Human Genetic Variant Pathogenicity Scoring With Dynamic Evidence
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Solution Overview
Problem
Current methods for evaluating the clinical significance of genetic variants, particularly those of unknown significance (VUS), are inadequate due to static and outdated mutation lists, lacking real-time updates and inconsistent assessments, leading to unclear guidance for healthcare providers.
Innovation Solution
A custom database and scoring technique that integrates variant-related data, including biological function, population frequency, co-occurrence with known variants, family segregation, and minor evidence, to determine a clinical significance score using a combination of function, frequency, co-occurrence, and family segregation scores, optionally with minor evidence, and generates standardized reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static mutation list is assembled at time of discovery, then the initial variant assessment is completed, but the list cannot be updated in real time leading to outdated information
Solution Approach 1:
The patent transforms the static mutation list into a dynamic system that automatically updates in real-time. The database continuously integrates new variant data, functional predictions, population frequencies, and clinical observations, allowing the system to adapt and evolve without manual intervention or time lags.
Solution Approach 2:
The system implements feedback loops where clinical observations, family segregation data, and functional assay results are continuously fed back into the database. This feedback mechanism allows the variant assessment to be automatically refined and updated based on new evidence, ensuring current and accurate information.
2Measurement precision
If comprehensive variant data collection is implemented, then assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive variant assessment into distinct modular components: functional predictions, population frequencies, co-occurrence patterns, family segregation analysis, and clinical observations. Each component is independently calculated and then integrated through a standardized scoring algorithm, making the complex system manageable and systematic.
Solution Approach 2:
The database system is designed as a universal platform that handles multiple types of variant data and assessment criteria through a single integrated framework. The same database structure and scoring algorithm can evaluate different variant types across multiple genes and disease contexts, reducing overall system complexity through standardization.
3Reliability
If real-time updates are implemented, then clinical guidance currency is improved, but data management complexity increases
Solution Approach 1:
The system is designed to automatically update itself without requiring manual intervention. The database automatically ingests new variant data, recalculates functional predictions, updates population frequencies, and regenerates clinical guidance reports in real-time, making the data management process self-service and reducing operational complexity.
Solution Approach 2:
The system performs preliminary data processing and validation before integration. Functional predictions, population frequencies, and other data elements are pre-processed and standardized before being incorporated into the database, which streamlines the real-time update process and reduces management complexity.
4Measurement precision
If multiple scoring criteria are integrated, then assessment comprehensiveness is improved, but computational requirements increase
Solution Approach 1:
The patent implements a tiered scoring approach where the most critical criteria (functional impact, population frequency, inheritance pattern) are given higher weights in the aggregation algorithm. This allows the system to achieve comprehensive assessment while optimizing computational resources by focusing on the most influential factors.
Data Source
AI summary
Provided are methods and systems for determining the clinical significance of a genetic variant. The methods entail determining, for the variant, (a) a function score based on known impact of the variant on a biological function of a cell or protein, (b) a frequency score based on the frequency of the variant in a population, (c) a co-occurrence score based on how the variant co-occurs with a reference variant having known clinical significance relating to a clinical disease or condition, and (d) a family segregation score based on how the variant segregates with a disease or condition in a family; and aggregating, on a computer, the function score, the frequency score, the co-occurrence score, the family segregation score to generate a clinical significance score indicating the clinical significance of the genetic variant.