Unraveling the shared genetic architecture of metabolic bone diseases via genomic structural equation modeling and multi-omics integration

Background: The shared genetic etiology underlying the comorbidity of skeletal degeneration and metabolic dysregulation remains poorly understood. This study aims to decipher the pleiotropic genetic architecture of multivariate metabolic bone diseases (mvMBDs) through an integrative summary-statistics framework. Methods: We applied Genomic Structural Equation Modeling (Genomic SEM) to integrate genome-wide association study (GWAS) summary statistics for six complex traits: osteoporosis (OP), chronic kidney disease (CKD), type 2 diabetes (T2D), alkaline phosphatase (ALP), high-density lipoprotein (HDL), and C-reactive protein (CRP). Subsequent analyses incorporated robust fine-mapping (SuSiE/FINEMAP), transcriptome-wide association studies (TWAS), and partitioned heritability assessments to identify putative functional loci and regulatory mechanisms. Results: Genomic SEM revealed significant genetic covariance among the six traits, successfully deriving a latent mvMBD construct. We identified 197 genomic risk loci, including 193 lead SNPs that did not achieve genome-wide significance in the six constituent univariate GWAS, predominantly localized in non-coding cis-regulatory elements (e.g., super-enhancers and intronic regions). Fine-mapping and TWAS prioritized critical pleiotropic genes (e.g., MACF1, CETP, SPP1, and HNF1A). Gene-set enrichment analyses identified enrichment for terms related to bone biology, lipid metabolism, and inflammation. Conclusion: Our findings robustly demonstrate that mvMBDs are not isolated clinical entities but rather interconnected systemic syndromes underpinned by glycolipid metabolic dysregulation and shared pleiotropic regulatory networks. This study establishes a novel theoretical paradigm for the genetic basis of metabolic bone diseases, offering promising functional targets for future precision medicine interventions.