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From phenotyping towards breeding strategies: using in vivo indicator traits and genetic markers to improve meat quality in an endangered pig breed

Published online by Cambridge University Press:  18 February 2015

A. D. M. Biermann ,
T. Yin ,
U. U. König von Borstel ,
K. Rübesam ,
B. Kuhn  and
S. König
A. D. M. Biermann*
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
T. Yin
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
U. U. König von Borstel
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
K. Rübesam
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
B. Kuhn
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
S. König
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a Witzenhausen, 37213 Germany
*
†E-mail: [email protected]
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Abstract

In endangered and local pig breeds of small population sizes, production has to focus on alternative niche markets with an emphasis on specific product and meat quality traits to achieve economic competiveness. For designing breeding strategies on meat quality, an adequate performance testing scheme focussing on phenotyped selection candidates is required. For the endangered German pig breed ‘Bunte Bentheimer’ (BB), no breeding program has been designed until now, and no performance testing scheme has been implemented. For local breeds, mainly reared in small-scale production systems, a performance test based on in vivo indicator traits might be a promising alternative in order to increase genetic gain for meat quality traits. Hence, the main objective of this study was to design and evaluate breeding strategies for the improvement of meat quality within the BB breed using in vivo indicator traits and genetic markers. The in vivo indicator trait was backfat thickness measured by ultrasound (BFiv), and genetic markers were allele variants at the ryanodine receptor 1 (RYR1) locus. In total, 1116 records of production and meat quality traits were collected, including 613 in vivo ultrasound measurements and 713 carcass and meat quality records. Additionally, 700 pigs were genotyped at the RYR1 locus. Data were used (1) to estimate genetic (co)variance components for production and meat quality traits, (2) to estimate allele substitution effects at the RYR1 locus using a selective genotyping approach and (3) to evaluate breeding strategies on meat quality by combining results from quantitative-genetic and molecular-genetic approaches. Heritability for the production trait BFiv was 0.27, and 0.48 for backfat thickness measured on carcass. Estimated heritabilities for meat quality traits ranged from 0.14 for meat brightness to 0.78 for the intramuscular fat content (IMF). Genetic correlations between BFiv and IMF were higher than estimates based on carcass backfat measurements (0.39 v. 0.25). The presence of the unfavorable n allele was associated with increased electric conductivity, paler meat and higher drip loss. The allele substitution effect on IMF was unfavorable, indicating lower IMF when the n allele is present. A breeding strategy including the phenotype (BFiv) combined with genetic marker information at the RYR1 locus from the selection candidate, resulted in a 20% increase in accuracy and selection response when compared with a breeding strategy without genetic marker information.

Keywords

endangered pig breedmeat qualityhalothane geneultrasound indicator traitsbreeding strategies
Type
Research Article
Information
animal , Volume 9 , Issue 6 , June 2015 , pp. 919 - 927
Copyright
© The Animal Consortium 2015 

