A SNP panel and online tool for checking genotype concordance through comparing QR codes.

Area of Science:

• Genomics
• Bioinformatics
• Precision Medicine

Background:

• High-throughput genotyping and sequencing generate vast amounts of genetic data.
• Sample mix-ups are a significant concern, with reported rates between 0.1% and 1%, impacting research and clinical applications.
• The increasing scale of genomic data necessitates robust methods for sample identification and tracking.

Purpose of the Study:

• To develop a method for uniquely identifying personal genomes using a minimal set of Single Nucleotide Polymorphisms (SNPs).
• To create a user-friendly web tool for checking genetic data concordance and detecting sample mix-ups.
• To reduce the complexity and number of markers required for genetic data labeling and tracking.

Main Methods:

• Identified a set of informative SNPs utilizing allele frequencies from the 1000 Genomes Project and ExAC Consortium.
• Incorporated SNPs for ABO blood type and sex prediction into the SNP panel.
• Developed a web interface (QRC) to extract, encode genotypes as QR codes, and compare them for concordance analysis.

Main Results:

• A panel of 80 fingerprinting SNPs was established for unique personal genome identification.
• The QRC web tool efficiently processes raw genetic data, generates QR codes, and reports concordance.
• The method significantly reduces the complexity and number of markers for genetic data labeling.

Conclusions:

• The developed SNP panel and QRC web tool offer an accessible and efficient solution for identifying personal genomes and ensuring genetic data accuracy.
• This approach is crucial for maintaining data integrity in the era of precision medicine and large-scale sequencing projects.
• The method provides a valuable resource for researchers and the public to verify the accuracy of complex genetic datasets.