Genomic structural equation modeling decodes skeletal aging: novel loci discovery and multisystem genetic crosstalk.

OBJECTIVE

Skeletal aging, a core determinant of systemic aging, poses a global public health challenge due to its association with chronic diseases and functional decline. This study aimed to decode the genetic architecture of skeletal aging by identifying novel loci and multi-system crosstalk using genomic structural equation modeling (Genomic SEM).

METHODS

We integrated genome-wide association study (GWAS) data from five musculoskeletal-related traits (osteoporosis [OP], osteoarthritis [OA], lumbar spinal stenosis [LSS], telomere length [TL], and low back pain [LBP]) across 462,933 to 472,174 European individuals. Genomic SEM, FUMA, FUSION, and fine-mapping tools (SuSIE/FINEMAP) were applied to model latent skeletal aging ("mvSAge") and identify causal variants, enriched pathways, and tissue-specific gene expression.

RESULTS

The latent factor model (CFI = 0.993, SRMR = 0.065) revealed shared genetic architecture among OP, OA, LSS, TL, and LBP. We identified 514 lead SNPs (P < 5 × 10⁻12), including 136 novel loci enriched in regulatory regions (e.g., brain putamen, frontal cortex). Fine-mapping prioritized causal variants (posterior probability > 0.95) near MTPAP, GRAMD4, and DPP8, implicating mitochondrial function and immune regulation. Transcriptome-wide analyses highlighted PPP6R3 (bone mineral density) and SLC33A1 (anticancer target) as key genes. Enrichment analyses linked mvSAge to Wnt signaling, ER stress, and Mendelian disorders (e.g., ALS). Chromosomes 1, 2, and 4 showed elevated heritability contributions, driven by conserved regulatory elements (RUNX2, SP7) and chromatin accessibility hotspots.

CONCLUSION

This study establishes mvSAge as a genetically cohesive construct and uncovers novel loci, pathways, and multi-system interactions underlying skeletal aging. These findings advance precision medicine strategies for aging-related musculoskeletal disorders.