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Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds

Published online by Cambridge University Press:  28 August 2014

A. Kause ,
L. Mikkola ,
I. Strandén  and
K. Sirkko
A. Kause*
Affiliation:
MTT Agrifood Research Finland, Biometrical Genetics, Myllytie 1, FI-31600 Jokioinen, Finland
L. Mikkola
Affiliation:
MTT Agrifood Research Finland, Biometrical Genetics, Myllytie 1, FI-31600 Jokioinen, Finland Department of Agricultural Sciences, University of Helsinki, FI-00014 Helsinki, Finland
I. Strandén
Affiliation:
MTT Agrifood Research Finland, Biometrical Genetics, Myllytie 1, FI-31600 Jokioinen, Finland
K. Sirkko
Affiliation:
Faba Co., Vantaa, Urheilutie 6, FI-01301 Vantaa, Finland
*
†E-mail: [email protected]
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Abstract

Profitability of beef production can be increased by genetically improving carcass traits. To construct breeding value evaluations for carcass traits, breed-specific genetic parameters were estimated for carcass weight, carcass conformation and carcass fat in five beef cattle breeds in Finland (Hereford, Aberdeen Angus, Simmental, Charolais and Limousin). Conformation and fat were visually scored using the EUROP carcass classification. Each breed was separately analyzed using a multitrait animal model. A total of 6879–19 539 animals per breed had phenotypes. For the five breeds, heritabilities were moderate for carcass weight (h2=0.39 to 0.48, s.e.=0.02 to 0.04) and slightly lower for conformation (h2=0.30 to 0.44, s.e.=0.02 to 0.04) and carcass fat (h2=0.29 to 0.44, s.e.=0.02 to 0.04). The genetic correlation between carcass weight and conformation was favorable in all breeds (rG=0.37 to 0.53, s.e.=0.04 to 0.05), heavy carcasses being genetically more conformed. The phenotypic correlation between carcass weight and carcass fat was moderately positive in all breeds (rP=0.21 to 0.32), implying that increasing carcass weight was related to increasing fat levels. The respective genetic correlation was the strongest in Hereford (rG=0.28, s.e.=0.05) and Angus (rG=0.15, s.e.=0.05), the two small body-sized British breeds with the lowest conformation and the highest fat level. The correlation was weaker in the other breeds (rG=0.08 to 0.14). For Hereford, Angus and Simmental, more conformed carcasses were phenotypically fatter (rP=0.11 to 0.15), but the respective genetic correlations were close to zero (rG=−0.05 to 0.04). In contrast, in the two large body-sized and muscular French breeds, the genetic correlation between conformation and fat was negative and the phenotypic correlation was close to zero or negative (Charolais: rG=−0.18, s.e.=0.06, rP=0.02; Limousin: rG=−0.56, s.e.=0.04, rP=−0.13). The results indicate genetic variation for the genetic improvement of the carcass traits, favorable correlations for the simultaneous improvement of carcass weight and conformation in all breeds, and breed differences in the correlations of carcass fat.

Keywords

beefEUROPfatgenetic correlationheritability
Type
Research Article
Information
animal , Volume 9 , Issue 1 , January 2015 , pp. 35 - 42
Copyright
© The Animal Consortium 2014 

