A robust method for simultaneous estimation of single gene and polygenic effects in dairy cows using externally estimated breeding values as prior information

The aim of this study was to develop a robust method to estimate single
gene and random polygenic animal effects simultaneously in a small
field dataset with limited pedigree information. The new method was
based on a Bayesian approach using additional prior information on the
distribution of externally estimated breeding values. The field dataset
consisted of 40 269 test-day records for milk performance traits for 1455
genotyped dairy cows for the 11 bp-deletion in the coding sequence of
the myostatin gene. For all traits, estimated additive effects of the
favoured wild-type allele (‘+’ allele) were smaller when applying the
new method in comparison with the application of a conventional
mixed inheritance test-day model. Dominance effects of the myostatin
gene showed the same behaviour but were generally lower than additive
effects. Robustness of methods was tested using a data-splitting
technique, based on the correlation of estimated breeding values from
two samples, with one-half of the data eliminated randomly from the
first sample and the remaining data eliminated from the second sample.
Results for 100 replicates showed that the correlation between split
datasets when prior information included was higher than the conventional
method. The new method led to more robust estimations for
genetic effects and therefore has potential for use when only a small
number of genotyped animals with field data and limited pedigree information
are available.