A Method for Predicting Allelic Variants of Single Nucleotide Polymorphisms.

IF 2.9 4区 医学 Q3 CHEMISTRY, MEDICINAL Current topics in medicinal chemistry Pub Date : 2024-09-05 DOI:10.2174/0115680266328330240828040922
Ekaterina Evgenyevna Tyagunova, Alexander Sergeevich Zakharov, Galina Valerievna Pavlova, Daria Alexandrovna Ogarkova, Natalia Alexandrovna Zhuchenko, Vladimir Alexeyevich Gushchin
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Abstract

Introduction: Single nucleotide polymorphisms (SNPs) are pivotal in clinical genetics, serving to link genotypes with disease susceptibility and response to environmental factors, including pharmacogenetics. They also play a crucial role in population genetics for mapping the human genome and localizing genes. Despite their utility, challenges arise when molecular genetic studies yield insufficient or uninformative data, particularly for socially significant diseases. This study aims to address these gaps by proposing a method to predict allelic variants of SNPs.

Method: Using quantitative PCR and analyzing body composition data from 150 patients with their voluntary informed consent, we employed IBM SPSS Statistics 29.0 for data analysis. Our prototype formula, exemplified by allelic variant ADRB2 (rs1042713) = 0.257 + 0.639 * allelic variant ADRB2 (rs1042714) - 0.314 * allelic variant ADRB3 (rs4994) + 0.191 * allelic variant PPARA (rs4253778) - 0.218 * allelic variant PPARD (rs2016520) + 0.027 * body weight + 0.00001 * body weight², demonstrates the feasibility of predicting SNP allelic variants.

Results: This method holds promise for diverse diseases, including those of significant social impact, due to its potential to streamline and economize molecular genetic research. Its ability to stratify disease risk in the absence of complete SNP data makes it particularly compelling for clinical and laboratory geneticists.

Conclusion: However, its translation into clinical practice necessitates the establishment of a comprehensive SNP database, especially for frequently analyzed SNPs within the implementing institution.

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预测单核苷酸多态性等位基因变异的方法。
导言:单核苷酸多态性(SNPs)在临床遗传学中举足轻重,可将基因型与疾病易感性和对环境因素的反应联系起来,包括药物遗传学。在群体遗传学中,SNP 在绘制人类基因组图谱和定位基因方面也发挥着至关重要的作用。尽管分子遗传学很有用,但当分子遗传学研究产生的数据不足或信息不充分时,尤其是对社会重大疾病而言,就会出现挑战。本研究旨在通过提出一种预测 SNPs 等位基因变异的方法来弥补这些不足:方法:我们使用定量 PCR 分析了 150 名患者的身体成分数据,并获得了他们的自愿知情同意。我们的原型公式为等位基因变异 ADRB2 (rs1042713) = 0.257 + 0.639 * 等位基因变异 ADRB2 (rs1042714) - 0.314 * 等位基因变异 ADRB3 (rs4994) + 0.191 * 等位基因变异体 PPARA (rs4253778) - 0.218 * 等位基因变异体 PPARD (rs2016520) + 0.027 * 体重 + 0.00001 * 体重²,证明了预测 SNP 等位基因变异体的可行性:结果:由于这种方法具有简化和节约分子遗传研究的潜力,因此有望用于各种疾病,包括对社会有重大影响的疾病。它能在缺乏完整 SNP 数据的情况下对疾病风险进行分层,这对临床和实验室遗传学家来说尤其具有吸引力:然而,要将其应用到临床实践中,就必须建立一个全面的 SNP 数据库,尤其是针对实施机构内经常分析的 SNP。
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来源期刊
CiteScore
6.40
自引率
2.90%
发文量
186
审稿时长
3-8 weeks
期刊介绍: Current Topics in Medicinal Chemistry is a forum for the review of areas of keen and topical interest to medicinal chemists and others in the allied disciplines. Each issue is solely devoted to a specific topic, containing six to nine reviews, which provide the reader a comprehensive survey of that area. A Guest Editor who is an expert in the topic under review, will assemble each issue. The scope of Current Topics in Medicinal Chemistry will cover all areas of medicinal chemistry, including current developments in rational drug design, synthetic chemistry, bioorganic chemistry, high-throughput screening, combinatorial chemistry, compound diversity measurements, drug absorption, drug distribution, metabolism, new and emerging drug targets, natural products, pharmacogenomics, and structure-activity relationships. Medicinal chemistry is a rapidly maturing discipline. The study of how structure and function are related is absolutely essential to understanding the molecular basis of life. Current Topics in Medicinal Chemistry aims to contribute to the growth of scientific knowledge and insight, and facilitate the discovery and development of new therapeutic agents to treat debilitating human disorders. The journal is essential for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important advances.
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