DOI QR코드

DOI QR Code

The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

  • Lee, Yun-Mi (Department of Biotechnology, Yeungnam University) ;
  • Dang, Chang-Gwon (Division of Animal Breeding and Genetics, National Institute of Animal Science, RDA) ;
  • Alam, Mohammad Z. (Department of Biotechnology, Yeungnam University) ;
  • Kim, You-Sam (Department of Biotechnology, Yeungnam University) ;
  • Cho, Kwang-Hyeon (Korea National College of Agriculture and Fisheries) ;
  • Park, Kyung-Do (Department of Animal Biotechnology, Chonbuk National University) ;
  • Kim, Jong-Joo (Department of Biotechnology, Yeungnam University)
  • Received : 2019.07.05
  • Accepted : 2019.11.25
  • Published : 2020.03.01

Abstract

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

Acknowledgement

Supported by : Rural Development Administration

References

  1. Mulder HA, Calus MPL, Druet T, Schrooten C. Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. J Dairy Sci 2012;95:876-89. https://doi.org/10.3168/jds.2011-4490 https://doi.org/10.3168/jds.2011-4490
  2. Boison SA, Santos DJA, Utsunomiya AHT, et al. Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips. J Dairy Sci 2015; 98:4969-89. https://doi.org/10.3168/jds.2014-9213 https://doi.org/10.3168/jds.2014-9213
  3. Gaspa G, Veerkamp RF, Calus MPL, Windig JJ. Assessment of genomic selection for introgression of polledness into Holstein Friesian cattle by simulation. Livest Sci 2015;179:86-95. https://doi.org/10.1016/j.livsci.2015.05.020 https://doi.org/10.1016/j.livsci.2015.05.020
  4. Winkelman AM, Johnson DL, Harris BL. Application of genomic evaluation to dairy cattle in New Zealand. J Dairy Sci 2015;98:659-75. https://doi.org/10.3168/jds.2014-8560 https://doi.org/10.3168/jds.2014-8560
  5. Garcia-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-Lopez FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci USA 2016; 113:E3995-4004. https://doi.org/10.1073/pnas.1519061113 https://doi.org/10.1073/pnas.1519061113
  6. Jattawa D, Elzo MA, Koonawootrittriron S, Suwanasopee T. Imputation accuracy from low to moderate density single nucleotide polymorphism chips in a Thai multibreed dairy cattle population. Asian-Australas J Anim Sci 2016;29:464-70. https://doi.org/10.5713/ajas.15.0291 https://doi.org/10.5713/ajas.15.0291
  7. Nguyen TTT, Bowman PJ, Haile-Mariam M, Pryce JE, Hayes BJ. Genomic selection for tolerance to heat stress in Australian dairy cattle. J Dairy Sci 2016;99:2849-62. https://doi.org/10.3168/jds.2015-9685 https://doi.org/10.3168/jds.2015-9685
  8. Gengler N, Mayeres P, Szydlowski M. A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose Belgian Blue cattle. Animal 2007;1:21-8. https://doi.org/10.1017/S175 1731107392628 https://doi.org/10.1017/S1751731107392628
  9. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980 https://doi.org/10.3168/jds.2007-0980
  10. Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 2009;92:4648-55. https://doi.org/10.3168/jds.2009-2064 https://doi.org/10.3168/jds.2009-2064
  11. Liu Z, Goddard ME, Reinhardt F, Reent R. A Single-step genomic model with direct estimation of marker effects. J Dairy Sci 2014;97:5833-50. https://doi.org/10.3168/jds.2014-7924 https://doi.org/10.3168/jds.2014-7924
  12. Interbull. 2017. Interbull routine genetic evaluation for dairy production traits. http://interbull.org/ib/geforms
  13. VanRaden PM, VanTassell CP, Wiggans GR, et al. 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
  14. Uemoto Y, Osawa T, Saburi J. Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle. Animal 2017;11:382-93. https://doi.org/10.1017/S1751731116001762 https://doi.org/10.1017/S1751731116001762
  15. Schaeffer LR. Multiple-country comparison of dairy sires. J Dairy Sci 1994;77:2671-8. https://doi.org/10.3168/jds.S0022-0302(94)77209-X https://doi.org/10.3168/jds.S0022-0302(94)77209-X
  16. Sullivan PG, VanRaden PM. Development of genomic GMACE. Interbull Bulltein 2009;40:157-61.
  17. Misztal I, Aguilar I, Legarra A, Vitezica Z. Manual for BLUPF90 family of programs. Athens, GA, USA: University of Georgia; 2015.
  18. Powell RL, Norman HD. Different lactations for estimating genetic merit of dairy cows. J Dairy Sci 1981;64:321-30. https://doi.org/10.3168/jds.S0022-0302(81)82569-6 https://doi.org/10.3168/jds.S0022-0302(81)82569-6
  19. Montaldo HH, Castillo-Juarez H, Valencia-Posadas M, Cienfuegos-Rivas EG, Ruiz-Lopez FJ. Genetic and environmental parameters for milk production, udder health, and fertility traits in Mexican Holstein cows. J Dairy Sci 2010;93:2168-75. https://doi.org/10.3168/jds.2009-2050 https://doi.org/10.3168/jds.2009-2050
  20. Stachowicz K, Sargolzaei M, Miglior F, Schenkel FS. Rates of inbreeding and genetic diversity in Canadian Holstein and Jersey cattle. J Dairy Sci 2011;94:5160-75. https://doi.org/10.3168/jds.2010-3308 https://doi.org/10.3168/jds.2010-3308
  21. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29.
  22. 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
  23. Christensen OF, Lund MS. Genomic prediction when some animals are not genotyped. Genet Sel Evol 2010;42:2. https://doi.org/10.1186/1297-9686-42-2 https://doi.org/10.1186/1297-9686-42-2
  24. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal 2012;6: 1565-71. https://doi.org/10.1017/S1751731112000742 https://doi.org/10.1017/S1751731112000742
  25. Ding X, Zhang Z, Li X, et al. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. J Dairy Sci 2013;96:5315-23. https://doi.org/10.3168/jds.2012-6194 https://doi.org/10.3168/jds.2012-6194
  26. Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS. Genomic selection in dairy cattle: The USDA experience. Annu Rev Anim Biosci 2016;5:309-27. https://doi.org/10.1146/annurev-animal-021815-111422
  27. Weigel KA. Genomic selection of dairy cattle: a review of methods, strategies, and impact. J Anim Breed Genet 2017;1:1-15. https://doi.org/10.12972/jabng.20170001
  28. Falconer DS, Mackay TFC. Introduction to quantitative genetics. Essex, England: Longman; 1966.
  29. 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
  30. Lund MS, van den Berg I, Ma P, Brondum RF, Su G. Review: How to improve genomic predictions in small dairy cattle populations. Animal 2016;10:1042-9. https://doi.org/10.1017/S1751731115003031 https://doi.org/10.1017/S1751731115003031