Genetic improvement in mastitis resistance: comparison of selection criteria from cross-sectional and random regression sire models for somatic cell score.
Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.
Odegård
J
J
Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, N-1432 As, Norway. jorgen.odegard@umb.no
Klemetsdal
G
G
Heringstad
B
B
D000818:Animals; D002417:Cattle; D002452:Cell Count / Q000662:veterinary; D003430:Cross-Sectional Studies; D005260:Female; D007113:Immunity, Innate / Q000235:genetics*; D007774:Lactation / Q000235:genetics*; D008297:Male; D008414:Mastitis, Bovine / Q000453:epidemiology / Q000235:genetics* / Q000517:prevention & control; D008892:Milk / Q000166:cytology; D008957:Models, Genetic; D012044:Regression Analysis; D012641:Selection, Genetic