Structural Variant Fingerprinting for Sample Swap Detection
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Solution Overview
Problem
Conventional methods for detecting sample swap and contamination in molecular diagnostics, such as those used in minimal residual disease detection, suffer from limited discriminatory power and complexity, particularly in PCR-based techniques, leading to inaccurate results due to shared SNPs and DNA contamination, which reduces sensitivity.
Innovation Solution
Utilizing structural variants, particularly germline structural variants (GSVs), to create a unique fingerprint for each patient by selecting low-frequency GSVs that are present on different chromosomes, and using digital PCR to detect their presence or absence in tumor and non-tumor DNA samples, thereby confirming sample identity and detecting contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SNP profiles are used for sample identification, then sample swap and contamination can be detected, but the discriminatory power is limited when the number of targets is reduced (e.g., in PCR)
Solution Approach 1:
The invention changes the genetic marker parameter from SNPs to structural variants (SVs). SVs provide higher discriminatory power because they involve larger genomic alterations (deletions, duplications, insertions, inversions, translocations) that are more unique to individuals compared to single base pair SNPs. This parameter change enables accurate sample identification in PCR-based techniques where the number of targets must be reduced.
2Measurement precision
If a large number of SNPs are tracked to account for shared SNPs among individuals, then sample identification accuracy improves, but the complexity and cost of analysis increases
Solution Approach 1:
The invention extracts and focuses on a specific subset of genetic markers - structural variants - that inherently provide higher discrimination power. By selecting SVs with low population allele frequencies (e.g., less than 10%), the method achieves high sample identification accuracy while analyzing fewer markers compared to tracking large numbers of SNPs. This extraction of key discriminatory features reduces analysis complexity.
3Reliability
If DNA contamination is detected by modeling sequence reads as a mixture, then contamination can be identified, but the quantification of each SNP variant becomes extremely difficult and reduces sensitivity
Solution Approach 1:
The invention changes from analyzing SNP-level variations to detecting structural variant presence/absence. SVs provide clearer binary signals (present or absent) that are easier to quantify and less susceptible to contamination-induced quantification errors. This parameter change improves both contamination detection reliability and maintains sensitivity by using more robust genetic markers.
Data Source
AI summary
The invention provides methods of sample identification using germline structural variants to assess sample contamination and sample swap.