The objectives of this study were to estimate genetic correlations among body condition scores (BCS) from various sources, dairy form, and measures of cow health. Body condition score and dairy form evaluated during routine type appraisal was obtained from the Holstein Association USA, Inc. A second set of BCS was obtained from Dairy Records Managements Systems (DRMS) and was recorded by producers that use PCDART dairy management software. Disease observations were obtained from recorded veterinarian treatments in several dairy herds in the United States. Estimated breeding values for diseases in Denmark were also obtained. Genetic correlations among BCS, dairy form, and cow health traits in the United States were generated with sire models. Models included fixed effects for age, DIM, and contemporary group. Random effects included sire, permanent environment, herd-year season for health traits, and error. Predicted transmitting abilities (PTA) for BCS and dairy form were correlated with estimated breeding values for disease in Denmark. The genetic correlation estimate between BCS from DRMS and BCS from the Holstein Association USA, Inc., was 0.85. The genetic correlation estimate between BCS and a composite of all diseases in the United States was -0.79, and PTA for BCS was favorably correlated with an index of resistance to disease other than mastitis in Denmark (0.27). Dairy form was positively correlated with a composite of all diseases in the United States (0.85) and was unfavorably correlated with an index for resistance to disease other than mastitis in Denmark (-0.29). Adjustment for protein yield PTA had a minimal affect on correlations between PTA for BCS or dairy form and disease in Denmark. Selection for higher body condition or lower dairy form with continued selection for yield may slow deterioration in cow health as a correlated response to selection for increased yield.

-The inverse of the gametic covariance matrix between relatives, G −1 , for a marked quantitative trait locus (QTL) is required in best linear unbiased prediction (BLUP) of breeding values if marker data are available on a QTL. A rapid method for computing the inverse of a gametic relationship matrix for a marked QTL without building G itself is presented. The algorithm is particularly useful due to the approach taken in computing inbreeding coefficients by having to compute only few elements of G. Numerical techniques for determining, storing, and computing the required elements of G and the nonzero elements of the inverse are discussed. We show that the subset of G required for computing the inbreeding coefficients and hence the inverse is a tiny proportion of the whole matrix and can be easily stored in computer memory using sparse matrix storage techniques. We also introduce an algorithm to determine the maximum set of nonzero elements that can be found in G −1 and a strategy to efficiently store and access them. Finally, we demonstrate that the inverse can be efficiently built using the present techniques for very large and inbred populations. gametic relationship / marker-assisted selection / best linear unbiased / prediction

Estimation of genetic parameters and accuracy of threshold model genetic predictions were investigated. Data were simulated for different population structures by using Monte Carlo techniques. Variance components were estimated by using threshold models and linear sire models applied to untransformed data, logarithmically transformed data, and transformation to Snell scores. Effects of number of categories (2, 5, and 10), incidence of categories (extreme, moderate, and normal), heritability in the underlying scale (.04, .20, and .50), and data structure (unbalanced and balanced) on accuracy of genetic prediction were investigated. The real importance of using a threshold model was to estimate genetic parameters. An expected heritability of .20 was estimated to be .22 and .10 by a threshold model and a linear model, respectively. Accuracy increased significantly with a larger number of categories, a more normal distribution of incidences, increased heritability, and more balanced data. Even threshold models were shown to be more efficient with more than two categories (e.g., binomial). Transformation of scale did not accomplish the purpose intended.

The superiority of selection schemes employing information about a known quantitative trait locus (QTL) over conventional schemes is examined for dairy cattle breeding schemes. Stochastic simulation of a dairy cattle population with selection practices, structures, and parameters similar to the US Holstein population was implemented. Additive genetic effects were estimated by an animal model. Two schemes were compared: a QTL-assisted selection scheme in which the genotype of a known QTL was accounted for in the animal model as a fixed factor, and a QTL-free selection scheme in which the QTL was simulated but was not fit separately in the animal model. Under the QTL-assisted selection scheme, all animals in the mixed model were assumed to be genotyped for the QTL. The effect of using QTL information on the genetic response, the frequency of the favorable QTL allele, and the accuracy of evaluation were examined. Moreover, the effect was studied in four distinct paths of selection: active sires, proven young bulls, bull dams, and first-lactation cows. Average superiority values of 4.6, 7.6, 11.7, and 1.1% for genetic response were observed over 16 yr of selection for active sires, young bulls, bull dams, and first-lactation cows, respectively. Frequency of the favorable QTL allele changed faster in bull dams than males, and was the slowest in first-lactation cows. Finally, accuracy of evaluation under the QTL-assisted selection scheme was higher than under the QTL-free selection scheme. Young bulls ofthe QTL-assisted selection scheme on average had 0.049 higher accuracy, and first-lactation cows had on average 0.185 higher accuracy than corresponding animals of the QTL-free selection scheme.

No abstract