Genomic selection (GS) can increase genetic gain per generation through early selection. GS is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Accuracies were investigated by simulation for a typical dairy cattle breeding setting. A genome consisting 3 chromosomes each with 100 cM in length was simulated. In order to create sufficient linkage disequilibrium after 50 generations of random mating in a finite population (Ne = 100), population was expanded to obtain intended population size (500 male and 500 female). Three measures of heritability (0.05, 0.30, 0.80) and four different numbers of markers (100, 200, 400, 800) were considered. Each simulation was replicated 10 times and results were averaged across replications. Six generations with only genotypes were generated to investigate the accuracy of breeding value over time. Accuracies without phenotypes ranged from 0.21 for threshold traits to 0.73 and from 0.24 to 0.74 for continuous traits. Accuracies were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Accuracies were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility. The results showed that using genomic selection can be useful for threshold traits which include some of important traits in animal breeding

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