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Quantification of low-level genetically modified (GM) seed presence in large seed lots: a case study of GM seed in Canadian flax breeder seed lots

Published online by Cambridge University Press:  05 August 2011

Eric G. Lamb  and
Helen M. Booker
Eric G. Lamb*
Affiliation:
Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SKS7N 5A8, Canada
Helen M. Booker
Affiliation:
Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SKS7N 5A8, Canada
*
*Correspondence Fax: +1306-966-1799 Email: [email protected]
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Abstract

The detection and quantification of the prevalence of genetically modified organism (GM) contamination in seed exports is a critical element of regulatory compliance. While the procedures to reliably detect high levels of GM contamination are well understood, no comparable statistical approaches are available for the quantification of levels of GM prevalence below the established detection rate of standard tests. We present a simple statistical approach based on simulation modelling for the quantification of low levels of GM contamination. The approach can be modified to match any sampling regime and can account for rates of false positive and negative assay results. The application of this method is demonstrated using the low level of contamination in Canadian flax breeder seed lots by the GM flax variety ‘Triffid’. We show that GM contamination is likely present in seed lots at rates between two GM seeds per million and six seeds per hundred thousand. We also show that this low level of presumed contamination is indistinguishable from the number of positive tests expected from a clean seed lot given the potential rates of false positive tests.

Keywords

CDC Triffidflaxgenetically modified oranismLinum usiatissimumseed purity analysisseed testingstatistical methodstransgenic seeds
Type
Research Article
Information
Seed Science Research , Volume 21 , Issue 4 , December 2011 , pp. 315 - 321
Copyright
Copyright © Cambridge University Press 2011

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References

Begg, G., Cullen, D., Iannetta, P. and Squire, G. (2007) Sources of uncertainty in the quantification of genetically modified oilseed rape contamination in seed lots. Transgenic Research 16, 51–63.CrossRefGoogle ScholarPubMed
Bennett, M.D. and Leich, U. (2005) Plant DNA C-values database (release 4.0, October 2005 edition). Royal Botanic Gardens, Kew. Available at http://data.kew.org/cvalues/ (accessed 8 July 2011).Google Scholar
Canadian Grain Commission (2010) Sampling and Testing Protocol for Canadian Flaxseed Exported to the European Union. Available at http://www.grainscanada.gc.ca/gmflax-lingm/stpf-peevl-eng.pdf (accessed 8 July 2011).Google Scholar
Crawley, M.J. (2007) The R Book. Chichester, UK, John Wiley & Sons.CrossRefGoogle Scholar
Emslie, K.R., Whaites, L., Griffiths, K.R. and Murby, E.J. (2007) Sampling plan and test protocol for the semiquantitative detection of genetically modified canola (Brassica napus) seed in bulk canola seed. Journal of Agricultural and Food Chemistry 55, 4414–4421.CrossRefGoogle ScholarPubMed
Hernandez-Suarez, C.M., Montesinos-Lopez, O.A., McLaren, G. and Crossa, J. (2008) Probability models for detecting transgenic plants. Seed Science Research 18, 77–89.CrossRefGoogle Scholar
Laffont, J.L., Remund, K.M., Wright, D., Simpson, R.D. and Gregoire, S. (2005) Testing for adventitious presence of transgenic material in conventional seed or grain lots using quantitative laboratory methods: statistical procedures and their implementation. Seed Science Research 15, 197–204.CrossRefGoogle Scholar
Law, A.V. (2007) Simulation modeling and analysis (4th edition). New York, NY, McGraw-Hill.Google Scholar
McHughen, A., Rowland, G.G., Holm, F.A., Bhatty, R.S. and Kenaschuk, E.O. (1997) CDC Triffid transgenic flax. Canadian Journal of Plant Science 77, 641–643.CrossRefGoogle Scholar
R Development Core Team (2009) R: A language and environment for statistical computing. Vienna, Austria, Foundation for Statistical Computing.Google Scholar
Remund, K.M., Dixon, D.A., Wright, D.L. and Holden, L.R. (2001) Statistical considerations in seed purity testing for transgenic traits. Seed Science Research 11, 101–120.Google Scholar
Whitaker, T.B., Freese, L., Giesbrecht, F.G. and Slate, A.B. (2001) Sampling grain shipments to detect genetically modified seed. Journal of AOAC International 84, 1941–1946.CrossRefGoogle ScholarPubMed
Supplementary material: File

Lamb Supplementary Material

Simulation model code and summary

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