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Artificial selection was performed using the best linear unbiased prediction (BLUP) of breeding values obtained from an animal model.
Moreover, the correlation between the estimated total breeding values obtained from individual and pooled data was surprisingly close to one.
Among the other traits, with moderate heritabilities, breeding values obtained from 25 markers captured approximately one-fifth of the overall correlation.
Predicted genomic breeding values obtained from the different models were compared for the training dataset that included all three lines, by computing correlations between the predictions in the validation data (Table 5).
Correlations were estimated between the EBV and DGV for each of the traits within the prediction set to demonstrate the association between breeding values obtained from marker effects and the best estimate of the true breeding values.
Correlations of breeding values obtained from PBLUP and GBLUP predictions ranged across traits from 0.70 to 0.86, from 0.70 to 0.86 between PBLUP and both Bayesian LASSO and BayesA and from 0.87 to 0.93 between PBLUP and HBLUP (Table 3).
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Correlations between the sire breeding values obtained by the different models are reported in Table 3.
This was tested with a few key variates and the breeding values obtained were very similar to those obtained from the model described.
The genomic breeding values obtained with the RRPCA model deviated most from those of the other models and had the lowest average correlations with the other models i.e. 0.91, 0.91 and 0.97 for lines B1, B2 and W1, respectively.
Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated information and, therefore, more reliable estimated breeding values were obtained.
Selection candidates of the last generation were ranked based on their estimated breeding values (EBV) obtained from the PED and IBD-GS models, respectively (high EBV are favorable), which were used to select 200 parents for the next generation.
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