Genomic selection in dairy cattle

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

Integrate cow and bull data in genomic evaluation for conformation traits and claw health

The two objectives of this study were to investigate and find methods to successfully integrate cow data in the bull reference population for genomic evaluation and to investigate the effect of adding reference cows on the DGV reliability for conformation traits and claw health. Information from about 25,000 bulls and about 15,000 cows was available. Bulls were genotyped with the Illumina 50K SNP chip and the cows with the Illumina 10K SNP chip. All animals were imputed to an equal 50K SNP set. After SNP edits 37,995 SNP remain for all animals. As phenotypes, yield deviations, deregressed proofs (DRPs) with adjustments for cows and DRPs calculated based on matrix deregression will be used. The three kinds of phenotypes will be validated to investigate the effect on the reliability of direct genomic value for conformation traits and claw health

Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls

Genomic selection has the potential to revolutionize dairy cattle breeding because young animals can be accurately selected as parents, leading to a much shorter generation interval and higher rates of genetic gain. The aims of this study were to assess the effects of genomic selection and reduction of the generation interval on the rate of genetic gain and rate of inbreeding. Furthermore, the merit of proven bulls relative to young bulls was studied. This is important for breeding organizations as it determines the relative importance of progeny testing. A closed nucleus breeding scheme was simulated in which 1,000 males and 1,000 females were born annually, 200 bulls were progeny tested, and 20 sires and 200 dams were selected to produce the next generation. In the "proven" (PROV) scenario, only cows with own performance records and progeny-tested bulls were selected as parents. The proportion of the genetic variance that was explained by simulated marker information (M) was varied from 0 to 100%. When M increased from 0 to 100%, the rate of genetic gain increased from 0.238 to 0.309 genetic standard deviations (s) per year (+30%), whereas the rate of inbreeding reduced from 1.00 to 0.42% per generation. Alternatively, when young cows and bulls were selected as parents (YNG scenario), the rate of genetic gain for M=0% was 0.292 s/yr but the corresponding rate of inbreeding increased substantially to 3.15% per generation. A realistic genomic selection scheme (YNG with M=40%) gave 108% higher rate of genetic gain (0.495 s/yr) and approximately the same rate of inbreeding per generation as the conventional system without genomic selection (PROV with M=0%). The rate of inbreeding per year, however, increased from 0.18 to 0.52% because the generation interval in the YNG scheme was much shorter. Progeny-testing fewer bulls reduced the rate of genetic gain and increased the rate of inbreeding for PROV, but had negligible effects for YNG because almost all sires were young bulls. In scenario YNG with M=40%, the best young bulls were superior to the best proven bulls by 1.27 s difference in genomic estimated breeding value. This superiority increased even further when fewer bulls were progeny tested. This stochastic simulation study shows that genomic selection in combination with a severe reduction in the generation interval can double the rate of genetic gain at the same rate of inbreeding per generation, but with a higher rate of inbreeding per year. The number of progeny-tested bulls can be greatly reduced, although this will slightly affect the quality of the proven bull team. Therefore, it is important for breeding organizations to predict the future demand for proven bull semen in light of the increasing superiority of young bull

Short communication: Ketone body concentration in milk determined by Fourier transform infrared spectroscopy: Value for the detection of hyperketonemia in dairy cows

The objective of this study was to evaluate Fourier transform infrared (FTIR) spectrometry to measure milk ketone bodies to detect hyperketonemic cows and compare this method with milk fat to protein ratio to detect hyperketonemia. Plasma and milk samples were obtained weekly from calving to wk 9 postpartum from 69 high-producing dairy cows. The reference test for hyperketonemia was defined as plasma concentration of ß-hydroxybutyrate (BHBA) =1,200 µmol/L. The weekly prevalence of hyperketonemia during the first 9 wk of lactation was, on average, 7.1%. Both BHBA and acetone in milk, determined by FTIR, had a higher sensitivity (80%) to detect hyperketonemia compared with milk fat to protein ratio (66%). Specificity was similar for the 3 diagnostic tests (71, 70, and 71%). In conclusion, FTIR predictions of BHBA or acetone in milk can detect cows with hyperketonemia in early lactation with a higher accuracy compared with the use of milk fat to protein ratio. Because of the high proportion of false-positive tests, there are concerns about the practical applicability of FTIR predictions of acetone, BHBA, and fat to protein ratio in milk to detect hyperketonemic cows

Genomic breeding value estimation using genetic markers, inferred ancestral haplotypes, and the genomic relationship matrix

peer reviewe