Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study

Abstract

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.