Estimating Daughter Yield Deviation and Validation of Genetic Trend for Somatic Cell Score in Holstein Cattle Using Random Regression Test Day Model

Authors
H. Khanzadeh
N. Ghavi Hossein-Zadeh
Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, P. O. Box: 41635-1314, Rasht, Islamic Republic of Iran.
Abstract
The objective of this study was to estimate Daughter Yield Deviations (DYDs) of bulls and Yield Deviations (YDs) for cows using a random regression model and validation of genetic trend using estimated DYDs and Method II of Interbull for test-day records of Somatic Cell Score (SCS) in the first lactation of Iranian Holsteins. Data set included the 108995 test day records collected by the Animal Breeding Center of Iran from 2001 to 2010. Results of the present study indicated that variation in YDs of cows at different stages of lactation corresponds closely with their Estimated Breeding Values (EBVs). Because YDs and DYDs are considered as an additional measure of an animal’s genetic merit, their correlation with EBVs is very important. The correlation between DYDs and EBVs of bulls for SCS was 0.88. High correlation estimates between DYDs and EBVs indicated that, in addition to EBV, the DYD can be an appropriate measure for dairy cattle breeding programs. The correlation increased with increase in the number of bull daughters and the average number of test-days of daughters. Estimated DYDs for each production year were used to validate the genetic trend obtained from the model which was used for genetic evaluation. Results indicated that genetic trend for SCS in the first lactation of Iranian Holsteins was slightly overestimated.

Keywords

1. Akaike, H. 1973. Information Theory and an Extension of the Maximum Likelihood Principle. Proceedings of the 2nd International Symposium on Information Theory, 2-8 September, Budapest, Hungary, PP. 267–281.
2. Boichard, D., Bonaiti, B., Barbat, A. and Mattalia, S. 1995 Three Methods to Validate the Estimation of Genetic Trend for Dairy Cattle. J. Dairy Sci., 78: 431–437.
3. Bonaiti, B., Boichard, D., Barbat, A. and Mattalia, S. 1994. Three Methods to Validate the Estimation of Genetic Trend in Dairy Cattle. Interbull Meeting, August 5-6, Ottawa, Ontario, Canada.
4. Freyer, G., Stricker, C. and Kuhn, C. 2002. Comparison of Estimated Breeding Values and Daughter Yield Deviations Used in Segregation and Linkage Analyses. Czech J. Anim. Sci. 47: 247-252.
5. Haile Mariam, M., Goddard, M. E. and Bowman, P. J. 2001. Estimates of Genetic Parameters for Daily Somatic Cell Count of Australian Dairy Cattle. J. Dairy Sci. 84: 1255-1264.
6. Kadarmideen, H. N. and Pryce J. E. 2001. Genetic and Economic Relationships between Somatic Cell Count and Clinical Mastitis and Their Use in Selection for Mastitis Resistance in Dairy Cattle. J. Anim. Sci. 73: 229-240.
7. Khanzadeh, H., Ghavi Hossein-Zadeh, N., Naserani, M. and Mohammad Nazari, B. 2013. Calculating Daughter Yield Deviations for Production Traits in Holstein Cattle Using Repeatability Animal and Random Regression Test Day Models. Livest. Sci. 157: 408-413.
8. Koivula, M., Negussie, E. and Mäntysaari, E. A. 2004. Genetic Parameters for Test-day Somatic Cell Count at Different Lactation Stages of Finnish Dairy Cattle. Livest. Prod. Sci. 90: 145-157.
9. Liu, Z., Reinhardt, F., Bunger, A. and Reents, R. 2004. Derivation and Calculation of Approximate Reliabilities and Daughter Yield Deviations of a Random Regression Test-Day Model for Genetic Evaluation of Dairy Cattle. J. Dairy Sci. 87: 1896-1907.
10. Liu, Z., Bunger, A. and Reents, R. 2003. Calculation and Use of Daughter Yield Deviations and Associated Reliabilities of Bulls under Multiple Trait Models. Interbull Bull., 31: 16-25.
11. Meyer K. 2006. WOMBAT: A Program for Mixed Model Analyses by Restricted Maximum Likelihood. User Notes. Animal Genetics and Breeding Unit, University of New England, Armidale, Australia.
12. Mrode, R. A. and Swanson, G. J. T. 1996. Genetic and Statistical Properties of Somatic Cell Count and Its Suitability as an Indirect Means of Reducing the Incidence of Mastitis in Dairy Cattle. Anim. Breed. Abstr., 64: 847-857.
13. Mrode, R. A. and Swanson, G. J. T. 2002. The Calculation of Cow and Daughter Yield Deviations and Partitioning of Genetic Evaluations when Using a Random Regression Model. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 19-23 August, Montpellier, France, PP. 51–54.
14. Mrode, R. A. and Swanson, G. J. T. 2004. Calculating Cow and Daughter Yield Deviations and Partitioning of Genetic Evaluations under a Random Regression Model. Livest. Prod. Sci., 86: 253-260.
15. Ødegard, J., Jensen, J., Klemetsdal, G., Madsen, P. and Heringstad, B. 2003. Genetic Analysis of Somatic Cell Score in Norwegian Cattle Using Random Regression Test-day Models. J. Dairy Sci., 86: 4103-4114.
16. Reents, R., Dekkers, J. C. M. and Schaeffer, L. R. 1994. Genetic Parameters of Test Day Somatic Cell Counts and Production Traits. Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, Guelph, Ontario, Canada, 17: 120-123.
17. SAS. 2002. SAS Users Guide Ver. 9.1. Statistics SAS Institute Inc., Cary, NC.
18. Sheikhloo, M., Shodja, J., Pirani, N., Alijani, S. and Sayadnejad, M. B. 2009. Genetics Evaluation and Calculating Daughter Yield Deviation of Bulls in Iranian Holstein Cattle for Milk and Fat Yields. Asian-Austr. J. Anim. Sci., 5: 611-617.
19. Szyda, J., Ptak, E., Komisarek, J. and Zarnecki, A. 2008. Practical Application of Daughter Yield Deviations in Dairy Cattle Breeding. J. Appl. Genet., 49: 183-191.
20. Theron, H. E., Kanfer, F. H. J. and Rautenbach, L. 2002. The Effect of Phantom Parent Groups on Genetic Trend Estimation. S. Afr. J. Anim. Sci., 322: 130-135.
21. Van Raden, P. M. and Wiggans, G. R. 1991. Derivation, Calculation, and Use of National Animal Model Information. J. Dairy Sci., 74: 2737-2746.
22. Van Steenbergen, E. J., Van der Lindde, C., De Roos, A. P. W., Harbers, A. G. F. and De Jong, G. 2005. Genetic Trend Validation in the PROTEJE Data and the Influence of Genetic Correlations on MACE EBVs. Interbull Bull., 33: 16-20.
Journal of Agricultural Science and Technology
Volume 18, Issue 6
November and December 2016
Pages 1467-1474