DOI QR코드

DOI QR Code

Genetic parameters for marbling and body score in Anglonubian goats using Bayesian inference via threshold and linear models

  • Figueiredo Filho, Luiz Antonio Silva (Federal Institute of Education, Science and Tecnology of Maranhao (IFMA)) ;
  • Sarmento, Jose Lindenberg Rocha (Federal University of Piaui (UFPI)) ;
  • Campelo, Jose Elivalto Guimaraes (Federal University of Piaui (UFPI)) ;
  • de Oliveira Almeida, Marcos Jacob (Brazilian Agricultural Research Corporation (Embrapa Meio Norte)) ;
  • de Sousa, Antonio Junior (Technical School of Teresina (CTT)) ;
  • da Silva Santos, Natanael Pereira (Federal University of Piau? (UFPI)) ;
  • da Silva Costa, Marcio (Federal University of Piau? (UFPI)) ;
  • Torres, Tatiana Saraiva (Federal University of Piaui (UFPI)) ;
  • Sena, Luciano Silva (Federal University of Piaui (UFPI))
  • Received : 2017.06.28
  • Accepted : 2018.02.08
  • Published : 2018.09.01

Abstract

Objective: The aim of this study was to estimate (co) variance components and genetic parameters for categorical carcass traits using Bayesian inference via mixed linear and threshold animal models in Anglonubian goats. Methods: Data were obtained from Anglonubian goats reared in the Brazilian Mid-North region. The traits in study were body condition score, marbling in the rib eye, ribeye area, fat thickness of the sternum, hip height, leg perimeter, and body weight. The numerator relationship matrix contained information from 793 animals. The single- and two-trait analyses were performed to estimate (co) variance components and genetic parameters via linear and threshold animal models. For estimation of genetic parameters, chains with 2 and 4 million cycles were tested. An 1,000,000-cycle initial burn-in was considered with values taken every 250 cycles, in a total of 4,000 samples. Convergence was monitored by Geweke criteria and Monte Carlo error chain. Results: Threshold model best fits categorical data since it is more efficient to detect genetic variability. In two-trait analysis the contribution of the increase in information and the correlations between traits contributed to increase the estimated values for (co) variance components and heritability, in comparison to single-trait analysis. Heritability estimates for the study traits were from low to moderate magnitude. Conclusion: Direct selection of the continuous distribution of traits such as thickness sternal fat and hip height allows obtaining the indirect selection for marbling of ribeye.

Keywords

Bayesian Inference;Carcass;Components of (co) Variance;Gibbs Sampling;Heritability

Acknowledgement

Supported by : Federal Institute of Education, Science and Tecnology of Maranhao, Federal University of Piaui

