Unveiling migraine subtype heterogeneity and risk loci: integrated genome-wide association study and single-cell transcriptomics discovery

作者：Shuxu Wei, Yan Quan, Xinyi Li, Suiqin Zhong, Ling Xiao, Chao Yang, Ronghuai Shen, Xiaojia Lu, Lingbin He, Youti Zhang, Xianxi Huang · 发表于：The Journal of Headache and Pain · 年份：2025 · DOI：10.1186/s10194-025-02128-7 · 被引用次数：5 · 研究领域：Migraine and Headache Studies、Health, Environment, Cognitive Aging、Functional Brain Connectivity Studies

BACKGROUND: Migraine, a debilitating neurological disorder with distinct subtypes (migraine with aura [MA] and migraine without aura [MO]), exhibits genetic and spatial heterogeneity that remains poorly understood. While genetic correlations between subtypes are established, spatially resolved molecular mechanisms driving their divergent clinical phenotypes-particularly in tissue microenvironments-are unclear, limiting targeted therapeutic development. METHODS: We integrated genome-wide association study (GWAS) data from FinnGen R11 and international cohorts with transcriptomic, epigenomic, and spatially resolved single-cell spatial transcriptomics (sc-ST) profiles. Genetic correlations and functional annotations were assessed using Linkage Disequilibrium Score Regression (LDSC), High-Definition Likelihood (HDL), and partitioned heritability analyses. A multi-omics framework combined Summary Mendelian Randomization (SMR) for expression and methylation quantitative trait loci (eQTL/mQTL), Functional Summary-based Imputation (FUSION), Multi-marker Analysis of GenoMic Annotation (MAGMA), Joint-Tissue Imputation Enhanced PrediXcan Analysis (JTI-PrediXcan), and the Polygenic Priority Score (PoPS) to systematically prioritize genes based on methodological robustness (≥ 2 analytical approaches) and cross-subtype consistency. Tissue-enriched specificity was validated via genetically informed spatial mapping of cells for complex traits (gsMap), a novel algorithm integrating sc-ST and ...