In practical implementations of marker-assisted selection economic and logistic restrictions frequently lead to incomplete genotypic data for the animals of interest. This may result in bias and larger standard errors of the estimated parameters and, as a consequence, reduce the benefits of applying marker-assisted selection. Our study examines the impact of the following factors: phenotypic information, depth of pedigree, and missing genotypes in the application of marker-assisted selection. Stochastic simulations were conducted to generate a typical dairy cattle population. Genetic parameters and breeding values were estimated using a two-step approach. First, pre-corrected phenotypes (daughter yield deviations (DYD) for bulls, yield deviations (YD) for cows) were calculated in polygenic animal models for the entire population. These estimated phenotypes were then used in marker assisted BLUP (MA-BLUP) evaluations where only the genotyped animals and their close relatives were included.Models using YD of cows (bull dams) in addition to DYD of bulls resulted in much smaller standard errors for the estimated variance components. The bias in DYD models was larger than in models including YD. Depth of pedigree had the strongest impact on the standard errors of all the estimated variance components. As expected, estimation of variance components was less precise with larger proportions of animals without genotypes in the pedigree. Accuracies of MA-BLUP breeding values for young bull candidates were strongly affected by the inclusion of cow information, but only marginally influenced by pedigree depth and proportions of genotyped animals.