과제정보
We extend our thanks to the University of Florida (Gainesville, Florida, USA), the Tropical Animal Genetic Special Research Unit (TAGU), the Dairy Farming Promotion Organization of Thailand, dairy-related organizations in Thailand, and Thai dairy farmers for their support and collaboration.
참고문헌
- 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;95:87689. https://doi.org/10.3168/jds.2011
- VanRaden PM, Null DJ, Sargolzaei M, et al. Genomic imputation and evaluation using highdensity Holstein genotypes. J Dairy Sci 2013;96:66878. https://doi.org/10.3168/jds.2012
- Jattawa D, Elzo MA, Koonawootrittriron S, Suwanasopee T. Comparison of genetic evaluations for milk yield and fat yield using a polygenic model and three genomic–polygenic models with different sets of SNP genotypes in Thai multibreeddairy cattle. Livest Sci 2015;181:5864. https://doi.org/10.1016/j.livsci.2015.10.008
- Boison SA, Utsunomiya ATH, Santos DJA, et al. Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle. J Dairy Sci 2017;100:547990. https://doi.org/10.3168/jds.2016
- 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/10.1186/1471216415478
- He S, Wang S, Fu W, Ding X, Zhang Q. Imputation of missing genotypes from lowto highdensity SNP panel in different population designs. Anim Genet 2015;46:17. https://doi.org/10.1111/age.12236
- 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-Australato Anim Sci 2016;29:464-70. https://doi.org/105713/ajas.15.0291 105713/ajas.15.0291
- Jattawa D, Koonawootrittriron S, Elzo MA, Suwanasopee T. Effects of using genomic imputation on dairy genomic evaluation in Thailand. Khon Kean Agric J 2016;44(Suppl 2):335-43.
- Boison SA, Neves HHR, Perez O'Brien AM, et al. Imputation of non-genotyped individuals using genotyped progeny in Nellore, a Bos indicus cattle breed. Livest Sci 2014;166:176-89. http://dx.doi.org/10.1016/j.livsci.2014.05.033
- Bouwman AC, Hickey JM, Calus MPL, Veerkamp RF. Imputation of non-genotyped individuals based on genotyped relatives: assessing the imputation accuracy of a real case scenario in dairy cattle. Genet Sel Evol 2014;46:6. https://doi.org/10.1186/1297-9686-46-6
- Bojnord NR, Aminafshar M, Honarvar M, Kashan NEJ. Imputation of ungenotyped individuals based on genotyped relatives using machine learning methodology. T Epigenet 2021;2:13-22. https://doi.org/10.22111/jep.2021.34789.1023
- Genetic ability of breeding cattle 2567: sire and dam summary 2024 [Internet]. Dairy Farming Promotion Organization of Thailand; 2024 [cited 2024 Mar15]. Available from: https://www.dpogenetics.com/images/siredam-summary/catalogue/CatalougeDPO2024.pdf
- Thailand Weather [Internet]. Thai Meteorological Department; 2024 [cited 2024 Feb1]. Available from: https://tmd-devazurewebsites.net/en
- Koonawootrittriron S, Elzo MA, Thongprapi T. Genetic trends in a holstein × other breeds multibreed dairy population in Central Thailand. Livest Sci 2009;122:186-92. https://doi.org/10.1016/j.livsci.2008.08.013
- Ritsawai P, Koonawootrittriron S, Jattawa D, Suwanasopee T, Elzo MA. Fraction of cattle breeds and their influence on milk production of Thai dairy cattle. In: Proceeding of 52nd Kasetsart Anuual Conference; 2014 Feb 4-7; Bangkok, Thailand; Kasetsart University; 2014. p. 25-32.
- Sargent FD, Lytton VH, Wall JOG Jr. Test interval method of calculating dairy herd improvement association records. J Dairy Sci 1968;51:170-9. https://doi.org/10.3168/jds.S0022-0302(68)86943-7
- Koonawootrittriron S, Elzo MA, Tumwasorn S, Sintala W. Prediction of 100-d and 305-d milk yields in a multibreed dairy herd in Thailand using monthly test-day records. Thai J Agric Sci 2001;34:163-74.
- Van Raden PM, Sun C. Fast imputation using medium- or low-coverage sequence data. In: Proceeding of 10th World Congress of Genetics Applied to Livestock Production; 2014 Aug 17-22; Vancouver, BC, Canada.
- 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
- Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci 2009;92:4656-63. https://doi.org/10.3168/jds.2009-2061
- Van Raden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org10.3168/jds.2007-0980
- Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In: Proceeding 7th World Congress on Genetics Applied to Livestock Production; 2002 Aug 19-23; Montpellier, France.
- BLUPF 90. AIREM LF90-Average Information REML with several options including EM-REML and heterogeneous residual variances (S. Tsuruta) [Internet]. Animal Breeding and Genetics Group; 2014 [cited 2023 Oct1]. Available from: http://nce.ads.uga.edu/wiki/doku.php?id=application_programs
- Meyer K, Houle D. Sampling based approximation of confi-dence intervals for functions of genetic covariance matrices. In: Proceedings of the 20th Conference of the Association for Advances in Animal Breeding Genetics; 2013 Oct 20-23; Napier, New Zealand. p. 523-6.
- Laodim T, Koonawootrittriron K, Elzo MA, Suwanasopee T. Genome-wide linkage disequilibrium in a Thai multibreed dairy cattle population. Livest Sci 2015;180:27-33. http://doi.org/10.1016/j.livsci.2015.06.021
- Bernardes PA, do Nascimento GB, Savegnago RP, et al. Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle. Sci Rep 2019;9:17920. https://doi.org/101038/s41598-019-54382-w 101038/s41598-019-54382-
- Piccoli, ML, Braccini J, Cardoso FF, Sargolzaei M, Larmer SG, Schenkel FS. Accuracy of genome-wide imputation in Braford and Hereford beef cattle. BMC Genet 2014;15:157. https://doi.org/10.1186/s12863-014-0157-9
- Berry DP, McHugh N, Randles S, et al. Imputation of non-genotyped sheep from the genotypes of their mates and resulting progeny. Animal 2018;12:191-8. https://doi.org10.1017/S1751731117001653
- Johnston J, Kistemaker G, Sullivan PG. Comparison of different imputation methods. In: Proceeding of the 2011 Interbull Meeting; 2011 Aug 26-29: Stavanger, Norway. p. 25-33.
- Larmer, SG, Sargolzaei M, Schenkel FS. Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds. Dairy Sci 2014;97:3128-41. https://doi.org/10.3168/jds.2013-6826
- Mitt M, Kals M, Parn K, et al. Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. Eur J Hum Genet 2017;25:869-76. https://doi.org/10.1038/ejhg.2017.51
- Cleveland MA, Hickey JM, Kinghorn BP. Genotype imputation for the prediction of genomic breeding values in non-genotyped and low-density genotyped individuals. BMC Proc 2011;5(Suppl 3):S6. https://doi.org/10.1186/1753-6561-5-S3-S6
- Hickey JM, Kinghorn BP, Tier B, van der Werf JH, Cleveland MA. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genet Sel Evol 2012;44:9. https://doi.org/10.1186/1297-9686-44-9
- Pimentel ECG, Wensch-Dorendorf M, Konig S, Swalve HH. Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genet Sel Evol 2013;45:12. https://doi.org/10.1186/1297-9686-45-12
- Pszczola M, Mulder HA, Calus MPL. Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle. J Dairy Sci 2011;94:431-41. https://doi.org/10.3168/jds.2009-2840