Testing for adventitious presence of transgenic material in conventional seed or grain lots using quantitative laboratory methods: statistical procedures and their implementation

Abstract

2005). Testing for adventitious presence of transgenic material in conventional seed or grain lots using quantitative laboratory methods: statistical procedures and their implementation. AbstractWhen the laboratory methods employed are qualitative, the statistical methodologies used in testing for the adventitious presence (AP) of transgenic material in conventional seed and grain lots are well defined. However, when the response from the method used by the laboratory is quantitative (e.g. percent transgenic DNA), the statistical methodologies developed for qualitative laboratory methods are not fully appropriate. In this paper, we present the details of procedures specific to quantitative laboratory methods. In particular we consider: (1) the assessment of quantitative laboratory method errors using linear modelling; and (2) the process of deciding whether or not a lot meets pre-specified purity standards, including the development of probability calculations needed to develop operating characteristic curves and estimate consumer and producer risks for a given lower quality limit (LQL), acceptable quality limit (AQL) and testing plan. We also describe implementation of this approach in a useful spreadsheet application.

Impact of genetic structures on haploid genome-based quantification of genetically modified DNA: theoretical considerations, experimental data in MON 810 maize kernels (Zea mays L.) and some practical applications

“…However, a number of analytical (physical treatments of matrices, particle sizes, DNA quality, analysts, chemical reagents, equipments, ...) and biological factors can influence the genetically modified (GM) DNA quantification results and contribute to the uncertainty of real-time PCR-based quantitative analysis (Holst-Jensen et al 2003;Corbisier et al 2005;Laffont et al 2005;Lipp et al 2005;Moreana et al 2005;Yoshimora et al 2005;Holst-Jensen et al 2006;Weighardt 2006).…”

Statistics based algorithm for exact quantitation of [biotechnology] seed traits without reference material

“…3000 balls-(16 red+14 blue+2970 black) emulating the mixture of A2704-12 and A5547-127, and (150 red+2850 black) emulating GTS 40-3-2. Mixture of 3000 balls was put into an opaque bag, thoroughly mixed, then random ball was taken out "blindly" by hand (like when playing lotto) and put into one of 20 numbered (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) glasses. Then the balls left in the bag were mixed again.…”

The relevance of sampling for the control of genetically modified organisms in the agri-food chain

“…Acceptance sampling plans aim to estimate the GMO content of a lot of seeds (or grains) through the use of qualitative analyses applied to a set of subsamples. This approach is based on statistical considerations and the GM content is expressed in % of seeds, as units or % of DNA (Remund et al, 2001;Laffont et al, 2005). An example of this approach is the statistical programme ISTÀ Seedcalc' (Remund et al, 2001), which can be used to design sampling and testing plans for purity/impurity estimation of seed lots for adventitious Relevance of sampling for control of GMOs in the agri-food chain 225 presence/absence or levels of GM seeds in conventional seed lots.…”