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References

Ayuso, D, González, A, Hernández, F, Corral, JM and Izquierdo, M 2013. Prediction of carcass composition, ham and foreleg weights, and lean meat yields of Iberian pigs using ultrasound measurements in live animals. Journal of Animal Science 91, 1884–1892.Google Scholar
Bach, H and Sack, E 1987. Handelsklassen für Schweinehälften. Auswertungs- und Informationsdienst für Ernährung, Landwirtschaft u. Forsten (AID), Bonn, Germany.Google Scholar
Bagheri, M, Miraie-Ashtiani, R, Moradi-Sharbabak, M, Nejati-Javaremi, A, Pakdel, A, von Borstel, UU, Pimentel, ECG and König, S 2013. Selective genotyping and logistic regression analyses to identify favorable SNP-genotypes for clinical mastitis and production traits in Holstein dairy cattle. Livestock Science 151, 140–151.Google Scholar
Biermann, ADM, Pimentel, ECG, Tietze, M, Pinent, T and König, S 2014. Implementation of genetic evaluation and mating designs for the endangered local pig breed, ‘Bunte Bentheimer’. Journal of Animal Breeding and Genetics 131, 36–45.Google Scholar
Borchers, N, Otto, G and Kalm, E 2007. Genetic relationship of drip loss to further meat quality traits in purebred Piétrains. Archives Animal Breeding 50, 84–91.Google Scholar
Dekkers, JC 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics 124, 331–341.Google Scholar
De Vries, AG, van der Wal, PG, Long, T, Eikelenboom, G and Merks, JWM 1994. Genetic parameters of pork quality and production traits in Yorkshire population. Livestock Production Science 40, 277–289.CrossRefGoogle Scholar
Dodenhoff, J, Schuster, M, Wittmann, W, Littmann, E, Lindner, J-P and Götz, K-U 2011. Leistungsprüfung und zuchtwertschätzung für tropfsaftverlust beim schwein in bayern. Züchtungskunde 83, 142–152.Google Scholar
Fernández, A, de Pedro, N, Núñez, N, Silió, L, García-Casco, J and Rodríguez, C 2003. Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs. Meat Science 64, 405–410.Google Scholar
Fujii, J, Otsu, K, Zorzato, F, De Leon, S, Khanna, VK, Weiler, JE, O'Brien, PJ and Maclennan, DH 1991. Identification of a mutation in porcine ryanodine receptor associated with malignat hyperthermia. Science 253, 448–451.Google Scholar
Gjerlaug-Enger, E, Aass, L and Vangen, O 2010. Genetic parameters of meat quality traits in two pig breeds measured by rapid methods. Animal 4, 1832–1843.CrossRefGoogle ScholarPubMed
Han, X, Jiang, T, Yang, H, Zhang, Q, Wang, W, Fan, B and Liu, B 2012. Investigation of four porcine candidate genes (H-FABP, MYOD1, UCP3 and MASTR) for meat quality in Large White pigs. Molecular Biology Reports 39, 6599–6605.Google Scholar
Henshall, JM and Goddard, ME 1999. Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression. Genetics 151, 885–894.Google Scholar
Lo, LL, McLaren, DG, McKeith, FK, Fernando, RL and Novakofski, J 1992. Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: ii. heritabilities and correlations. Journal of Animal Science 70, 2387–2396.Google Scholar
Madsen, P and Jensen, J 2000. A user’s guide to DMU. A package for analysing multivariate mixed models. Version 6, release 5. University of Aarhus, Tjele, Denmark.Google Scholar
Newcom, DW, Baas, TJ, Stalder, KJ and Schwab, CR 2005. Comparison of three models to estimate breeding values for percentage of loin intramuscular fat in Duroc swine. Journal of Animal Science 83, 750–756.CrossRefGoogle ScholarPubMed
Pimentel, ECG and König, S 2012. Genomic selection for the improvement of meat quality in beef. Journal of Animal Science 90, 3418–3426.Google Scholar
Pugliese, C and Sirtori, F 2012. Quality of meat products produced from southern European pig breeds. Meat Science 90, 511–518.Google Scholar
Rosenvold, K and Andersen, HJ 2003. Factors of significance for pork quality – a review. Meat Science 64, 219–237.Google Scholar
Schwab, CR, Baas, TJ and Stalder, KJ 2010. Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. ii. Genetic parameters and trends. Journal of Animal Science 88, 69–79.Google Scholar
Schwab, CR, Mote, BE, Du, Z-Q, Amoako, R, Baas, TJ and Rothschild, MF 2009. An evaluation of four candidate genes for use in selection programmes aimed at increased intramuscular fat in Duroc swine. Journal of Animal Breeding and Genetics 126, 228–236.Google Scholar
Sharma, BS, Jansen, GB, Karrow, NA, Kelton, D and Jlang, Z 2006. Detection and characterization of amplified length polymorphism markers for clinical mastitis in Canadian holsteins. Journal of Dairy Science 89, 3653–3663.Google Scholar
Suzuki, K, Irie, M, Kadowaki, H, Shibata, T, Kumagai, M and Nishida, A 2005. Genetic parameter estimates of meat quality traits in Duroc pigs selected for average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content. Journal of Animal Science 83, 2058–2065.Google Scholar
van Wijk, HJ, Arts, DJG, Matthews, JO, Webster, M, Ducro, BJ and Knol, EF 2005. Genetic parameters for carcass composition and pork quality estimated in a commercial production chain. Journal of Animal Science 83, 324–333.Google Scholar
Zhang, S, Knight, TJ, Stalder, KJ, Goodwin, RN, Lonergan, SM and Beitz, DC 2007. Effects of breed, sex, and halothane genotype on fatty acid composition of pork longissimus muscle. Journal of Animal Science 85, 583–591.Google Scholar