Asian-Australasian Journal of Animal Sciences

  • 제33권10호
  • /
  • Pages.1544-1557
  • /
  • 2020
  • /
  • 1011-2367(pISSN)
  • /
  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle

  • Park, Mi Na (Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration) ;
  • Alam, Mahboob (Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration) ;
  • Kim, Sidong (Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration) ;
  • Park, Byoungho (Poultry Research Institute, National Institute of Animal Science, Rural Development Administration) ;
  • Lee, Seung Hwan (Division of Animal and Dairy Science, Chungnam National University) ;
  • Lee, Sung Soo (Hanwoo Genetic Improvement Center, NongHyup Agribusiness Group Inc)
  • 투고 : 2018.12.10
  • 심사 : 2019.10.30
  • 발행 : 2020.10.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (rM1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (rM2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The rM1 estimates in the study were between 0.90 and 0.96 among five traits. The rM1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average rM2 estimates were much smaller than rM1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, rM2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of rM2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the rM2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based rM2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

키워드

참고문헌

  1. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980
  2. VanRaden PM, Van Tassell CP, Wiggans GR, et al. Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 2009;92:16-24. https://doi.org/10.3168/jds.2008-1514
  3. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 2009;92:433-43. https://doi.org/10.3168/jds.2008-1646
  4. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 2010;93:743-52. https://doi.org/10.3168/jds.2009-2730
  5. Baloche G, Legarra A, Salle G, et al. Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. J Dairy Sci 2014;97:1107-16. https://doi.org/10.3168/jds.2013-7135
  6. Carillier C, Larroque H, Robert-Granie C. Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population. Genet Sel Evol 2014;46:67. https://doi.org/10.1186/s12711-014-0067-3
  7. Cardoso FF, Gomes CCG, Sollero BP, et al. Genomic prediction for tick resistance in Braford and Hereford cattle. J Anim Sci 2015;93:2693-705. https://doi.org/10.2527/jas.2014-8832
  8. Kim S, Alam M, Park MN. Breeding initiatives for Hanwoo cattle to thrive as a beef industry- A review study. J Anim Breed Genom 2017;1:102-24. https://doi.org/10.12972/jabng.20170011
  9. Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 2014;15:478. https://doi.org/10.1186/1471-2164-15-478
  10. Aguilar I, Misztal I, Tsuruta S, Legarra A, Wang H. PREGSF90-POSTGSF90: Computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. In: Proceedings of the 10th World Congress of Genetics Applied to Livestock Production; 2014 Vancouver, Canada. https://doi.org/10.13140/2.1.4801.5045
  11. Boichard D. PEDIG: A FORTRAN package for pedigree analysis suited for large populations. 7th world congress on genetics applied to livestock production; 2002 August 19-23; Montpellier, France.
  12. Sargolzaei M, Iwaisaki H, Colleau JJ. CFC: A tool for monitoring genetic diversity. In: Proceedings of the 8th world congress on genetics applied to livestock production; 2006 August 13-18; Belo Horizonte, MG, Brasil. Minas Gerais, Brazil: Instituto Prociencia; 2006. p. 27-8.
  13. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics 1975;31:423-47. https://doi.org/10.2307/2529430
  14. Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A, Vitezica Z. Manual for BLUPF90 family of programs [Internet]. Athens, GA, USA: University of Georgia; c2015 [cited 2019 Oct 4]. Available from: http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=blupf90_all2.pdf
