The study evaluates the impact of genetic relationships on the accuracy of genome-assisted breeding values (GEBVs) in genomic selection. It shows that markers can capture genetic relationships among genotyped animals, which affects the accuracy of GEBVs. Strategies to validate the accuracy of GEBVs due to linkage disequilibrium (LD) are discussed. Simulations demonstrate that accuracies of GEBVs obtained by fixed regression–least squares (FR–LS), random regression–best linear unbiased prediction (RR–BLUP), and Bayes-B are nonzero even without LD. When LD is present, accuracies decrease rapidly in generations after estimation due to the decay of genetic relationships. However, there is a persistent accuracy due to LD, which can be estimated by modeling the decay of genetic relationships and the decay of LD. The impact of genetic relationships was greatest for RR–BLUP. The accuracy of GEBVs can result entirely from genetic relationships captured by markers, and to validate the potential of genomic selection, several generations have to be analyzed to estimate the accuracy due to LD. The method of choice was Bayes-B; FR–LS should be investigated further, whereas RR–BLUP cannot be recommended. Genomic selection relies on the ability to predict GEBVs with high accuracy over several generations without additional phenotyping. This requires LD between marker loci and quantitative trait loci (QTL). The study shows that markers can capture genetic relationships between individuals, which affects the accuracy of GEBVs. Even without LD, the accuracy of GEBVs can be nonzero due to genetic relationships. When LD is present, the accuracy of GEBVs is expected to be higher than accuracy due to LD alone. The study used simulations to analyze the accuracy of GEBVs over generations and to derive strategies for validating the advantage of GEBVs due to LD in practical applications. Three statistical models were used: FR–LS, RR–BLUP, and Bayes-B. These models estimate genome-wide SNP marker effects for computing GEBVs. The basic model underlying these methods can be written as y = 1μ + ∑k xk βk δk + e, where y is the vector of trait phenotypes, μ is the overall mean, xk is a column vector of marker genotypes at locus k, βk is the marker effect, δk is a 0/1-indicator variable, and e is the vector of random residual effects. The variance of y varies depending on the model used. The study found that the accuracy of GEBVs obtained with LE markers was always positive in the five generations considered. The maximum accuracy was obtained for fathers of individuals in the training data (generation 3), because each sire had 10 sons with trait phenotypes in the training data. As the number of LE markers increased, the accuracies of GEBVs for all males and females approached the accuracies of TP–BLUP.The study evaluates the impact of genetic relationships on the accuracy of genome-assisted breeding values (GEBVs) in genomic selection. It shows that markers can capture genetic relationships among genotyped animals, which affects the accuracy of GEBVs. Strategies to validate the accuracy of GEBVs due to linkage disequilibrium (LD) are discussed. Simulations demonstrate that accuracies of GEBVs obtained by fixed regression–least squares (FR–LS), random regression–best linear unbiased prediction (RR–BLUP), and Bayes-B are nonzero even without LD. When LD is present, accuracies decrease rapidly in generations after estimation due to the decay of genetic relationships. However, there is a persistent accuracy due to LD, which can be estimated by modeling the decay of genetic relationships and the decay of LD. The impact of genetic relationships was greatest for RR–BLUP. The accuracy of GEBVs can result entirely from genetic relationships captured by markers, and to validate the potential of genomic selection, several generations have to be analyzed to estimate the accuracy due to LD. The method of choice was Bayes-B; FR–LS should be investigated further, whereas RR–BLUP cannot be recommended. Genomic selection relies on the ability to predict GEBVs with high accuracy over several generations without additional phenotyping. This requires LD between marker loci and quantitative trait loci (QTL). The study shows that markers can capture genetic relationships between individuals, which affects the accuracy of GEBVs. Even without LD, the accuracy of GEBVs can be nonzero due to genetic relationships. When LD is present, the accuracy of GEBVs is expected to be higher than accuracy due to LD alone. The study used simulations to analyze the accuracy of GEBVs over generations and to derive strategies for validating the advantage of GEBVs due to LD in practical applications. Three statistical models were used: FR–LS, RR–BLUP, and Bayes-B. These models estimate genome-wide SNP marker effects for computing GEBVs. The basic model underlying these methods can be written as y = 1μ + ∑k xk βk δk + e, where y is the vector of trait phenotypes, μ is the overall mean, xk is a column vector of marker genotypes at locus k, βk is the marker effect, δk is a 0/1-indicator variable, and e is the vector of random residual effects. The variance of y varies depending on the model used. The study found that the accuracy of GEBVs obtained with LE markers was always positive in the five generations considered. The maximum accuracy was obtained for fathers of individuals in the training data (generation 3), because each sire had 10 sons with trait phenotypes in the training data. As the number of LE markers increased, the accuracies of GEBVs for all males and females approached the accuracies of TP–BLUP.