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, and they require 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 accurate and efficient predictive data analysis by focusing on independent correlations between genetic variants and medical conditions, reducing the need for resource-intensive operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional polygenic predictive data analysis systems are used to map relationships between genetic sequences and medical conditions, then comprehensive analysis coverage is achieved, but accuracy and reliability deteriorate when individual genetic variant interactions are not statistically significant
Solution Approach 1:
The patent segments the polygenic risk analysis into independent per-variant genetic risk scores, organizing them into structured genetic risk profiles. This segmentation allows each genetic variant to be evaluated independently with its own risk score, rather than relying on statistically significant interactions between variants. The genetic risk profile structure (including per-chromosome profile segments and per-functional-grouping profile segments) enables comprehensive coverage while maintaining reliability through independent variant assessment.
2Productivity
If traditional statistical operations are performed to analyze genetic variant interactions, then comprehensive relationship mapping is achieved, but computational resources and time requirements increase significantly
Solution Approach 1:
The patent extracts the essential predictive information from complex statistical operations by generating independent per-variant genetic risk scores. Instead of performing resource-intensive statistical operations to analyze variant interactions, the system extracts individual variant risk scores and organizes them into genetic risk profiles. This extraction approach maintains comprehensive analysis coverage while dramatically reducing computational complexity and resource requirements.
3Measurement precision
If per-variant genetic risk scores are generated for all genetic variants, then accuracy of predictive data analysis is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies local quality by organizing per-variant genetic risk scores into structured genetic risk profiles with specific organizational patterns (per-chromosome profile segments, per-functional-grouping profile segments). This local organization allows the system to maintain high measurement precision through comprehensive per-variant scoring while optimizing processing efficiency through structured data organization. The local quality structure enables selective processing and retrieval of risk scores based on chromosomal or functional grouping needs.
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.