Genomic selection is revolutionizing dairy cattle breeding by enabling the prediction of genomic breeding values (GEBV) based on dense genetic markers. These values capture the effects of quantitative trait loci (QTL) across the entire genome, allowing for more accurate selection decisions. GEBV are calculated using reference populations of progeny-tested bulls genotyped with approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results range from 20 to 67%, depending on trait heritability, reference population size, and statistical methods. The BLUP method, which assumes a normal distribution of marker effects, provides reliabilities comparable to more complex methods. GEBV reliability is significantly higher than that of parental average breeding values, which are currently used for selecting bull calves for progeny testing. This increase in reliability allows for earlier selection of bull calves, potentially doubling the rate of genetic gain in the dairy industry. Genomic selection has been implemented in dairy cattle breeding programs in Australia, New Zealand, the United States, and the Netherlands. These programs have shown that GEBV reliability is higher than traditional breeding values, with results varying by country. The reliability of GEBV for fertility traits is generally lower due to lower heritability and fewer records. Bayesian methods, such as BayesA, have shown slightly higher reliability than BLUP for most traits. However, including all SNP in Bayesian analysis did not consistently improve accuracy. The accuracy of GEBV depends on factors such as linkage disequilibrium (LD) between markers and QTL, the number of animals with phenotypes and genotypes in the reference population, trait heritability, and the distribution of QTL effects. Higher LD between adjacent markers increases GEBV accuracy. The number of phenotypic records and the heritability of the trait also influence GEBV accuracy. For low-heritability traits, a large number of records are needed to achieve high accuracy. Genomic selection allows for more accurate breeding value predictions for young animals, which can lead to more efficient breeding programs. This technology can reduce the generation interval by allowing bull calves to be selected and used earlier, potentially increasing genetic gain. However, challenges remain, including integrating genomic information into national evaluations, managing long-term genetic gain and inbreeding, and computational challenges. Additionally, genomic selection may lead to a more balanced direction of genetic gain, increasing the accuracy of fertility traits. The technology also has the potential to reduce inbreeding by capturing Mendelian sampling effects. Genomic selection is also being applied across different breeds, with challenges arising from differences in LD between markers and QTL across breeds. Using a multibreed reference population can improve the accuracy of GEBV across breeds. Nonadditive effects, such as dominance and epistasis, may also be considered in genomic selection to improve phenotype prediction. Overall, genomic selection is expected to significantlyGenomic selection is revolutionizing dairy cattle breeding by enabling the prediction of genomic breeding values (GEBV) based on dense genetic markers. These values capture the effects of quantitative trait loci (QTL) across the entire genome, allowing for more accurate selection decisions. GEBV are calculated using reference populations of progeny-tested bulls genotyped with approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results range from 20 to 67%, depending on trait heritability, reference population size, and statistical methods. The BLUP method, which assumes a normal distribution of marker effects, provides reliabilities comparable to more complex methods. GEBV reliability is significantly higher than that of parental average breeding values, which are currently used for selecting bull calves for progeny testing. This increase in reliability allows for earlier selection of bull calves, potentially doubling the rate of genetic gain in the dairy industry. Genomic selection has been implemented in dairy cattle breeding programs in Australia, New Zealand, the United States, and the Netherlands. These programs have shown that GEBV reliability is higher than traditional breeding values, with results varying by country. The reliability of GEBV for fertility traits is generally lower due to lower heritability and fewer records. Bayesian methods, such as BayesA, have shown slightly higher reliability than BLUP for most traits. However, including all SNP in Bayesian analysis did not consistently improve accuracy. The accuracy of GEBV depends on factors such as linkage disequilibrium (LD) between markers and QTL, the number of animals with phenotypes and genotypes in the reference population, trait heritability, and the distribution of QTL effects. Higher LD between adjacent markers increases GEBV accuracy. The number of phenotypic records and the heritability of the trait also influence GEBV accuracy. For low-heritability traits, a large number of records are needed to achieve high accuracy. Genomic selection allows for more accurate breeding value predictions for young animals, which can lead to more efficient breeding programs. This technology can reduce the generation interval by allowing bull calves to be selected and used earlier, potentially increasing genetic gain. However, challenges remain, including integrating genomic information into national evaluations, managing long-term genetic gain and inbreeding, and computational challenges. Additionally, genomic selection may lead to a more balanced direction of genetic gain, increasing the accuracy of fertility traits. The technology also has the potential to reduce inbreeding by capturing Mendelian sampling effects. Genomic selection is also being applied across different breeds, with challenges arising from differences in LD between markers and QTL across breeds. Using a multibreed reference population can improve the accuracy of GEBV across breeds. Nonadditive effects, such as dominance and epistasis, may also be considered in genomic selection to improve phenotype prediction. Overall, genomic selection is expected to significantly