Genetic Risk Profiling via Phenotype Interaction Analysis
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Solution Overview
Problem
Current genetic screening methods provide general risk levels for disease contraction, which may not be actionable for patients, and determining precise individualized risk based on genetic variants and phenotypes is computationally intensive and limited to considering a few phenotypes at a time.
Innovation Solution
Systems and methods that identify phenotypes impacting disease progression for carriers of genetic variants, generating tailored risk profiles by analyzing the impact of varying phenotypes within carrier populations, allowing for the identification of additive and multiplicative effects on disease risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If formal interaction analysis is performed to determine precise individualized risk based on phenotypes and genetics, then measurement precision of disease risk is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the comprehensive interaction analysis into two distinct phases: (1) a training phase that pre-computes phenotype interaction weights and risk multipliers from reference population data, and (2) an application phase that applies these pre-computed factors to individual patients. This segmentation transforms the computationally intensive full interaction analysis into efficient lookups and calculations for clinical use.
Solution Approach 2:
The system performs preliminary action by pre-computing phenotype interaction effects, risk multipliers, and weighting factors from large reference datasets before clinical application. These pre-computed interaction profiles are stored and readily applied to individual patients, eliminating the need to perform exhaustive interaction analyses in real-time clinical settings.
2Measurement precision
If comprehensive phenotype analysis is performed to generate tailored risk profiles, then measurement precision of disease risk is improved, but time consumption increases
Solution Approach 1:
The analysis is segmented into offline training computations and online clinical applications. The time-consuming comprehensive phenotype interactions are analyzed in advance during system setup, while individual patient assessments utilize these pre-analyzed interaction profiles for rapid risk calculation.
Solution Approach 2:
Comprehensive phenotype interaction analyses are performed as preliminary actions during system initialization and training phases. These pre-computed interaction profiles enable rapid individualized risk assessment during clinical use without repeating the exhaustive analysis for each patient.
3Ease of operation
If general genetic screening is used to provide disease risk information, then ease of operation is maintained, but measurement precision of disease risk deteriorates
Solution Approach 1:
The system introduces phenotype interaction profiles as an intermediary layer between simple genetic screening and complex interaction analysis. These pre-computed profiles serve as mediators that translate basic genetic variant data into refined risk estimates by incorporating known phenotype interactions, maintaining operational simplicity while improving precision.
Solution Approach 2:
The system enhances the basic genetic risk parameter by applying phenotype interaction multipliers and weighting factors. This parameter transformation converts simple presence/absence genetic data into refined risk estimates that account for modifying phenotypes, improving precision without complicating the core screening process.
Data Source
AI summary
Systems and methods herein provide for identifying phenotypes that impact disease progression for carriers of genetic variants. One method includes identifying, from a larger population of gene sequences, a first plurality of gene sequences of probands having a genetic variant that is susceptible to contracting a primary phenotype. For each of a plurality of interacting phenotypes, the method includes identifying, from the first plurality of gene sequences, a second plurality of gene sequences of probands having the interacting phenotype, determining a difference between an odds ratio indicating a likelihood of probands having the primary phenotype for the second plurality of gene sequences and an odds ratio indicating a likelihood of probands having the primary phenotype for the first plurality of gene sequences, and selecting the interacting phenotype based on the difference. The method also includes identifying a risk of contracting the primary phenotype for each of the selected interacting phenotypes.