Genetic influence of the brain imaging phenotypes, brain and cerebrospinal fluid metabolites and brain genes on migraine subtypes: a Mendelian randomization and multi-omics study

May 20, 2025The journal of headache and pain

Genetic links between brain imaging, brain and spinal fluid chemicals, and brain genes with different types of migraine

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Abstract

Significant genetic correlations were identified with 73 brain imaging phenotypes and 40 metabolites for overall migraine.

Genetic correlations were found between migraine and 71 brain imaging phenotypes and 37 metabolites for migraine with aura.
Migraine without aura showed genetic correlations with 49 brain imaging phenotypes and 62 metabolites.
Six brain imaging phenotypes and two metabolites were identified as causally linked to overall migraine.
Reverse analysis indicated overall migraine causally influenced four brain imaging phenotypes and three metabolites.
Two genes, FAM83B and CIB2, were identified as having inhibitory effects across most brain regions.

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BACKGROUND: Migraine is a complex neurological disorder with high prevalence but unclear pathogenesis. Numerous studies have suggested that migraine is associated with alterations in brain imaging phenotypes (BIPs) and dysregulation of cerebrospinal fluid (CSF) and brain metabolism; however, causal evidence remains limited. Mendelian randomization (MR) offers a powerful approach for inferring causality using genetic instruments.

METHODS: Firstly, we conducted linkage disequilibrium score regression (LDSC) to evaluate genetic correlations between migraine, including the migraine with aura (MA) and migraine without aura (MO) subtypes, and BIPs, CSF, and brain metabolites. Traits that showed genetic correlations with migraine, MA, or MO were retained for subsequent MR analysis with the corresponding migraine phenotype. Traits showing significant correlations were analyzed using bidirectional two-sample MR (TSMR), followed by two-step TSMR to identify cross-omics mediation effects. Additionally, We also applied summary-data-based MR (SMR) to detect brain-region-specific genes with potential causal effects. Enrichment analyses (KEGG, GO, PPI, transcription factor, and miRNA networks) were conducted to further explore underlying mechanisms.

RESULTS: LDSC identified significant genetic correlations with 73 BIPs and 40 metabolites for overall migraine, 71 BIPs and 37 metabolites for MA, and 49 BIPs and 62 metabolites for MO. Enrichment analysis revealed that genetically associated metabolites were predominantly involved in amino acid metabolic pathways. TSMR identified 6 BIPs and 2 metabolites causally linked to overall migraine, 3 BIPs and 3 metabolites to MA, and 2 BIPs and 5 metabolites to MO. Most migraine-related BIPs mapped to the parietal lobe. Reverse MR analysis showed that overall migraine causally influenced 4 BIPs and 3 metabolites, while MA and MO affected 1 BIP and 1 metabolite, and 3 BIPs and 1 metabolite, respectively. Mediation analysis revealed five significant mediation pathways were identified. SMR analysis identified FAM83B and CIB2 consistently showing inhibitory effects across most regions. Enrichment analysis showed that these genes were predominantly involved in immune activation and cell adhesion.

CONCLUSIONS: Our study integrates cross-omics analyses to investigate the causal links between brain structure, metabolic alterations, gene expression, and migraine including its MA and MO subtypes. These findings provide novel insights into the pathophysiological mechanisms and potential targets for intervention across migraine subtypes.

Key numbers

Genetic Correlations Identified

BIPs significantly correlated with overall migraine

0.760

Causal Effect on Migraine Risk

Odds Ratio for increased thickness of the right superior parietal gyrus

Mediation Pathways Identified

Significant pathways linking BIPs and metabolites to migraine

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Genetic influence of the brain imaging phenotypes, brain and cerebrospinal fluid metabolites and brain genes on migraine subtypes: a Mendelian randomization and multi-omics study

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