Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Genetic relationship between purebred and synthetic pigs for growth performance using single step method

  • Hong, Joon Ki (National Institute of Animal Science, Rural Development Administration) ;
  • Cho, Kyu Ho (National Institute of Animal Science, Rural Development Administration) ;
  • Kim, Young Sin (National Institute of Animal Science, Rural Development Administration) ;
  • Chung, Hak Jae (National Institute of Animal Science, Rural Development Administration) ;
  • Baek, Sun Young (National Institute of Animal Science, Rural Development Administration) ;
  • Cho, Eun Seok (National Institute of Animal Science, Rural Development Administration) ;
  • Sa, Soo Jin (National Institute of Animal Science, Rural Development Administration)
  • Received : 2020.04.24
  • Accepted : 2020.08.21
  • Published : 2021.06.01
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Abstract

Objective: The objective of this study was to estimate the genetic correlation (rpc) of growth performance between purebred (Duroc and Korean native) and synthetic (WooriHeukDon) pigs using a single-step method. Methods: Phenotypes of 15,902 pigs with genotyped data from 1,792 pigs from a nucleus farm were used for this study. We estimated the rpc of several performance traits between WooriHeukDon and purebred pigs: day of target weight (DAY), backfat thickness (BF), feed conversion rate (FCR), and residual feed intake (RFI). The variances and covariances of the studied traits were estimated by an animal multi-trait model that applied the Bayesian inference. Results: rpc within traits was lower than 0.1 for DAY and BF, but high for FCR and RFI; in particular, rpc for RFI between Duroc and WooriHeukDon pigs was nearly 1. Comparison between different traits revealed that RFI in Duroc pigs was associated with different traits in WooriHeukDon pigs. However, the most of rpc between different traits were estimated with low or with high standard deviation. Conclusion: The results indicated that there were substantial differences in rpc of traits in the synthetic WooriHeukDon pigs, which could be caused by these pigs having a more complex origin than other crossbred pigs. RFI was strongly correlated between Duroc and WooriHeukDon pigs, and these breeds might have similar single nucleotide polymorphism effects that control RFI. RFI is more essential for metabolism than other growth traits and these metabolic characteristics in purebred pigs, such as nutrient utilization, could significantly affect those in synthetic pigs. The findings of this study can be used to elucidate the genetic architecture of crossbred pigs and help develop new breeds with target traits.

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References

  1. Wientjes YCJ, Calus MPL. Board invited review: the purebred-crossbred correlation in pigs: a review of theory, estimates, and implications. J Anim Sci 2017;95:3467-78. https://doi.org/10.2527/jas.2017.1669
  2. Juarez M, Clemente I, Polvillo O, Molina A. Meat quality of tenderloin from Iberian pigs as affected by breed strain and crossbreeding. Meat Sci 2009;81:573-9. https://doi.org/10.1016/j.meatsci.2008.10.016
  3. Wei M, van der Werf JHJ. Maximizing genetic response in crossbreds using both purebred and crossbred information. Anim Sci 1994;59:401-13. https://doi.org/10.1017/S0003356100007923
  4. Bijma P, van Arendonk JAM. Maximizing genetic gain for the sire line of a crossbreeding scheme utilizing both purebred and crossbred information. Anim Sci 1998;66:529-42. https://doi.org/10.1017/S135772980000970X
  5. Tusell L, Gilbert H, Riquet J, Mercat MJ, Legarra A, Larzul C. Pedigree and genomic evaluation of pigs using a terminal-cross model. Genet Sel Evol 2016;48:32. https://doi.org/10.1186/s12711-016-0211-3
  6. Habier D, Gotz KU, Dempfle L. Estimation of genetic parameters on test stations using purebred and crossbred progeny of sires of the Bavarian Pietrain. Livest Sci 2007;107:142-51. https://doi.org/10.1016/j.livsci.2006.09.012
  7. Edea Z, Hong JK, Jung JH, et al. Detecting selection signatures between Duroc and Duroc synthetic pig populations using high-density SNP chip. Anim Genet 2017;48:473-7. https://doi.org/10.1111/age.12559
  8. Gilbert H, Billon Y, Brossard L, et al. Review: divergent selection for residual feed intake in the growing pig. Animal 2017;11:1427-39. https://doi.org/10.1017/S175173111600286X
  9. Harris AJ, Patience JF, Lonergan SM, Dekkers CJM, Gabler NK. Improved nutrient digestibility and retention partially explains feed efficiency gains in pigs selected for low residual feed intake. J Anim Sci 2012;90(Suppl 4):164-6. https://doi.org/10.2527/jas.53855
  10. Sargolzaei M, Iwaisaki H, Colleau JJ. CFC: a tool for monitoring genetic diversity. In: 8th World Congress on Genetics Applied to Livestock Production; 2006 Aug 13-18: Belo Horizonte, MG, Brasil.
