Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Development of an index that decreases birth weight, promotes postnatal growth and yet minimizes selection intensity in beef cattle

  • Received : 2023.09.07
  • Accepted : 2023.11.06
  • Published : 2024.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: The main goal of our current study was to improve the growth curve of meat animals by decreasing the birth weight while achieving a finishing weight that is the same as that before selection but at younger age. Methods: Random regression model was developed to derive various selection indices to achieve desired gains in body weight at target time points throughout the fattening process. We considered absolute and proportional gains at specific ages (in weeks) and for various stages (i.e., early, middle, late) during the fattening process. Results: The point gain index was particularly easy to use because breeders can assign a specific age (in weeks) as a time point and model either the actual weight gain desired or a scaled percentage gain in body weight. Conclusion: The point gain index we developed can achieve the desired weight gain at any given postnatal week of the growing process and is an easy-to-use and practical option for improving the growth curve.

Keywords

Acknowledgement

Togashi thanks the staff members of LIAJ for their generous support; Drs. J.E.O. Rege and H.A. Fitzhugh, Jr., for support and advice while Togashi was in Addis Ababa; Dr. K. Hammond for support and advice while Togashi was in Armidale; and Dr. C. Y. Lin for his enthusiastic support while Lin was in Sapporo.

References

  1. Gregory KE. Symposium on performance testing in beef cattle: Evaluating postweaning performance in beef cattle. J Anim Sci 1965;24:248-58. https://doi.org/10.2527/jas1965.241248x 
  2. Cartwright TC. Selection criteria for beef cattle for the future. J Anim Sci 1970;30:706-11. https://doi.org/10.2527/jas1970.305706x 
  3. Dickerson GE, Kunzi N, Cundiff LV, Koch RM, Arthaud VH, Gregory KE. Selection criteria for efficient beef production. J Anim Sci 1974;39:659-73. https://doi.org/10.2527/jas1974.394659x 
  4. Fitzhugh HA. Analysis of growth curves and strategies for altering their shape. J Anim Sci 1974;42:1036-51. https://doi.org/10.2527/jas1976.4241036x 
  5. Foulley JL. Some considerations on selection criteria and optimization for terminal sire breeds. Genet Sel Evol 1976; 8:89. https://doi.org/10.1186/1297-9686-8-1-89 
  6. Schaeffer LR, Dekkers JCM. Random regressions in animal models for test-day production in dairy cattle. 5th WCGALP; 1994 August 11; Guelph, Canada. p.443-46. 
  7. Jamrozik J, Schaeffer LR, Dekkers JCM. Genetic evaluation of dairy cattle using test day yields and random regression model. J Dairy Sci 1997;80:1217-26. https://doi.org/10.3168/jds.S0022-0302(97)76050-8 
  8. Togashi K, Lin CY. Modifying the lactation curve to improve lactation milk and persistency. J Dairy Sci 2003;86:1487-93. https://doi.org/10.3168/jds.S0022-0302(03)73734-5 
  9. Togashi K, Lin CY. Development of an optimal index to improve lactation yield and persistency with the least selection intensity. J Dairy Sci 2004;87:3047-52. https://doi.org/10.3168/jds.S0022-0302(04)73437-2 
  10. Boligon AA, Mercadante MEZ, Forni S, Lobo RB, Albuquerque LG. Covariance functions for body weight from birth to maturity in Nellore cows. J Anim Sci 2010;88:849-59. https://doi.org/10.2527/jas.2008-1511 
  11. Meyer K. Scope for a random regression model in genetic evaluation of beef cattle for growth. Livest Prod Sci 2004;86:69-83. https://doi.org/10.1016/S0301-6226(03)00142-8 
  12. Meyer K. Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet Sel Evol 2005;37;473-500. https://doi.org/10.1186/1297-9686-37-6-473 
  13. Mota LFM, Martins PGMA, Littiere TO, Abreu LRA, Silva MA, Bonafe CM. Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials. Animal 2018;12:667-74. https://doi.org/10.1017/S1751731117001951 
