Integrative genomic SEM analysis of shared genetic architecture and candidate genes for insulin resistance-related metabolic traits.

Insulin resistance is a complex metabolic state involving impaired insulin sensitivity, compensatory insulin secretion, abnormal lipid metabolism, and altered fat distribution. However, the shared genetic architecture underlying these correlated phenotypes remains incompletely defined. In this study, we applied Genomic Structural Equation Modeling to genome-wide association summary statistics of fasting insulin, insulin sensitivity index, triglycerides, and waist-to-hip ratio to construct an insulin resistance-related latent genetic factor. SNP-level association analysis was then performed for the latent factor, followed by risk locus annotation, fine-mapping, transcriptome-wide association analysis, gene-level prioritization, enrichment analysis, and exploratory structural biology assessment of selected missense substitutions. The single-factor model showed acceptable but not optimal approximate fit and captured a biologically consistent genetic pattern, with positive loadings for fasting insulin, triglycerides, and waist-to-hip ratio and a negative loading for insulin sensitivity index. The latent factor showed detectable SNP heritability and revealed 3,067 genome-wide significant candidate SNPs, with Q_SNP retained as a complementary SNP-level heterogeneity diagnostic.. Functional annotation identified 100 genomic risk loci and 1,530 FDR-significant genes in MAGMA. Fine-mapping prioritized multiple candidate regions, including loci near PPARG, RSPO3, SLC38A11, SLC39A8, BCL2, GDF5, and SPATA5. TWAS and FOCUS further highlighted candidate genes involved in adipocyte biology, glucose-lipid metabolism, insulin secretion, and expression regulation, including PPARG, MLXIPL, TCF7L2, FAM13A, CCDC92, and SLC22A3. Enrichment analyses suggested that genetic signals associated with the insulin resistance-related latent factor were mainly linked to fat distribution, cardiometabolic traits, adipose tissue, mesenchymal cell lineages, and active regulatory genomic regions. In a separate exploratory analysis, AlphaFold3 modeling, ThermoMPNN prediction, and molecular dynamics simulations suggested structural perturbations for three ultra-rare missense substitutions in nominated candidate proteins: BCL2-W188G, GDF5-C400Y, and RSPO3-C129Y. These substitutions were selected using functional-impact annotations and modeling feasibility, were not GWAS lead or fine-mapped causal variants, and do not establish the mechanisms underlying the GWAS associations. These findings provide an integrated genetic and structural framework for understanding shared mechanisms underlying insulin resistance-related metabolic abnormalities, while emphasizing the need for further experimental validation.