  15. Weng Z, Zhang Z, Ding X, et al. Application of imputation methods to genomic selection in Chinese Holstein cattle. J Anim Sci Biotechnol 2012;3:6. https://doi.org/10.1186/2049-1891-3-6
  16. Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM. Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics 2013;193:347-65. https://doi.org/10.1534/genetics.112.147983
  17. Mehrban H, Lee DH, Moradi MH, Cho CI, Naserkheil M, Ibanez-Escriche N. Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture. Genet Sel Evol 2017;49:1. https://doi.org/10.1186/s12711-016-0283-0
  18. Shin EG, Lee SH, Yoon D. Accuracy of genomic estimated breeding value with Hanwoo cows in the commercial farms. J Agric Life Sci 2018;52:91-8. https://doi.org/10.14397/jals.2018.52.2.91
  19. Lee SH, Kim HC, Lim D, et al. Prediction of genomic breeding values of carcass traits using whole genome SNP data in Hanwoo (Korean cattle). CNU J Agric Sci 2012;39:357-64. https://doi.org/10.7744/CNUJAS.2012.39.3.357
  20. Park B, Choi T, Kim S, Oh SH. National genetic evaluation (system) of Hanwoo (Korean native cattle). Asian-Australas J Anim Sci 2013;26:151-6. https://doi.org/10.5713/ajas.2012.12439
  21. Choi TJ, Alam M, Cho CI, et al. Genetic parameters for yearling weight, carcass traits, and primal-cut yields of Hanwoo cattle. J Anim Sci 2015;93:1511-21. https://doi.org/10.2527/jas.2014-7953
  22. Lee DH. Methods for genetic parameter estimations of carcass weight, longissimus muscle area and marbling score in Korean cattle. J Anim Sci Technol 2004;46:509-16. https://doi.org/10.5187/JAST.2004.46.4.509
  23. Hwang JM, Kim S, Choy YH, Yoon BH, PC J. Genetic parameter estimation of carcass traits of Hanwoo steers. J Anim Sci Technol 2008;50:613-20. https://doi.org/10.5187/JAST.2008.50.5.613
  24. Roh SH, Kim BW, Kim HS, et al. Comparison between REML and Bayesian via Gibbs sampling algorithm with a mixed animal model to estimate genetic parameters for carcass traits in Hanwoo (Korean native cattle). J Anim Sci Technol 2004; 46:719-28. https://doi.org/10.5187/JAST.2004.46.5.719
  25. Roh SH, Kim CY, Won YS, Park CJ, Lee SS, Lee JG. Studies on genetic parameter estimation and sire selection to ultrasound measurement traits of Hanwoo. J Anim Sci Technol 2010;52:1-8 https://doi.org/10.5187/JAST.2010.52.1.001
  26. Kim JB, Kim DJ, Lee JK, Lee CY. Genetic relationship between carcass traits and carcass price of Korean cattle. Asian-Australas J Anim Sci 2010;23:848-54. https://doi.org/10.5713/ajas.2010.90555
  27. Lee DH, Kim HC. Genetic relationship between ultrasonic and carcass measurements for meat qualities in Korean steers. Asian-Australas J Anim Sci 2004;17:7-12. https://doi.org/10.5713/ajas.2004.7
  28. Choi T, Lim D, Park B, et al. Accuracy of genomic breeding value prediction for intramuscular fat using different genomic relationship matrices in Hanwoo (Korean cattle). Asian-Australas J Anim Sci 2017;30:907-11. https://doi.org/10.5713/ajas.15.0983
  29. Lee SS, Lee SH, Choi TJ, et al. Estimation of the accuracy of genomic breeding value in Hanwoo (Korean cattle). J Anim Sci Technol 2013;55:13-8. https://doi.org/10.5187/JAST.2013.55.1.13
  30. Badke YM, Bates RO, Ernst CW, Fix J, Steibel JP. Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation. G3 (Bethesda) 2014;4:623-31. https://doi.org/10.1534/g3.114.010504
  31. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 2008;3:e3395. https://doi.org/10.1371/journal.pone.0003395
  32. Clark SA, Hickey JM, Daetwyler HD, van der Werf JH. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol 2012;44:4. https://doi.org/10.1186/1297-9686-44-4
  33. Berry DP, Garcia JF, Garrick DJ. Development and implementation of genomic predictions in beef cattle. Anim Front 2016; 6:32-8. https://doi.org/10.2527/af.2016-0005

피인용 문헌

  1. Genetic Analysis of Major Carcass Traits of Korean Hanwoo Males Raised for Thirty Months vol.11, pp.6, 2020, https://doi.org/10.3390/ani11061792
  2. Main regulatory factors of marbling level in beef cattle vol.14, 2020, https://doi.org/10.1016/j.vas.2021.100219