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Non-linear recursive models for growth traits in the Pirenaica beef cattle breed

Published online by Cambridge University Press:  27 March 2014

A. González-Rodríguez*
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal. Facultad de Veterinaria, Universidad de Zaragoza, C. Miguel Servet, 177, 50013 Zaragoza, Spain
E. F. Mouresan
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal. Facultad de Veterinaria, Universidad de Zaragoza, C. Miguel Servet, 177, 50013 Zaragoza, Spain
J. Altarriba
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal. Facultad de Veterinaria, Universidad de Zaragoza, C. Miguel Servet, 177, 50013 Zaragoza, Spain
C. Moreno
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal. Facultad de Veterinaria, Universidad de Zaragoza, C. Miguel Servet, 177, 50013 Zaragoza, Spain
L. Varona
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal. Facultad de Veterinaria, Universidad de Zaragoza, C. Miguel Servet, 177, 50013 Zaragoza, Spain
*
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Abstract

One of the main goals of selection schemes in beef cattle populations is to increase carcass weight at slaughter. Live weights at different growth stages are frequently used as selection criteria under the hypothesis that they usually have a high and positive genetic correlation with weight at slaughter. However, the presence of compensatory growth may bias the prediction ability of early weights for selection purposes. Recursive models may represent an interesting alternative for understanding the genetic and phenotypic relationship between weight traits during growth. For the purposes of this study, the analysis was performed for three different set of data from the Pirenaica beef cattle breed: weight at 120 days (W120) and at 210 days (W210); W120 and carcass weight at slaughter at 365 days (CW365); W210 and CW365. The number of records for each analysis was 8592, 4648 and 3234, respectively. A pedigree composed of 56323 individuals was also included. The statistical model comprised sex, year-season of birth, herd and slaughterhouse, plus a non-linear recursive dependency between traits. The dependency was modeled as a polynomial up to the 4th degree and models were compared using a Logarithm of Conditional Predictive Ordinates. The results of model comparison suggest that the best models were the 3rd degree polynomial for W120-W210 and W120-CW365 and the 2nd degree polynomial for W210-CW365. The posterior mean estimates for heritabilities ranged between 0.29 and 0.44 and the posterior mean estimates of the genetic correlations were null or very low, indicating that the relationship between traits is fully captured by the recursive dependency. The results imply that the predictive ability of the performance of future growth is low if it is only based on records of early weights. The usefulness of slaughterhouse records in beef cattle breeding evaluation is confirmed.

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Full Paper
Copyright
© The Animal Consortium 2014 

