Computational Model for Genomic Variant Pathogenicity Scoring
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Solution Overview
Problem
Current methods fail to effectively diagnose and treat complex medical disorders, particularly those influenced by noncoding genomic RNA regulatory sequences and sequence variants, as they do not adequately account for the biochemical regulatory effects of these variants on phenotypic variations.
Innovation Solution
A computational model is trained to evaluate genomic variants by aggregating their effects on biochemical regulatory processes, using chromatin and RNA binding protein profiles, to determine pathogenicity scores for diagnosing and treating medical disorders such as autism spectrum disorder, Alzheimer's disease, and other complex traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic methods are used, then simplicity and ease of operation are maintained, but diagnostic accuracy and reliability for complex medical disorders deteriorate
Solution Approach 1:
A computational model serves as an intermediary between raw genetic data and diagnostic conclusions. The model processes multiple genetic variants and their biochemical regulatory effects, aggregating them into a cumulative pathogenicity score that informs diagnosis. This intermediary handles the complexity of integrating multiple data sources while providing a clear diagnostic output.
Solution Approach 2:
The diagnostic approach segments the genome into specific loci known to harbor pathogenic variants affecting biochemical processes. By focusing analysis on these segmented regions rather than the entire genome, the method improves diagnostic accuracy for complex disorders while managing computational complexity through targeted analysis.
2Reliability
If cumulative pathogenicity scoring is implemented, then diagnostic accuracy improves, but computational requirements and analysis time increase
Solution Approach 1:
Genomic loci are pre-identified and selected based on prior knowledge of which regions harbor pathogenic variants affecting biochemical processes. This preliminary selection of relevant loci reduces the scope of subsequent analysis, allowing cumulative pathogenicity scoring to be performed more efficiently while maintaining high diagnostic reliability.
3Loss of information
If noncoding genomic RNA regulatory sequences are analyzed, then understanding of phenotypic variations improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The computational model acts as an intermediary that processes difficult-to-measure noncoding genomic RNA regulatory sequences. The model translates these complex regulatory effects into quantifiable pathogenicity scores, making the information from noncoding regions accessible and actionable for diagnosis without requiring direct manual measurement of each regulatory element.
Data Source
AI summary
Processes to identify variants that affect biochemical regulation are described. Generally, models are used to identify variants that affect biochemical regulation, which can be used in several downstream applications. A pathogenicity of identified variants is also determined in some instances, which can also be used in several. Various methods further develop research tools, perform diagnostics, and treat individuals based on identified variants.