Table 1

Accuracy and bias of predicted GBVs in Data I
Method Trait A Trait B Trait C
MCBayes π = 0 rTBV,pGBV 0.788 ± 0.051 0.581 ± 0.103 0.453 ± 0.090
bTBV,pGBV 0.994 ± 0.038 1.048 ± 0.264 1.00 ± 0.370
0 < π <1 rTBV,pGBV 0.753 ± 0.060 0.580 ± 0.117 0.364 ± 0.137
bTBV,pGBV 1.070 ± 0.064 1.149 ± 0.340 1.016 ± 0.364
varBayes π = 0 rTBV,pGBV 0.754 ± 0.061 0.570 ± 0.113 0.383 ± 0.117
bTBV,pGBV 1.054 ± 0.051 0.994 ± 0.233 0.899 ± 0.247
0 < π <1 rTBV,pGBV 0.716 ± 0.070 0.548 ± 0.122 0.347 ± 0.131
bTBV,pGBV 0.894 ± 0.054 0.834 ± 0.186 0.636 ± 0.202
single-trait π =0 rTBV,pGBV 0.783 ± 0.051 0.469 ± 0.083 0.455 ± 0.076
(MCBayes) bTBV,pGBV 0.978 ± 0.037 1.020 ± 0.301 0.970 ± 0.259
0 < π <1 rTBV,pGBV 0.778 ± 0.050 0.491 ± 0.114 0.483 ± 0.101
bTBV,pGBV 1.089 ± 0.054 1.110 ± 0.634 1.061 ± 0.338

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.

Hayashi and Iwata

Hayashi and Iwata BMC Bioinformatics 2013 14:34   doi:10.1186/1471-2105-14-34

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