Integrative genomic SEM analysis of shared genetic architecture and candidate genes for insulin resistance–related metabolic traits
4 affiliations
- 1Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China.
- 2Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- 3Key Laboratory of Sichuan Province Ophthalmopathy Prevention & Cure and Visual Function Protection With TCM, Chengdu, China.
- 4The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing, China.
Abstract
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.
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BibTeX
@article{li2026integrative,
title = {{Integrative genomic SEM analysis of shared genetic architecture and candidate genes for insulin resistance–related metabolic traits}},
author = {ShuYan Li and Yi Liang and Guangwen Zhu and Lijiao Wu and Hong Li and Yu Li and Qiu Chen},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361601},
url = {https://doi.org/10.1371/journal.pone.0361601},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrative genomic SEM analysis of shared genetic architecture and candidate genes for insulin resistance–related metabolic traits
AU - ShuYan Li
AU - Yi Liang
AU - Guangwen Zhu
AU - Lijiao Wu
AU - Hong Li
AU - Yu Li
AU - Qiu Chen
PY - 2026
DA - 2026/10/29
JO - PLOS One
DO - 10.1371/journal.pone.0361601
UR - https://doi.org/10.1371/journal.pone.0361601
AB - 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.
SN - 1932-6203
ER -