Across breed genomic evaluation in cattle
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Genomic evaluation techniques have been a huge success in the dairy cattle industry, as they allow accurate enough estimation of breeding values at a young age to allow selection decisions to be made at an earlier stage, thereby increasing the rate of genetic progress per annum. The success of genomic selection techniques relies on the existence of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) across the population of interest; LD persists across larger distances within breeds than across breeds. Therefore, most success so far has been for selection within breeds, but the industry is keen for “across breed” evaluations to be developed, both in a multi-breed scenario which would allow evaluations for breeds that are numerically too small to carry out evaluations within breeds, and also for the evaluation of crossbred animals. This thesis investigates the potential for applying genomic selection techniques in both the multi-breed and crossbred scenarios. Chapter 2 examines the potential for a multi-breed reference population to improve the accuracy of genomic evaluation for a numerically small breed, for a range of production and non-production traits. The results provide evidence that forming a multi-breed reference population for two closely related breeds (Holstein and Friesian) results in a higher accuracy of GEBVs for the smaller breed, particularly when more phenotypic records are added via the single-step GBLUP method, and when a higher density SNP chip is used. Chapter 3 examines the crossbred scenario, whereby GEBVs are calculated for crossbred individuals based on a crossbred reference population. The population used for analysis was a highly crossbred African population, and GEBVs were calculated for three groups of animals chosen according to whether they had a high or low proportion of imported dairy genetics. Accuracy of prediction was higher than expected, and provided proof of concept for applying genomic selection techniques in crossbred African cattle populations. Chapter 4 investigates the potential for using novel SNPs derived from sequence data in order to estimate genomic relationships across cattle breeds, deploying data from two closely related breeds, Fleckvieh and Simmental, and a further distant European breed, the Brown Swiss. Novel SNPs were selected from sequence based on their putative impact on the genome, with impacts being inferred by SNP annotation software snpEff. Results showed that genomic relationships calculated using novel SNPs have a high correlation with genomic relationships calculated using SNPs common to the Illumina BovineHD SNP chip, though between-breed correlations were lower than those within breeds. The results presented in this thesis demonstrate that utilising a multi-breed reference population can improve the accuracy of prediction for a numerically small breed, and that genomic prediction of highly crossbred individuals is also feasible. However, differences between breeds and also types of crossbred animal suggest that no one solution can be used for all across-breed evaluations, and further research will be needed to allow commercial implementation in further populations.