Journal of Animal Reproduction and Biotechnology (한국동물생명공학회지)

The Korean Society of Animal Reproduction and Biotechnology (한국동물생명공학회)
권호 표지
DOI QR코드

DOI QR Code

Genetic evaluation and accuracy analysis of commercial Hanwoo population using genomic data

  • Gwang Hyeon Lee (Department of Applied Biotechnology, The Graduate School of Hankyong National University) ;
  • Yeon Hwa Lee (Department of Applied Biotechnology, The Graduate School of Hankyong National University) ;
  • Hong Sik Kong (Department of Applied Biotechnology, The Graduate School of Hankyong National University)
  • Received : 2023.02.18
  • Accepted : 2023.03.14
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study has evaluated the genomic estimated breeding value (GEBV) of the commercial Hanwoo population using the genomic best linear unbiased prediction (GBLUP) method and genomic information. Furthermore, it analyzed the accuracy and realized accuracy of the GEBV. 1,740 heads of the Hanwoo population which were analyzed using a single nucleotide polymorphism (SNP) Chip has selected as the test population. For carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS), the mean GEBVs estimated using the GBLUP method were 3.819, 0.740, -0.248, and 0.041, respectively and the accuracy of each trait was 0.743, 0.728, 0.737, and 0.765, respectively. The accuracy of the breeding value was affected by heritability. The accuracy was estimated to be low in EMA with low heritability and high in MS with high heritability. Realized accuracy values of 0.522, 0.404, 0.444, and 0.539 for CWT, EMA, BFT, and MS, respectively, showing the same pattern as the accuracy value. The results of this study suggest that the breeding value of each individual can be estimated with higher accuracy by estimating the GEBV using the genomic information of 18,499 reference populations. If this method is used and applied to individual selection in a commercial Hanwoo population, more precise and economical individual selection is possible. In addition, continuous verification of the GBLUP model and establishment of a reference population suitable for commercial Hanwoo populations in Korea will enable a more accurate evaluation of individuals.

Keywords

References

  1. Byun SK, Kim DH, Oh JD, Lee HK. 2021. The analysis on evaluation of genomic breeding value in Brindle cattle using reference population of Hanwoo. J. Anim. Breed. Genom. 5:91-98.
  2. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7.
  3. Gao N, Teng J, Ye S, Yuan X, Huang S, Zhang H, Zhang X, Li J, Zhang Z. 2018. Genomic prediction of complex phenotypes using genic similarity based relatedness matrix. Front. Genet. 9:364.
  4. Garrick DJ. 2007. Equivalent mixed model equations for genomic selection. J. Anim. Sci. 85:376.
  5. Goddard M. 2009. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245-257. https://doi.org/10.1007/s10709-008-9308-0
  6. Heffner EL, Sorrells ME, Jannink JL. 2009. Genomic selection for crop improvement. Crop Sci. 49:1-12. https://doi.org/10.2135/cropsci2008.08.0512
  7. Henderson CR. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423-447. https://doi.org/10.2307/2529430
  8. Henderson CR. 1984. Applications of Linear Models in Animal Breeding. University of Guelph, Guelph.
  9. Henderson CR and Quaas RL. 1976. Multiple trait evaluation using relatives' records. J. Anim. Sci. 43:1188-1197. https://doi.org/10.2527/jas1976.4361188x
  10. Jang MJ, Lim DJ, Park WC, Park JE. 2022. Estimation of genetic prediction accuracy using convolutional neural network in Hanwoo. J. Korea Acad. Ind. Coop. Soc. 23:516-523.
  11. Kim DH. 2021. Studies on the genetic evaluation of Hanwoo using the genomic information [Doctoral dissertation, Jeonbuk National University]. RISS. http://www.riss.kr/link?id=T15777082
  12. Kim EH, Sun DW, Kang HC, Kim JY, Myung CH, Lee DH, Lee SH, Lim HT. 2021. A study of the genomic estimated breeding value and accuracy using genotypes in Hanwoo steer (Korean cattle). Korean J. Agric. Sci. 48:681-691. https://doi.org/10.7744/KJOAS.20210057
  13. Lee JJ. 2012. Genetic evaluation for carcass traits of Hanwoo using pedigree and SNP marker-derived relationship matrix [Doctoral dissertation, Chungbuk National University]. RISS. http://www.riss.kr/link?id=T12647293
  14. Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829. https://doi.org/10.1093/genetics/157.4.1819
  15. Misztal I, Tsuruta S, Lourenco D, Masuda Y, Aguilar I, Legarra A, Vitezica Z. 2018. Manual for BLUPF90 Family of Programs. University of Georgia, Athens.
  16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81:559-575. https://doi.org/10.1086/519795
  17. Son JH, Koo YM, Jeoung YH, Kim JI, Cha DH, Kim SJ, Choi TJ, Park MN, Lee DH, Lee JH. 2021. Estimation of the genetic gain of carcass traits in Hanwoo Korean proven bull and cow. J. Agric. Life Sci. 55:81-88. https://doi.org/10.14397/jals.2021.55.3.81
  18. VanRaden PM. 2007. Genomic measures of relationship and inbreeding. Interbull Bull. 37:33-36.
  19. VanRaden PM. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423. https://doi.org/10.3168/jds.2007-0980
  20. Visscher PM, Medland SE, Ferreira MA, Morley KI, Zhu G, Cornes BK, Montgomery GW, Martin NG. 2006. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2:e41.
  21. Zhang Z, Todhunter RJ, Buckler ES, Van Vleck LD. 2007. Technical note: use of marker-based relationships with multipletrait derivative-free restricted maximal likelihood. J. Anim. Sci. 85:881-885. https://doi.org/10.2527/jas.2006-656