Integrative multi-omics analysis identifies endocrine-disrupting chemical-related molecular mechanisms in migraine
Abstract
Background: Migraine is a highly prevalent, heritable neurological disorder with a marked female predominance, indicating substantial hormonal contributions to disease pathophysiology. Endocrine-disrupting chemicals (EDCs) are ubiquitous environmental contaminants that interfere with hormonal signaling and have been linked to multiple diseases, yet their molecular contribution to migraine remains unclear. This study aims to identify EDC-related genes contributing to migraine risk and explore underlying mechanisms using an integrative multi-omics and causal inference framework.
Methods: We curated 181 EDCs and identified 1,116 EDC-related genes through chemical-gene interaction network analysis, followed by integration of multi-omics quantitative trait loci datasets (eQTL, sQTL, mQTL, and pQTL) to derive genetic instruments. Summary data-based Mendelian randomization (SMR) was performed to assess the effects of genetically predicted molecular traits on migraine risk using genome-wide association study (GWAS) data from the OpenGWAS and GWASCatalog. HEIDI testing and Bayesian colocalization analyses were conducted to distinguish pleiotropy from linkage and to prioritize genes with shared causal variants, followed by transcriptome-wide association study (TWAS) for validation. Multi-omics integration and differential gene expression analysis were used to characterize regulatory mechanisms. Finally, molecular docking and molecular dynamics simulations were performed to evaluate…
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