Ancestry-Specific Genetic Risk Score Calculation
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Solution Overview
Problem
Current genetic risk prediction methods fail to account for an individual's ancestry, leading to imprecise and inaccurate predictions due to the use of ancestry-agnostic proxy genetic variants, which can be in linkage disequilibrium (LD) in one population but not another.
Innovation Solution
The method involves determining an individual's ancestry and calculating a genetic risk score (GRS) using ancestry-specific genetic variants derived from subjects of the same ancestry, selecting proxy variants based on LD patterns within the individual's ancestral population, and incorporating these into a trait-associated database for personalized risk predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ancestry-agnostic genetic variants are used for risk prediction, then the method is simple and broadly applicable, but the prediction accuracy decreases due to population-specific linkage disequilibrium patterns
Solution Approach 1:
The patent applies local quality by transitioning from uniform ancestry-agnostic genetic variants to ancestry-specific genetic variants tailored to each population group. Different ancestral populations (e.g., European, African, Asian) have distinct linkage disequilibrium patterns, so the method selects genetic variants and LD proxies specific to each population's local genetic architecture, thereby improving prediction accuracy without sacrificing broad applicability.
Solution Approach 2:
The patent changes the parameter of genetic variant selection from fixed ancestry-agnostic variants to dynamically selected ancestry-specific variants based on the individual's determined ancestry. This parameter change allows the system to adapt to population-specific LD patterns, resolving the contradiction between method simplicity and prediction accuracy.
2Measurement precision
If ancestry-specific genetic variants are used for risk prediction, then the prediction accuracy improves, but the method complexity increases due to ancestry determination and population-specific variant selection
Solution Approach 1:
The patent applies preliminary action by performing ancestry determination early in the workflow, before genetic risk score calculation. By pre-classifying individuals into ancestral populations and pre-selecting appropriate ancestry-specific genetic variants and LD proxies for each population, the system streamlines the overall process despite the added complexity of ancestry-specific analysis.
Solution Approach 2:
The patent introduces ancestry determination as an intermediary step that mediates between the individual's genotype data and the appropriate genetic risk prediction model. This intermediary classifies the individual into an ancestral population, which then guides the selection of appropriate ancestry-specific variants and LD patterns, managing complexity through structured intermediate processing.
3Measurement precision
If ancestry-specific linkage disequilibrium patterns are accounted for, then the genetic risk score accuracy improves, but computational requirements and data processing complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the global population into distinct ancestral segments (e.g., European, African, Asian, admixed populations), each with its own LD patterns and risk variants. This segmentation allows the system to process each population's genetic data with population-appropriate parameters, improving accuracy while managing computational complexity through modular, population-specific analysis pipelines.
Data Source
AI summary
Disclosed herein are methods and systems for calculating genetic risk scores (GRS) representing the likelihood that an individual will develop a specific trait based on the ancestry of the individual. Also provided are methods and systems for providing a recommendation to the individual to modify a behavior related to a specific trait, based on the individual's GRS for that trait.