A genome wide selection (GWS) platform was developed for prediction of genetic merit in dairy cattle. The critical components of the GWS platform included a genome wide SNP analysis assay representing 15,036 SNPs, 1546 progeny tested Holstein Friesian sires with EBV (ABV) for 42 lactation performance traits, and a series of complexity reduction methods with internal and external cross validation. Derived Molecular Breeding Values (MBV) using a fraction of the available SNP information, were shown to have high predictive value for genetic merit (r=0.65-0.87 with ABV) in bulls not used in the training data from which the SNP effects were derived. GWS can be used in the absence of SNP location and pedigree to make potentially highly accurate predictions of genetic merit at an early age from DNA analyses.

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.