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Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

Published online by Cambridge University Press:  21 March 2012

A. M. Haberland ,
U. König von Borstel ,
H. Simianer  and
S. König
A. M. Haberland*
Affiliation:
Department of Animal Sciences, University of Goettingen, 37075 Goettingen, Germany
U. König von Borstel
Affiliation:
Department of Animal Sciences, University of Goettingen, 37075 Goettingen, Germany
H. Simianer
Affiliation:
Department of Animal Sciences, University of Goettingen, 37075 Goettingen, Germany
S. König
Affiliation:
Department of Animal Breeding, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany
*
†E-mail: ahaberl@gwdg.de
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Abstract

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (rTI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of rmg = 0.5. For a low heritability trait (h2 = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles rTI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases rTI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.

Keywords

accuracy of selectionbreeding strategiesgeneration intervalgenomic selectionsport horse
Type
Full Paper
Information
animal , Volume 6 , Issue 9 , September 2012 , pp. 1369 - 1376
Copyright
Copyright © The Animal Consortium 2012

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References

Calus, MP, Meuwissen, TH, de Roos, AP, Veerkamp, RF 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553–561.CrossRefGoogle ScholarPubMed
Corbin, LJ, Blott, SC, Swinburne, JE, Vaudin, M, Bishop, SC, Woolliams, JA 2010. Linkage disequilibrium and historical effective population size in the Thoroughbred horse. Animal Genetics 41, 8–15.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Pong-Wong, R, Villanueva, B, Woolliams, JA 2010. The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.CrossRefGoogle ScholarPubMed
Dekkers, JCM 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. Journal of Animal Breeding and Genetics 124, 331–341.CrossRefGoogle ScholarPubMed
Dubois, C, Ricard, A 2007. Efficiency of past selection of the French Sport Horse: Selle Français breed and suggestions for the future. Livestock Science 112, 161–171.CrossRefGoogle Scholar
Gerber Olsson, E, Árnason, Th, Näsholm, A, Philipsson, J 2000. Genetic parameters for traits at performance test of stallions and correlations with traits at progeny tests in Swedish Warmblood horses. Livestock Production Science 65, 81–89.CrossRefGoogle Scholar
German Equestrian Federation (FN) 2008. Breeding program of the German Riding Horse. In Jahrbuch Sport und Zucht 2008, Deutsche Reiterliche Vereinigung, 84pp. FN-Verlag, Warendorf, Germany.Google Scholar
Habier, D, Fernando, RL, Dekkers, JCM 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.CrossRefGoogle ScholarPubMed
Hamann, H, Distl, O 2008. Genetic variability in Hanoverian Warmblood horses using pedigree analysis. Journal of Animal Science 86, 1503–1513.CrossRefGoogle ScholarPubMed
Hasler, H, Flury, C, Menet, S, Haase, B, Leeb, T, Simianer, H, Poncet, PA, Rieder, S 2011. Genetic diversity in an indigenous horse breed – implications for mating strategies and the control of future inbreeding. Journal of Animal Breeding and Genetics 128, 394–406.CrossRefGoogle Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ, Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Jaitner, J, Reinhardt, F 2008. Beschreibung Integrierte Zuchtwertschätzung Pferd. Retrieved February 24, 2011, from http://www.vit.de/index.php?id=zws-pferd.Google Scholar
Koenen, EPC, Aldridge, LI, Philipsson, J 2004. An overview of breeding objectives for warmblood sport horses. Livestock Production Science 88, 77–84.CrossRefGoogle Scholar
König, S, Swalve, HH 2009. Application of selection index calculations to determine selection strategies in genomic breeding programs. Journal of Dairy Science 92, 5292–5303.CrossRefGoogle ScholarPubMed
König, S, Simianer, H, Willam, A 2009. Economic evaluation of genomic breeding programs. Journal of Dairy Science 92, 382–391.CrossRefGoogle ScholarPubMed
König von Borstel, U, Euent, S, Graf, P, König, S, Gauly, M 2011. Equine behaviour and heart rate in temperament tests with or without rider or handler. Physiology and Behavior 104, 454–463.CrossRefGoogle ScholarPubMed
Long, CR, Walker, SC, Wang, RT, Westhusin, ME 2003. New commercial opportunities for advanced reproductive technologies in horses, wildlife, and companion animals. Theriogenology 59, 139–149.CrossRefGoogle ScholarPubMed
