Estimation of the Accuracy of Genomic Breeding Value in Hanwoo (Korean Cattle)

한우의 유전체 육종가의 정확도 추정

  • 이승수 (농촌진흥청 국립축산과학원) ;
  • 이승환 (농촌진흥청 국립축산과학원) ;
  • 최태정 (농촌진흥청 국립축산과학원) ;
  • 최연호 (농촌진흥청 국립축산과학원) ;
  • 조광현 (농촌진흥청 국립축산과학원) ;
  • 최유림 (농촌진흥청 국립축산과학원) ;
  • 조용민 (농촌진흥청 국립축산과학원) ;
  • 김내수 (충북대학교) ;
  • 이중재 (충북대학교)
  • Received : 2013.01.28
  • Accepted : 2013.02.26
  • Published : 2013.02.28


This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.


GBLUP;GEBV;SNP;Cross-validation;Genomic selection


Supported by : 농촌진흥청


  1. Cho, C. I. and Lee, D. H. 2011. Study on Genetic Evaluation using Genomic Information in Animal Breeding - Simulation Study for Estimation of Marker Effects. J. Anim. Sci. Tech. (Kor.) 53(1):1-6.
  2. Forni, S., Aguilar, I. and Misztal, I. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution. 43:1.
  3. Habier, D, Tetens, J., Seefried, F. R., Lichtner, P. and Thaller, G. 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution. 42:5.
  4. Habier, D., Fernando, R. L. and Dekkers, J. C. M. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 177:2389-2397
  5. Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. and Goddard, M. E. 2009a. Accuracy of genomic breeding values in multi-breed dairy cattle popilations. Genetics Selection Evolution. 41:51.
  6. Hayes, B. J., Visscher P. M. and Goddard, M. E. 2009b. Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res. 91:47-60.
  7. Hwang, J. M., Kim, S. D., Choy, Y. H., Yoon, H. B. and Park, C. J. 2008. Genetic Parameter Estimation of Carcass Traits of Hanwoo Steers. J. Anim. Sci. Tech. (Kor.) 50(5):613-620.
  8. Kim, H. S., Hwang, J. M., Choi, T. J., Park, B. H., Cho, K. H., Park, C. J, and Kim S. D. 2010. Research on the Reformation of the Selection Index for Hanwoo Proven Bull. J. Anim. Sci. Tech. (Kor.) 52(2):83-90.
  9. Lee, J. H. 2011. Study on Methodology for Estimating Breeding Values using Genomic Information. Ph. D. thesis, Hankyoung National University.
  10. Lee, J. J. 2012. Genetic Evaluation for Carcass Traits of Hanwoo using Pedigree and SNP Marker-Derived Relationship Matrix. Ph. D. thesis, Chungbuk National University.
  11. Lee, S. H., Lim D. J., Jang, G. W., Cho, Y. M., Choi, B. H., Kim S. D., Oh S. J., Lee J. H., Yoon D. H., Park E. W., Lee, H. K., Hong, S. K. and Yang B. S. 2012. Genome Wide Association Study to Identity QTL for Growth Traits in Hanwoo. J. Anim. Sci. Tech. (Kor.) 54(5):1-10.
  12. Meuwissen, T. 2003. 'Genomic Selection : the future of marker assisted selection and animal breeding'. FAO.
  13. Misztal, I., Legarra, A. and Aguilar, I. 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci. 92:4648-4655.
  14. Rolf, M. M., Taylor, J. F., Schnabel, R. D., Mckay, S. D., Mcclure, M. C., Northcutt, S. L., Kerley, M. S. and Weaber, R. L. 2010. Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle. BMC. Genetics 11:24.
  15. SAS Institute Inc. 2004. SAS OnlineDoc$^{(R)}$ 9.1.3. Cary, NC: SAS Institute Inc.
  16. Su, G., Brondum, R. F., Ma, P., Guldbrandtsen, B., Aamand, G. P. and Lund, M. S. 2012. Comparison of genomic predcitions using medium-density (${\sim}54,000$) and high-density (${\sim}777,000$) single uncleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. J. Dairy Sci. 95:4657-4665.
  17. VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. dairy sci. 91:4414-4423.
  18. VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F. and Schenkel, F. S. 2009. Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.
  19. Visscher, P. M., Medland, S. E., Ferreira, M. A., Morley, K. I., Zhu, G., Cornes, B. K., Montgomery, G. W. and Martin, N. G. 2006. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLOS Genetic 2(3):e41.
  20. Whittaker, J. C., Thompson, R. and Denham, M. C. 2000. Marker-assisted selection using ridge regression. Genetical Research. 75:249-252.
  21. Meuwissen, T. H. E., Hayes, B. J. and Goddard, M. E. 2001. Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps. Genetics. 157: 1819-1829.

Cited by

  1. Accuracy of Genomic Estimated Breeding Value with Hanwoo Cows in the Commercial Farms vol.52, pp.2, 2018,