A Method for Predicting Allelic Variants of Single Nucleotide Polymorphisms
Ekaterina Evgenyevna Tyagunova1,2, Alexander Sergeevich Zakharov3, Galina Valerievna Pavlova1
1Federal State Autonomous Educational Institution of Higher Education First Moscow State Medical University of the Ministry Healthcare of the Russian Federation Named After I. M. Sechenov (Sechenov University), Moscow, Russia.
Current Topics in Medicinal Chemistry
|September 6, 2024
Summary
This study introduces a novel method to predict single nucleotide polymorphism (SNP) allelic variants, improving genetic research efficiency for socially significant diseases. The approach aids in disease risk stratification when complete SNP data is unavailable.
Area of Science:
- Genetics
- Bioinformatics
- Clinical Genetics
Background:
- Single nucleotide polymorphisms (SNPs) are crucial genetic markers linking genotype to disease susceptibility and pharmacogenetics.
- Challenges exist in molecular genetic studies due to insufficient data, especially for socially significant diseases.
- Existing methods require comprehensive SNP data, limiting research scope.
Purpose of the Study:
- To develop and validate a predictive method for SNP allelic variants.
- To address data gaps in molecular genetic studies.
- To enhance disease risk stratification and streamline genetic research.
Main Methods:
- Quantitative PCR and body composition data from 150 patients were analyzed.
- Statistical analysis was performed using IBM SPSS Statistics 29.0.
- A prototype formula was developed to predict SNP allelic variants based on existing data and body weight.
Main Results:
- The developed method demonstrates feasibility in predicting SNP allelic variants.
- This approach can streamline and economize molecular genetic research.
- It enables disease risk stratification even with incomplete SNP data.
Conclusions:
- The predictive method shows promise for diverse diseases, including those with significant social impact.
- Implementation requires a comprehensive SNP database for clinical practice.
- This method offers a valuable tool for clinical and laboratory geneticists facing data limitations.
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