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The implications of selecting for KTs and the NKTs of HWRN and DTR on the gain that might be achievable in the production population was examined using individual-tree breeding values generated from mixed-model analysis.
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To generate different levels of heritability (h), different magnitudes of residual terms were added to the simulated true breeding values, generating phenotypic values representing ten levels of h, ranging from 0.05 to 0.5 in steps of 0.05.
Based on a real dairy cattle dataset, breeding values were generated in the last generation of sires to mimic genomic pre-selection.
In the simulation, breeding values are generated for three types of individuals, queens, sires and worker groups, and subsequently the phenotypes for colonies are generated.
But which metric measures the value generated from your product?
P-values generated from Chi-square tests.
Artificial selection was performed using the best linear unbiased prediction (BLUP) of breeding values obtained from an animal model.
The statistical properties of breeding values resulting from the mixed model approach still hold for a population under selection.
More specifically, for each iteration from the model, we computed the average breeding values per generation of (i) actual estimated breeding values from the population and (ii) simulated breeding values under drift (rbv function in MCMCglmm).
Estimated breeding values from routine breeding estimation were used for fertility traits.
GS is based on the prediction of breeding values from individual genotypes (estimated genomic breeding values, GEBV).
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