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. 2025 Aug 18;26(1):185.
doi: 10.1186/s10194-025-02128-7.

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

Shuxu Wei #  1 , Yan Quan #  2 , Xinyi Li  1 , Suiqin Zhong  1 , Ling Xiao  1 , Chao Yang  3 , Ronghuai Shen  1 , Xiaojia Lu  1 , Lingbin He  1 , Youti Zhang  2 , Xianxi Huang  4   5
Affiliations

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

Shuxu Wei et al. J Headache Pain. .

Abstract

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 GWAS data to map subtype-associated cellular architectures at single-cell resolution across embryonic tissues.

Results: LDSC and HDL confirmed strong genetic correlations between MA and MO. But they showed divergent functional architectures in functional genomic annotations, with MA enriched in conserved regulatory elements (e.g., Backgrd_Selection_StatL2_0, enrichment = 1.38, P = 5.47 × 10-6) and MO in vascular pathways (e.g., GERP.NSL2_0, enrichment = 2.12, P = 1.04 × 10-6). Sc-ST revealed spatially divergent niches: MA showed prenatal enrichment in neural crest-derived tissues (jaw primordium, p = 0.0039) and hypothalamic microglial adjacencies, aligning with neuroimmune regulation, while MO exhibited peripheral tropism in vascular smooth muscle and gut-brain interfaces, corroborated by LDSC-SEG/MAGMA vascular pathways. Multi-omics integration identified high-confidence cross-subtype genes (LRP1 [PoPS: Overall = 3.67, MO = 0.80], PHACTR1 [PoPS: Overall = 2.65, MA = 0.33, MO = 1.28], STAT6 [PoPS: Overall = 3.00, MO = 2.29], RDH16, TTC24, ZBTB39, FHL5, MEF2D, NAB2, UFL1, and REEP3) supported by ≥ 2 methods. Subtype-specific genes included MA-associated neuronal regulators (CACNA1A, KLHDC8B) and MO-specific vascular/metabolic genes (e.g., ACO2, BCAR1, CCDC134).

Conclusion: Our study delineates spatially constrained mechanisms underlying migraine heterogeneity: MA arises from neuroimmune-epigenetic dysregulation, while MO is driven by vascular-metabolic perturbations. Key genes and pathways provide actionable targets for subtype-specific therapies. By bridging genetic architecture with spatial biology, we redefine migraine pathogenesis and precision intervention strategies.

Keywords: Migraine subtypes; Multi-omics integration; Precision medicine; Single-cell spatial transcriptomics; Therapeutic targets.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The dataset used in this study is publicly available, and ethical approval and informed consent were obtained before implementation. Therefore, our study requires no additional informed consent or ethical approval. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of our study. GWAS: Genome-Wide Association Study; mQTL: Methylation Quantitative Trait Loci; eQTL: Expression Quantitative Trait Loci; TWAS: Transcriptome-Wide Association Study; BSGS: Brisbane Systems Genetics Study; LBC: Lothian Birth Cohort; GTEx: Genotype-Tissue Expression Project; LDSC: Linkage Disequilibrium Score Regression; HDL: High-Definition Likelihood; S-LDSC: Sparse Linkage Disequilibrium Score Regression; LDSC-SEG: Linkage Disequilibrium Score Regression-Stratified by Expression of Genes; gsMap: genetically informed spatial mapping of cells for complex traits; SMR: Summary Mendelian Randomization; FDR: False Discovery Rate; HEIDI: Heterogeneity in Dependent Instruments Test; MAGMA: Multi-marker Analysis of GenoMic Annotation; JTI: Joint-Tissue Imputation; PrediXcan: Predicting Gene Expression; UTMOST: Unified Test for Molecular Signature; PoPS: Polygenic Priority Score; gnomAD: Genome Aggregation Database; pLI: Probability of Loss-of-Function Intolerance; ENCODE: Encyclopedia of DNA Elements; MA: Migraine with Aura; MO: Migraine without Aura; CACNA1A: Calcium Voltage-Gated Channel Subunit Alpha1 A; KLHDC8B: Kelch Domain Containing 8B; ACO2: Aconitase 2; BCAR1 (Breast Cancer Anti-Estrogen Resistance 1
Fig. 2
Fig. 2
Partitioned Heritability Analysis of Genetic Correlations Between Overall Migraine and Subtypes Using LDSC. This figure illustrates the partitioned heritability contributions of distinct genomic components to the aggregate genetic correlations between overall migraine, migraine with aura (MA), and migraine without aura (MO), as quantified via Linkage Disequilibrium Score Regression (LDSC). The analysis delineates the proportion of heritability attributable to specific functional annotations (e.g., regulatory elements, conserved regions) for each subtype, highlighting subtype-specific and shared genetic architectures
Fig. 3
Fig. 3
Bidirectional bar plot of tissue enrichment results (LDSC-SEG) across 53 tissues. The dashed line indicates the significance threshold (P = 0.05). Positive bars represent tissues with enrichment for migraine risk, while negative bars reflect depletion. Tissues surpassing the threshold (P < 0.05) are highlighted in bold. LDSC-SE: Linkage Disequilibrium Score Correction-Stratified Expression Gene
Fig. 4
Fig. 4
MAGMA analysis results for overall migraine and its subtypes. From left to right: Manhattan plots of significant genes, bar plots of enriched pathways for significant genes, and bar plots of tissue enrichment results for significant genes. From top to bottom: analysis results for overall migraine, migraine with aura (MA), and migraine without aura (MO). MAGMA: Multi-marker Analysis of GenoMic Annotation
Fig. 5
Fig. 5
Genome-wide spatial association signals of MA and MO-associated genes identified by gsMap. From top to bottom, the Manhattan plots of MA and MO, respectively. Chromosomal positions (x-axis) and -log10p values (y-axis) are shown. MA: migraine with aura; MO: migraine without aura; gsMap: genetically informed spatial mapping of cells for complex traits
Fig. 6
Fig. 6
Spatial mapping of MA and MO-associated cellular patterns in E16.5 mouse embryonic single-cell spatial transcriptomics (ST) data, generated by gsMap algorithm across 25 organs. MA: migraine with aura; MO: migraine without aura; gsMap: genetically informed spatial mapping of cells for complex traits
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