Journal of Animal Reproduction and Biotechnology (한국동물생명공학회지)

The Korean Society of Animal Reproduction and Biotechnology (한국동물생명공학회)
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Genetic evaluation for economic traits of commercial Hanwoo population using single-step GBLUP

  • Gwang Hyeon Lee (Department of Applied Biotechnology, The Graduate School of Hankyong National University) ;
  • Khaliunaa Tseveen (Department of Applied Biotechnology, The Graduate School of Hankyong National University) ;
  • Yoon Seok 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 : 2023.12.12
  • Accepted : 2023.12.19
  • Published : 2023.12.31
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Abstract

Background: Recently, the single-step genomic best linear unbiased prediction (ssGBLUP) method, which incorporates not only genomic information but also phenotypic information of pedigree, is under study. In this study, we performed a ssGBLUP analysis on a commercial Hanwoo population using phenotypic, genotypic, and pedigree data. Methods: The test population comprised Hanwoo 1,740 heads raised in four regions of Korea, while the reference population used Hanwoo 18,499 heads raised across the country and two-generation pedigree data. Analysis was performed using genotype data generated by the Hanwoo 50 K SNP beadchip. Results: The mean Genome estimated breeding values (GEBVs) estimated using the ssGBLUP methods for carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS) were 7.348, 1.515, -0.355, and 0.040, respectively, while the accuracy of each trait was 0.749, 0.733, 0.769, and 0.768, respectively. When the correlation analysis between the GEBVs as a result of this study and the actual slaughter performance was confirmed, CWT, EMA, BFT, and MS were reported to be 0.519, 0.435, 0.444, and 0.543, respectively. Conclusions: Our results suggest that the ssGBLUP method enables a more accurate evaluation because it conducts a genetic evaluation of an individual using not only genotype information but also phenotypic information of the pedigree. Individual evaluation using the ssGBLUP method is considered effective for enhancing the genetic ability of farms and enabling accurate and rapid improvements. It is considered that if more pedigree information of reference population is collected for analysis, genetic ability can be evaluated more accurately.

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References

  1. 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.
  2. Chen CY, Misztal I, Aguilar I, Legarra A, Muir WM. 2011. Effect of different genomic relationship matrices on accuracy and scale. J. Anim. Sci. 89:2673-2679. https://doi.org/10.2527/jas.2010-3555
  3. Choi TJ, Alam M, Cho CI, Lee JG, Park B, Kim S, Koo Y, Roh SH. 2015. Genetic parameters for yearling weight, carcass traits, and primal-cut yields of Hanwoo cattle. J. Anim. Sci. 93:1511-1521. https://doi.org/10.2527/jas.2014-7953
  4. Choy YH, Park BH, Choi TJ, Choi JG, Cho KH, Lee SS, Choi YL, Koh KC, Kim HS. 2012. Estimation of relative economic weights of hanwoo carcass traits based on carcass market price. Asian-Australas. J. Anim. Sci. 25:1667-1673. https://doi.org/10.5713/ajas.2012.12397
  5. Forni S, Aguilar I, Misztal I. 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet. Sel. Evol. 43:1.
  6. Gao N, Teng J, Pan R, Li X, Ye S, Li J, Zhang H, Zhang X, Zhang Z. 2019. Accuracy of whole genome prediction with single-step GBLUP in a Chinese yellow-feathered chicken population. Livest. Sci. 230:103817.
  7. Garrick DJ. 2007. Equivalent mixed model equations for genomic selection. J. Anim. Sci. 85:376.
  8. Gordo DG, Espigolan R, Tonussi RL, Junior GA, Bresolin T, Magalhaes AF, Feitosa FL, Baldi F, Carvalheiro R, Tonhati H, de Oliveira HN, Chardulo LA, de Albuquerque LG. 2016. Genetic parameter estimates for carcass traits and visual scores including or not genomic information. J. Anim. Sci. 94:1821-1826. https://doi.org/10.2527/jas.2015-0134
  9. Henderson CR. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423-447. https://doi.org/10.2307/2529430
  10. Henderson CR. 1984. Applications of Linear Models in Animal Breeding. University of Guelph, Guelph.
  11. 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
  12. 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
  13. Kim EH, Sun DW, Kang HC, Myung CH, Kim JY, Lee DH, Lee SH, Lim HT. 2022. Estimated of genomic estimated breeding value and accuracy analysis according to the amount of genotypes in the full-sib family. J. Agric. Life Sci. 56:171-178. https://doi.org/10.14397/jals.2022.56.6.171
  14. Kim SJ, Choi TJ, Son JI, Lee DM, Lee JJ, Lee JG, Lim HT, Koo YM. 2022. Estimation of genomic estimated breeding value(GEBV) and reliability for Hanwoo carcass traits using ssGBLUP. J. Anim. Breed. Genom. 6:57-72.
  15. Lee GH, Lee YH, Kong HS. 2023. Genetic evaluation and accuracy analysis of commercial Hanwoo population using genomic data. J. Anim. Reprod. Biotechnol. 38:32-37. https://doi.org/10.12750/JARB.38.1.32
  16. Lee J, Cheng H, Garrick D, Golden B, Dekkers J, Park K, Lee D, Fernando R. 2017. Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genet. Sel. Evol. 49:2.
  17. Lopez BI, Lee SH, Shin DH, Oh JD, Chai HH, Park W, Park JE, Lim D. 2020. Accuracy of genomic evaluation using imputed high-density genotypes for carcass traits in commercial Hanwoo population. Livest. Sci. 241:104256.
  18. 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
  19. Misztal I, Lourenco D, Legarra A. 2020. Current status of genomic evaluation. J. Anim. Sci. 98:skaa101.
  20. Misztal I, Tsuruta S, Lourenco D, Masuda Y, Aguilar I, Legarra A, Vitezica Z. 2014. Manual for BLUPF90 Family of Programs. University of Georgia, Athens.
  21. Park MN, Alam M, Kim S, Park B, Lee SH, Lee SS. 2020. Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle. Asian-Australas. J. Anim. Sci. 33:1544-1557. https://doi.org/10.5713/ajas.18.0936
  22. 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
  23. Song H, Zhang J, Zhang Q, Ding X. 2019. Using different single-step strategies to improve the efficiency of genomic prediction on body measurement traits in pig. Front. Genet. 9:730.
  24. VanRaden PM. 2007. Genomic measures of relationship and inbreeding. Interbull Bull. 37:33-36.
  25. VanRaden PM. 2012. Avoiding bias from genomic pre-selection in converting daughter information across countries. Interbull Bull. 45.
  26. VanRaden PM. 2020. Symposium review: how to implement genomic selection. J. Dairy Sci. 103:5291-5301. https://doi.org/10.3168/jds.2019-17684
  27. Xiang T, Nielsen B, Su G, Legarra A, Christensen OF. 2016. Application of single-step genomic evaluation for crossbred performance in pig. J. Anim. Sci. 94:936-948. https://doi.org/10.2527/jas.2015-9930
  28. Zhang Z, Todhunter RJ, Buckler ES, Van Vleck LD. 2007. Technical note: use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. J. Anim. Sci. 85:881-885. https://doi.org/10.2527/jas.2006-656