  14. Pribyl J, Krejcova1 H, Pribylova J, Misztal I, Bohmanova J, Stipkova M. Trajectory of body weight of performance tested dual-purpose bulls. Czech J Anim Sci 2007;52:315-24. https://doi.org/10.17221/2340-CJAS 
  15. Togashi K, Adachi K, Kurogi K, et al. Predicting the rate of inbreeding in populations undergoing four-path selection on genomically enhanced breeding values. Anim Biosci 2022;35:804-13. https://doi.org/10.5713/ab.21.0350 
  16. Togashi K, Lin CY. Selection for milk production and persistency using eigenvectors of the random regression coefficient matrix. J Dairy Sci 2006;89:4866-73. https://doi.org/10.3168/jds.S0022-0302(06)72535-8 
  17. 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 
  18. 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 
  19. Christensen OF. Compatibility of pedigree-based and markerbased relationship matrices for single-step genetic evaluation. Genet Sel Evol 2012:44:37-46. https://doi.org/10.1186/1297-9686-44-37 
  20. Henderson CR. Applications of linear models in animal breeding. Guelph, ON, Canada: University of Guelph; 1984. 
  21. Dekkers JCM. Asymptotic response to selection on best linear unbiased predictors of breeding values. Anim Prod 1992;54:351-60. https://doi.org/10.1017/S0003356100020808 
  22. Togashi K, Kurogi K, Adachi K, et al. Asymptotic response to four-path selection due to index and single trait selection according to genomically enhanced breeding values. Livest Sci 2020;231:103846. https://doi.org/10.1016/j.livsci.2019.103846 
  23. Searle SR. Matrix Algebra for the Biological Science. 1st ed. Hoboken, NJ, USA: John Wiley & Sons; 1966. 
  24. Takeda M, Uemoto Y, Inoue K, et al. Evaluation of feed efficiency traits for genetic improvement in Japanese black cattle. J Anim Sci 2018;96:797-805. https://doi.org/10.1093/jas/skx054 
  25. Onogi A, Ogino A, Sato A, Kurogi K, Yasumori T, Togashi K. Development of a structural growth curve model that considers the causal effect of initial phenotypes. Genet Sel Evol 2019;51:19-27. https://doi.org/10.1186/s12711-019-0461-y 
  26. Teixeira BB, Mota RR, Lobo RB, et al. Genetic evaluation of growth traits in nellore cattle through multi-trait and random regression models. Czech J Anim Sci 2018;63:212-21. https://doi.org/10.17221/21/2017-CJAS 
  27. Ferreira JL, Lopes FB, Pereira LS, et al. Estimation of (co) variances for growth traits in Nellore cattle raised in the Humid Tropics of Brazil by random regression. Cienc Agrar 2015;36:1713-23. https://doi.org/10.5433/1679-0359.2015v36n3p1713 
  28. National Livestock Breeding Center. The trend of genetic ability in Japanese Black cattle. Nishigo, Fukushima, Japan: National Livestock Breeding Center; c2022 [cited 2023 June 20]. Available from: https://www.nlbc.go.jp/kachikukairyo/iden/ 
  29. Harris, DL. Expected and predicted progress from index selection involving estimates of population parameters. Biometrics 1964;20:46-72. https://doi.org/10.2307/2527617 
  30. Heidhues T. Relative accuracy of selection indices based on estimated genotypic and phenotypic parameters. Biometrics 1961;17:502-3. 
  31. Misztal I. Estimation of heritabilities and genetic correlations by time slices using predictivity in large genomic models. bioRxiv 2023. https://doi.org/10.1101/2023.06.28.546953 
  32. James JW. Index selection with restrictions. Biometrics 1968;24:1015-8. https://doi.org/10.2307/2528888 
  33. Kempthorne O, Nordskog AW. Restricted selection indices. Biometrics 1959;15:10-9. https://doi.org/10.2307/2527598 
  34. Ma J, Chen J, Gan M, et al. Gut microbiota composition and diversity in different commercial swine breeds in early and finishing growth stages. Animals (Basel) 2022;12:1607. https://doi.org/10.3390/ani12131607 
  35. Koivula M, Sevon-Aimonen ML, Stranden I, et al. Genetic (co)variances and breeding value estimation of Gompertz growth curve parameters in Finnish Yorkshire boars, gilts and barrows. J Anim Breed Genet 2008;125:168-75. https://doi.org/10.1111/j.1439-0388.2008.00726.x