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Comparison of genomic predictions for carcass and reproduction traits in Berkshire, Duroc and Yorkshire populations in Korea

  • Iqbal, Asif (Department of Biotechnology, Yeungnam University) ;
  • Choi, Tae-Jeong (Swine Science Division, National Institute of Animal Science, RDA) ;
  • Kim, You-Sam (Department of Biotechnology, Yeungnam University) ;
  • Lee, Yun-Mi (Department of Biotechnology, Yeungnam University) ;
  • Alam, M. Zahangir (Department of Biotechnology, Yeungnam University) ;
  • Jung, Jong-Hyun (Jung P&C Institute) ;
  • Choe, Ho-Sung (Department of Animal Biotechnology, Chonbuk National University) ;
  • Kim, Jong-Joo (Department of Biotechnology, Yeungnam University)
  • Received : 2018.09.07
  • Accepted : 2019.06.02
  • Published : 2019.11.01

Abstract

Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

Acknowledgement

Supported by : Rural Development Administration, Chonbuk National University

References

  1. Arranz JJ, Bayon Y, San Primitivo F. Comparison of protein markers and microsatellites in differentiation of cattle populations. Anim Genet 1996;27:415-9. https://doi.org/10.1111/j.1365-2052.1996.tb00508.x
  2. Andersson L. Genetic dissection of phenotypic diversity in farm animals. Nat Rev Genet 2001;2:130-8. https://doi.org/10.1038/35052563 https://doi.org/10.1038/35052563
  3. Goddard ME, Hayes BJ. Genomic selection. J Anim Breed Genet 2007;124:323-30. https://doi.org/10.1111/j.1439-0388.2007.00702.x https://doi.org/10.1111/j.1439-0388.2007.00702.x
  4. Dekkers JCM, van der Werf JHJ. Strategies, limitations, and opportunities for marker-assisted selection in livestock. "Marker Assisted Selection (MAS) in Crops, Livestock, Forestry and Fish: Current Status and the Way Forward", FAO Invited Book;2007. Chapter 10.
  5. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29.
  6. Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol 2009;41:51. https://doi.org/10.1186/1297-9686-41-51 https://doi.org/10.1186/1297-9686-41-51
  7. Liu T, Qu H, Luo C, et al. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genet 2014;15:110. https://doi.org/10.1186/s12863-014-0110-y
  8. Lee SH, Clark S, van der Werf JHJ. Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship. PloS One 2017;12:e0189775. https://doi.org/10.1371/journal.pone.0189775 https://doi.org/10.1371/journal.pone.0189775
  9. Lee SH, Weerasinghe WM, Wray NR, Goddard ME, van der Werf JH. Using information of relatives in genomic prediction to apply effective stratified medicine. Sci Rep 2017;7:42091. https://doi.org/10.1038/srep42091 https://doi.org/10.1038/srep42091
  10. 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 https://doi.org/10.3168/jds.2008-1646
  11. 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 https://doi.org/10.3168/jds.2008-1514
  12. Berton MP, Dourado RC, Lima FBF, et al. Growing-finishing performance and carcass yield of pigs reared in a climate-controlled and uncontrolled environment. Int J Biometeorol 2015;59:955-60. https://doi.org/10.1007/s00484-014-0908-3 https://doi.org/10.1007/s00484-014-0908-3
  13. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75. https://doi.org/10.1086/519795 https://doi.org/10.1086/519795
  14. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 2009;84:210-23. https://doi.org/10.1016/j.ajhg.2009.01.005 https://doi.org/10.1016/j.ajhg.2009.01.005
  15. Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml User Guide Release 3.0. Hemel Hempstead, UK: VSN International Ltd.; 2009.
  16. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76-82. https://doi.org/10.1016/j.ajhg.2010.11.011 https://doi.org/10.1016/j.ajhg.2010.11.011
  17. Badke YM, Bates RO, Ernst CW, Fix J, Steibel JP. Accuracy of estimation of genomic breeding values in pigs using lowdensity genotypes and imputation. G3 (Bethesda, Md). 2014;4: 623-31. https://doi.org/10.1534/g3.114.010504
  18. Hwang IH, Park BY, Cho SH, Kim JH, Choi YS, Lee JM. Identification of muscle proteins related to objective meat quality in Korean native black Pig. Asian-Australas J Anim Sci 2004;17:1599-607. https://doi.org/10.5713/ajas.2004.1599 https://doi.org/10.5713/ajas.2004.1599
  19. Schwab CR, Baas TJ, Stalder KJ, Nettleton D. Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. I. Direct and correlated phenotypic responses to selection. J Anim Sci 2009;87:2774-80. https://doi.org/10.2527/jas.2008-1335 https://doi.org/10.2527/jas.2008-1335
  20. Baby S, Hyeong KE, Lee YM, et al. Evaluation of genome based estimated breeding values for meat quality in a berkshire population using high density single nucleotide polymorphism chips. Asian-Australas J Anim Sci 2014;27:1540-7. https://doi.org/10.5713/ajas.2014.14371 https://doi.org/10.5713/ajas.2014.14371
  21. Kuhlers DL, Nadarajah N, Jungst SB, Anderson BL. Genetic selection for real-time ultrasound loin eye area in a closed line of Landrace pigs. Livest Prod Sci 2001;72:225-31. https://doi.org/10.1016/S0301-6226(01)00222-6 https://doi.org/10.1016/S0301-6226(01)00222-6
  22. Akanno EC, Schenkel FS, Sargolzaei M, Friendship RM, Robinson JAB. Opportunities for genome-wide selection for pig breeding in developing countries. J Anim Sci 2013;91:4617-27. https://doi.org/10.2527/jas.2013-6102 https://doi.org/10.2527/jas.2013-6102
  23. Cleveland MA, Forni S, Garrick DJ, Deeb N. Prediction of genomic breeding values in a commercial pig population. Proceedings of the 9th World Congress Genetics Applied to Livestock Production; 2010 Aug 1-6; Leipzig, Germany.
  24. Cleveland MA, Hickey JM. Practical implementation of costeffective genomic selection in commercial pig breeding using imputation. J Anim Sci 2013;91:3583-92. https://doi.org/10.2527/jas.2013-6270
  25. Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol 2010; 42:5. https://doi.org/10.1186/1297-9686-42-5 https://doi.org/10.1186/1297-9686-42-5
  26. Goddard M. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 2009;136:245-57. https://doi.org/10.1007/s10709-008-9308-0 https://doi.org/10.1007/s10709-008-9308-0
  27. Uimari P, Sevon Aimonen ML, Serenius T. 2014. Reliability of genomic selection of reproduction traits in Finnish Yorkshire pig breed. Manuscript n. 926. Proceedings of the 10th World Congress Genetics Applied to Livestock Production, August 2014, Vancouver BC, Canada.
  28. Forni S, Aguilar I, Misztal I. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol 2011;43:1. https://doi.org/10.1186/1297-9686-43-1 https://doi.org/10.1186/1297-9686-43-1