Network-informed multi-trait genomic analysis decodes the shared genetic architecture and therapeutic landscape of pelvic floor disorders.

Pelvic floor disorders, including pelvic organ prolapse and urinary incontinence, represent a common health burden with substantial clinical comorbidity, but their shared genetic architecture remains incompletely understood. We performed a multi-trait genomic analysis of six pelvic-floor-related phenotypes using publicly available GWAS summary statistics from FinnGen R12 and the GWAS Catalog, with Pan-UKBB summary statistics used only for cross-ancestry validation. Linkage disequilibrium score regression and Genomic Structural Equation Modeling identified two broad latent genetic dimensions: a structural factor related to anatomical prolapse and a functional factor related to urinary and bowel dysfunction. MAGMA gene-based analysis identified 267 significant genes enriched for extracellular matrix organization and urogenital developmental pathways, including WNT4, LOXL1, ESR1, WT1, HNF1B, and FGFR2. An exploratory network-informed machine-learning framework incorporating protein-protein interaction topology improved gene prioritization over a baseline Random Forest model and highlighted biologically plausible hub genes. Cross-ancestry analysis of Pan-UKBB female genital prolapse supported directional portability of European-discovered signals and identified WNT4 as a directionally concordant locus. These findings provide a systems-level map of pelvic-floor-related genetic architecture and prioritize candidate genes for future functional validation.