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) |
1 | 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 DOI |
2 | 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 DOI |
3 | 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 DOI |
4 | 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 DOI |
5 | 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 DOI |
6 | 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 DOI |
7 | 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 DOI |
8 | 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 DOI |
9 | 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 DOI |
10 | 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 DOI |
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 DOI |
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 DOI |
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 DOI |
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 DOI |
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 DOI |
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 DOI |
20 | 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 DOI |
21 | VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980 DOI |
22 | 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 DOI |
23 | 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 DOI |
24 | Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29. DOI |
25 | 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 DOI |
26 | 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 DOI |
27 | 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 DOI |
28 | 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 DOI |
29 | 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 |
30 | Falconer DS, Mackay TFC. Introduction to quantitative genetics. Essex, England: Longman; 1966. |