamma distribution, ²QTL effects pre-defined to explain at least 0.5% of genetic variance (GV), ³QTL with r² > 0.7 with at least one selected SNP, ⁴GV = Genetic Variance, ⁵accuracy of phenotype prediction, ⁶accuracy of genomic prediction