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.

A procedure has been developed for the prediction of genetic merit and the simultaneous assessment of multiple genotypes for subsequent use in gene detection. The system utilises a large volume of genotype information but ignores pedigree. With a simple additive model of inheritance, high correlations between estimates of molecular breeding value and highly reliable progeny test estimated breeding values were observed (0.70–0.77).

The method of partial least squares was applied to the prediction of genetic merit using whole genome scan data consisting of 10715 SNP. The method is particularly suited to data sets that have many more markers than observations and in which markers are collinear due to high linkage disequilibrium. A SNP ranking method was applied to select a subset of markers which have equal predictive power compared to using all SNP simultaneously.

Single nucleotide polymorphic (SNP) chips enable us to account for variation within genomes very well. It is possible to associate this variation with variation in phenotypes and so predict genetic merit of young individuals better than ancestral indexes. This has significant implications for livestock industries as to accuracy and timing of selection decisions. and how resources are allocated to maximize the returns from investments in genotypic and phenotypic data collection. High density genotyping platforms will exacerbate the problem of the joint analysis of individuals with heterogeneous amounts of genotypic information.