CNV Detection via Statistical Tag Counting
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Solution Overview
Problem
Current methods for detecting copy number variations (CNVs) in genetic material, particularly for noninvasive prenatal diagnostics and cancer monitoring, face limitations such as insufficient sensitivity and sequencing bias due to low levels of circulating cell-free DNA and the inherent nature of genomic information.
Innovation Solution
A statistical approach that accounts for variability in sequencing data to determine CNVs by calculating single chromosome doses or segment doses based on sequence tag ratios, allowing for the detection of fetal aneuploidies and other chromosomal abnormalities from a mixture of nucleic acids without the need for invasive sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sequencing methods are used to detect CNVs in circulating cell-free DNA, then the testing can be performed noninvasively, but the sensitivity is insufficient due to low levels of cfDNA
Solution Approach 1:
The method segments the genome into multiple discrete loci and counts sequence tags at each locus independently. By dividing the genomic analysis into separate countable units across multiple loci, the method amplifies the detectable signal from low-abundance cfDNA, enabling reliable CNV detection even when total cfDNA concentration is low.
Solution Approach 2:
The invention transitions from analyzing continuous sequencing depth ratios to counting discrete sequence tag occurrences at specific loci. This dimensional shift from continuous to discrete measurement provides enhanced sensitivity by accumulating count data across multiple independent loci, effectively amplifying the signal from limited cfDNA molecules.
2Measurement precision
If conventional sequencing approaches are used, then the process is relatively simple, but sequencing bias occurs due to the inherent nature of genomic information
Solution Approach 1:
The method introduces sequence tags as intermediary markers at specific genomic loci. These tags serve as mediators that convert complex genomic information into simple, countable discrete units. By counting tag occurrences rather than analyzing continuous sequencing data, the method eliminates sequencing bias while maintaining analytical simplicity.
Solution Approach 2:
The invention changes the measurement parameter from continuous sequencing depth (prone to bias) to discrete sequence tag counts (resistant to bias). This parameter transformation fundamentally removes sequencing bias effects while the statistical framework maintains methodological simplicity through standardized counting procedures across multiple loci.
3Reliability
If multiple sequencing runs are performed to achieve sufficient statistical power, then detection reliability improves, but the time and cost increase
Solution Approach 1:
The method achieves sufficient statistical power by segmenting the genome into numerous independent loci, each contributing discrete count data. The aggregation of statistical evidence across many segmented loci provides robust power in a single sequencing run, eliminating the need for multiple runs while maintaining high detection reliability.
Solution Approach 2:
The invention merges statistical information from multiple independent loci into a unified CNV detection framework. By combining count data across numerous loci simultaneously in a single sequencing experiment, the method achieves the statistical power that would otherwise require multiple separate sequencing runs, thereby reducing time and cost.
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.