Superiority of genomic selection (GS) is possible only when high density single nucleotide polymorphism (SNP) panels are used to track QTLs affecting traits. Even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. To reduce the cost of GS, it is often the case that a larger portion of the population is genotyped with low-density SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of errors is unavoidable. Such rate of errors tends to increase with the increase of the generational interval between reference and testing generations. Thus, it is reasonable to assume that accuracy of GEBVs will be affected by imputation errors. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation on the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between training and validation sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by around 0.5% per generations. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in 1 or 2 generations with a decrease rate of around 20–25% per generation. When the reference population is updated by 1 or 5% every generation, the decay in accuracy was only 8 to 11% after 7 generations. These results indicate that imputed genotypes provide a viable alternative, as long the reference and training populations are appropriately updated.