Genetic Correlation Patterns Between Somatic Cell Score and Protein Yield in the Italian Holstein-Friesian Population

• Published in: Journal of dairy science 2008-10, Vol.91 (10), p.4013-4021
• Main Authors: Samoré, A.B., Groen, A.F., Boettcher, P.J., Jamrozik, J., Canavesi, F., Bagnato, A.
• Format: Article
• Language: English
• Subjects: 1st 3 lactations; Animal productions; Animals; Biological and medical sciences; Cattle - genetics; clinical mastitis; dairy cows; dairy-cattle; environmental correlations; Female; Food industries; functional longevity; Fundamental and applied biological sciences. Psychology; genetic correlation; genetic correlation pattern; Holstein; Holstein-Friesian cow; lactation; Lactation - genetics; Milk - chemistry; Milk - cytology; Milk and cheese industries. Ice creams; milk protein yield; Milk Proteins - analysis; milk-yield; Models, Genetic; parity (reproduction); phenotypic relationships; protein yield; random regression; somatic cell count; somatic cell score; survival analysis; Terrestrial animal productions; test-day model; Vertebrates
• ISSN: 0022-0302; 1525-3198
• DOI: 10.3168/jds.2007-0718

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.