1. REGRESSION MODEL y ijklm = BD i + b j A j + HYS k + b dstate D l + b sstate S l + b sd (S×SD m ) + b dherd F m + b sherd G m + e ijklm, y = ME milk yield, ME fat yield, ME protein yield, SCS, fat % or protein % BD = the fixed effect of breed b j = coefficient for fixed regression on age nested within parity (A) HYS = fixed effect of herd-year-season b dstate = coefficient for fixed regression on dam record nested within state (D) b sstate = coefficient for fixed regression on sire PTA nested within state (S) b sd = coefficient for fixed regression on the interaction between sire PTA and herd standard deviation (SD) b dherd = coefficient for random regression on dam record nested within herd (F) b sherd = coefficient for random regression on sire PTA nested within herd (G) e = random residual *b dherd and b sherd were assumed to be correlated Figure 1. Correlations between principal components for herd heritability and sire misidentification rate for 230 herds Figure 2. Relationship between herd misidentification rate and a standardized principal component for all herd heritability measures for 230 herds Figure 3. Average predicted misidentification rate by the number of first lactation cows entering a herd per year CONCLUSIONS -Regression techniques can be used to estimate individual herd heritability - Daughter - dam heritability estimates were roughly twice daughter - sire heritability estimates -Higher misidentification is associated with lower herd heritability estimates - Herd heritability estimates could be used to identify progeny test herds that are candidates for DNA parent verification - Misidentification rates were predicted to be highest in larger herds Acknowledgements -Funding provided by Agricultural Research Service, USDA. -DNA parent verification was provided Alta Genetics, Inc. and Genetic Visions, Inc. -Data from the National Genetic Improvement Program was provided by AgriTech Analytics (Visalia, CA), AgSource Cooperative Services (Verona, WI), Dairy Records Management Systems (Raleigh, NC), DHI Computing Services (Provo, UT), and Texas DHIA (College Station, TX). References Dechow, C. D., and H. D. Norman. 2007. Within-herd heritability estimated with daughter-parent regression for yield and somatic cell score. J. Dairy Sci. 90:482-492. Van Vleck, L. D. 1970. Misidentification in estimating the paternal sib correlation. J. Dairy Sci. 53:1469- 1474. RESULTS Table 1. Average daughter-dam and daughter-sire heritability estimates for 20,920 herds Table 2. Correlation of herd misidentification rate with daughter-dam and daughter-sire heritability for 230 herds 1 1 All correlations significant at P<0.001 Relationship of Herd-Heritability with Sire Misidentication and Entry Into a Proven Sire Lineup C. D. Dechow 1, H. D. Norman* 2, and N. R. Zwald 3 1 The Pennsylvania State University, University Park, 2 Animal Improvement Programs Laboratory, Beltsville, MD, 3 Alta Genetics, Inc., Watertown, WI. ABSTRACT The objectives of this study were to estimate individual herd heritabilities for all herds in a large dataset and to estimate the relationship of individual herd heritability with sire misidentication rate. Milk, fat and protein yield and somatic cell score (SCS) were extracted from the national dairy database. Paternity verification results from DNA marker analysis were provided by Alta Genetics, Inc. for 160 herds and from Accelerated Genetics for 75 herds. The number of cows tested per herd ranged from 3 to 274. Herd heritability was calculated with daughter-dam regression and daughter sire predicted transmitting ability (PTA) regression using 7,084,953 records from 20,920 herds. Herd heritabilities were estimated with regression models in ASREML that included fixed breed, age within parity, herd-year-season of calving, dam records nested within state, and sire PTA within state; random regression coefcients were dam records and sire PTA within herd. Average daughter-dam herd heritability estimates ranged from 0.26 (SCS) to 0.73 (protein percent), whereas average daughter-sire herd heritability ranged from 0.11 for SCS to 0.42 for protein percent. Correlations between herd heritability and sire misidentication rate ranged from -0.28 to -0.43. The correlation between a principal component for all measures of herd heritability and sire misidentication rate was -0.50. Higher herd heritabilities were associated with lower sire misidentification rates and individual herd heritability estimates could be used to identify progeny test herds that are candidates for parent verification with DNA marker analysis. INTRODUCTION - Individual herd heritabilities can be generated with daughter – dam and daughter – sire PTA regression (Dechow & Norman, 2007) - Misidentification reduces heritability estimates (Van Vleck, 1970) - Individual herd heritabilities could help identify herds with poor sire identification OBJECTIVES -Estimate individual herd heritability for all herds in the national database -Determine the relationship between individual herd heritability and sire misidentification rate DATA & EDITS - 7,084,953 records for six traits -ME Milk, ME Fat, ME protein, SCS, Fat %, Protein % -Lactations 1 through 5 -Calving between August 2000 and August 2005 -Identified sire with ≥50% reliability for PTAM METHODS -Dam records were pre- adjusted for age, parity and herd-year-season of calving -Herd phenotypic standard deviation for each trait was determined -Herd heritability was generated for all herds simultaneously with a regression model -Three principal components of heritability were generated 1.First principal component of daughter-sire heritability estimates 2.First principal component of daughter-dam heritability estimates 3.First principal component of all heritability estimates -Heritability and principal components were merged with DNA parent verification results for 230 herds -Herd heritability and misidentification rate were used to develop a misidentification rate prediction formula -Misidentification was predicted for all 20,920 herds DERIVATION OF HERD HERITABILITY ESTIMATES Daughter-Dam Heritability:2 * regression of daughter performance on dam performance = 2 * [b dstate + b dherd ] Daughter-Sire Heritability:Daughter – sire PTA regression used to estimate genetic variance = [(b sstate + b sd (SD m ) + b sherd )SD US ] 2 / R [SD m ] 2 SD US = genetic standard deviation assumed in US genetic evaluations R= Average sire PTA reliability for the herd T66