Low cost genotyping of individuals or families using genomic markers with constantly increasing density is currently being introduced in genetic improvement programs for agricultural animal and crop species. Use of dense genomic markers can increase the accuracy of predicting additive genetic merit especially for selection of candidates that do not yet have own or progeny records . Such application of dense genomic markers, usually called genomic selection (GS), can reduce the cost of running an intensive breeding program due to potential reductions in the number of individuals tested for own or progeny performance, a shorter generation interval , and simultaneously greatly enhance the genetic gain from the program without a concomitant extra increase in the accumulation of inbreeding . Currently, this has led to implementation of GS methods in many large scale commercial dairy cattle breeding programs [4–6]. Implementation in other animal and plant species are being initiated and is expected to increase considerably in the near future .
The current industry standard in dairy cattle breeding is use of 50 K chips such as the Illumina Bovine SNP50 BeadChip  but other options, ranging from low density 3 K chips to high density 800 K chips, are also commercially available. Increasing the density of genetic markers is expected to increase the amount of genetic variance that can be explained by markers due to increased linkage disequilibrium between markers and causative loci . A further step is the use of complete sequencing of individual genomes either based on direct sequence data or based on sequence data for selected individuals and imputing genotypes for animals that has been genotyped with lower marker density
In human genetics very high density chips have been used in large scale studies . However, genome wide association studies (GWAS) have generally not been able to identify individual genes that can explain a large proportion of total genetic variance of complex traits recorded in humans. A typical model trait in humans is height that has been investigated in many studies. This is a trait measured with an accuracy that is comparable to the accuracy of daughter yield deviations of dairy sires that are progeny-tested using large daughter groups. Height in humans is known to have a heritability around 0.8 . In several well designed large scale studies with the use of very high density SNPs, numerous loci have been identified that are significantly associated with human height, yet each of them typically only account for a very small fraction of total phenotypic variance. Collectively these loci are only responsible for up to 5% of the total phenotypic variance in human height [11, 12]. This has lead to discussions among human geneticists about the missing heritability of complex traits . Suggested explanations have included dominance and epistatic interactions, genotype by environment interactions and common epigenetic factors causing resemblance between relatives .
As mentioned above the total variance explained by previously identified causal loci is usually only a small fraction of total genetic variance in the populations investigated. In GWAS very stringent significance thresholds are necessary due to the very large number of statistical tests that are conducted when searching the whole genome using high density SNP marker panels. This will only allow loci with large effects to become statistically significant. However,  showed that a considerable proportion of additive genetic variance can be explained by a very large number of SNPs and their effects can be predicted simultaneously using appropriate statistical models. Such models also includes the SNPs with small effects as long as they are associated with the trait of interest This association may be either due to SNPs being located within causative loci, being in linkage disequilibrium (LD) with causative genes, or due to markers tracing parts of familial additive genetic relationships among genotyped animals . These results are well in line with results from dairy cattle where a large proportion of additive genetic can be explained using dense markers.
If not all genetic variance can be explained by markers then, in order to ensure optimal predictions, the remaining genetic variance should be accounted for in other ways. A simple approach is to combine predicted breeding values based on genomic information with traditional breeding values based on pedigree using selection index theory . An alternative method is to include both genomic and pedigree relationships in the analysis simultaneously. Previous studies have report that a model including a residual polygenic effect slightly increases reliability and reduce bias in prediction of future records [17, 18]. Such a method requires the partitioning of genetic variance accounted for by genomic information and remaining genetic variance accounted for by pedigree relationships.
Recently, [19, 20], proposed a one-step method combining marker based genomic relationships and pedigree-based relationships into a single relationship matrix. An important factor in this procedure is that marker-based and pedigree-based relationship coefficients must be expressed on the same scale, i.e. the variance of the genomic and the classical additive genetic effects must refer to the same base population, and the proportion of total genetic variance explained by markers and the remaining genetic variance must be known. Currently implemented multistep procedures may also need to be on the same scale in order to ensure derivation of optimum combined predictions.
The purpose of this study was to evaluate the amount of additive genetic variation in production and fitness related traits in dairy cattle, to quantify the amount of additive genetic variation that can be explained using genomic markers with different density, and to quantify the amount of genomic variance that can be ascribed to individual chromosomes. The value of increasing the density of marker information for predicting genetic merit was also assessed using subsets of available markers.