Low Coverage Whole Genome Sequencing for Rare Variant Detection
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Solution Overview
Problem
Current genome-wide association studies (GWAS) face challenges in analyzing less common genetic variants due to high costs and limitations in low-coverage whole genome sequencing (lcWGS), which often focus on high-frequency variants, leaving less frequent loci unanalyzed despite their potential strong statistical association with traits.
Innovation Solution
Implementing low-coverage whole genome sequencing (lcWGS) to collect and analyze a large set of samples with associated phenotypes, focusing on less common variants (Minor Allele Frequency <1%), and using imputation techniques to estimate missing genotypes, combined with statistical association methods to identify significant associations across different populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If low-coverage whole genome sequencing is used to reduce costs, then cost-effectiveness improves, but the ability to analyze less common variants deteriorates
Solution Approach 1:
The patent segments the genome analysis into two distinct approaches: low-coverage whole genome sequencing for common variants and exome sequencing for less common variants. This segmentation allows each method to be optimized for its appropriate variant type, with low-coverage WGS providing cost-effective analysis of common variants while exome sequencing provides the necessary depth for detecting less common variants.
Solution Approach 2:
The patent applies different sequencing depths to different genomic regions based on the expected variant frequency. Common variant regions are analyzed using lower coverage (more cost-effective), while less common variant regions in the exome are analyzed using higher coverage (more precise). This local quality adjustment optimizes both cost and accuracy for different genomic contexts.
2Productivity
If low-coverage whole genome sequencing focuses on high-frequency variants, then analysis efficiency improves, but the ability to detect less frequent loci deteriorates
Solution Approach 1:
The patent merges two complementary sequencing approaches: low-coverage whole genome sequencing and exome sequencing. The low-coverage WGS provides efficient screening of common variants across the entire genome, while exome sequencing adds comprehensive coverage of less common variants in coding regions. Together, they achieve both high analysis efficiency and broad variant frequency coverage.
Solution Approach 2:
The combined sequencing approach serves multiple functions: it efficiently detects common variants through low-coverage WGS while simultaneously capturing less common variants through exome sequencing. This multi-functional system addresses both common and rare variant detection needs within a unified analytical framework.
3Measurement precision
If whole genome sequencing is performed at high coverage, then variant detection accuracy improves, but cost increases significantly
Solution Approach 1:
The patent applies partial sequencing coverage strategically: low-coverage whole genome sequencing is used for common variants where lower depth is sufficient, while higher coverage exome sequencing is applied specifically to regions containing less common variants. This partial action approach avoids the excessive cost of uniform high-coverage whole genome sequencing while maintaining necessary accuracy for rare variant detection.
Solution Approach 2:
The patent changes the coverage parameter based on the genomic region and expected variant frequency. Instead of maintaining a single high coverage level across the entire genome, the system adjusts coverage depth: lower coverage for common variant regions and higher coverage for exome regions where less common variants are expected. This parameter optimization achieves cost-effectiveness while preserving detection accuracy where needed.
Data Source
AI summary
Techniques for next generation sequencing (NGS), and more particularly, to techniques for applying low coverage whole genome sequencing (lcWGS) in genome wide association studies (GWAS). One aspect includes performing a lcWGS of a biological sample from a subject to obtain a set of reads, determining an inference of a phenotype from the set of reads, obtaining self-reported data from the subject, executing a first query on eligibility criteria for a plurality of genomic routes to obtain a set of genomic routes that satisfy the first query, executing a second query on routing criteria for the set of genomic routes to obtain a subset of ranked genomic routes that satisfy the second query, and selecting one or more genomic routes from the subset of ranked genomic routes based on the ranking of each of the one or more genomic routes.