Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

Table 3 Accuracy of predicting phenotype in each successive generation based on training on generation 1 for pedigree BLUP (PBLUP) and four genomic methods (BayesB, BayesC, BayesC0 and GBLUP)

Validation generation	Method of training in generation 1
Validation generation	PBLUP^a	BayesB^b	BayesC^b	BayesC0	GBLUP^c
G2	0.21	0.36	0.34	0.32	0.31
G3	0.02	0.22	0.22	0.17	0.17
G4	0.12	0.32	0.31	0.28	0.28
G5	0.01	0.27	0.25	0.20	0.20
G6	0.12	0.42	0.39	0.35	0.35
G7	−0.05	0.28	0.28	0.21	0.21
G8	0.10	0.35	0.33	0.19	0.20
Average^d	0.07	0.32	0.30	0.25	0.25

^aPedigree-based BLUP
^bMixture models assumed the fraction of SNPs with 0 effect (π) of 0.99
^cGBLUP was fitted as BayesC0 with genetic and residual scale factors having 100° of freedom
^dAverage of the 7 individual generation results