Genomic Data Annotation System for Polygenic Risk Prediction
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Solution Overview
Problem
Current clinical genetic tests for polygenic conditions have low accuracy, particularly for non-European ancestry individuals, leading to false negatives and delayed detection of life-threatening conditions, as they primarily focus on coding regions and miss non-coding variants crucial for disease risk prediction.
Innovation Solution
A system that converts unannotated genomic data into a standardized format, matches it with annotation data from diverse sources, and generates annotated variant loci, providing a polygenic risk score and personalized disease prevention or treatment recommendations by integrating non-coding variant information and functional annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical genetic tests focus on coding regions only, then the test complexity and cost are reduced, but the measurement precision and reliability of disease risk prediction deteriorate due to missing non-coding variants
Solution Approach 1:
The system segments the genome into coding and non-coding regions, and further segments non-coding regions into functional elements (promoters, enhancers, etc.). This segmentation allows comprehensive analysis of all genomic regions while organizing the complexity into manageable categories, thereby improving prediction accuracy without overwhelming computational burden
Solution Approach 2:
The system introduces an intermediary layer of functional annotation data that bridges raw genomic sequences and disease risk predictions. This intermediary layer includes pre-computed functional scores and regulatory element annotations that simplify the interpretation of non-coding variants, improving accuracy while managing complexity through intermediate representation
2Adaptability or versatility
If clinical genetic tests use standardized annotation formats, then the ease of operation and data integration are improved, but the adaptability to diverse data sources and varying data qualities deteriorates
Solution Approach 1:
The system dynamically adjusts annotation parameters and filtering thresholds based on data source characteristics and quality metrics. Different data sources receive customized processing parameters while all outputs are standardized to a common format, thereby maintaining both adaptability to diverse sources and ease of operation through uniform output standards
3Measurement precision
If polygenic risk scores are calculated using current methods, then the productivity and speed of genetic testing are maintained, but the measurement precision deteriorates due to low accuracy particularly for non-European ancestry individuals
Solution Approach 1:
The system performs preliminary actions by pre-computing functional annotations, regulatory element mappings, and population-specific parameter sets before actual risk calculation. This preliminary preparation enables faster and more accurate polygenic risk scoring for diverse populations without increasing real-time computational burden, thereby improving accuracy while maintaining speed
Data Source
AI summary
In variants, the method can include receiving a subject's unannotated genomic data, optionally generating annotated variant loci, and optionally determining a risk score for the subject. The method can function to: provide genomic data analysis to a user; predict disease risk; and/or provide recommendations for screenings, treatment, and/or lifestyle changes.