SNP Markers Predicting Multiple Sclerosis Severity
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Solution Overview
Problem
Current methods for predicting the severity of Multiple Sclerosis (MS) are inadequate, as they rely on categorical approaches and have not identified reliable genetic markers for disease severity, limiting the ability to personalize treatment and manage the disease effectively.
Innovation Solution
The use of specific single nucleotide polymorphisms (SNPs) such as rs2059283 and rs12927173, along with others in Linkage Disequilibrium, to predict MS severity, combined with interferon-beta treatment for managing the disease.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If categorical approaches are used to predict MS severity, then the prediction method is simple, but the reliability of genetic markers is insufficient
Solution Approach 1:
The patent segments the complex genetic prediction problem into multiple discrete SNP markers (rs2059283, rs12927173, and others in Linkage Disequilibrium). Each SNP is analyzed independently to determine its nucleotide type, and these individual predictions are combined to stratify patients into severity groups. This segmentation transforms an unreliable categorical approach into a reliable multi-marker system while maintaining manageable complexity through standardized genotyping protocols.
2Adaptability or versatility
If genetic markers are identified to predict MS severity, then personalized treatment can be enabled, but the difficulty of detecting and measuring severity increases
Solution Approach 1:
The patent introduces SNP genetic markers as intermediary indicators that mediate between complex disease severity characteristics and treatment decisions. Instead of directly measuring complex clinical severity parameters, the method uses easily detectable SNP nucleotide types (A, T, C, or G) as proxies. These genetic intermediaries correlate with disease severity and enable personalized treatment stratification while simplifying the detection and measurement process through standard genotyping techniques.
3Measurement precision
If multiple SNPs are used for prediction, then the measurement precision of severity increases, but the device complexity of genotyping increases
Solution Approach 1:
The patent merges multiple SNP detection functions into a unified genotyping system that simultaneously analyzes SNPs rs2059283, rs12927173, and other linked markers. By combining these detection functions into an integrated approach using Linkage Disequilibrium relationships, the system achieves high measurement precision for severity prediction while avoiding the complexity of independently analyzing each SNP. The merged system leverages the correlation between SNPs to reduce redundant measurements and simplify the overall genotyping process.
Data Source
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AI summary
The present invention relates to the use of SNPs in predicting susceptibility and/or severity of Multiple Sclerosis in an individual. The SNPs are located in the introns of the glycosylation enzymes MGAT5 and XYLTl, 3' of HIFlAN, within introns of MEGF11, FGF14, PDE9A and CDH13 and within desert regions of 4q34 and 17p13.