A robust method for simultaneous estimation of single gene and polygenic effects in dairy cows using externally...

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.