Asian-Australasian Journal of Animal Sciences

  • 제32권12호
  • /
  • Pages.1836-1843
  • /
  • 2019
  • /
  • 1011-2367(pISSN)
  • /
  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Application of single-step genomic evaluation using social genetic effect model for growth in pig

  • Hong, Joon Ki (National Institute of Animal Science, Rural Development Administration) ;
  • Kim, Young Sin (National Institute of Animal Science, Rural Development Administration) ;
  • Cho, Kyu Ho (National Institute of Animal Science, Rural Development Administration) ;
  • Lee, Deuk Hwan (Department of Animal Life Resources, Hankyong University) ;
  • Min, Ye Jin (National Institute of Animal Science, Rural Development Administration) ;
  • Cho, Eun Seok (National Institute of Animal Science, Rural Development Administration)
  • 투고 : 2019.03.07
  • 심사 : 2019.08.14
  • 발행 : 2019.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

키워드

참고문헌

  1. Bijma P, Muir WM, Van Arendonk JA. Multilevel selection 1: quantitative genetics of inheritance and response to selection. Genetics 2007;175:277-88. https://doi.org/10.1534/genetics.106.062711
  2. Bergsma R, Kanis E, Knol EF, Bijma P. The contribution of social effects to heritable variation in finishing traits of domestic pigs (Sus scrofa). Genetics 2008;178:1559-70. https://doi.org/10.1534/genetics.107.084236
  3. Duijvesteijn N. Sociable swine: prospects of indirect genetic effects for the improvement of productivity, welfare and quality [Ph. D. thesis]. Wageningen, NL, USA: Wageningen University; 2014.
  4. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980
  5. 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
  6. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Singlestep methods for genomic evaluation in pigs. Animal 2012;6:1565-71. https://doi.org/10.1017/S1751731112000742
  7. Sargolzaei M, Iwaisaki H, Colleau J. CFC: A tool for monitoring genetic diversity. 8th World Congress on Genetics Applied to Livestock Production; 2006 Aug 13-18: Belo Horizonte, MG, Brasil. CD-ROM Communication; 2006.
  8. Ramos AM, Crooijmans RP, Affara NA, et al. Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PloS One 2009;4:e6524. https://doi.org/10.1371/journal.pone.0006524
  9. Wiggans GR, VanRaden PM, Bacheller LR, et al. Selection and management of DNA markers for use in genomic evaluation. J Dairy Sci 2010;93:2287-92. https://doi.org/10.3168/jds.2009-2773
  10. Aguilar I, Misztal I, Legarra A, Tsuruta S. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J Anim Breed Genet 2011;128:422-8. https://doi.org/10.1111/j.1439-0388.2010.00912.x
  11. Arango J, Misztal I, Tsuruta S, Culbertson M, Herring W. Estimation of variance components including competitive effects of Large White growing gilts. J Anim Sci 2005;83:1241-6. https://doi.org/10.2527/2005.8361241x
  12. Bijma P. Estimating indirect genetic effects: precision of estimates and optimum designs. Genetics 2010;186:1013-28. https://doi.org/10.1534/genetics.110.120493
  13. Bijma P. Multilevel selection 4: modeling the relationship of indirect genetic effects and group size. Genetics 2010;186:1029-31. https://doi.org/10.1534/genetics.110.120485
  14. Misztal I, Tsuruta S, Strabel T, et al. BLUPF90 and related programs (BGF90). The 7th World Congress on Genetics Applied to Livestock Production; 2002 Aug 19-23: Montpellier, France.
  15. 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
  16. 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
  17. Misztal I, Aguilar I, Legarra A, Lawlor T. Choice of parameters for single-step genomic evaluation for type. J Dairy Sci 2010;93(Suppl 1):533.
  18. Tsuruta S, Misztal I, Aguilar I, Lawlor TJ. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J Dairy Sci 2011;94:4198-204. https://doi.org/10.3168/jds.2011-4256
  19. Chen C, Misztal I, Aguilar I, et al. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. J Anim Sci 2011;89:23-8. https://doi.org/10.2527/jas.2010-3071
  20. Koivula M, Stranden I, Poso J, Aamand GP, Mantysaari EA. Single-step genomic evaluation using multitrait random regression model and test-day data. J Dairy Sci 2015;98:2775-84. https://doi.org/10.3168/jds.2014-8975
  21. Tsuruta S, Lourenco DAL, Misztal I. Bias in singlestep genomic evaluations attributable to unknown parent group estimates. J. Dairy Sci 2013;96(E-Suppl. 1):75 (Abstr.). https://doi.org/10.3168/jds.2012-5946
