Deterministic predictions of the accuracy of genomic breeding values in selection candidates with no phenotypes have been derived based on the heritability of the trait, number of phenotyped and genotyped animals in the reference population where the marker effects are estimated, the effective population size and the length of the genome. We assessed the value of these deterministic predictions given the results that have been achieved in Holstein and Jersey dairy cattle. We conclude that the deterministic predictions are useful guide for establishing the size of the reference populations which must be assembled in order to predict genomic breeding values at a desired level of accuracy in selection candidates.

We genotyped overlapping samples of Australian dairy bulls using three different SNP chips (15k, 25k and 54k). These chips have different but complementary coverage hence increasing the number of animals and the density and coverage of SNPs to 74k in a combined dataset. A combined analysis of the data from these three SNP chips showed a four fold increase in the coverage of the genome by haplotype blocks over bovine hapmap reported previously (Khatkar et al. 2007). An analysis of contiguous runs of homozygosity revealed long stretches (up to 49.39 Mb) of homozygosity on chromosome 1 in many bulls. Distribution of these segments of homozygosity in a sample of bulls is presented. The results for one chromosome are described in detail.

Use of high density Single Nucleotide Polymorphic (SNP) marker information allows for prediction of genetic merit via genome wide selection and for localization of markers in gene regions of biological interest through Genome Wide Association Studies (GWAS). We report on a replicated GWAS in dairy cattle using 1,945 progeny tested bulls genotyped with three high density SNP panels representing 63,678 informative SNP. Single SNP genotypes were analysed against deregressed EBV for protein percent and fat percent using a mixed linear model accounting for SNP and animal polygenic effects. The 127,356 analyses (63,678 informative SNP by two traits) across the two data sets identified 143 and 87 significant (P<0.05, corrected for False Discovery Rate) associations for protein % in data set 1 and 2 respectively, whilst for fat % 102 and 61 significant associations were identified in the two data sets respectively. Outputs from selected SNP analyses are discussed for significance and pleiotropic effects and compared against integrated QTL meta-assembly from public domain studies.