解密抑郁症与痛经之间的基因相互作用:孟德尔随机研究。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Shuhe Liu, Zhen Wei, Daniel F Carr, John Moraros
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引用次数: 0

摘要

背景:本研究旨在通过综合利用各种资源中的基因组、转录组和蛋白质相互作用数据/信息的创新方法,探讨抑郁症与痛经之间的联系:本研究旨在采用一种综合的创新方法,结合来自各种资源的基因组、转录组和蛋白质相互作用数据/信息,探讨抑郁症与痛经之间的联系:方法:采用双样本、双向和多变量孟德尔随机化(MR)方法确定痛经与抑郁症之间的因果关系。利用全基因组关联研究(GWAS)数据确定与痛经和抑郁症相关的遗传变异,然后对共同的遗传影响因素进行共定位分析。通过分析公共数据库中的表达量性状位点(eQTL)数据,确定了相关组织中的目标基因。此外,还利用 STRING 数据库构建了蛋白质-蛋白质相互作用(PPI)网络,以分析已识别蛋白质之间的相互作用:结果:磁共振分析证实抑郁症对痛经有明显的因果效应['几率比'(95% 置信区间)= 1.51 (1.19, 1.91),P = 7.26 × 10-4]。相反,没有证据支持痛经对抑郁症的因果效应(P = .74)。利用 GWAS 和 eQTL 数据进行的遗传分析确定了多个基因的单核苷酸多态性,包括 GRK4、TRAIP 和 RNF123,表明抑郁症可能通过这些遗传途径影响生殖功能,并通过 PPI 网络分析呈现了详细情况。共定位分析强调了rs34341246(RBMS3)是一个潜在的共享因果变异体:这项研究表明,抑郁症对痛经有重大影响,并确定了参与这种相互作用的关键基因和蛋白质。研究结果突出表明,有必要采取综合的临床和公共卫生方法,对痛经妇女进行抑郁筛查,并提出新的有针对性的预防策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study.

Background: This study aims to explore the link between depression and dysmenorrhea by using an integrated and innovative approach that combines genomic, transcriptomic, and protein interaction data/information from various resources.

Methods: A two-sample, bidirectional, and multivariate Mendelian randomization (MR) approach was applied to determine causality between dysmenorrhea and depression. Genome-wide association study (GWAS) data were used to identify genetic variants associated with both dysmenorrhea and depression, followed by colocalization analysis of shared genetic influences. Expression quantitative trait locus (eQTL) data were analyzed from public databases to pinpoint target genes in relevant tissues. Additionally, a protein-protein interaction (PPI) network was constructed using the STRING database to analyze interactions among identified proteins.

Results: MR analysis confirmed a significant causal effect of depression on dysmenorrhea ['odds ratio' (95% confidence interval) = 1.51 (1.19, 1.91), P = 7.26 × 10-4]. Conversely, no evidence was found to support a causal effect of dysmenorrhea on depression (P = .74). Genetic analysis, using GWAS and eQTL data, identified single-nucleotide polymorphisms in several genes, including GRK4, TRAIP, and RNF123, indicating that depression may impact reproductive function through these genetic pathways, with a detailed picture presented by way of analysis in the PPI network. Colocalization analysis highlighted rs34341246(RBMS3) as a potential shared causal variant.

Conclusions: This study suggests that depression significantly affects dysmenorrhea and identifies key genes and proteins involved in this interaction. The findings underline the need for integrated clinical and public health approaches that screen for depression among women presenting with dysmenorrhea and suggest new targeted preventive strategies.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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