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Comparison of Genetic Parameter Estimates of Total Sperm Cells of Boars between Random Regression and Multiple Trait Animal Models

  • Oh, S.-H. (North Carolina A&T State University) ;
  • See, M.T. (North Carolina State University)
  • Received : 2007.07.09
  • Accepted : 2008.02.27
  • Published : 2008.07.01

Abstract

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

Keywords

Genetic Correlation;Boar;Semen;Random Regression Model;Multiple Trait Animal Model

References

  1. Boldman, K. G., L. A. Kriese, L. D. Van Vleck, C. P. Van Tassell and S. D. Kachman. 1995. A Manual for Use of MTDFREML. A set of programs to obtain estimates of variances and covariances (Draft). U.S. Department of Agriculture, Agricultural Research Service.
  2. Meyer, K. and W. G. Hill. 1997. Estimation of genetic and phenotypic covariance functions for longitudinal or 'repeated' records by restricted maximum likelihood. Livest. Prod. Sci. 47:185-200. https://doi.org/10.1016/S0301-6226(96)01414-5
  3. Morant, S. V. and A. Gnanasakthy. 1989. A new approach to the mathematical formulation of lactation curves. Anim. Produc. 49:151-162. https://doi.org/10.1017/S000335610003227X
  4. Oh, S.-H., M. T. See, T. E. Long and J. M. Galvin. 2006. Genetic parameters for various random regression models to describe total sperm cells per ejaculate over the reproductive lifetime of boars. J. Anim. Sci. 84:538-545. https://doi.org/10.2527/2006.843538x
  5. Meyer, K. 2001. Estimates of direct and maternal covariance functions for growth of Australian beef calves from birth to weaning. Genet. Sel. Evol. 33:487-514. https://doi.org/10.1186/1297-9686-33-5-487
  6. Masek, N., J. Kuciel, J. Masek and L. Maca. 1977. Genetical analysis of indicators for evaluating boar ejaculates. Acta Universitatis Agriculturae, Facultas Agronomica, Brno. 25:133-139.
  7. Meyer, K. 1998. Modeling 'repeated' records: covariance functions and random regression models to analyse animal breeding data. 6th World Congr. Genet. Appl. Livest. Prod. 25:517-520.
  8. Meyer, K. 2000. Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livest. Prod. Sci. 65:19-38. https://doi.org/10.1016/S0301-6226(99)00183-9
  9. Huang, Y. T. and R. K. Johnson. 1996. Effect of selection for size of testes in boars on semen and testis traits. J. Anim. Sci. 74:750-760. https://doi.org/10.2527/1996.744750x
  10. Huisman, A. E., R. F. Veerkamp and J. A. M. van Arendonk. 2002. Genetic parameters for various random regression models to describe the weight data of pigs. J. Anim. Sci. 80:575-582. https://doi.org/10.2527/2002.803575x
  11. Brandt, H. and G. Grandjot. 1998. Genetic and environmental effects on male fertility of AI boars. Proc. 6th World Congr. Genet. Appl. Livest. Prod. 23:527-530.
  12. Du Mesnil du Buisson, F., M. Paquignon and M. Courot. 1978. Boar sperm production: use in artificial insemination - a review. Livest. Prod. Sci. 5:293-302. https://doi.org/10.1016/0301-6226(78)90057-X
  13. Henderson, C. R. 1984. Applications of linear models in animal breeding. Univ. of Guelph, Guelph, Canada.
  14. Reents, R., J. C. M. Dekkers and L. R. Schaeffer. 1995. Genetic evaluation for somatic cell score with a test day model for multiple lactations. J. Dairy Sci. 78:2858-2870. https://doi.org/10.3168/jds.S0022-0302(95)76916-8
  15. Van der Werf, J. H. J., M. E. Goddard and K. Meyer. 1998. The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records. J. Dairy Sci. 81:3300-3308. https://doi.org/10.3168/jds.S0022-0302(98)75895-3
  16. Olori, V. E., W. G. Hill, B. J. McGuirk and S. Brotherstone. 1999. Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model. Livest. Prod. Sci. 61:53-63. https://doi.org/10.1016/S0301-6226(99)00052-4
  17. Strabel, T. and I. Misztal. 1999. Genetic parameters for first and second lactation milk yields of Polish Black and White Cattle with random regression test-day models. J. Dairy Sci. 82:2805-2810. https://doi.org/10.3168/jds.S0022-0302(99)75538-4

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