Novel use of derived genotype probabilities to discover significant dominance effects for milk production traits in dairy cattle.

The estimation of dominance effects requires the availability of direct phenotypes, i.e., genotypes and phenotypes in the same individuals. In dairy cattle, classical QTL mapping approaches are, however, relying on genotyped sires and daughter-based phenotypes like breeding values. Thus, dominance effects cannot be estimated. The number of dairy bulls genotyped for dense genome-wide marker panels is steadily increasing in the context of genomic selection schemes. The availability of genotyped cows is, however, limited. Within the current study, the genotypes of male ancestors were applied to the calculation of genotype probabilities in cows. Together with the cows' phenotypes, these probabilities were used to estimate dominance effects on a genome-wide scale. The impact of sample size, the depth of pedigree used in deriving genotype probabilities, the linkage disequilibrium between QTL and marker, the fraction of variance explained by the QTL, and the degree of dominance on the power to detect dominance were analyzed in simulation studies. The effect of relatedness among animals on the specificity of detection was addressed. Furthermore, the approach was applied to a real data set comprising 470,000 Holstein cows. To account for relatedness between animals a mixed-model two-step approach was used to adjust phenotypes based on an additive genetic relationship matrix. Thereby, considerable dominance effects were identified for important milk production traits. The approach might serve as a powerful tool to dissect the genetic architecture of performance and functional traits in dairy cattle.