Genetic Variant Analysis via Computational Badges for Structural Variants
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Solution Overview
Problem
Current methods for clinical genome analysis and variant interpretation in next-generation sequencing are manual, time-consuming, and costly, with high computational complexity and a shortage of specialized personnel, limiting the scalability of diagnostic processes for Mendelian diseases and precision medicine.
Innovation Solution
A computer-implemented method for inferring structural variants by constructing badges to span genomic regions, identifying genetic variants, determining ploidy, and reporting changes in gene or regulatory element dosage, which automates the process and prioritizes variants associated with disease phenotypes, reducing false positives and improving diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert interpretation is used for variant analysis, then diagnostic accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an automated computational system that acts as an intermediary between raw sequencing data and clinical diagnosis. This system uses machine learning models and algorithms to process variants, serving as a bridge that reduces manual work while maintaining diagnostic accuracy through validated computational methods
Solution Approach 2:
The patent creates computational models that replicate expert interpretation capabilities. By training machine learning systems on expert-annotated data, the system copies and automates the decision-making processes of human experts, enabling scalable analysis without requiring proportional increases in specialized personnel
2Measurement precision
If manual curation protocols are used, then variant interpretation quality is improved, but scalability is limited due to shortage of specialized personnel
Solution Approach 1:
The patent implements self-service mechanisms where the computational system automatically performs variant filtering, prioritization, and interpretation without requiring continuous human intervention. The system serves itself by using trained models to make decisions, only requiring human oversight for complex cases, thereby enabling scalability while maintaining quality
Solution Approach 2:
The patent changes the operational parameters of variant analysis from manual expert review to automated computational processing. By adjusting parameters such as filtering thresholds, confidence scores, and prioritization criteria through systematic calibration, the system achieves scalable operation while maintaining interpretation quality comparable to manual methods
3Ease of manufacture
If computational complexity of data processing is reduced, then cost is decreased, but diagnostic accuracy may be compromised
Solution Approach 1:
The patent segments the complex data processing pipeline into distinct modular stages: quality control, variant calling, filtering, prioritization, and interpretation. Each segment is optimized independently with appropriate computational complexity, allowing cost-effective processing while maintaining overall diagnostic accuracy through systematic progression through each stage
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most clinically relevant variants rather than analyzing all possible variants exhaustively.通过使用优先级排序和过滤策略，系统对高置信度变体进行详细分析，而对低置信度变体采用简化处理，从而在保证诊断准确性的同时降低计算成本
Data Source
AI summary
Provided are methods for identifying gene variants associated with a phenotype, for example by inferring and scoring of structural variants from whole-genome or exome data or processing a set of genes against a known set of genes having known variants associated with a set of phenotypes, and optionally determining how likely each of the genes are to cause the phenotype.