Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-17T07:04:28.771Z Has data issue: false hasContentIssue false

Probability models for detecting transgenic plants

Published online by Cambridge University Press:  01 June 2008

Carlos M. Hernández-Suárez
Affiliation:
Facultad de Ciencias, Universidad de Colima, Bernal Díaz del Castillo No. 340 Col. Villas San Sebastián, C.P. 28045Colima, Colima, México
Osval A. Montesinos-López
Affiliation:
Facultad de Telemática, Universidad de Colima, Bernal Díaz del Castillo No. 340 Col. Villas San Sebastián, C.P. 28045Colima, Colima, México
Graham McLaren
Affiliation:
Biometrics and Bioinformatics Unit, Crop Informatics Laboratory (CRIL), International Rice Research Institute (IRRI), DAPO Box 7777, Manila, Philippines
José Crossa*
Affiliation:
Biometrics and Statistics Unit, Crop Informatics Laboratory (CRIL), International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, MéxicoDF, México
*
*Correspondence j.crossa@cgiar.org

Abstract

When detecting the adventitious presence of transgenic plants (AP), it is important to use an appropriate testing method in the laboratory. Dorfman's group testing method is effective for reducing the number of laboratory analyses, but does not consider the case where AP is diluted below the sensitivity of the analyses, which causes the rate of false negatives to increase. The objective of this study is to propose binomial and negative binomial probabilistic models for determining the required sample size (n), number of pools (g), and size of the pool (k) for detecting individuals possessing AP with a probability ≥ (1 − α) (for a small α) given: (1) pool size (k); (2) estimated proportion of individuals with AP in the population (p); (3) concentration of the trait of interest (AP) in individual seeds (w); and (4) detection limit of the test (c) (AP concentration in a pool below which it cannot be detected). The proposed models consider the different rates of false positives (δ) and false negatives (λ), and the assessment of consumer and producer risks. Results have shown that when using the negative binomial, a required sample size n can be determined that guarantees a high probability that m individuals or g pools containing AP will be found. The pools formed have an optimum size, such that one element with AP will be detected at a low cost. The negative binomial distribution should be used when it is known that the proportion of individuals with AP in the population is p < 0.1; thus, it is guaranteed that m individuals or g pools of individuals with AP will be detected with high probability.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bartko, J.J. (1962) A note on the negative binomial distribution. Technometrics 4, 609610.Google Scholar
Cleveland, D.A., Soleri, D., Aragón-Cuevas, F., Crossa, J. and Gepts, P. (2005) Detecting (trans)gene flow to landraces in centers of crop origin: lessons from the case of maize in Mexico. Environmental Biosafety Research 4, 197208.CrossRefGoogle ScholarPubMed
Cochran, W.G. (1980) Técnicas de Muestreo. México, Editorial Continental.Google Scholar
Dorfman, R. (1943) The detection of defective members of large populations. Annals of Mathematical Statistics 14, 436440.Google Scholar
Federer, W.T. (1991) Statistics and society. Data collection and interpretation. New York, Marcel Dekker.Google Scholar
Gerrard, D.J. and Cook, R.D. (1972) Inverse binomial sampling as a basis for estimating negative binomial population densities. Biometrics 28, 971980.Google Scholar
Guenther, W.C. (1969) Modified sampling, binomial and hypergeometric cases. Technometrics 11, 639647.CrossRefGoogle Scholar
Haldane, J.B.S. (1945) On a method of estimating frequencies. Biometrika 33, 222225.Google Scholar
Kalton, G. and Anderson, D.W. (1986) Sampling rare populations. Journal of the Royal Statistical Society. Series A (General) 149, 6582.Google Scholar
Kay, S. and Paoletti, C. (2002) Sampling strategies for GMO detection and/or quantification.European Commission Report, Code EUR20239EN, Joint Research Centre Publication Office. Available online athttp://bgmo.jrc.ec.europa.eu/home/docs.htm#articles2002 (accessed 10 October 2007).Google Scholar
Kay, S. and Van den Eede, G. (2001) The limits of GMO detection. Nature Biotechnology 19, 405.Google Scholar
Kotz, S., Johnson, N.L. and Read, C.B. (1988) Encyclopedia of statistical sciences, Vol. 3 (1st edition). Toronto, Wiley.Google Scholar
Laffont, J.-L., Remund, K.M., Wright, D., Simpson, R.D. and Grégoire, 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, 197204.Google Scholar
Montgomery, D.C. (1997) Introduction to statistical quality control (3rd edition). New York, Wiley.Google Scholar
Morris, K.W. (1963) A note on direct and inverse binomial sampling. Biometrika 50, 544545.Google Scholar
Ortiz-García, S., Ezcurra, E., Schoel, B., Acevedo, F., Soberón, J. and Snow, A.A. (2005a) Absence of detectable transgenes in local landraces of maize in Oaxaca, Mexico (2003–2004). Proceedings of the National Academy of Sciences, USA 102, 1233812343.Google Scholar
Ortiz-García, S., Ezcurra, E., Schoel, B., Acevedo, F., Soberón, J. and Snow, A.A. (2005b) Correction. Proceedings of the National Academy of Sciences, USA 102, 18242.Google Scholar
Ortiz-García, S., Ezcurra, E., Schoel, B., Acevedo, F., Soberón, J. and Snow, A.A. (2005c) Reply to Cleveland et al.'s ‘Detecting (trans)gene flow to landraces in centers of crop origin: lessons from the case of maize in Mexico’. Environmental Biosafety Research 4, 209215.Google Scholar
Patil, G.P. (1960c) On the evaluation of the negative binomial distribution with examples. Technometrics 2, 501505.CrossRefGoogle Scholar
Quist, D. and Chapela, I.H. (2001) Transgenic DNA introgressed into traditional maize landraces in Oaxaca, Mexico. Nature 414, 541543.Google Scholar
Quist, D. and Chapela, I.H. (2002) Biodiversity (Communications arising (reply)): suspect evidence of transgenic contamination. Maize transgene results in Mexico are artefacts. Nature 416, 602.CrossRefGoogle Scholar
Remund, K.M., Dixon, D.A., Wright, D.L. and Holden, R.L. (2001) Statistical considerations in seed purity testing for transgenic traits. Seed Science Research 11, 101119.Google Scholar
USDA/GIPSA (2000a) Sampling for the detection of biotech grains. Washington, DC, United States Department of Agriculture. Available at website http://archive.gipsa.usda.gov/biotech/sample2.htm (accessed 20 November 2007)..Google Scholar
USDA/GIPSA (2000b) Practical application of sampling for the detection of biotech grains. Washington, DC, United States Department of Agriculture. Available at website http://archive.gipsa.usda.gov/biotech/sample1.htm (accessed 20 November 2007)..Google Scholar