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The animal model exploits information from relatives to estimate breeding values of each related individual in the pedigree (Lynch and Walsh 1998).
This model is, thus, a multiple-trait model with two "pseudo-traits", reflecting the breeding value for the trait in breeds 1 and 2. This model resembles the MACE model [ 18] in which the breeding values of each bull in different countries are seen as different, correlated traits.
Phenotypes of both traits were produced by adding normally distributed error terms, sampled from N 0, ), to the true breeding values of each individual.
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After partitioning the breeding value of each individual, the accuracy of estimated additive effects was calculated as the correlation between the TBV due to additive effects (TBV Add ) and the GEBV due to additive effects (GEBV Add ).
Breeding values of clinical mastitis for each lactation were estimated using a multi-trait random regression test-day animal model.
Breeding values of random selection generations were estimated with each of the three genomic estimation methods BM, PLSR and GBLUP.
Second, breeding values of Brown Swiss bulls were estimated applying the BLUP algorithm.
All gains were estimated using predicted breeding values of individual traits (i.e. RNC or NC) using spatial analyses.
Ratings are given to seedlots based on the contribution and breeding values of parents represented (Scion, unpublished data).
Also these estimates were used to predict the breeding values of generation 1002.
The estimated variance components and breeding values of all three models were compared.
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