Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

Table 6 Average number of variant effects per non-zero distribution (variances 0.0001\( \sigma \) ² _g, 0.001\( \sigma \) ² _g, and 0.01\( \sigma \) ² _g) of BayesR SEQ and BayesRC Lact models^a

Trait	Model	Number of Variant Effects per Distribution
		N(0,0.0001\( \sigma \) ² _g)		N(0,0.001\( \sigma \) ² _g)		N(0,0.01\( \sigma \) ² _g)
		AUS	DANZ	AUS	DANZ	AUS	DANZ
Milk Yield	BayesR SEQ	4263	5239	60	91	7	9
Milk Yield	BayesRC Lact	4276	5294	56	89	9	11
Fat Yield	BayesR SEQ	4769	5969	14	28	5	8
Fat Yield	BayesRC Lact	4774	5841	24	43	7	10
Protein Yield	BayesR SEQ	4604	6292	40	38	5	6
Protein Yield	BayesRC Lact	4641	6292	39	41	7	8

^a Results are for Milk, Fat and Protein Yield in both the DANZ and AUS training sets

ISSN: 1471-2164