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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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A new statistical method predicts genetically modified organism (GMO) events in soybean samples. This approach optimizes GMO testing workflows by analyzing quantification cycle (Cq) differences, reducing wasted resources on non-quantifiable samples.

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Area of Science:

  • Food Science
  • Molecular Biology
  • Biotechnology

Background:

  • European Union (EU) regulations mandate testing for genetically modified organisms (GMOs) in food and feed.
  • Current GMO testing workflows involve DNA extraction, screening, identification, and quantification.
  • Low-level GMO presence can lead to false positives during screening, causing resource inefficiency.

Purpose of the Study:

  • To develop a statistical framework to predict the presence of soybean GMO events.
  • To optimize the GMO testing workflow by minimizing resource expenditure on non-quantifiable samples.
  • To provide a proof of concept for statistically optimizing GMO testing compliance.

Main Methods:

  • A statistical framework was developed using quantification cycle (Cq) values from real-time PCR.
  • Analysis focused on the difference in Cq values (ΔCq) between specific screening elements (P35S, T-nos, CP4 epsps) and a reference gene (lectin).
  • The approach was validated in-house using real-life and spiked soybean samples.

Main Results:

  • The developed statistical framework successfully predicted the presence of soybean GMO events.
  • The method demonstrated feasibility in distinguishing between quantifiable and non-quantifiable GMO positives.
  • In-house verification confirmed the approach's effectiveness on various sample types.

Conclusions:

  • The statistical framework offers a method to optimize GMO testing workflows.
  • This approach can significantly reduce time and resources in laboratories by preemptively identifying potentially non-quantifiable samples.
  • The study provides a foundation for statistically driven optimization in GMO compliance testing.