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Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation

  • Danai Jattawa (Department of Animal Science, Faculty of Agriculture, Kasetsart University) ;
  • Thanathip Suwanasopee (Department of Animal Science, Faculty of Agriculture, Kasetsart University) ;
  • Mauricio A. Elzo (Tropical Animal Genetic Special Research Unit (TAGU), Kasetsart University) ;
  • Skorn Koonawootrittriron (Department of Animal Science, Faculty of Agriculture, Kasetsart University)
  • 투고 : 2024.05.14
  • 심사 : 2024.08.26
  • 발행 : 2025.03.01

초록

Objective: This study assessed the impact of incorporating imputed single nucleotide polymorphism (SNP) information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy. Methods: Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects. Results: The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic estimated breeding values from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003). Conclusion: Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.

키워드

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

  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;95:876­89. https://doi.org/10.3168/jds.2011­
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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.
  9. 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
  10. 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
  11. 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
  12. 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
  13. Thailand Weather [Internet]. Thai Meteorological Department; 2024 [cited 2024 Feb1]. Available from: https://tmd-devazurewebsites.net/en
  14. 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
  15. 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.
  16. 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
  17. 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.
  18. 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.
  19. 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
  20. 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
  21. Van Raden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org10.3168/jds.2007-0980
  22. 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.
  23. 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
  24. 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.
  25. 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
  26. 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-
  27. 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
  28. 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
  29. 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.
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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