Genomic selection in dairy cattle

Abstract

The objectives of this Ph.D. thesis were (1) to optimise genomic selection in dairy cattle with respect to the accuracy of predicting total genetic merit and (2) to optimise a dairy cattle breeding program using genomic selection. The study was performed using a combination of real data sets and simulations. Real data sets consisted of dense marker genotypes of progeny tested bulls that had accurate phenotypes derived from their daughters’ performance records. Through cross-validation, the reliability of genomic predictions was assessed for Bayesian models that fitted either marker genotypes, ancestral haplotypes or genomic relationships. Haplotype-based methods gave the most reliable predictions and provided opportunities to limit computer requirements for analysing very large data sets. The reliability of genomic predictions across breeds was studied using simulated marker data. The data was simulated such that it showed the same the patterns of linkage disequilibrium (LD) as observed within and between Holstein, Angus, and Jersey cattle from the Netherlands, Australia, and New Zealand. It was concluded that the most reliable genomic predictions can be obtained when the reference populations of each breed are combined, whereas for diverged breeds at least 300,000 markers are required to ensure that the LD between markers and QTL persists across breeds. Using a simulated genomic selection scheme, it was shown that the annual rate of genetic gain in dairy cattle may double compared to current progeny test schemes, without compromising the rate of inbreeding. To achieve such a high rate of genetic gain, the generation interval needs to be reduced significantly, as young bulls will prove to be superior to progeny tested bulls. It is expected that in the near future many animals will be genotyped and very high marker densities will be inferred by imputation techniques. This may result in genomic predictions that are persistent across breeds and generations. Large scale genotyping of cows may enable genomic selection for novel traits and the integration of genomic information in herd management processes