Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: April 15, 2002
Publication Date: May 1, 2002
Citation: VAN TASSELL, C.P., WIGGANS, G.R., PHILPOT, J.C., MISZTAL, I. IMPROVEMENTS IN DYSTOCIA NATIONAL GENETIC EVALUATION SYSTEM AND DATA PROCESSING. JOURNAL OF DAIRY SCIENCE. vol. 85 (Suppl. 1), pp. 33.
The Animal Improvements Programs Laboratory (AIPL) of USDA assumed responsibility for conducting the national genetic evaluation for dystocia and maintaining the associated database in 1999. Current evaluations use a sire model. Adding a maternal grandsire (MGS) effect to the model is expected to improve accuracy by partially accounting for merit of mates and differences in maternal ability of the dams. Dystocia data were migrated to a relational database integrated with the AIPL production database. This database design was implemented to allow more rigorous data edits by comparing with the production data and to improve MGS identification (ID) rate by utilizing pedigree records from production records with dam ID sufficiently unique to match production pedigrees. Improvements in MGS ID rate are important for implementation of a sire-MGS model. Processing information for 8,861,363 records is available: 568,396 were duplicates or updates of existing records; 33,378 records were rejected; and, the remaining 8,259,589 records (>93%) were accepted. MGS ID increased from 57.2% to 73.1% by integrating the dystocia and production data. Sire and sire-MGS models were compared using data from the August 2001 national genetic evaluation. This data included over 5 million birth score records after requiring sire and MGS ID, dam and sire breed of Holstein or Red and White, single birth, and birth 1980 or later. The sire model included herd-year, year-season, sex of calf, parity of dam, birth year group of sire, and sire. MGS and birth year group of MGS were added to the sire-MGS model. Herd-year, sire and MGS were random effects. Variance for herd-year was set to 10% of residual variance, residual variance was set to 1, heritability to .16, maternal variance to 40% of the direct genetic variance, and correlation between direct and maternal to -.3. Correlation between sire solution from the two models was .93. For the sire-MGS model, the range of solutions for sire effects was 1.36 and for MGS solutions the range was .42. Integration of dystocia data with production data resulted in substantial increase in MGS ID rate. A sire-MGS model is feasible for the dystocia data set and provides similar sire evaluations to a sire model.