No abstract

Data on 23,196 cows were extracted from the Finnish system for recording health data and merged with information on SCS and 305-d milk production to study 1) the genetic and phenotypic correlations of clinical mastitis (within 150 d postpartum) and SCS across the first three lactations and 2) the genetic relationships between the traits for individual lactations. (Co)variance components were estimated using linear multitrait REML and the expectation-maximization algorithm. Heritability estimates for separate lactations were distinctly higher for somatic cell score (0.14 to 0.19) than for clinical mastitis (0.02 to 0.05). Genetic correlations of the same traits among different lactations were positive and moderate to high, suggesting that, in practice, clinical mastitis and SCS can be considered as the same traits for different lactations. Genetic correlations of clinical mastitis and SCS varied from 0.37 for first lactation to 0.68 for third lactation, implying that clinical mastitis and SCC monitor different aspects of udder health. A clear, antagonistic genetic association existed between clinical mastitis and milk production, but the genetic correlation of SCS and milk production did not differ from 0.

The objectives of this study were to evaluate the feasibility of use of the test-day (TD) single-step genomic BLUP (ssGBLUP) using phenotypic records of Nordic Red Dairy cows. The critical point in ssGBLUP is how genomically derived relationships (G) are integrated with population-based pedigree relationships (A) into a combined relationship matrix (H). Therefore, we also tested how different weights for genomic and pedigree relationships affect ssGBLUP, validation reliability, and validation regression coefficients. Deregressed proofs for 305-d milk, protein, and fat yields were used for a posteriori validation. The results showed that the use of phenotypic TD records in ssGBLUP is feasible. Moreover, the TD ssGBLUP model gave considerably higher validation reliabilities and validation regression coefficients than the TD model without genomic information. No significant differences were found in validation reliability between the different TD ssGBLUP models according to bootstrap confidence intervals. However, the degree of inflation in genomic enhanced breeding values is affected by the method used in construction of the H matrix. The results showed that ssGBLUP provides a good alternative to the currently used multi-step approach but there is a great need to find the best option to combine pedigree and genomic information in the genomic matrix.

Single nucleotide polymorphism (SNP) data enable the estimation of inbreeding at the genome level. In this study, we estimated inbreeding levels for 19,075 Finnish Ayrshire cows genotyped with a low-density SNP panel (8K). The genotypes were imputed to 50K density, and after quality control, 39,144 SNPs remained for the analysis. Inbreeding coefficients were estimated for each animal based on the percentage of homozygous SNPs (F ), runs of homozygosity (F ) and pedigree (F ). Phenotypic records were available for 13,712 animals including non-return rate (NRR), number of inseminations (AIS) and interval from first to last insemination (IFL) for heifers and up to three parities for cows, as well as interval from calving to first insemination (ICF) for cows. Average F was 0.02, F 0.06 and F 0.63. A correlation of 0.71 was found between F and F , 0.66 between F and F and 0.94 between F and F . Pedigree-based inbreeding coefficients did not show inbreeding depression in any of the traits. However, when F or F was used as a covariate, significant inbreeding depression was observed; a 10% increase in F was associated with 5 days longer IFL0 and IFL1, 2 weeks longer IFL3 and 3 days longer ICF2 compared to non-inbred cows.

Three random regression models were developed for routine genetic evaluation of Danish, Finnish, and Swedish dairy cattle. Data included over 169 million test-day records with milk, protein, and fat yield observations from over 8.7 million dairy cows of all breeds. Variance component analyses showed significant differences in estimates between Holstein, Nordic Red Cattle, and Jersey, but only small to moderate differences within a breed across countries. The obtained variance component estimates were used to build, for each breed, their own set of covariance functions. The covariance functions describe the animal effects on milk, protein, and fat yields of the first 3 lactations as 9 different traits, assuming the same heritabilities and a genetic correlation of unity across countries. Only 15, 27, and 7 eigenfunctions with the largest eigenvalues were used to describe additive genetic animal effects and nonhereditary animal effects across lactations and within later lactations, respectively. These reduced-rank covariance functions explained 99.0 to 99.9% of the original variances but reduced the number of animal equations to be solved by 44%. Moderate rank reduction for nonhereditary animal effects and use of one-third-smaller measurement error correlations than obtained from variance component estimation made the models more robust against extreme observations. Estimation of the genetic levels of the countries' subpopulations within a breed was found sensitive to the way the breed effects were modeled, especially for the genetically heterogeneous Nordic Red Cattle. Means to ensure that only additive genetic effects entered the estimated breeding values were to describe the crossbreeding effects by fixed and random cofactors and the calving age effect by an age × breed proportion interaction, and to model phantom parent groups as random effects. To ensure that genetic variances were the same across the 3 countries in breeding value estimation, as suggested by the variance component estimates, the applied multiplicative heterogeneous variance adjustment method had to be tailored using country-specific reference measurement error variances. Results showed the feasibility of across-country genetic evaluation of cows and sires based on original test-day phenotypes. Nevertheless, applying a thorough model validation procedure is essential throughout the model building process to obtain reliable breeding values.