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J. Dairy Sci. 89:4846-4857
© American Dairy Science Association, 2006.

Genotype by Environment Interaction for Somatic Cell Score Across Bulk Milk Somatic Cell Count and Days in Milk

M. P. L. Calus*,1, L. L. G. Janss*,{dagger} and R. F. Veerkamp*

* Animal Sciences Group, P.O. Box 65, 8200 AB Lelystad, The Netherlands
{dagger} Statistical Animal Genetics Group, Institute of Animal Science Swiss Federal Institute of Technology, ETH Zentrum, CH 8092 Zurich, Switzerland

1 Corresponding author: mario.calus{at}wur.nl

The objective of this paper was to investigate the importance of a genotype x environment interaction (G x E) for somatic cell score (SCS) across levels of bulk milk somatic cell count (BMSCC), number of days in milk (DIM), and their interaction. Variance components were estimated with a model including random regressions for each sire on herd test-day BMSCC, DIM, and the interaction of BMSCC and DIM. The analyzed data set contained 344,029 test-day records of 24,125 cows, sired by 182 bulls, in 461 herds comprising 13,563 herd test-days. In early lactation, considerable G x E effects were detected for SCS, indicated by 3-fold higher genetic variance for SCS at high BMSCC compared with SCS at low BMSCC, and a genetic correlation of 0.72 between SCS at low and at high BMSCC. Estimated G x E effects were smaller during late lactation. Genetic correlations between SCS at the same level of BMSCC, across DIM, were between 0.43 and 0.89. The lowest genetic correlation between SCS measures on any 2 possible combinations of BMSCC and DIM was 0.42. Correlated responses in SCS across BMSCC and DIM were, on some occasions, less than half the direct response to selection in the response environment. Responses to selection were reasonably high among environments in the second half of the lactation, whereas responses to selection between environments early and late in lactation tended to be low. Selection for reduced SCS yielded the highest direct response early in lactation at high BMSCC.

Key Words: somatic cell count • genotype x environment interaction • reaction norm model • test-day model




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