Structural Variant Genotyping with Probabilistic Read Counting
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Solution Overview
Problem
Existing methods for genotyping structural variants (SVs) face challenges in achieving consistent accuracy across various lengths and types of SVs, particularly for insertion, deletion, and inversion variants, using conventional approaches.
Innovation Solution
A method and system for genotyping SVs using targeted sequencing and next-generation sequencing (NGS) technology, employing unique amplicons for wild-type and variant alleles, and a probabilistic model to determine genotypes based on read counts, with adjustable parameters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional genotyping methods are used, then the process is simple, but the accuracy varies across different SV lengths and types
Solution Approach 1:
The patent modifies reference genomes by introducing structural variant contigs with specific parameters (breakpoint positions, variant types, lengths) to create a modified reference genome that accounts for diverse SV characteristics. This parameter-based approach enables consistent genotyping across different SV types by adjusting reference parameters rather than using a single conventional method
Solution Approach 2:
The patent segments the reference genome into wild-type target regions and structural variant target regions (contigs), allowing separate handling and mapping of reads to appropriate regions. This segmentation enables the system to process different SV types (deletions, insertions, inversions) with specialized reference regions while maintaining overall genotyping consistency
2Measurement precision
If targeted sequencing with unique amplicons is used, then genotyping accuracy improves, but the device complexity increases
Solution Approach 1:
The patent employs universal primer binding sites that can amplify both wild-type and variant alleles across different SV types through a single targeted sequencing reaction. This multi-functional primer design eliminates the need for separate assays for different SV types, reducing overall system complexity while maintaining high genotyping accuracy through unique amplicon discrimination
3Reliability
If probabilistic models with adjustable parameters are used, then genotyping reliability improves, but the computational requirements increase
Solution Approach 1:
The patent replaces complex mechanical or manual genotyping processes with computational probabilistic models that calculate genotype probabilities based on read counts. This substitution uses software-based probability calculations instead of complex hardware or manual methods, achieving high reliability through statistical modeling while keeping computational requirements manageable through efficient probability algorithms
Data Source
AI summary
Methods for determining genotypes of structural variants in a sample genome, may include: amplifying nucleic acid sequences at targeted locations in the sample genome by a panel targeting a plurality of structural variant marker to generate sequence reads; mapping the sequence reads to a modified reference genome to produce aligned sequence reads, wherein the modified reference genome includes a wild-type target region and a structural variant target region; for each structural variant marker, determining a read count for a wild-type allele and a read count for a structural variant allele; determining a probability for each possible genotype, wherein the possible genotypes include a homozygous wild-type genotype, a heterozygous genotype and a homozygous structural variant genotype; and selecting the genotype with a maximum probability value to provide an estimated genotype corresponding to the structural variant marker of the sample.