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References

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, 115–120.Google Scholar
Amer, PR, Simm, G, Keane, MG, Diskin, MG and Wickham, BW 2001. Breeding objectives for beef cattle in Ireland. Livestock Production Science 67, 223–239.Google Scholar
Decker, JE, Pires, JC, Conant, GC, McKay, SD, Heaton, MP, Chen, K, Cooper, A, Vilkki, J, Seabury, CM, Caetano, AR, Johnson, GS, Brenneman, RA, Hanotte, O, Eggert, LS, Wiener, P, Kim, JJ, Kim, KS, Sonstegard, TS, VanTassell, CP, Neibergs, HL, McEwan, JC, Brauning, R, Coutinho, LL, Babar, ME, Wilson, GA, McClure, MC, Rolf, MM, Kim, J, Schnabel, RD and Taylor, JF 2009. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proceedings of the National Academy of Sciences USA 106, 18644–18649.Google Scholar
Dijkstra, J, Oldenbroek, JK, Korver, S and van der Werf, JHJ 1990. Breeding for veal and beef production in Dutch Red and White cattle. Livestock Production Science 25, 183–196.Google Scholar
Eriksson, S, Näsholm, A, Johansson, K and Philipsson, J 2003. Genetic analyses of field-recorded growth and carcass traits for Swedish beef cattle. Livestock Production Science 84, 53–62.CrossRefGoogle Scholar
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics, 4th edition. Longman Group, Essex, UK.Google Scholar
Fouilloux, M-N, Renand, G, Gaillard, J and Ménissier, F 1999. Genetic parameters of beef traits of Limousin and Charolais progeny-tested AI sires. Genetics Selection Evolution 31, 465–489.Google Scholar
Hazel, LN 1943. The genetic basis of constructing selection indices. Genetics 28, 476–490.CrossRefGoogle Scholar
Hickey, JM, Keane, MG, Kenny, DA, Cromie, AR and Veerkamp, RF 2007. Genetic parameters for EUROP carcass traits within different groups of cattle in Ireland. Journal of Animal Science 85, 314–321.Google Scholar
Hirooka, H, Groen, AF and van der Werf, JHJ 1998. Estimation of additive and non-additive genetic parameters for carcass traits on bulls in dairy, dual purpose and beef cattle breeds. Livestock Production Science 54, 99–105.Google Scholar
Houle, D 1992. Comparing evolvability and variability of quantitative traits. Genetics 130, 195–204.Google Scholar
Kause, A, Saloniemi, I, Morin, JP, Haukioja, E, Hanhimäki, S and Ruohomäki, K 2001. Seasonally varying diet quality and the quantitative genetics of development time and body size in birch feeding insects. Evolution 55, 1992–2001.Google Scholar
Kempthorne, O and Nordskog, AW 1959. Restricted selection indices. Biometrics 15, 10–19.Google Scholar
Koots, KR and Gibson, JP 1996. Realized sampling variances of estimates of genetic parameters and the difference between genetic and phenotypic correlations. Genetics 143, 1409–1416.CrossRefGoogle ScholarPubMed
Koots, KR, Gibson, JP, Smith, C and Wilton, JW 1994. Analyses of published genetic parameter estimates for beef production traits. 1. Heritability. Animal Breeding Abstracts 62, 309–338.Google Scholar
Liinamo, AE, Ojala, M and van Arendonk, JAM 1999. Relationships of body weight and carcass quality traits with first lactation milk production in Finnish Ayrshire cows. Livestock Production Science 60, 271–279.Google Scholar
Madsen, P and Jensen, J 2008. DMU – a package for analysing multivariate mixed models, version 6. Retrieved September 20, 2013, from http://www.dmu.agrsci.dk.Google Scholar
Marshall, DM 1994. Breed differences and genetic parameters for body composition traits in beef cattle. Journal of Animal Science 72, 2745–2755.Google Scholar
More O’Ferrall, GJ, Joseph, RL, Tarrant, PV and McGloughlin, P 1989. Phenotypic and genetic parameters of carcass and meat quality traits in cattle. Livestock Production Science 21, 35–47.Google Scholar
Pabiou, T, Fikse, WF, Amer, PR, Cromie, AR, Näsholm, A and Berry, DP 2012. Genetic relationships between carcass cut weights predicted from video image analysis and other performance traits in cattle. Animal 6, 1389–1397.CrossRefGoogle ScholarPubMed
Pabiou, T, Fikse, WF, Näsholm, A, Cromie, AR, Drennan, MJ, Keane, MG and Berry, DP 2009. Genetic parameters for carcass cut weight in Irish beef cattle. Journal of Animal Science 87, 3865–3876.CrossRefGoogle ScholarPubMed
Parkkonen, P, Liinamo, AE and Ojala, M 2000. Estimates of genetic parameters for carcass traits in Finnish Ayrshire and Holstein–Friesian. Livestock Production Science 64, 203–213.Google Scholar
Ríos-Utrera, A and van Vleck, LD 2004. Heritability estimates for carcass traits of cattle: a review. Genetics and Molecular Research 3, 380–394.Google Scholar
Roff, D 1996. The evolution of genetic correlations: an analysis of patterns. Evolution 50, 1392–1403.CrossRefGoogle ScholarPubMed
Sarti, FM, Pieramati, C, Lubricchio, E, Giontella, A, Lasagna, E and Panella, F 2013. Genetic parameters for the weight and yields of carcass cuts in Chianina cattle. Journal of Animal Science 91, 4099–4103.Google Scholar
Steppan, SJ, Phillips, PC and Houle, D 2002. Comparative quantitative genetics: evolution of the G matrix. Trends in Ecology and Evolution 17, 320–327.CrossRefGoogle Scholar
Tarrés, J, Fina, M, Varona, L and Piedrafita, J 2011. Carcass conformation and fat cover scores in beef cattle: a comparison of threshold linear models vs grouped data models. Genetics Selection Evolution 43, 16.Google Scholar
van der Werf, JHJ, van der Waaij, EH, Groen, AF and de Jong, G 1998. An index for beef and veal characteristics in dairy cattle based on carcass traits. Livestock Production Science 54, 11–20.Google Scholar
Veselá, Z, Vostrý, L and Šafus, P 2011. Linear and linear-threshold model for genetic parameters for SEUROP carcass traits in Czech beef cattle. Czech Journal of Animal Science 56, 414–425.CrossRefGoogle Scholar
Wolfová, M, Nitter, G, Wolf, J and Fiedler, J 2001. Impact of crossing system on relative economic weights of traits in purebred pig populations. Journal of Animal Breeding and Genetics 118, 389–402.Google Scholar