Lund, MS, de Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, FR, Su, G 2010. Improving genomic prediction by EuroGenomics collaboration. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 1–6 August, Leipzig, Germany, ISBN 978-3-00-031608-1.Google Scholar
Lynch, M, Walsh, B 1998. In genetics and analysis of quantitative traits. Sinauer Associates Inc., Sunderland, MA.Google Scholar
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Niemann, B 2009. Untersuchungen zu Veränderungen im Zuchtgeschehen und deren Auswirkungen auf die Hannoveraner Pferdezucht. PhD, Goettingen University.Google Scholar
Philipsson, J, Árnason, Th, Bergsten, K 1990. Alternative selection strategies for performance of the Swedish Warmblood horse. Livestock Production Science 24, 273–285.CrossRefGoogle Scholar
Pieramati, C, Pepe, M, Silvestrelli, M, Bolla, A 2003. Heritability estimation of osteochondrosis dissecans in Maremmano horses. Livestock Production Science 79, 249–255.CrossRefGoogle Scholar
Qanbari, S, Pimentel, ECG, Tetens, J, Thaller, G, Lichtner, P, Sharifi, AR, Simianer, H 2010. The pattern of linkage disequilibrium in German Holstein cattle. Animal Genetics 41, 346–356.CrossRefGoogle ScholarPubMed
Schade, W 1996. Entwicklung eines Besamungszuchtprogrammes für die hannoversche Warmblutzucht. PhD, Goettingen University.Google Scholar
Schaeffer, LR 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.CrossRefGoogle ScholarPubMed
Schober, M 2003. Schätzung von genetischen Effekten beim Auftreten von Osteochondrosis dissecans beim Warmblutpferd. PhD, Goettingen University.Google Scholar
Schorm, G 1983. Analyse der phänotypischen Entwicklung des Warmblutpferdes von der Geburt bis zum 3jährigen Pferd und Einflüsse von genetischen und umweltbedingten Faktoren. PhD, Leipzig University.Google Scholar
Simianer, H 2009. The potential of genomic selection to improve litter size in pig breeding programs. In Proceedings of 60th Annual Meeting of the European Association for Animal Production, Barcelona, Spain, August 24–27, 2009. Wageningen Academic Publishers, the Netherlands.Google Scholar
Sonesson, AK, Meuwissen, THE 2009. Testing strategies for genomic selection in aquaculture breeding programs. Genetics Selection Evolution 41, 37.CrossRefGoogle ScholarPubMed
Spelman, RJ, Garrick, DJ 1998. Genetic and economic responses for within-family marker-assisted selection in dairy cattle breeding schemes. Journal of Dairy Science 81, 2942–2950.CrossRefGoogle ScholarPubMed
Stock, KF, Distl, O 2007. Genetic correlations between performance traits and radiographic findings in the limbs of German Warmblood riding horses. Journal of Animal Science 85, 31–41.CrossRefGoogle ScholarPubMed
Stricker, C, Fernando, RL 2008. Genomewide genetic evaluation: how many individuals to genotype? International postgraduate course and workshop ‘Whole Genome Association and Genomic Selection’, September 1–8, Salzburg, Austria.Google Scholar
Thorén Hellsten, E, Viklund, Å, Koenen, EPC, Ricard, A, Bruns, E, Philipsson, J 2006. Review of genetic parameters estimated at stallion and young horse performance tests and their correlations with later results in dressage and show-jumping competition. Livestock Science 103, 1–12.CrossRefGoogle Scholar
VanRaden, PM, Van Tassel, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF, Schenkel, FS 2009. Invited Review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Van Hoogmoed, LM, Snyder, JR, Thomas, HL, Harmon, FA 2003. Retrospective evaluation of equine prepurchase examinations performed. Equine Veterinary Journal 35, 375–381.CrossRefGoogle ScholarPubMed
von Lengerken, G, Schwark, H-J 2002. Exterieur und Leistungen in der Pferdezucht – Alleskönner oder Spezialisten. Archiv für Tierzucht, Dummerstorf 45, 68–79.Google Scholar
Wade, CM, Giulotto, E, Sigurdsson, S, Zoli, M, Gnerre, S, Imsland, F, Lear, TL, Adelson, DL, Bailey, E, Bellone, RR, Blöcker, H, Distl, O, Edgar, RC, Garber, M, Leeb, T, Mauceli, E, MacLeod, JN, Penedo, MCT, Raison, JM, Sharpe, T, Vogel, J, Andersson, L, Antczak, DF, Biagi, T, Binns, MM, Chowdhary, BP, Coleman, SJ, Della Valle, G, Fryc, S, Guérin, G, Hasegawa, T, Hill, EW, Jurka, J, Kiialainen, A, Lindgren, G, Liu, J, Magnani, E, Mickelson, JR, Murray, J, Nergadze, SG, Onofrio, R, Pedroni, S, Piras, MF, Raudsepp, T, Rocchi, M, Røed, KH, Ryder, OA, Searle, S, Skow, L, Swinburne, JE, Syvänen, AC, Tozaki, T, Valberg, SJ, Vaudin, M, White, JR, Zody, MC 2009, Broad Institute Genome Sequencing Platform, Broad Institute Whole Genome Assembly Team, Lander ES and Lindblad-Toh KGenome sequence, comparative analysis, and population genetics of the domestic horse. Science 326, 865–867.CrossRefGoogle ScholarPubMed