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Table 1 Accuracy and bias of predicted GBVs in Data I

From: A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

Method    Trait A Trait B Trait C
MCBayes π = 0 r TBV,pGBV 0.788 ± 0.051 0.581 ± 0.103 0.453 ± 0.090
   b TBV,pGBV 0.994 ± 0.038 1.048 ± 0.264 1.00 ± 0.370
  0 < π <1 r TBV,pGBV 0.753 ± 0.060 0.580 ± 0.117 0.364 ± 0.137
   b TBV,pGBV 1.070 ± 0.064 1.149 ± 0.340 1.016 ± 0.364
varBayes π = 0 r TBV,pGBV 0.754 ± 0.061 0.570 ± 0.113 0.383 ± 0.117
   b TBV,pGBV 1.054 ± 0.051 0.994 ± 0.233 0.899 ± 0.247
  0 < π <1 r TBV,pGBV 0.716 ± 0.070 0.548 ± 0.122 0.347 ± 0.131
   b TBV,pGBV 0.894 ± 0.054 0.834 ± 0.186 0.636 ± 0.202
single-trait π =0 r TBV,pGBV 0.783 ± 0.051 0.469 ± 0.083 0.455 ± 0.076
(MCBayes)   b TBV,pGBV 0.978 ± 0.037 1.020 ± 0.301 0.970 ± 0.259
  0 < π <1 r TBV,pGBV 0.778 ± 0.050 0.491 ± 0.114 0.483 ± 0.101
   b TBV,pGBV 1.089 ± 0.054 1.110 ± 0.634 1.061 ± 0.338
  1. Averages and standard errors based on 100 replicates of simulated data are listed for prediction accuracy, rpGBV,TBV, and bias, bpGBV,TBV, of each trait. For the prior probability that a SNP has zero effect, π, we considered two settings, in which π was fixed at 0, meaning the inclusion of all SNPs in the model, and π was varied over 0 < π <1 and inferred from the data.