Genetic Risk Score Calculation Using Bayesian Analysis
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Solution Overview
Problem
Current methods for assessing genetic disease risk rely heavily on family medical history but lack efficient computational tools to integrate and analyze this information effectively, leading to suboptimal risk prediction and diagnosis.
Innovation Solution
A computer-implemented method that assigns genotypes to family members based on inheritance modes and analyzes Mendelian and Bayesian probabilities to determine genetic and family history risk scores, integrating age and diagnosis information to predict genetic family history risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If family medical history analysis is performed manually or with basic tools, then the assessment can be conducted, but the accuracy and comprehensiveness of risk prediction is insufficient
Solution Approach 1:
The patent segments the family medical history analysis into distinct computational modules: pedigree construction from unstructured data, genotype assignment based on inheritance modes, Mendelian risk calculation, and Bayesian risk integration. Each module handles a specific aspect of the analysis independently, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces computational algorithms as intermediaries between raw family medical history data and clinical risk assessment. These algorithms automatically process unstructured pedigree information, apply genetic inheritance rules, and generate standardized risk scores, eliminating manual analysis limitations while maintaining clinical interpretability.
2Reliability
If comprehensive family history data is collected and analyzed, then the diagnostic capability is improved, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary automated processing of family medical history data by automatically constructing pedigrees from unstructured text, pre-assigning genotypes based on inheritance patterns, and pre-calculating risk scores before clinical review. This preliminary computational action reduces the time required for comprehensive analysis while maintaining diagnostic reliability.
Solution Approach 2:
The patent transforms unstructured family history narratives into standardized structured parameters (pedigree charts, genotype assignments, risk scores) that can be processed efficiently by computational algorithms. This parameter transformation enables comprehensive data analysis to be performed rapidly while preserving all diagnostic information.
Data Source
AI summary
A computer-implemented method comprising: assigning, a plurality of family members of a patient, at least one genotype, respectively; determining a genetic risk score indicating a likelihood of the patient having the condition using Mendelian and/or Bayesian analysis; determining a family history risk score indicating a likelihood of the patient having the condition based on: a number of family members of the patient are known to have or have had the condition and who are not known to have died from the condition and at least one family member's age upon diagnosis with the condition, and a number of family members of the patient are known to have died from the condition and at least one family member's age upon death; and determining a genetic family history risk score of the patient having the condition based on the genetic risk score and the family history risk score.