References

  1. Faria CU, Magnabosco CU, Albuquerque LG, et al. Genetic analysis for visual scores of bovines with the linear and threshold bayesian models. Pesq Agropec Bras 2008;43:835-41. https://doi.org/10.1590/S0100-204X2008000700007
  2. Gianola D, Foulley JL. Sire evaluation for ordered categorical data with a threshold model. Gen Sel Evol 1983;15:201-24. https://doi.org/10.1186/1297-9686-15-2-201
  3. Luo MF, Boettcher PJ, Schaeffer LR, Dekkers JCM. Estimation of genetic parameters of calving ease in first and second parities of Canadian Holsteins using Bayesian methods. Livest Prod Sci 2002;74:175-84. https://doi.org/10.1016/S0301-6226(01)00294-9
  4. Misztal I. Programs for analysis of mixed linear and threshold models with support for REML- type variance component estimation and maternal grandsire model. CMMAT2. Athens, AL, USA: University of Georgia; 1989.
  5. Sorensen DA, Andersen S, Gianola D, Korsgaard I. Bayesian inference in threshold models using Gibbs sampling. Genet Sel Evol 1995;27:229-49. https://doi.org/10.1186/1297-9686-27-3-229
  6. Van Tassell CP, Van Vleck LD, Gregory KE. Bayesian analysis of twinning and ovulation rates using a multiple-trait threshold model and Gibbs sampling. J Anim Sci 1998;76:2048-61. https://doi.org/10.2527/1998.7682048x
  7. Ribeiro SDA. Rational breeding of goats, Sao Paulo, Brazil: Nobel; 1998.
  8. United States Department of Agriculture (USDA): Standards for grades of Slaughter cattle and standards for grades of carcass beef. Agricultural Marketing Services, USDA. Washington, DC, USA: Government Printing Office; 1997.
  9. Figueiredo Filho LAS, Sarmento JLR, Campelo JEG, Santos NPS, Sousa Junior A. Measures Carcass traits by ultrasound in goats. Rev Bras Saude Prod Anim 2012;13:804-14. https://doi.org/10.1590/S1519-99402012000300018
  10. Misztal J. Fortran programs [Internet]. Athens, GA, USA: University of Georgia; 2002 [cited 2015 Jan 15]. Available from: http://nce.ads.uga.edu/wiki/doku.php?id=readme.gibbs3
  11. Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian Statistics. Oxford, UK: Oxford University Press; 1992.
  12. Sorensen DA, Gianola D. Likelihood, Bayesian and MCMC methods in quantitative genetics: statistics for biology and health, New York, USA: Springer-Verlag; 2002.
  13. Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A. Bayesian measures of model complexity and fit. J R Statist Soc B 2002; 64:583-616. https://doi.org/10.1111/1467-9868.00353
  14. Kass RE, Raftery AE. Bayes factors. J Am Statist Assoc 1995; 90:773-95. https://doi.org/10.1080/01621459.1995.10476572
  15. Dempster AP. The direct use of likelihood for significance testing. Statist Comput 1997;7:247-52. https://doi.org/10.1023/A:1018598421607
  16. Van Tassell CP, Van Vleck LD. Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance component inference. J Anim Sci 1996;74:2586-97. https://doi.org/10.2527/1996.74112586x
  17. Figueiredo Filho LA, Do OA, Sarmento JLR, Santos NPS, Torres TS. Genetic parameters for carcass traits and body size in sheep for meat production. Trop Anim Health Prod 2015;48:215-8.
  18. Brito EA, Sousa WH, Ramos JPF, Oliveira Junior S. Qualitative features of the carcass of three genetic groups of goats and sheep finished in confinement. Rev Tecnol Cien Agropec 2009; 3:47-52.
  19. Hammond J. Farm animals: their breeding, growth, and inheritance. London, UK: Edward Arnold & Co.; 1965.
  20. Suguisawa L, Vargas Junior FM, Marques ACW, et al. Carcass characteristics and meat quality by ultrasonography in confined lamps. In: 10th Brazilian Meeting of Animal Science (ZOOTEC); 2008 May 27-29, Joao Pessoa, PB, Brazil.
  21. Bonacina MS, Osorio MTM, Osorio JCS, Correa GF, Hashimoto JH. The influence of sex and finishing system on carcass and meat quality of Texel $\times$ Corriedale lambs. R Bras Zootec 2011; 40:1242-49. https://doi.org/10.1590/S1516-35982011000600012
  22. Santos NPS, Sarmento JLR, Pimenta Filho EC, et al. Environmental and genetic aspects of litter size in goats using linear and threshold bayesian models. Arq Bras Med Vet Zootec 2013;65:885-93. https://doi.org/10.1590/S0102-09352013000300038
  23. Fernandes PB, Santana LA, Ferreira FR, et al. Use of linear and threshold models for the estimation of variance components for pregnant probability from Murrah buffaloes. In: 24th Brazilian Meeting of Animal Science (ZOOTEC); 2014 May 12-14, Vitoria, ES, Brazil.
  24. Faria CU, Andrade WBF, Pereira CF, Silva RP, Lobo RB. Bayesian analysis for carcass traits in Polled Nelore. Cienc Rural 2015;45:317-22. https://doi.org/10.1590/0103-8478cr20140331
  25. Rosa GT, Pires CC, Silva JHS, Motta OS. Muscle, fat and bone allometric growth in Texel lambs carcasses cuts in relation to the feeding methods and slaughter weight. Cienc Rural 2005; 35:870-6. https://doi.org/10.1590/S0103-84782005000400019
  26. Koury Filho W, Albuquerque LG, Forni S, et al. Genetic parameters estimates for visual scores and their association with body weight in beef cattle. R Bras Zootec 2010;39:1015-22. https://doi.org/10.1590/S1516-35982010000500011
  27. Falconer DS. Introduction to quantitative genetics. Vicosa, MG, Brazil: Editora da Universidade Federal de Vicosa; 1987.
  28. Sainz RD, Araujo FRC. Types of cattle of bovine and swine. In: 1st Brazilian Meeting of Meet Science and Technology; 2001 Oct 22-25; Sao Pedro, SP, Brazil.