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Table 2 The estimated heritability for different traits in Bayesian and GBLUP models based on the reference dataset

From: Genomic prediction based on selected variants from imputed whole-genome sequence data in Australian sheep populations

 BayesR (top)0.094 0.0650.1290.1630.1050.1120.2190.611
 BayesR (50k)0.1960.1550.2280.3760.1890.2190.3930.547
 BayesR (50k + top)0.2010.1600.2420.3780.1890.2240.4000.634
 BayesRC (50k + top)0.1900.1590.2300.3590.1610.2170.3840.672
 BayesR (HD)0.2240.1850.2740.4090.2220.2420.4330.596
 GBLUP (top)0.1020.0700.1340.1660.1080.1160.2340.619
 GBLUP (50k)0.2000.1500.2290.3800.1890.2160.3990.569
 GBLUP (50k + top [1GRM])0.2170.1580.2500.4000.2030.2320.4090.679
 GBLUP (50k + top [2GRMs])a0.191 (0.116 + 0.075)0.152 (0.111 + 0.041)0.226 (0.128 + 0.098)0.356 (0.252 + 0.105)0.142 (0.046 + 0.096)0.212 (0.152 + 0.060)0.388 (0.271 + 0.117)0.681 (0.154 + 0.527)
 GBLUP (HD)0.2320.1820.2750.4230.2330.2500.4390.620
 GBLUP (WGS)0.2310.1910.2850.4390.2400.2540.4530.643
  1. CCFAT carcass fat depth at C site, CEMD carcass eye muscle depth, PEMD post-weaning eye muscle depth, IMF intermuscular fat percentage, SF5 shear force measured at day 5 after slaughter, PWT post-weaning weight, YCFW yearling clean fleece weight, YFD yearling fiber diameter
  2. aThe genetic variance explained by two GRM fitted in the model were divided to the phenotypic variance and then were added up to calculate the overal heritability. The first and the second value in the parentheses are the heritability estimates related to 50k and top SNPs, respectively