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Sources of sire-specific genetic variance for birth and weaning weight in Bruna dels Pirineus beef calves

Published online by Cambridge University Press:  03 July 2012

M. Fina
Affiliation:
Grup de Recerca en Remugants, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
L. Varona
Affiliation:
Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, 50013 Zaragoza, Spain
J. Piedrafita
Affiliation:
Grup de Recerca en Remugants, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
J. Casellas*
Affiliation:
Grup de Recerca en Remugants, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
*
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Abstract

This research investigated two sources of sire-specific genetic effects on the birth weight (BWT) and weaning weight (WWT) of Bruna dels Pirineus beef calves. More specifically, we focused on the influence of genes located in the non-autosomal region of the Y chromosome and the contribution of paternal imprinting. Our analyses were performed on 8130 BWT and 1245 WWT records from 12 and 2 purebred herds, respectively, they being collected between years 1986 and 2010. All animals included in the study were registered in the Yield Recording Scheme of the Bruna dels Pirineus breed. Both BWT and WWT were analyzed using a univariate linear animal model, and the relevance of paternal imprinting and Y chromosome-linked effects were checked by the deviance information criterion (DIC). In addition to sire-specific and direct genetic effects, our model accounted for random permanent effects (dam and herd-year-season) and three systematic sources of variation, that is, sex of the calf (male or female), age of the dam at calving (six levels) and birth type (single or twin). Both weight traits evidenced remarkable effects from the Y chromosome, whereas paternal imprinting was only revealed in WWT. Note that differences in DIC between the preferred model and the remaining ones exceed 39 000 and 2 800 000 DIC units for BWT and WWT, respectively. It is important to highlight that Y chromosome accounted for ∼2% and ∼6% of the total phenotypic variance for BWT and WWT, respectively, and paternal imprinting accounted for ∼13% of the phenotypic variance for WWT. These results revealed two relevant sources of sire-specific genetic variability with potential contributions to the current breeding scheme of the Bruna dels Pirineus beef cattle breed; moreover, these sire-specific effects could be included in other beef cattle breeding programs or, at least, they must be considered and appropriately analyzed.

