NGS Genetic Variant Interpretation With ACMG Logic Trees
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Solution Overview
Problem
Existing methods struggle to accurately and efficiently interpret the pathogenicity of genetic variants detected by next-generation sequencing (NGS) using the ACMG guidelines, complicating disease diagnosis and research integration.
Innovation Solution
A logic tree algorithm that classifies genetic variants based on ACMG guidelines by integrating clinical characteristic, SNP frequency, repeat sequence, protein domain, and in-silico prediction information from various databases, determining pathogenicity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ACMG guidelines are used to classify genetic variants, then the pathogenicity classification can be standardized, but the complexity of integrating information and determining pathogenicity increases
Solution Approach 1:
The patent divides the complex ACMG guidelines into a structured decision tree with multiple branches, where each branch represents a specific criterion (P1-P10 for pathogenic, B1-B7 for benign). This segmentation allows systematic evaluation of each criterion independently, reducing the cognitive load and complexity of integrating all 28 criteria while maintaining comprehensive coverage of the classification system.
Solution Approach 2:
The decision tree structure enables dynamic evaluation where the pathogenicity determination changes based on the sequence of criteria met. The system adapts its evaluation path based on previous findings, allowing for flexible and efficient classification that can reach conclusions faster by prioritizing high-weight criteria first, thereby reducing overall processing complexity.
2Measurement precision
If comprehensive criteria are applied to determine pathogenicity, then the accuracy improves, but the time required for analysis increases
Solution Approach 1:
The decision tree performs preliminary evaluation of high-weight criteria (such as P1-P4 for pathogenic and B1-B3 for benign) before proceeding to lower-weight criteria. This preliminary action allows the system to quickly identify and classify variants that meet strong evidence thresholds, avoiding unnecessary time spent evaluating all criteria for every variant, while maintaining high accuracy through comprehensive criteria evaluation when needed.
Solution Approach 2:
The system applies partial evaluation by stopping at the first criterion that provides sufficient evidence for classification. If a variant meets strong pathogenic criteria (P1-P4) or strong benign criteria (B1-B3), the system can conclude the analysis without evaluating all remaining criteria, thereby reducing analysis time while maintaining adequate accuracy through the use of high-weight criteria.
3Reliability
If detailed evaluation of all criteria is performed, then the classification reliability improves, but the ease of operation decreases
Solution Approach 1:
The patent segments the complex ACMG guidelines into a visual decision tree with clear branching paths, making the evaluation process more intuitive and easier to operate. Each branch represents a specific criterion with clear decision points, allowing users to systematically navigate the classification process without having to manually track multiple criteria simultaneously, thereby improving ease of operation while maintaining high reliability.
Solution Approach 2:
The decision tree acts as an intermediary structure that mediates between the complex ACMG guidelines and the user's analysis process. It translates the abstract criteria into concrete, step-by-step decision paths, serving as a bridge that simplifies operation while ensuring reliable classification by systematically applying all necessary criteria through the tree structure.
Data Source
AI summary
The types of genetic variants detected by NGS are very wide and not all genetic variants always lead to diseases, and thus it is difficult to quickly and accurately interpret the meaning of disease relevance for detected genetic variants. The present invention relates to a method of interpreting genetic variants based on nucleic acid sequencing. The method of interpreting genetic variants according to the present invention provides a logic tree for interpreting NGS variant data, which can classify the pathogenicity of genetic variants based on the ACMG guidelines and determine the level of pathogenicity of the genetic variants, and thus it is expected to be widely used in the life sciences and medical health fields.