  11. Choi JG, Cho CI, Choi IS, et al. Genetic parameter estimation in seedstock Swine population for growth performances. Asian-Australas J Anim Sci 2013;26:470-5. https://doi.org/10.5713/ajas.2012.12454
  12. Cai W, Casey DS, Dekkers JCM. Selection response and genetic parameters for residual feed intake in Yorkshire swine. J Anim Sci 2008;86:287-98. https://doi.org/10.2527/jas.2007-0396
  13. Ramos AM, Crooijmans RPMA, 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
  14. Wiggans GR, Sonstegard TS, VanRaden PM, et al. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. J Dairy Sci 2009;92:3431-6. https://doi.org/10.3168/jds.2008-1758
  15. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th world congress on genetics applied to livestock production. 2002. pp. 743-4.
  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. 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
  18. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980
  19. Choy YH, Mahboob A, Cho CI, et al. Genetic parameters of pre-adjusted body weight growth and ultrasound measures of body tissue development in three seedstock pig breed populations in Korea. Asian-Australas J Anim Sci 2015;28:1696-702. https://doi.org/10.5713/ajas.14.0971
  20. Noguera JL, Varona L, Babot D, Estany J. Multivariate analysis of litter size for multiple parities with production traits in pigs: I. Bayesian variance component estimation. J Anim Sci 2002;80:2540-7. https://doi.org/10.1093/ansci/80.10.2540
  21. Arango J, Misztal I, Tsuruta S, Culbertson M, Herring W. Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows. J Anim Sci 2005;83:499-506. https://doi.org/10.2527/2005.833499x
  22. Chen P, Baas TJ, Mabry JW, Dekkers JCM, Koehler KJ. Genetic parameters and trends for lean growth rate and its components in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs. J Anim Sci 2002;80:2062-70. https://doi.org/10.1093/ansci/80.8.2062
  23. Suzuki K, Ishida M, Kadowaki H, Shibata T, Uchida H, Nishida A. Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs. J Anim Sci 2006;84:2026-34. https://doi.org/10.2527/jas.2005-660
  24. Hicks C, Satoh M, Ishii K, Kuroki S, Fujiwara T, Furukawa T. Effect of sex on estimates of genetic parameters for daily gain and ultrasonic backfat thickness in swine. Asian-Australas J Anim Sci 1999;12:677-81. https://doi.org/10.5713/ajas.1999.677
  25. Hoque MA, Kadowaki H, Shibata T, Oikawa T, Suzuki K. Genetic parameters for measures of residual feed intake and growth traits in seven generations of Duroc pigs. Livest Sci 2009;121:45-9. https://doi.org/10.1016/j.livsci.2008.05.016
  26. Mrode RA, Kennedy BW. Genetic variation in measures of food efficiency in pigs and their genetic relationships with growth rate and backfat. Anim Sci 1993;56:225-32. https://doi.org/10.1017/S0003356100021309
  27. Kadarmideen HN, Schworer D, Ilahi H, Malek M, Hofer A. Genetics of osteochondral disease and its relationship with meat quality and quantity, growth, and feed conversion traits in pigs. J Anim Sci 2004;82:3118-27. https://doi.org/10.2527/2004.82113118x
  28. Do DN, Strathe AB, Jensen J, Mark T, Kadarmideen HN. Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds. J Anim Sci 2013;91:4069-79. https://doi.org/10.2527/jas.2012-6197
