Mathematical representations of correlations among yield traits and somatic cell score on test day

Major advances in genetic evaluation techniques

The past quarter-century in genetic evaluation of dairy cattle has been marked by evolution in methodology and computer capacity, expansion in the array of evaluated traits, and globalization. Animal models replaced sire and sire-maternal grandsire models and, more recently, application of Bayesian theory has become standard. Individual test-day observations have been used more effectively in estimation of lactation yield or directly as input to evaluation models. Computer speed and storage are less limiting in choosing procedures. The increased capabilities have supported evaluation of additional traits that affect the net profitability of dairy cows. The importance of traits other than yield has increased, in a few cases due to an antagonistic relationship with yield. National evaluations combined internationally provide evaluations for bulls from all participating countries on each of the national scales, facilitating choices from among many more bulls. Selection within countries has increased inbreeding and the use of similar genetics across countries reduces the previously available outcross population. Concern about inbreeding has prompted changes in evaluation methodology and mating practices, and has promoted interest in crossbreeding. In just the past decade, distribution of genetic evaluations has gone from mailed paper or computer tapes for a limited audience to publicly accessible, request-driven distribution via the Internet. Among the distributed information is a choice of economic indices that combine an increasing array of traits into numbers reflecting breeding goals under different milk-pricing conditions. Considerable progress in genomics and the mapping of the bovine genome have identified markers for some deleterious recessive genes, but broader benefits of marker-assisted selection are still in the future. A possible exception is the proprietary use of DNA testing by semen producers to select among potential progeny test bulls. The collection and analysis of industry-wide data to evaluate genetic merit will continue to be the most important tool for genetic progress into the foreseeable future.

Evaluation of Autoregressive Covariance Structures for Test-Day Records of Holstein Cows: Estimates of Parameters

The objective of this study was to compare test-day (TD) models with autoregressive covariance structures for the estimation of genetic and environmental components of variance for milk, fat and protein yields, and somatic cell score (SCS) in Holstein cows. Four models were compared: model I (CS model) was a simple TD repeatability animal model with compound symmetry covariance structure for environmental effects, model II (ARpe model) and model III (ARe model) had first-order autoregressive covariance structures for TD permanent or residual environmental effects, respectively, and model IV (305-d model) was a simple animal model using 305-d records. Data were 106,472 first-lactation TD records of 12,071 Holstein cows calving from 1996 through 2001. Likelihood ratio tests indicated that ARpe and ARe models fit the data significantly better than the CS model. The ARe model resulted in slightly smaller estimates of genetic variance and heritability than did the CS model. Estimates of residual variance were always smaller with the CS model than with the ARe model with the autoregressive covariance structure among TD residual effects. Estimates of heritability with different TD models were in the range of 0.06 to 0.11. The 305-d model resulted in estimates of heritability in the range of 0.11 to 0.36. The autoregressive covariance structure among TD residual effects may help to prevent bias in heritability estimates for milk, fat and protein yields, and SCS.