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References

Altarriba, J, García-Cortés, A, Moreno, C and Varona, L 1996. Situación y perspectivas de la mejora genética de la raza vacuna Pirenaica. Información Técnica Económica Agraria 92 A, 107116.Google Scholar
Altarriba, J, Yagüe, G, Moreno, C and Varona, L 2009. Exploring the possibilities of genetic improvement from traceability data an example in the Pirenaica beef cattle. Livestock Science 125, 115120.CrossRefGoogle Scholar
Beef Improvement Federation 2010. Guidelines for uniform beef improvement programs. In Beef improvement federation, 9th edition (ed. LV Cundiff, LD Van Vleck and WD Hohenboken), pp. 1725. Raleigh, North Carolina, USA.Google Scholar
Blanco, M, Villalba, D, Ripoll, G, Sauerwein, H and Casasus, I 2009. Effects of early weaning and breed on calf performance and carcass and meat quality in autumm-born bull calves. Livestock Science 120, 103105.Google Scholar
Bouquet, A, Fouilloux, MN, Renand, G and Phocas, F 2010. Genetic parameters for growth, muscularity, feed efficiency and carcass trait of young beef bulls. Livestock Science 129, 3848.CrossRefGoogle Scholar
Geisser, S 1993. Monograph on statistics and applied probability 55. In Predictive inference: an introduction, pp. 88107. Chapman & Hall, London, England.CrossRefGoogle Scholar
Gelfand, AE, Dey, DK and Chang, H 1992. Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian statistics (ed. JM Bernardo, JO Berger, AP Dawid and AFM Smith), pp. 147167. Oxford University Press, London, England.Google Scholar
Gelfand, AE, Hills, SE, Racine-Poon, A and Smith, AFM 1990. Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association 85, 972985.CrossRefGoogle Scholar
Gianola, D and Sorensen, D 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 14071424.CrossRefGoogle ScholarPubMed
Golden, BL, Garrick, DJ, Newman, S and Enns, RM 1998. Economically relevant traits a framework for the next generation of EPDs, Review. Retrieved 11 June 2013, from http://www.beef.org.nz/research/breeding/beefert.pdf.Google Scholar
González-Rodríguez, A, Altarriba, J, Moreno, C and Varona, L 2011. Análisis genético multicarácter del crecimiento en vacuno Pirenaico. Información Técnica Económica Agraria 2, 458460.Google Scholar
Henderson, CR 1984. Applications of linear models in animal breeding. University of Guelp, Guelp, Ontario, Canada.Google Scholar
Ibáñez-Escriche, N, López de Maturana, E, Noguera, JL and Varona, L 2010. An application of change-point recursive models to the relationship between litter size and number of stillborns in pigs. Journal of Animal Science 88, 34933503.Google Scholar
Ibáñez-Escriche, N, Varona, L, Casellas, J, Quintanilla, R and Noguera, JL 2009. Bayesian threshold analysis of direct and maternal genetic parameters for piglet mortality at farrowing in Large White, Landrace and Pietrain populations. Journal of Animal Science 87, 8087.CrossRefGoogle ScholarPubMed
Legarra, A, Varona, L and López de Maturana, E 2008. TM Threshold Model. Retrieved 11 June 2013, from http://snp.toulouse.inra.fr/~alegarra/manualtm.pdf.Google Scholar
López de Maturana, E, Wu, XL, Gianola, D, Weigel, KA and Rosa, GJM 2009. Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics 181, 277287.Google Scholar
MacNeil, MD 2003. Genetic evaluation of an index of birth weight and yearling weight to improved efficiency of beef production. Journal of Animal Science 81, 24252433.CrossRefGoogle ScholarPubMed
Meyer, K, Carrick, MJ and Donnelly, BJ 1993. Genetic parameters for growth traits of Australian beef cattle from a multibreed selection experimental. Journal of Animal Science 71, 26142622.CrossRefGoogle Scholar
Misztal, I 2012. RENUM – data preparation program for sire and animal models. Retrieved 11 June 2013, from http://nce.ads.uga.edu/~ignacy/numpub/renum/.Google Scholar
Neto, SG, Bezerra, LR, Medeiros, AN, Ferreira, MA, Pimenta, EC, Candido, EP and Oliveira, RL 2011. Feed restriction and compensatory growth in Guzera females. Asian-Australasian Journal of Animal Science 24, 791799.CrossRefGoogle Scholar
Raftery, AE and Lewis, SM 1992. How many iterations in the Gibbs Sampler. In Bayesian stadistics IV (ed. JM Bernardo, JO Berger, AP Dawid and AFM Smith), pp. 763774. Oxford University Press, New York, USA.CrossRefGoogle Scholar
Ríos-Utrera, A and Van Vleck, LD 2004. Heritability estimates for carcass traits of cattle: a review. Genetics and Molecular Research 3, 380394.Google Scholar
Smith, CA and Hodnett, GE 1962. Compensatory growth of cattle on natural grasslands of Northern Rhodesia. Nature 195, 919920.CrossRefGoogle Scholar
Sorensen, D and Gianola, D 2002. Likelihood. Bayeian and McMC methods in quantitative genetics. Springer, New York, USA.Google Scholar
Valente, BD, Rosa, GJM, Gianola, D, Wu, XL and Weigel, K 2013. Is structural equation advantageous for the genetic improvement of multiple traits? Genetics 113, 151209.Google Scholar
Varona, L and Sorensen, D 2014. Joint analysis of binomial and continuous traits with a recursive model: a case study using mortality and litter size of pigs. Genetics 113.Google Scholar
Varona, L, Sorensen, D and Thompson, R 2007. Analysis of little size and average litter weight in pigs using a recursive model. Genetics 177, 17911799.CrossRefGoogle Scholar
Varona, L, Moreno, C and Altarriba, J 2012. Genetic correlation of longevity with growth, post-morten, docility and some morphological traits in the Pirenaica beef cattle breed. Animal 6, 873879.Google Scholar
Varona, L, Moreno, C, García Cortés, LA and Altarriba, J 1997. Multiple trait genetic analysis of underlying biological variables of production functions. Livestock Production Science 47, 201209.Google Scholar