DOI QR코드

DOI QR Code

Genotype by environment interaction for somatic cell score in Holstein cattle of southern Brazil via reaction norms

  • 투고 : 2020.01.15
  • 심사 : 2020.04.20
  • 발행 : 2021.04.01

초록

Objective: The objective of this study was to evaluate the genetic behavior of a population of Holstein cattle in response to the variation of environmental temperature by analyzing the effects of genotype by environment interaction (GEI) through reaction norms for the somatic cell score (SCS). Methods: Data was collected for 67,206 primiparous cows from the database of the Paraná Holstein Breeders Association in Brazil, with the aim of evaluating the temperature effect, considered as an environmental variable, distinguished under six gradients, with the variation range found being 17℃ to 19.5℃, over the region. A reaction norm model was adopted utilizing the fourth order under the Legendre polynomials, using the mixed models of analysis by the restricted maximum likelihood method by the WOMBAT software. Additionally, the genetic behavior of the 15 most representative bulls was assessed, in response to the changes in the temperature gradient. Results: A mean score of 2.66 and a heritability variation from 0.17 to 0.23 was found in the regional temperature increase. The correlation between the environmental gradients proved to be higher than 0.80. Distinctive genetic behaviors were observed according to the increase in regional temperature, with an observed increase of up to 0.258 in the breeding values of some animals, as well as a reduction in the breeding of up to 0.793, with occasional reclassifications being observed as the temperature increased. Conclusion: Non-relevant GEI for SCS were observed in Holstein cattle herds of southern Brazil. Thus, the inclusion of the temperature effect in the model of genetic evaluation of SCS for the southern Brazilian Holstein breed is not required.

