The objectives of this study were to estimate heritabilities within herds participating in Dairy Herd Improvement and determine the relationship of the individual herd heritability with sire misidentification rate. Individual herd heritabilities for milk, fat, and protein yield and somatic cell score (SCS) were calculated with daughter-dam regression and daughter-sire predicted transmitting ability (PTA) regression using 4,712,166 records from 16,336 herds available for August 2000 evaluations and 7,084,953 records from 20,920 herds available for August 2006 evaluations. Herd heritabilities were estimated using regression models that included fixed breed, age within parity, herd-year-season of calving, dam records nested within state, sire PTA within state, and an interaction between sire PTA and herd variance; random regression coefficients were dam records within herd and sire PTA within herd. Average daughter-dam herd heritability estimates ranged from 0.21 (SCS in 2000) to 0.73 (protein percentage in 2006), whereas daughter-sire herd heritability ranged from 0.10 (SCS in 2000) to 0.42 (protein percentage in 2006). Verification of sire identification with DNA marker analysis was provided by Accelerated Genetics and Alta Genetics Inc. Daughter-sire herd heritability was more strongly correlated with sire misidentification rate than daughter-dam herd heritability. The correlation between the first principal component for all measures of herd heritability and sire misidentification rate was -0.38 (176 herds) and -0.50 (230 herds) in 2000 and 2006, respectively. Herd heritability can be estimated with simple regression techniques for several thousand herds simultaneously. The herd heritability estimates were correlated negatively with sire misidentification rates and could be used to identify herds that provide inaccurate data for progeny testing.

Data were collected for 11 yr in five institutional herds in Virginia to determine the response to selection for milk yield and to examine correlated response in other traits. A randombred control population allowed separation of genetic and environmental effects. The genetic base was the same for both control and selection groups, and little directional selection took place in the control population. The control line was generated from 12 sire lines initiated by 8 unproven sires sampled by artificial insemination and sons of 4 proven bulls. Four bulls high for Predicted Difference milk were used in the selection group each year. By three methods of analysis, best linear unbiased prediction, deviation of selection from control group means, and least squares differences between selection and control for milk, fat, and fat percentage were: 370, 374, 438 kg; 10, 8, 6 kg; and -.02, -.02, and -.10%. These agree closely with expectations from modified contemporary comparisons of the bulls. Differences in fat percentages were more variable although all analyses indicated a negative correlated response to selection for milk yield. Generation effects varied with method of analysis but were generally small and agreed with differences in sire Predicted Differences. Response differed across herds.

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The objective of any well designed progeny test programme is to identify those individuals that have inherited the favourable genes from his parents. Bulls that enter a progeny test programme have been highly selected on a set of selection criteria. The criteria vary among organizations based upon their breeding philosophy and their prediction of the future economic value of various traits. The accuracy of choosing among this highly selected group is quite low. Increasing the accuracy of selection before progeny testing is the greatest potential application of genetic marker technology. Markers associated with traits of importance can greatly enhance traditional selection methods by increasing the prospect of an individual having the desired characteristics. Genetic marker-assisted selection can greatly increase the actual genetic merit of traits of economic importance