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Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle

Published online by Cambridge University Press:  24 November 2016

M. M. Judge
Affiliation:
Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy P61 P302, Co. Cork, Ireland Department of Biological Sciences, Cork Institute of Technology, Bishopstown T12 P928, Co. Cork, Ireland
M. M. Kelleher
Affiliation:
Irish Cattle Breeding Federation, Highfield House, Bandon P72 X050, Co. Cork, Ireland
J. F. Kearney
Affiliation:
Irish Cattle Breeding Federation, Highfield House, Bandon P72 X050, Co. Cork, Ireland Department of Biological Sciences, Cork Institute of Technology, Bishopstown T12 P928, Co. Cork, Ireland
R. D. Sleator
Affiliation:
Department of Biological Sciences, Cork Institute of Technology, Bishopstown T12 P928, Co. Cork, Ireland
D. P. Berry*
Affiliation:
Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
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Abstract

Angus and Hereford beef is marketed internationally for apparent superior meat quality attributes; DNA-based breed authenticity could be a useful instrument to ensure consumer confidence on premium meat products. The objective of this study was to develop an ultra-low-density genotype panel to accurately quantify the Angus and Hereford breed proportion in biological samples. Medium-density genotypes (13 306 single nucleotide polymorphisms (SNPs)) were available on 54 703 commercial and 4042 purebred animals. The breed proportion of the commercial animals was generated from the medium-density genotypes and this estimate was regarded as the gold-standard breed composition. Ten genotype panels (100 to 1000 SNPs) were developed from the medium-density genotypes; five methods were used to identify the most informative SNPs and these included the Delta statistic, the fixation (Fst) statistic and an index of both. Breed assignment analyses were undertaken for each breed, panel density and SNP selection method separately with a programme to infer population structure using the entire 13 306 SNP panel (representing the gold-standard measure). Breed assignment was undertaken for all commercial animals (n=54 703), animals deemed to contain some proportion of Angus based on pedigree (n=5740) and animals deemed to contain some proportion of Hereford based on pedigree (n=5187). The predicted breed proportion of all animals from the lower density panels was then compared with the gold-standard breed prediction. Panel density, SNP selection method and breed all had a significant effect on the correlation of predicted and actual breed proportion. Regardless of breed, the Index method of SNP selection numerically (but not significantly) outperformed all other selection methods in accuracy (i.e. correlation and root mean square of prediction) when panel density was ⩾300 SNPs. The correlation between actual and predicted breed proportion increased as panel density increased. Using 300 SNPs (selected using the global index method), the correlation between predicted and actual breed proportion was 0.993 and 0.995 in the Angus and Hereford validation populations, respectively. When SNP panels optimised for breed prediction in one population were used to predict the breed proportion of a separate population, the correlation between predicted and actual breed proportion was 0.034 and 0.044 weaker in the Hereford and Angus populations, respectively (using the 300 SNP panel). It is necessary to include at least 300 to 400 SNPs (per breed) on genotype panels to accurately predict breed proportion from biological samples.

