Journal of stomatology, oral and maxillofacial surgery · 2025
Multivariate genomic analysis elucidates the genetic architecture of shared components of burning mouth syndrome.
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Lian Qiwu, Dong Linsheng, Zhou Qiaoyu, Yuan Qian
Аннотация
BACKGROUND: Burning mouth syndrome (BMS) is a chronic oral pain condition with unclear etiology. We sought to delineate the shared genetic architecture underlying BMS by modeling a latent BMS factor from multiple related traits.
METHODS: We analyzed summary statistics from six published GWASs of BMS-related phenotypes (Sjögren's syndrome, anxiety, depression, fibromyalgia, migraine, and dysphagia) using genomic structural equation modeling (genomic SEM) to construct an indirectly measured, multivariate GWAS for a latent BMS factor. We applied LDSC for genetic correlation and genomic control, FUMA/MAGMA for locus- and gene-level mapping, FINEMAP and SuSiE for fine-mapping, and performed transcriptome-wide association studies (TWAS) with FUSION followed by fine-mapping with FOCUS. We further conducted pathway and cell-type enrichment, and evaluated chromosome-level polygenic risk burden using PRS-CS.
RESULTS: The common-factor genomic SEM showed good model fit and revealed substantial shared genetic covariance among the six traits. We identified nine genome-wide significant loci for the latent BMS factor. Fine-mapping highlighted credible variants at loci including MRPL49P1 and R3HDML. TWAS prioritized genes such as STAC3 associated with higher BMS risk and LRP1 associated with lower BMS risk. Enrichment analyses implicated conserved and enhancer regions, and suggested neuronal and immune-related cellular contributions. PRS analyses indicated higher polygenic burden on chromosomes 1 and 2.
CONCLUSIONS: Leveraging genomic SEM on six BMS-related GWASs, we provide a comprehensive, multiscale map of BMS genetic architecture, identifying nine significant loci and prioritizing candidate genes and pathways with potential pathophysiological relevance. These results nominate testable targets for functional follow-up and may inform future treatment strategies for BMS.
METHODS: We analyzed summary statistics from six published GWASs of BMS-related phenotypes (Sjögren's syndrome, anxiety, depression, fibromyalgia, migraine, and dysphagia) using genomic structural equation modeling (genomic SEM) to construct an indirectly measured, multivariate GWAS for a latent BMS factor. We applied LDSC for genetic correlation and genomic control, FUMA/MAGMA for locus- and gene-level mapping, FINEMAP and SuSiE for fine-mapping, and performed transcriptome-wide association studies (TWAS) with FUSION followed by fine-mapping with FOCUS. We further conducted pathway and cell-type enrichment, and evaluated chromosome-level polygenic risk burden using PRS-CS.
RESULTS: The common-factor genomic SEM showed good model fit and revealed substantial shared genetic covariance among the six traits. We identified nine genome-wide significant loci for the latent BMS factor. Fine-mapping highlighted credible variants at loci including MRPL49P1 and R3HDML. TWAS prioritized genes such as STAC3 associated with higher BMS risk and LRP1 associated with lower BMS risk. Enrichment analyses implicated conserved and enhancer regions, and suggested neuronal and immune-related cellular contributions. PRS analyses indicated higher polygenic burden on chromosomes 1 and 2.
CONCLUSIONS: Leveraging genomic SEM on six BMS-related GWASs, we provide a comprehensive, multiscale map of BMS genetic architecture, identifying nine significant loci and prioritizing candidate genes and pathways with potential pathophysiological relevance. These results nominate testable targets for functional follow-up and may inform future treatment strategies for BMS.