Genomic evaluation,breed identification,and discovery of a haplotype affecting fertility for Ayrshire dairy cattle

Genomic evaluations of dairy cattle in the United States have been available for Brown Swiss, Holsteins, and Jerseys since 2009. As of January 2013, 1,023 Ayrshires had genotypes in the North American database. Evaluation accuracy was assessed using genomic evaluations based on 646 bulls with 2008 traditional evaluations to predict daughter performance of up to 180 bulls in 2012. Mean gain in reliability over parent average for all traits was 8.2 percentage points. The highest gains were for protein yield (16.9 percentage points), milk yield (16.6 percentage points), and stature (16.2 percentage points). Twelve single nucleotide polymorphisms were useful for Ayrshire breed determination. Fewer breed-determining SNP were available for Ayrshires than for Holsteins, Jerseys, and Brown Swiss because of the similarity of Ayrshires and Holsteins. A haplotype that affects fertility was identified on chromosome 17 and traces back in the genotyped population to the bull Selwood Betty’s Commander (born in 1953). The haplotype carrier frequency for genotyped Ayrshires was 26.1%. Sire conception rate was decreased by 4.3 ± 2.5 percentage points for carriers of the haplotype as determined by 618 matings of carrier sire by carrier maternal grandsire. Genomic evaluations for Ayrshires were officially implemented in the United States in April 2013.     

Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G^?1) and pedigree (A^?1₂₂) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G^?1 and A^?1₂₂ matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G^?1 and A^?1₂₂ matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.     

Realized genetic selection differentials in Canadian Ayrshire,Jersey, and Brown Swiss dairy cattle populations

Estimated breeding values of a selection index, production, durability, health, and fertility traits from Canadian Ayrshire, Jersey, and Brown Swiss bulls and cows were used to study genetic selection differentials (GSD). The bulls and cows were born from 1950 and 1960, respectively. The GSD for the 3 Canadian dairy populations were studied along the 4-path selection model: sire-to-bull (SB), dam-to-bull (DB), sire-to-cow (SC), and dam-to-cow (DC) pathways. We also determined the variations in realized GSD due to herd and herd × year of conception in addition to the effects of some environmental factors on realized GSD of the SC and DC paths. The mean realized GSD of the DB were higher than those of other paths and were increasing for lifetime performance index, 305-d milk yield, 305-d fat yield, and 305-d protein yield in all 3 dairy cattle populations. We observed no clear trends in realized GSD for type traits in all 3 dairy cattle breeds except for the apparent increasing trends in realized GSD of mammary system, dairy strength, and feet and legs in the DB and SC paths of the Ayrshire breed. No clear patterns were observed in the realized GSD of daughter fertility in the SB, DB, and SC paths of all dairy cattle breeds. Realized GSD for somatic cell score showed increasing and favorable trends in the 3 most influential selection paths (SB, DB, and SC). Year of conception influenced realized GSD of artificial insemination bulls in Ayrshire, Jersey, and Brown Swiss dairy populations. Selection emphases for the SC path generally increased with time. There was considerable variation among herds in selection pressures applied in the SC and DC pathways but no clear association with housing system or region. This study demonstrates that variations exist among herds of minor dairy cattle breeds in their selection for economically important traits. These variations offer opportunities for further improvements in these populations.     

Prior (co)variances can improve multiple-trait across-country evaluations of weakly linked bull populations

National genetic evaluation results for fore udder attachment from 9 Ayrshire populations were used to assess the impact of different uses of prior genetic correlations in multiple-trait across-country evaluations (MACE) on predicted international genetic merit. These Ayrshire populations were poorly connected; that is, 2% of the bulls had evaluations in 2 or more countries. Genetic correlations from the Holstein populations in the same countries were used as prior information to improve inferences of location parameters and international genetic merits. Fully Bayesian analyses using Gibbs sampling and computationally less demanding traditional MACE assuming a weighted average of prior and estimated Ayrshire genetic correlations were compared for 3 different prior degrees of belief and for different groups of bulls. Posterior means of genetic correlations estimated by Gibbs sampling were on average higher (+0.2) than those estimated by REML. Posterior heritabilities differed up to 0.2 units from those assumed in national genetic evaluations. Predicted genetic merit and international sire rankings of bulls with daughter information in the country of interest were not affected substantially by method of analysis and even less by varying prior degree of belief. Method of analysis had a larger impact on predicted genetic merit for bulls without daughter information in the country of interest. Here the average correlation between predicted genetic merit in different analyses ranged from 0.62 to 0.99. The predictive ability for young and randomly chosen bulls favored Bayesian MACE. The prior degree of belief did not have much impact on sire rankings and predictive ability, but intermediate prior degree of belief tended to perform best. All MACE analyses yielded nearly unbiased predictions. Traditional MACE assuming a simple weighted average of prior and estimated Ayrshire genetic correlations has been implemented by Interbull for routine international genetic evaluations.