A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

Table 10 The prediction performance evaluated by the Area under curve (AUC) of GBLUP, BayesR and HyB_BR on seven diseases

Diseases	GBLUP		BayesR		HyB_BR
Diseases	AUC	h ²	AUC	h ²	AUC	h ²
BD	0.63(0.0135)	0.71	0.63(0.0131)	0.63	0.64(0.0174)	0.63
CAD	0.58(0.0116)	0.38	0.59(0.0118)	0.38	0.58(0.0131)	0.38
CD	0.60(0.0134)	0.69	0.65(0.0159)	0.61	0.65(0.0158)	0.61
HT	0.58(0.0125)	0.53	0.58(0.0131)	0.52	0.58(0.0140)	0.51
RA	0.58(0.0109)	0.50	0.70(0.0104)	0.45	0.70(0.0107)	0.45
T1D	0.64(0.0133)	0.66	0.86(0.0099)	0.63	0.86(0.0102)	0.63
T2D	0.59(0.0139)	0.59	0.60(0.0117)	0.52	0.60(0.0122)	0.52

the heritability (h ²) is estimated by the equation $ {h}^2=\raisebox{1ex}{${\upsigma}_{\mathrm{g}}^2$}\!\left/ \!\raisebox{-1ex}{$\left({\upsigma}_{\mathrm{e}}^2+{\upsigma}_{\mathrm{g}}^2\right)$}\right. $; σ ²_e is derived separately by three methods; fixed genetic variance of σ ²_g for BayesR and HyB_BR is obtained from GCTA

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