Researchers from the Belgian Federal Institute Sciensano, working within the DARWIN project, have developed a genetic fingerprinting method that enhances the detection of gene-edited organisms in the food chain. This proof-of-concept demonstrates how genome database mining, combined with advanced sequencing and machine learning, can make it possible to accurately identify even subtle genetic modifications introduced through New Genomic Techniques (NGTs).
The study, published in Food Research International, represents a major step toward regulatory compliance, consumer trust, and traceability in food systems.
Breakthrough in Gene-Edited Rice Detection
The research focused on a genome-edited Nipponbare rice line with a single CRISPR-Cas-induced single nucleotide variation (SNV). Using whole-genome sequencing, researchers confirmed no off-target mutations and created a unique genetic fingerprint combining:
- The on-target mutation site
- Cultivar-specific barcodes made from pairs of SNVs unique to a rice variety
By analyzing more than 3,000 publicly available rice genomes, the team applied machine learning to identify these minimal marker sets, forming a reliable genetic barcode for each cultivar.
High Sensitivity and Accuracy
The results revealed that the approach could detect and identify genome-edited rice lines at very low levels (0.9% and 0.1%), proving its sensitivity for food-chain monitoring.
This means that even organisms with subtle genetic edits—often challenging to trace—can, in principle, be uniquely identified when prior genomic information is available.
Benefits for Food Safety and Regulation
According to Nancy Roosens, Head of Division at Sciensano, the method is best suited for gene-edited organisms with a fully sequenced and well-characterized genetic background, especially when supported by open-access genome databases.
Key potential benefits include:
- Supporting EU regulatory discussions on gene-edited crops
- Enhancing transparency in food systems
- Improving traceability for consumers and regulators
- Boosting scientific knowledge on innovative plant breeding technologies
However, the researchers emphasize that routine application will require overcoming challenges, including the need for broader genomic data sharing and efficient cataloging of modifications.
Implications for the Future of NGT Detection
This genetic fingerprint strategy highlights a promising path toward robust detection methods for new genomic techniques. It also strengthens the goals of the DARWIN project, which aims to deliver reliable tools ensuring food system transparency.
As gene-edited crops and foods become more common, having reliable methods for unambiguous detection will be essential for maintaining consumer trust and regulatory oversight.
Study Reference
Marie-Alice Fraiture et al. Genetic fingerprints derived from genome database mining allow accurate identification of genome-edited rice in the food chain via targeted high-throughput sequencing. Food Research International (2025).
DOI: 10.1016/j.foodres.2025.117218