Type
Breeding and genetics
Copyright
Copyright © The Animal Consortium 2012

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References

Beef Improvement Federation (BIF) 1986. Beef Improvement Federation Guidelines. North Carolina University Press, North Carolina.Google Scholar
Bünger, L, Renne, U, Dietl, G, Pirchner, F 1995. Paternal effects on the parent–offspring correlation for body-weight traits in mice. Journal of Animal Breeding and Genetics 112, 455461.Google Scholar
Casellas, J, Piedrafita, J 2002. Correction factors for weight productive traits up to weaning in the Bruna dels Pirineus beef cattle breed. Animal Research 51, 4350.Google Scholar
Casellas, J, Piedrafita, J, Varona, L 2007. Bayes factor for testing between different structures of random genetic groups: a case study using weaning weight in Bruna dels Pirineus beef cattle. Genetics Selection Evolution 39, 3953.Google Scholar
Eriksson, S, Näsholm, A, Johansson, K, Philipsson, J 2004. Genetic relationships between calving and carcass traits for Charolais and Hereford cattle in Sweden. Journal of Animal Science 82, 22692276.CrossRefGoogle ScholarPubMed
Geman, S, Geman, D 1984. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721741.Google Scholar
Geyer, CJ 1992. Practical Markov chain Monte Carlo. Statistical Science 7, 473483.Google Scholar
Gilks, WR, Richardson, S, Spiegelhalter, DJ 1996. Markov Chain Monte Carlo in Practice. Chapman & Hall, London, UK.Google Scholar
Golden, BL, Bourdon, RM, Snelling, WM 1994. Additive genetic groups for animals evaluated in more than one breed association national cattle evaluation. Journal of Animal Science 72, 25592567.CrossRefGoogle ScholarPubMed
Goodall, JJ, Schmutz, SM 2007. IGF2 gene characterization and association with eye area in beef cattle. Animal Genetics 38, 154161.CrossRefGoogle ScholarPubMed
Henderson, CR 1973. Sire evaluation and genetics trends. Proceedings of the Animal Breeding Genetics Symposium in honor of Dr. Jay L Lush, ASAS-ADSA, Champaign, Ilinois, USA, pp. 10–41.Google Scholar
Jeon, JT, Carlborg, Ö, Törnsten, A, Giuffra, E, Amarger, V, Lundström, P, Andersson, L 1999. A paternally expressed QTL affecting skeletal and cardiac muscle mass in pigs maps to the IGF2 locus. Nature Genetics 21, 157158.CrossRefGoogle Scholar
Kieffer, NM, Cartwright, TC 1968. Sex chromosome polymorphism in domestic cattle. Journal of Heredity 59, 3537.CrossRefGoogle ScholarPubMed
De Koning, DJ, Rattink, AP, Harlizius, B, Groenen, MAM, Brascamp, EW, Van Arendonk, JAM 2001a. Detection and characterization of quantitative trait loci for growth and reproduction in pigs. Livestock Production Science 72, 185198.CrossRefGoogle Scholar
De Koning, DJ, Harlizius, B, Rattink, AP, Groenen, MAM, Brascamp, EW, Van Arendonk, JAM 2001b. Detection and characterization of quantitative trait loci for meat quality traits in pigs. Journal of Animal Science 79, 28122819.Google Scholar
Engellandt, Th, Tier, B 2002. Genetic variances due to imprinted genes in cattle. Journal of Animal Breeding and Genetics 119, 154165.Google Scholar
Maxon, SC 1990. The evolution of the mammalian Y Chromosome. Behavior Genetics 20, 109126.Google Scholar
Menéndez-Buxadera, A, Carleos, C, Baro, JA, Villa, A, Cañón, J 2008. Multi-trait and random regression approaches for addressing the wide range of weaning ages in Asturiana de los Valles beef cattle for genetic parameter estimation. Journal of Animal Science 86, 278286.Google Scholar
Mujibi, FDN, Crews, DG 2009. Genetic parameters for calving ease, gestation length, and birth weight in Charolais cattle. Journal of Animal Science 87, 27592766.Google Scholar
Neugebauer, N, Räder, I, Schild, HJ, Zimmer, D, Reinsch, N 2010. Evidence for parent-of-origin effects on genetic variability on beef traits. Journal of Animal Science 88, 523532.CrossRefGoogle ScholarPubMed
Nezer, C, Moreau, L, Brouwers, B, Coppieters, W, Detilleux, J, Hanset, R, Karim, L, Kvasz, A, Leroy, P, Georges, M 1999. An imprinted QTL with major effect on muscle mass and fat deposition maps to the IGF2 locus in pigs. Nature Genetics 21, 155156.CrossRefGoogle Scholar
Potter, WL, Upton, PC 1979. Y chromosome morphology of cattle. Australian Veterinary Journal 55, 539541.Google Scholar
Quintanilla, R, Varona, L, Pujol, MR, Piedrafita, J 1999. Maternal animal model with correlation between maternal environmental effects of related dams. Journal of Animal Science 77, 29042917.Google Scholar
Raftery, AE, Lewis, SM 1992. How many iterations in the Gibbs sampler? In Bayesian Statistics IV (ed. JM Bernardo, JO Berger, AP Dawid and AFM Smith), pp. 763774. Oxford University Press, New York, NY, USA.Google Scholar
Reinsch, N, Engellandt, TH, Schild, HJ, Kalm, E 1999. Lack of evidence for bovine Y-chromosomal variation in beef traits. A Bayesian analysis of Simmental data. Journal of Animal Breeding and Genetics 118, 437445.Google Scholar
Schoeman, SJ 1989. Recent research into the production potential of indigenous cattle with special reference to the Sanga. South African Journal of Animal Science 19, 5561.Google Scholar
Schwenker, P, Maxon, SC 1986. Effect of DBA 1/Bg Y-chromosomes on testis weight and aggression. Behavior Genetics 16, 357363.Google Scholar
Serra, X, Gil, M, Gispert, M, Guerrero, L, Oliver, MA, Sañudo, C, Campo, MM, Panea, B, Olleta, JL, Quintanilla, R, Piedrafita, J 2004. Characterisation of young bulls of the Bruna dels Pirineus cattle breed (selected from old Brown Swiss) in relation to carcass, meat quality and biochemical traits. Meat Science 66, 425436.Google Scholar
Sluyter, F, Van Oortmerssen, GA, De Ruiter, AJH, Koolhass, JM 1996. Aggression in wild house mice: current state of affairs. Behavior Genetics 26, 489496.Google Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP 1998. Bayesian deviance, the effective number of parameters and the complexity of arbitrarily complex models. Technical Report, Medical Research Council, Biostatistics Unit, Cambridge, UK.Google Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP, Van der Linde, A 2002. Bayesian measures of model complexity and fit. Journal of Royal Statistical Society Series B 64, 583639.Google Scholar
Tarrés, J, Fina, M, Piedrafita, J 2010. Connectedness among herds of beef cattle bred under natural service. Genetic Selection Evolution 42, 16.Google Scholar
Tinker, ED, Frahm, RR, Buchanan, DS 1988. Comparison of Gelbvieh and Limousin sires in a terminal crossbreeding system. Journal of Animal Science 66, 13551362.Google Scholar
VanRaden, PM, Klaaskate, EJH 1993. Genetic evaluation of length of productive life including predicted longevity of live cows. Journal of Dairy Science 78, 27582764.Google Scholar
Villalba, D, Casasús, I, Sanz, A, Estany, J, Revilla, R 2000. Preweaning growth curves in Brown Swiss and Pirenaica calves with emphasis on individual variability. Journal of Animal Science 78, 11321140.Google Scholar
Westell, RA, Quaas, RL, Van Vleck, LD 1988. Genetic groups in an animal model. Journal of Dairy Science 71, 13101318.Google Scholar
Wright, S 1922. Coefficients of inbreeding and relationship. American Naturalist 56, 330338.Google Scholar