Applications of the Illumina BovineSNP50 BeadChip in Genetic Improvement of Beef Cattle
The release of the Illumina BovineSNP50 BeadChip in late 2007 has drawn attention from cattle breeders around the world to develop breeding programs that leverage association of these single nucleotide polymorphism (SNP) with economically important quantitative trait loci (QTL). In that context this project has come to study applications of the SNP panel in beef cattle. Analysis of linkage disequilibrium (LD) existing in Angus, Charolais, and crossbred animals revealed the pattern of LD within each breed group, as well as the persistence of LD phase between pairs of the breed groups. This is important for genomic selection where SNP are trained in one population and used to predict breeding value for animals in another population. Detection of chromosome regions potentially carrying QTL or causative mutations affecting the phenotypic variation in economically important traits was presented at individual SNP and haplotype levels. There were 269 SNP associated (P<0.001) with birth weight (BWT), weaning weight (WWT), average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), residual feed intake (RFI). They explained 1.64% - 8.06% of the phenotypic variation in these traits. There were 520 SNP associated (P<0.001) with carcass quality traits, namely hot carcass weight, back fat thickness, ribeye area, marbling scores, lean yield grade by Beef Improvement Federation, steak tenderness, and six rib dissection traits. These SNP explained 1.90 - 5.89% of the phenotypic variance of the traits. Many of the significant SNP were located on chromosome 6. Six haplotypes were found associated (P<0.05) with ADG, DMI, and RFI. In order for genomic selection to happen in beef cattle, higher density SNP panels should be made available at low genotyping cost. However, the cost of genotyping animals for high density SNP chip is still high, thus genotype imputation has come to practice. The last chapter of this thesis compared two approaches presently used in genotype imputation, investigated factors affecting imputation accuracy, as well as the impact of imputation accuracy on genomic estimated breeding value (GEBV). It proved that the highest possible accuracy of GEBV is attainable with sufficiently large groups of reference animals.