CNV Detection via Statistical Sequence Tag Frequency Analysis
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Solution Overview
Problem
Current methods for detecting copy number variations (CNVs) in genetic material, particularly for prenatal diagnostics and cancer monitoring, face limitations such as insufficient sensitivity and sequencing bias due to low levels of circulating cell-free DNA, necessitating more reliable and non-invasive techniques.
Innovation Solution
A statistical approach that accounts for variability in sequencing data to determine CNVs by calculating single chromosome doses and comparing them to threshold values, enabling the detection of fetal aneuploidies and other chromosomal abnormalities from a mixture of nucleic acids in a test sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional invasive procedures (amniocentesis) are used to obtain cells for karyotype analysis, then diagnostic reliability is improved, but patient safety deteriorates due to invasive risks
Solution Approach 1:
The patent extracts and analyzes cell-free DNA (cfDNA) circulating in maternal blood plasma, eliminating the need for invasive procedures. By detecting fetal chromosomal abnormalities through cfDNA sequencing and statistical analysis of sequence tag frequencies, the method achieves diagnostic reliability without amniocentesis or other invasive sampling
Solution Approach 2:
The patent uses cell-free DNA in maternal blood as an intermediary to indirectly detect fetal chromosomal abnormalities. Instead of directly sampling fetal cells through invasive procedures, the method analyzes cfDNA that contains fetal genetic information, serving as a safe mediator between the diagnostic goal and the fetus
2Object-affected harmful factors
If cfDNA sequencing is used for noninvasive detection, then patient safety is improved, but measurement precision deteriorates due to low cfDNA levels and sequencing bias
Solution Approach 1:
The patent transforms the detection parameter from direct cfDNA concentration measurement to statistical analysis of sequence tag frequency distributions. By modeling expected frequency distributions and comparing observed data against these models, the method achieves high detection sensitivity despite low cfDNA levels and sequencing biases
Solution Approach 2:
The patent implements a feedback mechanism where the statistical model continuously refines its analysis by comparing observed sequence tag frequencies against expected distributions. The system uses the observed data to validate and adjust the detection thresholds, improving measurement precision through iterative statistical evaluation
3Measurement precision
If statistical analysis of sequence tag frequencies is performed, then measurement precision is improved, but device complexity increases due to computational requirements
Solution Approach 1:
The patent segments the complex statistical analysis into distinct computational modules: sequence tag frequency counting, distribution model generation, hypothesis testing, and confidence interval calculation. This segmentation allows the complex analysis to be performed systematically through standardized computational steps that can be implemented in software
Data Source
AI summary
The invention provides a method for determining copy number variations (CNV) of a sequence of interest in a test sample that comprises a mixture of nucleic acids that are known or are suspected to differ in the amount of one or more sequence of interest. The method comprises a statistical approach that accounts for accrued variability stemming from process-related, interchromosomal and inter-sequencing variability. The method is applicable to determining CNV of any fetal aneuploidy, and CNVs known or suspected to be associated with a variety of medical conditions. CNV that can be determined according to the method include trisomies and monosomies of any one or more of chromosomes 1-22, X and Y, other chromosomal polysomies, and deletions and/or duplications of segments of any one or more of the chromosomes, which can be detected by sequencing only once the nucleic acids of a test sample.