  29. Li X, Kennedy BW. Genetic parameters for growth rate and backfat in Canadian Yorkshire, Landrace, Duroc, and Hampshire pigs. J Anim Sci 1994;72:1450-4. https://doi.org/10.2527/1994.7261450x
  30. Saintilan R, Merour I, Brossard L, et al. Genetics of residual feed intake in growing pigs: relationships with production traits, and nitrogen and phosphorus excretion traits. J Anim Sci 2013;91:2542-54. https://doi.org/10.2527/jas.2012-5687
  31. Gunsett FC. Linear index selection to improve traits defined as ratios. J Anim Sci 1984;59:1185-93. https://doi.org/10.2527/jas1984.5951185x
  32. Saintilan R, Merour I, Schwob S, Sellier P, Bidanel J, Gilbert H. Genetic parameters and halothane genotype effect for residual feed intake in Pietrain growing pigs. Livest Sci 2011;142:203-9. https://doi.org/10.1016/j.livsci.2011.07.013
  33. Boddicker N, Gabler NK, Spurlock ME, Nettleton D, Dekkers JCM. Effects of ad libitum and restricted feed intake on growth performance and body composition of Yorkshire pigs selected for reduced residual feed intake. J Anim Sci 2011;89:40-51. https://doi.org/10.2527/jas.2010-3106
  34. Kennedy BW, van der Werf JH, Meuwissen TH. Genetic and statistical properties of residual feed intake. J Anim Sci 1993;71:3239-50. https://doi.org/10.2527/1993.71123239x
  35. Godinho RM, Bergsma R, Silva FF, et al. Genetic correlations between feed efficiency traits, and growth performance and carcass traits in purebred and crossbred pigs. J Anim Sci 2018;96:817-29. https://doi.org/10.1093/jas/skx011
  36. Robertson A. The sampling variance of the genetic correlation coefficient. Biometrics 1959;15:469-85. https://doi.org/10.2307/2527750
  37. Sevillano CA, ten Napel J, Guimaraes SEF, Silva FF, Calus MPL. Effects of alleles in crossbred pigs estimated for genomic prediction depend on their breed-of-origin. BMC Genomics 2018;19:740. https://doi.org/10.1186/s12864-018-5126-7
  38. Nakavisut S, Crump R, Suarez M, Graser HU. Genetic correlations between the performance of purebred and crossbred pigs. In: Proceedings of the Association for the Advancement of Animal Breeding and Genetics. 2005. pp. 99-102.
  39. Godinho RM, Bergsma R, Silva FF, et al. Genetic correlations between feed efficiency traits, and growth performance and carcass traits in purebred and crossbred pigs. J Anim Sci 2018;96:817-29. https://doi.org/10.1093/jas/skx011
  40. Grubbs JK, Fritchen AN, Huff-Lonergan E, Dekkers JCM, Gabler NK, Lonergan SM. Divergent genetic selection for residual feed intake impacts mitochondria reactive oxygen species production in pigs. J Anim Sci 2013;91:2133-40. https://doi.org/10.2527/jas.2012-5894
  41. Colpoys JD, Abell CE, Young JM, et al. Effects of genetic selection for residual feed intake on behavioral reactivity of castrated male pigs to novel stimuli tests. Appl Anim Behav Sci 2014;159:34-40. https://doi.org/10.1016/j.applanim.2014.06.013
  42. Dunkelberger JR, Boddicker NJ, Serao NVL, Young JM, Rowland RRR, Dekkers JCM. Response of pigs divergently selected for residual feed intake to experimental infection with the PRRS virus. Livest Sci 2015;177:132-41. https://doi.org/10.1016/j.livsci.2015.04.014