Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing. (1st February 2016) - Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C.J.; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy
Highlights: A NGS workflow is developed to analyse GMOs. Transgene inserts are detected, proven to be integrated, and identified. A statistical framework is developed to support the NGS workflow. The framework is experimentally validated and influential factors are assessed. Samples encountered in routine analysis are investigated for NGS feasibility. Abstract: Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.