Unraveling the pathogenic mechanisms of osteoarthritis and obesity: An integration of GWAS, cellular specificity, and spatial transcriptomics

Abstract

Objective This study aims to systematically elucidate the shared and specific genetic basis of osteoarthritis (OA) and obesity by integrating large-scale genome-wide association study (GWAS) summary statistics, cross-tissue quantitative trait loci (QTLs), and single-cell and spatial transcriptomic data. Method The research employed a multi-omics integrative analysis pipeline. First, a meta-analysis was conducted on GWAS data for OA and obesity. Next, tissue- and spatial-specific enrichment analyses were performed using methods such as QTLEnrich, MAGMA, and gsMap. Key steps included the application of single-cell analysis, Cell-stratified mendelian randomization (csMR), and the ECLIPSER/CELLECT framework to identify specific cell types. Finally, hub genes were identified using hdWGCNA. Results The results revealed significant enrichment of genetic risk signals for OA and obesity in brain tissues, including the cortex and pituitary gland. At the cellular level, T cells were identified as the highest-priority shared cell type for both diseases. Hub genes—GSN, CALD1, EBF1, LHFPL6, and TIMP3—were identified through co-expression network analysis. Spatial transcriptomic analysis further mapped the genetic risk signals to brain regions during embryonic development. Conclusion This study precisely anchors the genetic risk of OA and obesity to specific brain regions, cell types, and developmental time windows, providing a novel perspective for understanding the pathological mechanisms of OA.