Dynamic Sliding Window for Rare Variant Correlation
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Solution Overview
Problem
Current genomic analysis techniques, such as GWAS and gene-based collapsing analysis, face limitations in precision when identifying rare genetic variants associated with specific traits, as common variants can mask the influence of nearby rare variants.
Innovation Solution
A sliding window approach is implemented, where the size of the window dynamically varies to maintain a consistent range of individuals carrying qualifying variants, allowing for iterative statistical analysis and correlation detection across genomic coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gene-based collapsing analysis is used to analyze rare variants, then the precision of identifying rare variant associations is improved, but the difficulty of selecting the exact genomic portion for analysis increases
Solution Approach 1:
The patent implements a dynamic sliding window approach where the window size and position are adjusted iteratively based on the distribution of qualifying variants and their association with the trait. The window dynamically expands or contracts to maintain optimal statistical power while capturing rare variants, resolving the difficulty of selecting fixed genomic portions.
Solution Approach 2:
The method changes the parameter of window size dynamically during the analysis process. By adjusting the window size based on the number of qualifying variants and their statistical association with the trait, the system optimizes the balance between capturing enough variants for statistical power and maintaining precision in identifying true associations.
2Reliability
If common variants are included in the analysis window, then the statistical power is increased, but the ability to detect rare variant associations is reduced due to masking effects
Solution Approach 1:
The sliding window approach applies local quality by focusing analysis on specific genomic regions with appropriate density of qualifying variants. Rather than uniformly analyzing all variants, the method creates localized analysis windows that concentrate on regions where rare variants are present, reducing masking effects while maintaining statistical power through iterative refinement.
Solution Approach 2:
The method uses partial action by analyzing only specific portions of the genome at any given time through the sliding window, rather than analyzing all variants simultaneously. This partial focus on regions with qualifying variants allows detection of rare variant associations without the masking effect of including all common variants across the entire genome.
3Ease of manufacture
If a fixed-size sliding window is used for analysis, then the computational simplicity is maintained, but the statistical consistency across different genomic regions is reduced
Solution Approach 1:
The patent transitions from a static fixed-size window to a dynamic sliding window that adapts its size and position based on the underlying data characteristics. This dynamic adjustment maintains statistical consistency across different genomic regions by ensuring each window contains an appropriate number of qualifying variants, while the iterative nature preserves computational feasibility.
Solution Approach 2:
The method implements feedback by iteratively analyzing the distribution of qualifying variants and their association with the trait, then using this information to adjust the sliding window position and size. This feedback loop ensures statistical consistency across genomic regions while maintaining computational simplicity through algorithmic automation.
Data Source
AI summary
Systems and methods are provided for analysis of genetic data. One embodiment is a system that includes a memory storing sequence data and trait data. The system also includes a controller that identifies qualifying variants within the sequence data. The controller generates a sliding window comprising a selection of sequential variants, wherein a number of the individuals in the population carrying a qualifying variant at the sliding window is within a predetermined range, and iteratively: performs a statistical analysis that indicates whether qualifying variants at genomic coordinates within a region occupied by the sliding window are correlated with the trait, and moves the sliding window across at least one variant along a chromosomal direction, while adjusting a number of variants encompassed by the sliding window to maintain a number of the individuals in the population carrying a qualifying variant at the sliding window within the predetermined range.