Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 16, 2003
Publication Date: October 1, 2003
Citation: Van Tassell, C.P., Wiggans, G.R., Misztal, I. 2003. Implementation of a sire-maternal grandsire model for evaluation of calving ease in the United States. Journal of Dairy Science. 86(10):3366-3373. Interpretive Summary: A sire-maternal grandsire (MGS) threshold model was implemented for genetic evaluation for calving ease (dystocia) in the US in 2002. The new model replaced a sire model. The Animal Improvement Programs Laboratory (AIPL) assumed responsibility for conducting the national genetic evaluation for calving ease and maintaining the associated database in 1999. The dystocia data were migrated to a relational database that is integrated with the AIPL national database of production data including lactations and pedigree back to 1960. The MGS identification (ID) rate was increased from 57 to 73% by utilizing production pedigree information. The new evaluation system made it possible to incorporate MGS in the model and allow for prediction of genetic merit for bulls used as service sires and for impact on daughters calving difficulty. The service sire calving ease evaluations can be used to determine semen usage in a herd, and daughter calving ease evaluations can be added to the net merit index to improve profitability.
Technical Abstract: The objective of this study was to add a maternal grandsire (MGS) effect to the existing sire model for national calving ease genetic evaluations. The Animal Improvement Programs Laboratory (AIPL) of USDA assumed responsibility for conducting the national genetic evaluation for calving ease and maintaining the associated database in 1999. Existing evaluations used a sire threshold model. Adding a MGS effect to the model was 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 allowed more rigorous data edits by comparing with the production data and improved MGS identification (ID) rate by utilizing pedigree records from production records with dam ID sufficiently unique to match production pedigrees. Integration of dystocia data with production data increased MGS ID rate from 58% to 73%. In addition, nearly 200,000 duplicate or correction records were identified using the new edit system. Sire and sire-MGS models were compared using over 10 millions observations available for the August 2002 national genetic evaluation. The sire model included herd-year, season, sex of calf, parity of dam, birth year group of sire, and sire. For the sire-MGS model, MGS and birth year group of MGS were added, year-seasons rather than years were used, and sex of calf and parity of dam were combined into a single effect. Herd-year, sire and MGS were random effects. Variance components used for the sire model were those previously used in the national evaluation and for the sire-MGS model were estimated in a separate study. Correlations between predicted genetic merits and between reliabilities for sire percent difficult births in heifers for the two models exceeded 80%, indicating agreement, yet with some significant change in evaluations. A sire-MGS model was implemented in August 2002 for the national calving ease genetic evaluation system.