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.

Real-time Polymerase Chain Reaction (PCR) based assays are widely used to estimate the content of genetically modified (GM) materials in food, feed and seed. It has been known that the genetic structures of the analyte can significantly influence the GM content expressed by the haploid genome (HG) % estimated using real-time PCR assays; this kind of influence is also understood as the impact of biological factors. The influence was first simulated at theoretical level using maize as a model. We then experimentally assessed the impact of biological factors on quantitative results, analysing by quantitative real-time PCR six maize MON 810 hybrid kernels with different genetic structures: (1) hemizygous from transgenic male parent, (2) hemizygous from transgenic female parent and (3) homozygous at the transgenic locus. The results obtained in the present study showed clear influences of biological factors on GM DNA quantification: 1% of GM materials by weight (wt) for the three genetic structures contained 0.39, 0.55 and 1.0% of GM DNA by HG respectively, from quantitative real-time PCR analyses. The relationships between GM wt% and GM HG% can be empirically established as: (1) in the case of the presence of a single GM trait: GM HG% = GM wt% x (0.5 +/- 0.167Y), where Y is the endosperm DNA content (%) in the total DNA of a maize kernel, (2) in the case of the presence of multiple GM traits: GM HG% = N x GM wt% x (0.5 +/- 0.167Y), where N is the number of GM traits (stacked or not) present in an unknown sample. This finding can be used by stakeholders related to GMO for empirical prediction from one unit of expression to another in the monitoring of seed and grain production chains. Practical equations have also been suggested for haploid copy number calculations, using hemizygous GM materials for calibration curves.