  22. Onogi A, Komatsu T, Shoji N, et al. Genomic prediction in Japanese Black cattle: application of a single-step approach to beef cattle. J Anim Sci 2014;92:1931-8. https://doi.org/10.2527/jas.2014-7168
  23. Harris BL, Winkelman A, Johnson D. Large-scale single-step genomic evaluation for milk production traits. Proceedings of the 2012 Interbull Meeting; 2012 May 28-31: Cork, Ireland. No. 46.
  24. Misztal I, Aggrey SE, Muir WM. Experiences with a singlestep genome evaluation. Poult Sci 2013;92:2530-4. https://doi.org/10.3382/ps.2012-02739
  25. Bergsma R, Mathur P, Kanis E, Verstegen MW, Knol EF, Van Arendonk JA. Genetic correlations between lactation performance and growing-finishing traits in pigs. J Anim Sci 2013;91:3601-11. https://doi.org/10.2527/jas.2012-6200
  26. Chen CY, Kachman SD, Johnson RK, Newman S, Van Vleck LD. Estimation of genetic parameters for average daily gain using models with competition effects. J Anim Sci 2008;86:2525-30. https://doi.org/10.2527/jas.2007-0660
  27. Putz A, Tiezzi F, Maltecca C, Gray KA, Knauer MT. A comparison of accuracy validation methods for genomic and pedigreebased predictions of swine litter size traits using Large White and simulated data. J Anim Breed Genet 2018;135:5-13. https://doi.org/10.1111/jbg.12302
  28. Martini JWR, Schrauf MF, Garcia-Baccino CA, et al. The effect of the H-1 scaling factors ${tau}$ and ${\omega}$ on the structure of H in the single-step procedure. Genet Sel Evol 2017;50:16. https://doi.org/10.1186/s12711-018-0386-x
  29. Wilson AJ, Morrissey M, Adams M, et al. Indirect genetics effects and evolutionary constraint: an analysis of social dominance in red deer, Cervus elaphus. J Evol Biol 2011;24:772-83. https://doi.org/10.1111/j.1420-9101.2010.02212.x
  30. Alemu SW, Bijma P, Moller SH, Janss L, Berg P. Indirect genetic effects contribute substantially to heritable variation in aggression-related traits in group-housed mink (Neovison vison). Genet Sel Evol 2014;46:30. https://doi.org/10.1186/1297-9686-46-30
  31. Moore AJ, Brodie ED, Wolf JB. Interacting phenotypes and the evolutionary process: I. direct and indirect genetic effects of social interactions. Evolution 1997;51:1352-62. https://doi.org/10.1111/j.1558-5646.1997.tb01458.x
  32. Sartori C, Mantovani R. Indirect genetic effects and the genetic bases of social dominance: evidence from cattle. Heredity 2012;110:3-9. https://doi.org/10.1038/hdy.2012.56
  33. Camerlink I, Turner SP, Bijma P, Bolhuis JE. Indirect genetic effects and housing conditions in relation to aggressive behaviour in pigs. PloS One 2013;8:e65136. https://doi.org/10.1371/journal.pone.0065136
  34. Reimert I, Rodenburg TB, Ursinus WW, et al. Backtest and novelty behavior of female and castrated male piglets, with diverging social breeding values for growth. J Anim Sci 2013;91:4589-97. https://doi.org/10.2527/jas.2013-6673
  35. Reimert I, Rodenburg TB, Ursinus WW, Kemp B, Bolhuis JE. Responses to novel situations of female and castrated male pigs with divergent social breeding values and different backtest classifications in barren and straw-enriched housing. Appl Anim Behav Sci 2014;151:24-35. https://doi.org/10.1016/j.applanim.2013.11.015
  36. Samore AB, Fontanesi L. Genomic selection in pigs: state of the art and perspectives. Italian J Anim Sci 2016;15:211-32. https://doi.org/10.1080/1828051X.2016.1172034
  37. D'Eath R, Conington J, Lawrence A, Olsson I, Sandoe P. Breeding for behavioural change in farm animals: practical, economic and ethical considerations. Anim Welf 2010;19:17-27.
  38. Rodenburg T, Bijma P, Ellen E, et al. Breeding amiable animals? Improving farm animal welfare by including social effects in breeding programmes. Anim Welf 2010;19 (Suppl 1):77-82.
  39. Camerlink I, Ursinus WW, Bijma P, Kemp B, Bolhuis JE. Indirect genetic effects for growth rate in domestic pigs alter aggressive and manipulative biting behaviour. Behav Genet 2015;45:117-26. https://doi.org/10.1007/s10519-014-9671-9
  40. Hong JK, Jeong YD, Cho ES, et al. A genome-wide association study of social genetic effects in landrace pigs. Asian-Australas J Anim Sci 2018;31:784-90. https://doi.org/10.5713/ajas.17.0440
  41. Hong JK, Kim KH, Hwang HS, Lee JK, Eom TK, Rhim SJ. Behaviors and body weight of suckling piglets in different social environments. Asian-Australas J Anim Sci 2017;30:902-6. https://doi.org/10.5713/ajas.16.0653

피인용 문헌

  1. Single-step genome-wide association study for social genetic effects and direct genetic effects on growth in Landrace pigs vol.10, 2019, https://doi.org/10.1038/s41598-020-71647-x