Cross-variant Polygenic Risk Modeling for Genetic Data Analysis
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Solution Overview
Problem
Existing polygenic predictive data analysis systems face challenges in accuracy and reliability due to their inability to effectively map relationships between genetic sequences and medical conditions, particularly when individual genetic variant interactions are not statistically significant, leading to loss of valuable information and resource-intensive statistical operations.
Innovation Solution
The use of cross-variant polygenic risk modeling that generates per-variant genetic risk scores and risk profiles, allowing for more precise analysis by independently describing correlations between genetic variants and medical conditions, reducing the need for resource-intensive operations through log of odds ratios and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional polygenic predictive data analysis is used, then the system can process genetic data, but the accuracy and reliability are reduced due to inability to effectively map relationships between genetic sequences and medical conditions
Solution Approach 1:
The patent segments the genetic analysis into per-variant genetic risk scores for individual genetic variants. Each variant is independently evaluated and scored, allowing the system to capture subtle individual contributions that traditional aggregate methods miss. This segmentation enables more precise mapping between specific genetic variants and medical conditions, improving reliability without losing information.
Solution Approach 2:
The patent transforms the analysis by changing from aggregate polygenic risk scores to per-variant risk scores with functional annotations. This parameter change includes adding functional grouping dimensions (e.g., gene ontology categories, pathway memberships) to each variant's risk score, enabling more nuanced interpretation and improving the mapping between genetic data and medical conditions.
2Measurement precision
If traditional statistical operations are performed on individual genetic variants, then comprehensive analysis is achieved, but resource-intensive operations are required
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing per-variant genetic risk scores and their functional annotations during a training phase. This preliminary computation includes calculating odds ratios and establishing variant-disease associations in advance, so that during actual predictive analysis, the system can quickly retrieve and apply these pre-computed values without performing resource-intensive statistical operations in real-time.
Solution Approach 2:
The patent creates simplified copies of complex genetic relationships in the form of pre-computed per-variant risk scores and functional annotations. Instead of performing full statistical operations on raw genetic data during each analysis, the system uses these copied representations that capture the essential relationships, dramatically improving computational efficiency while maintaining measurement precision.
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.