Gene-Phenotype Graph Analysis for Faster Variant Interpretation
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Solution Overview
Problem
Current methods for identifying clinically significant genetic variants in exome or genome samples are time-consuming and inefficient, requiring manual review of long lists of variants and lacking a standardized approach for linking genes to phenotypes.
Innovation Solution
A graph-based algorithm and clinical decision tool that converts gene-to-phenotype associations into a standardized format, using assertions to quickly identify and score genes relevant to input phenotypes, facilitating rapid interpretation of sequencing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to identify clinically significant genetic variants, then accuracy can be maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual review processes with an automated computational system that uses graph-based algorithms to identify genes associated with phenotypes. The system automatically processes sequencing data, links genes to phenotypes through standardized assertions, and generates prioritized gene lists, eliminating the need for manual examination while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service by automatically performing the entire workflow from data input to gene identification without requiring manual intervention. The automated algorithm processes the input phenotypes, queries the knowledge base, and generates output gene lists independently, allowing the system to serve itself rather than requiring continuous human review.
2Measurement precision
If comprehensive gene-phenotype linking is performed, then identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the complex gene-phenotype linking process into discrete, manageable components: (1) receiving input phenotypes, (2) querying standardized assertions from a knowledge base, (3) processing results through a graph-based algorithm, and (4) generating output gene lists. This segmentation reduces processing complexity by breaking down the comprehensive linking task into sequential steps.
Solution Approach 2:
The patent introduces standardized assertions as an intermediary layer between phenotypes and genes. These assertions serve as pre-processed, standardized knowledge units that mediate the connection between input phenotypes and target genes, simplifying the processing complexity by providing a structured intermediate representation rather than requiring direct complex pattern matching.
3Ease of operation
If standardized format for gene-phenotype assertions is implemented, then ease of operation improves, but data format complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming diverse gene-phenotype association data into a standardized format with consistent parameters and structure. The system converts various input formats into a unified representation using standardized assertions, which simplifies operations by providing a consistent data interface while managing format complexity through controlled transformation.
Data Source
AI summary
The present disclosure relates to systems and methods for identifying genes associated with phenotypes. A list of phenotypes is provided as input and the systems and methods automatically provide an output with a list of genes associated with the phenotypes provided. The systems and method analyze assertions linking a gene to a phenotype using a graph-based algorithm to identify the genes associated with the phenotypes.