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The correlation was calculated using the validation bulls from all replicates of a specified a max, after their EBVs were corrected by the mean EBV of their respective validation data set.
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This measure of reliability is unbiased only if the validation bulls come from a random sample but the bulls in this study were selected on the basis of PI.
Table 7 shows the correlations observed between deregressed EBV, without adjusting for the reliability contributed by the daughters' performance, and DGV or GEBV estimates for the validation bulls.
Welfare of growing bulls was evaluated using the previously validated partial A-Index.
Training and validation data sets were generated systematically using the additive-genetic relationships between bulls derived from the pedigree in order to study the impact of additive-genetic relationship information on accuracy of GEBVs by cross-validation.
For HOL-NOR 557 bulls from the EuroGenomics project [ 4] were genotyped using the 777K chip and these bulls were used as reference to impute the 777K markers for the bulls genotyped with the Illumina 50K SNP chip.
When ignoring this correction, the validation reliabilities of genomic predictions for CH bulls using the joint reference dataset were unrealistically high at 0.69, 0.54 and 0.60 for milk yield, fat yield and protein yield, respectively.
The Holstein validation bulls were progeny tested during or after 2004 (n = 400), and the Jersey validation bulls were progeny tested after 2004 as well (n = 77).
Using the high-density assay, the accuracy of prediction of validation bulls with sire in the training data was not consistently higher than for validation bulls without sire in the training data for all traits.
Use the bull's eye as the target.
The 9th generation of young bulls was then generated using either the bulls from the cryobank or the group of 10 sires selected as described in previous sections.
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