Type
Research Article
Copyright
© The Animal Consortium 2016 

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References

Alexander, DH, Novembre, J and Lange, K 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome Research 19, 16551664.Google Scholar
Berry, DP, McClure, MC, Waters, S, Weld, R, Flynn, P, Creevey, C, Kearney, F, Cromie, A and Mullen, M 2013. Development of a custom genotyping panel for dairy and beef cattle breeding and research. In Advances in Animal Biosciences Vol. 4, ed. S Athanasiadou, AS Chaudhry, M Denwood, DP Eckersall, J Flockhart, DA Kenny, T King, A Mather, RW Mayes, DM Nash, RI Richardson, JA Rooke, MT Rose, C Rymer, K Sinclair, MA Steel, S Waters, BT Wolf and ARG Wylie), p. 249. Cambridge University Press, Nottingham, UK.Google Scholar
Boichard, D, Chung, H, Dassonneville, R, David, X, Eggen, A, Fritz, S, Gietzen, KJ, Hayes, B, Lawley, CT, Sonstegard, TS, Van Tassell, CP, VanRaden, PM, Viaud-Martinez, KA and Wiggans, GR 2012. Design of a bovine low-density SNP array optimized for imputation. PLoS One 7, e34130.CrossRefGoogle ScholarPubMed
Ding, L, Wiener, H, Abebe, T, Altaye, M, Go, RCP, Kercsmer, C, Grabowski, G, Martin, LJ, Khurana Hershey, GK, Chakorborty, R and Baye, T 2011. Comparison of measures of marker informativeness for ancestry and admixture mapping. BMC Genomics 12, 622.Google Scholar
Dodds, KG, Auvrey, B, Newman, SN and Mc Ewan, J 2014. Genomic breed prediction in New Zealand sheep. BMC Genomics 15, 92.Google ScholarPubMed
Daetwyler, HD, Capitan, A, Pausch, H, Stothard, P, van Binsbergen, R, Brondum, RF, Liao, X, Djari, A, Rodriguez, SC, Grohs, C, Esquerre, D, Bouchez, O, Rossignol, M, Klopp, C, Rocha, D, Fritz, S, Eggen, A, Bowman, PJ, Coote, D, Chamberlain, AJ, Anderson, C, Van Tassell, CP, Hulsegge, I, Goddard, ME and Guldbrandtsen, B 2014. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics 46, 858865.CrossRefGoogle ScholarPubMed
Frkonja, A, Gredler, B, Schnyder, U, Curik, I and Solkner, J 2012. Prediction of breed composition in an admixture cattle population. Animal Genetics 43, 696703.Google Scholar
Hulsegge, B, Calus, MPL, Windig, JJ, Hoving-Bolink, AH, Maurice van Eijndhoven, MHT and Hiemstra, SJ 2013. Selection of SNP from 50K and 777K arrays to predict the breed origin in cattle. Journal of Animal Science 91, 51285134.Google Scholar
Kersbergen, P, van Duijn, K, Kloosterman, AD, den Dunnen, JT, Kayser, M and de Knijff, P 2009. Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans. BMC Genetics 10, 69.Google Scholar
Kuehn, LA, Keele, JW, Bennett, GL, Mc Daneld, TG, Smith, TPL, Snelling, WM, Sonstegard, TS and Thallman, RM 2011. Predicting breed composition using breed frequencies of 50 000 markers from the US Meat Animal Research Centre 2000 Bull Project. Journal of Animal Science 89, 17421750.Google Scholar
Lewis, J, Abas, Z, Dadousis, C, Lykidis, D, Paschou, P and Drineas, P 2011. Tracing cattle breeds with principle components analysis ancestry informative SNPs. PLoS One 6, e18007.Google Scholar
Negrini, R, Nicoloso, L, Crepaldi, P, Milanesi, E, Colli, L, Chegdani, F, Pariset, L, Dunner, S, Leveziel, H and Ajmone Marsan, P 2008. Assessing SNP markers for assigning individuals to cattle populations. Animal Genetics 40, 1826.Google Scholar
Nielsen, R, Paul, JS, Albrechtsen, A and Song, YS 2011. Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics 12, 443451.Google Scholar
Paschou, P, Ziv, E, Burchard, EG, Choudhry, S, Rodriguez-Cintron, W, Mahoney, MW and Drineas, P 2007. PCA-correlated SNPs for structure identification in worldwide human populations. PLoS Genetics 3, 9: e160.CrossRefGoogle ScholarPubMed
R Development Core Team 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rosenberg, NA, Li, LM, Ward, R and Pritchard, JK 2003. Informativeness of genetic markers for inference of ancestry. American Journal of Human Genetics 73, 14021422.Google Scholar
SAS Institute Inc 2012. Base SAS® 9.3 Procedures Guide: Statistical Procedures, 2nd edition. SAS Institute Inc., , Cary, NC, USA.Google Scholar
Sevane, N, Armstrong, E, Cortés, O, Wiener, P, Wong, RP and Dunner, S, andthe GemQual Constortium 2013. Association of bovine meat quality traits with genes included in the PPARG and PPARGC1A networks. Meat Science 94, 328335.Google Scholar
Shriver, MD, Smith, MW, Jin, L, Marcini, A, Akey, JM, Deka, R and Ferrell, RE 1997. Ethnic-affiliation estimation by use of population-specific DNA markers. American Journal of Human Genetics 60, 957964.Google Scholar
Solkner, J, Frkonja, A, Raadsma, HW, Jonas, E, Thaller, G, Gootwine, E, Seriussi, E, Fuerst, C, Egger-Danner, C and Gredler, B 2010. Estimation of individual levels of admixture in crossbred populations from SNP chip data: examples with sheep and cattle populations. Retrieved on 10 January 2016 from https://journal.interbull.org/index.php/ib/article/view/1159.Google Scholar
Wilkinson, S, Wiener, P, Archibald, AL, Law, A, Schnabel, RD, McKay, SD, Taylor, JF and Ogden, R 2011. Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC Genomics 12, 45.Google Scholar
Weir, BS and Cockerham, CC 1984. Estimating F-statistics for the analysis of population structure. Evolution 38, 13581370.Google Scholar
Weir, BS and Hill, WG 2002. Estimating F-statistics. Annual Review of Genetics 36, 721750.Google Scholar
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