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This study investigated theaccuracy of imputation from LD (7K) to 54K panel and compared accuracy ofgenomic prediction with or without the X chromosome information, based on data ofNordic Holstein bulls. Beagle and Findhap were used for imputation. Averagedover two imputation datasets, the allele correct rates of imputation usingFindhap were 98.2% for autosomal markers, 89.7% for markers on the pseudoautosomal region of the X chromosome, and 96.4% for X-specific markers. Theallele correct rates were 98.9%, 91.2% and 96.8%, respectively, when usingBeagle. Genomic predictions were carried out for 15 traits based on 54K markerdata, imputed 54K for test animals, and imputed 54K for half of referenceanimals. GBLUP models with or without residual polygenic effect were used forgenomic prediction. For all three data sets, genomic prediction using allmarkers gave slightly higher reliability than prediction excluding the X chromosome.Averaged over 15 traits, the gains in reliability from the X chromosome rangedfrom 0.3% to 0.5% points among the three data sets and models. Using a model with a G-matrix accounting for sex-linkedrelationship appropriately or a model which divided genomic breeding value intoan autosomal component and an X chromosomal component did not led to betterprediction based on the present data where all animals were bulls. A modelincluding polygenic effect did not recover the loss of prediction accuracy dueto exclusion of the X chromosome. It is recommended using markers on the X chromosomefor routine genomic evaluation

Using a combined multi-breed reference population, this study explored the influence of model specification and the effect of including a polygenic effect on the reliability of genomic breeding values (DGV and GEBV). The combined reference population consisted of 2986 Swedish Red Breed (SRB) and Finnish Ayrshire (FAY) dairy cattle. Bayesian methodology (common prior and mixture models with different prior distribution settings for the marker effects) as well as a best linear unbiased prediction with a genomic relationship matrix [genomic best linear unbiased predictor (GBLUP)] was used in the prediction of DGV. Mixture models including a polygenic effect were used to predict GEBV. In total, five traits with low, high and medium heritability were analysed. For the models using a mixture prior distribution, reliabilities of DGV tended to decrease with an increasing proportion of markers with small effects. The influence of the inclusion of a polygenic effect on the reliability of DGV varied across traits and model specifications. Average correlation between DGV with the Mendelian sampling term, across traits, was highest (R =0.25) for the GBLUP model and decreased with increasing proportion of markers with large effects. Reliabilities increased when DGV and parent average information were combined in an index. The GBLUP model with the largest gain across traits in the reliability of the index achieved the highest DGV mean reliability. However, the polygenic models showed to be less biased and more consistent in the estimation of DGV regardless of the model specifications compared with the mixture models without the polygenic effect.