Genetic Variant Prioritization via Composite Scoring
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Solution Overview
Problem
Current algorithms for predicting the functional effect of genetic variants yield discordant results, making it challenging to determine which variants are clinically significant for disease diagnosis and therapy, particularly in next-generation sequencing data analysis.
Innovation Solution
A method and system that integrate pathogenicity scores with prevalence and mutation rate data from disease and healthy populations to generate a composite priority score for genetic variants, reducing false positives and improving the reliability of variant prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple algorithms are used to predict the functional effect of variants, then the coverage of variant assessment is improved, but the discrepancy and discordance in results increase
Solution Approach 1:
The patent combines multiple independent variant prediction algorithms (SIFT, PolyPhen-2, LRT, MutationTaster) into a unified scoring system that integrates their results. Instead of using algorithms separately which produce discordant results, the invention merges their predictions into a composite priority score that resolves conflicts and provides consistent variant prioritization across all algorithms.
Solution Approach 2:
The invention creates a composite scoring system that integrates predictions from multiple algorithms along with prevalence and mutation rate data. This composite approach combines heterogeneous information sources (different algorithm scores, population frequencies, mutation rates) into a unified priority score, similar to how composite materials combine different substances to achieve superior properties.
2Productivity
If pathogenicity scores from algorithms are used alone, then the prediction speed is maintained, but the false positive rate increases
Solution Approach 1:
The system incorporates feedback from population data (prevalence in disease vs. healthy populations and mutation rates) to adjust and refine the algorithm predictions. This feedback mechanism allows the system to maintain fast algorithm-based screening while using population data to filter out false positives, thereby improving reliability without sacrificing prediction speed.
3Measurement precision
If comprehensive data analysis including prevalence and mutation rates is performed, then the accuracy of variant prioritization is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the variant prioritization process into distinct computational modules: (1) algorithm-based pathogenicity scoring, (2) prevalence calculation in disease and healthy populations, (3) mutation rate determination, and (4) composite priority score integration. This segmentation allows each module to be optimized independently and processed efficiently, reducing overall computational complexity while maintaining comprehensive analysis.
4Reliability
If multiple scoring metrics are integrated to produce a priority score, then the reliability of variant classification is improved, but the system complexity increases
Solution Approach 1:
The patent creates a universal priority scoring system that can process and integrate multiple different types of input data (algorithm predictions, prevalence rates, mutation rates) through a single unified framework. This multi-functional system handles diverse data types using consistent computational logic, improving classification reliability while avoiding the need for separate complex systems for each data type.
Data Source
AI summary
Systems and methods involve generating a priority score for a variant of a gene based on its potential significance to a disease or other condition. The prevalence of a variant, the mutation rate of a gene containing a variant, and/or pathogenicity scores associated with a variant may be utilized to determine a priority score. Priority scores may be calculated for multiple variants, and the variants may be ranked based on the generated priority scores.