키워드

참고문헌

  1. Zavadilova L, Stipkova M, Svitakova A, Krupova Z, Kasna E. Genetic parameters for clinical mastitis, fertility and somatic cell score in czech holstein cattle. Ann Anim Sci 2017;17:1007-18. https://doi.org/10.1515/aoas-2017-0006
  2. Taubert H, Rensing S, Stock KF, Reinhardt F. Development of a breeding value for mastitis based on SCS-results. Interbull Bull 2013:161-5.
  3. Bondan C, Folchini JA, Noro M, Quadros DL, Machado KM, Gonzalez FHD. Milk composition of Holstein cows: a retrospective study. Cienc Rural 2018;48:e20180123. https://doi.org/10.1590/0103-8478cr20180123
  4. Weigel KA, Shook GE. Genetic selection for mastitis resistance. Vet Clin North Am Food Anim Pract 2018;34:457-72. https://doi.org/10.1016/j.cvfa.2018.07.001
  5. Duran Aguilar M, Roman Ponce SI, Ruiz Lopez FJ, et al. Genome-wide association study for milk somatic cell score in holstein cattle using copy number variation as markers. J Anim Breed Genet 2017;134:49-59. https://doi.org/10.1111/jbg.12238
  6. Govignon-Gion A, Dassonneville R, Baloche G, Ducrocq V. Multiple trait genetic evaluation of clinical mastitis in three dairy cattle breeds. Animal 2016;10:558-65. https://doi.org/10.1017/S1751731115002529
  7. Streit M, Reinhardt F, Thaller G, Bennewitz J. Reaction norms and genotype-by-environment interaction in the German Holstein dairy cattle. J Anim Breed Genet 2012;129:380-9. https://doi.org/10.1111/j.1439-0388.2012.00999.x
  8. Falconer DS. Introduction to quantitative genetics. 3rd ed. Harlow, UK: Longman Scientific & Technical; 1989.
  9. Tiezzi F, de los Campos G, Parker Gaddis KL, Maltecca C. Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle. J Dairy Sci 2017;100:2042-56. https://doi.org/10.3168/jds.2016-11543
  10. Dingemanse NJ, Wolf M. Between-individual differences in behavioural plasticity within populations: causes and consequences. Anim Behav 2013;85:1031-9. https://doi.org/10.1016/j.anbehav.2012.12.032
  11. Alvares CA, Stape JL, Sentelhas PC, de Moraes Goncalves JL, Sparovek G. Koppen's climate classification map for Brazil. Meteorol Z 2013;22:711-28. https://doi.org/10.1127/0941-2948/2013/0507
  12. SAS Institute Inc. SAS 9.1.3 Help and documentation. Cary, NC, USA: SAS Institute Inc; 2013.
  13. Schaeffer LR. Application of random regression models in animal breeding. Livest Prod Sci 2004;86:35-45. https://doi.org/10.1016/S0301-6226(03)00151-9
  14. Meyer K. WOMBAT-a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J Zhejiang Univ Sci B 2007;8:815-21. https://doi.org/10.1631/jzus.2007.B0815
  15. Cardoso FF, Tempelman RJ. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction. J Anim Sci 2012;90:2130-41. https://doi.org/10.2527/jas.2011-4333
  16. MdAPeA. Instrucao normativa Nº 31, de 29 de junho de 2018.
  17. United States Department of Agriculture. Determining U.S. milk quality using bulk-tank somatic cell counts, 2018. Fort Collins, CO, USA: USDA; 2018.
  18. de Paula MC, Martins EN, da Silva LOC, de Oliveira CAL, Valotto AA, Ribas NP. Interacao genotipo × ambiente para producao de leite de bovinos da raca Holandesa entre bacias leiteiras no estado do Parana. Rev Bras Zootec 2009;38:467-73. https://doi.org/10.1590/S1516-35982009000300010
  19. Haiduck Padilha A, Alfonzo EPM, Daltro DS, Torres HAL, Braccini Neto J, Cobuci JA. Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model. Anim Prod Sci 2019;59:207-15. https://doi.org/10.1071/AN16835
  20. Alam M, Cho CI, Choi TJ, et al. Estimation of genetic parameters for somatic cell scores of Holsteins using multi-trait lactation models in Korea. Asian-Australas J Anim Sci 2015;28:303-10. https://doi.org/10.5713/ajas.13.0627
  21. Kheirabadi K, Razmkabir M. Genetic parameters for daily milk somatic cell score and relationships with yield traits of primiparous Holstein cattle in Iran. J Anim Sci Technol 2016;58:38. https://doi.org/10.1186/s40781-016-0121-5
  22. Bohlouli M, Alijani S, Naderi S, Yin T, Konig S. Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions. J Dairy Sci 2019;102:488-502. https://doi.org/10.3168/jds.2018-15329
  23. Meyer K. Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet Sel Evol 2005;37:473. https://doi.org/10.1186/1297-9686-37-6-473
  24. Pegolo NT, Albuquerque LG, Lobo RB, de Oliveira HN. Effects of sex and age on genotype × environment interaction for beef cattle body weight studied using reaction norm models1. J Anim Sci 2011;89:3410-25. https://doi.org/10.2527/jas.2010-3520
  25. Carvalheiro R, Costilla R, Neves HHR, Albuquerque LG, Moore S, Hayes BJ. Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions. Genet Sel Evol 2019;51:29. https://doi.org/10.1186/s12711-019-0470-x
  26. Morrissey MB, Liefting M. Variation in reaction norms: statistical considerations and biological interpretation. Evolution 2016;70:1944-59. https://doi.org/10.1111/evo.13003
  27. Robertson A. Experimental design on the measurement of heritabilities and genetic correlations: biometrical genetics. New York, USA: Pergamon; 1959. pp. 219-26.
  28. Kolmodin R, Strandberg E, Danell B, Jorjani H. Reaction norms for protein yield and days open in Swedish red and white dairy cattle in relation to various environmental variables. Acta Agric Scand A Anim Sci 2004;54:139-51. https://doi.org/10.1080/09064700410032040
  29. van der Veen AA, ten Napel J, Oosting SJ, Bontsema J, van der Zijpp AJ, Groot Koerkamp PWG. Robust performance: principles and potential applications in livestock production systems. In: Proceedings of the Joint International Agricultural Conference 2009; 2009 Jul 6-8: Netherlands. pp.173-80.
  30. Bohlouli M, Shodja J, Alijani S, Pirany N. Interaction between genotype and geographical region for milk production traits of Iranian Holstein dairy cattle. Livest Sci 2014;169:1-9. https://doi.org/10.1016/j.livsci.2014.08.010
  31. Aubin-Horth N, Renn SCP. Genomic reaction norms: using integrative biology to understand molecular mechanisms of phenotypic plasticity. Mol Ecol 2009;18:3763-80. https://doi.org/10.1111/j.1365-294X.2009.04313.x