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Table 2 Accuracies and bias of predicted genetic effects (mean over 100 replicates) for the three breeding schemes

From: Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

\(r_{{u_{DS} }}\) \({\mathbf{A}}\) \({\mathbf{G}}\)
SGM_DGE SGM_TBV CGM_DGE SGM_DGE SGM_TBV CGM_DGE
Accuracy of GESC
 − 0.5 0.201 0.318 0.204 0.244 0.437 0.269
 0 0.347 0.430 0.341 0.449 0.556 0.436
 0.5 0.481 0.504 0.457 0.614 0.656 0.596
Accuracy of DGE
 − 0.5 0.582 0.623 0.588 0.770 0.811 0.774
 0 0.583 0.604 0.580 0.768 0.792 0.765
 0.5 0.580 0.586 0.577 0.765 0.777 0.764
Bias of GESC
 − 0.5 0.39 1.00 0.39 0.38 0.98 0.41
 0 0.92 1.01 0.94 0.94 0.99 0.93
 0.5 1.54 1.05 1.50 1.52 1.00 1.51
Bias of DGE
 − 0.5 0.99 0.99 0.97 1.00 1.01 0.97
 0 0.98 0.99 1.01 0.99 1.00 1.00
 0.5 0.99 1.00 1.02 0.99 1.00 1.02
  1. Accuracy of predicted genetic effects of selection criteria (GESC) was calculated as the correlation between GESC and true TBV. Bias of GESC was the regression coefficient of true values of TBV on predicted values of GESC
  2. SGM_DGE used a social genetic model (SGM) with selection criteria based on direct genetic values (DGE); SGM_TBV used a SGM with selection criteria based on total breeding values (TBV); and CGM_DGE used the classical genetic model (CGM) with selection criteria based on DGE. Breeding schemes used either pedigree- (\({\mathbf{A}}\)) or genomic-based (\({\mathbf{G}}\)) relationships to predict genetic effects. The three breeding schemes were compared by making different assumptions for the trait simulated with SGE variance (\(\sigma_{{u_{S} }}^{2}\)) of 0.01 and correlation (\(r_{{u_{DS} }}\)) between SGE and DGE at − 0.5, 0, and 0.5. Group members were allocated at random. Accuracy and bias of predicted genetic effects were computed based on animals at generation \(t\) = 4