統合的機能ゲノミクスとファインマッピングが多変量肥満GWASの調節メカニズムとその心血管代謝への影響を特定する

Obesity is a systemic disorder with heterogeneous fat distribution and complex metabolic complications. Conventional genome-wide association studies (GWAS) typically analyze individual obesity-related traits separately, limiting the identification of shared genetic architecture and key regulatory mechanisms, particularly those involving non-coding variants.

We integrated GWAS data for five obesity traits (body mass index, waist circumference, visceral fat, liver fat, and body fat percentage) using genomic structural equation modeling (GSEM) to construct a multivariate phenotype (mvObesity). Functional genomic integration combined adipose chromatin accessibility, enhancer promoter interactions, and expression quantitative trait loci (eQTL) data with transcriptome-wide and proteome-wide (TWAS and PWAS) analyses, fine-mapping, and colocalization. Trait-relevant cell types were identified using single-cell and single-cell polygenic association of GWAS (scPagwas) analyses.

Multi-omics integration in adipose tissue identified 799 independent SNPs across 548 loci, including 45 previously unreported signals. Fine-mapping and TWAS defined 150 high-confidence candidate genes enriched for neuronal signaling, synaptic organization, and lipid metabolism pathways. MAGMA-based enrichment further revealed significant overrepresentation in brain regions such as the cerebellum, hippocampus, and hypothalamus, indicating central regulatory involvement. Single-cell analyses highlighted adipocytes, preadipocytes, and smooth muscle cells as major genetically influenced types, while cross-tissue TWAS and scRNA-seq supported coordinated neuro-metabolic transcriptional regulation. Multi-omic prioritization identified key genes such as MED13L, GBE1, CADM2, PIK3R3, ERBB4, and PTK2B and demonstrated significant genome-wide and local genetic overlap between mvObesity and cardiometabolic traits.

This multivariate, multi-omics framework delineates a cross-tissue neuro-adipose regulatory axis underlying obesity, providing mechanistic insight and a genetically informed candidate framework for future precision metabolic intervention research.