Genetic Risk Analysis Using Saliva Sampling and Selective Genotyping
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Solution Overview
Problem
Current genetic health risk tests are inefficient due to the need for inconvenient remote testing, inaccurate risk determinations due to insufficient clinical cases, and issues with false positives and false negatives caused by data overload and overlapping ranges.
Innovation Solution
A genetic-based biological sample analysis system and method that uses qualitative genotyping to detect clinically relevant genetic variants associated with specific diseases, such as lung and liver disease, by analyzing genomic DNA from saliva samples and employing a genetic risk model to classify user-specific risks into predetermined categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If genetic health risk tests are conducted at remote locations under professional prescription, then testing accuracy and reliability are improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The testing process is divided into two independent parts: a consumer-friendly at-home sample collection kit and a professional laboratory analysis service. Users can easily collect saliva samples at home using simple instructions, while the complex genetic analysis is performed by certified laboratories, combining the ease of home testing with the reliability of professional analysis.
Solution Approach 2:
A third-party service provider acts as an intermediary between the user and the laboratory. The service provider coordinates the entire process including sample receipt, validation, analysis, and result delivery, eliminating the need for users to directly interact with laboratory facilities while maintaining professional standards.
2Measurement precision
If comprehensive genetic data is collected and stored, then measurement precision and risk determination accuracy are improved, but data storage requirements and system complexity increase
Solution Approach 1:
The system extracts and analyzes only the specific genetic variants and markers relevant to the requested disease risk assessment, rather than storing and processing entire genomes. This selective extraction maintains measurement precision for the specific question while dramatically reducing data storage requirements.
Solution Approach 2:
The system performs partial genetic analysis focused on specific gene regions and variants known to be associated with the disease in question, rather than comprehensive whole-genome sequencing. This partial action provides sufficient accuracy for risk assessment while minimizing data processing and storage needs.
3Measurement precision
If clinical cases data is used for risk determination, then measurement precision is improved, but reliability deteriorates when clinical cases are insufficient or lack consensus
Solution Approach 1:
The system incorporates multiple validation layers before delivering risk determinations: expert review of the genetic variants, verification against established medical literature, assessment of clinical case sufficiency, and provision of uncertainty qualifiers. This beforehand cushioning prevents unreliable determinations from being presented to users.
Solution Approach 2:
The system continuously monitors and updates its risk determinations based on new clinical evidence and feedback from the scientific community. When new research emerges or clinical case data becomes available, the system revises its risk assessments to maintain both precision and reliability.
Data Source
AI summary
Genetic-based biological sample analysis systems and methods for detecting a user-specific genetic health risk related to a disease are disclosed herein. The systems and methods comprise obtaining user specific data of a user for detecting a likelihood of occurrence of a user-specific disease. Generation of a user profile causes a user test kit to be delivered to the user for collecting a biological sample comprising genomic deoxyribonucleic acid (DNA) of the user. Lab-based genetic analysis output, defining allele(s) of the user selected from related clinically relevant allelic variant genotype(s), is determined from the biological sample, and a classification is output by a genetic risk model defining a predetermined risk category based on the user's identified allele(s). A user-specific genetic health risk determination is generated based on the classification of the user and the lab-based genetic analysis output and is provided to the user for display on a user interface.