Multivariate Genome-Wide Association Analysis Identifies Genetic Susceptibility Loci for Metabolic Syndrome in the Korean Genome and Epidemiology Study

Abstract

Metabolic syndrome (MetS), characterized by a cluster of interrelated metabolic abnormalities including central obesity, elevated blood pressure, dysglycemia, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol (HDL-C), substantially increases type 2 diabetes and cardiovascular disease risk. Conventional genome-wide association studies (GWASs) analyze individual traits or a dichotomized MetS status, only partially capturing its heritability and potentially overlooking variants with shared (pleiotropic) effects across correlated traits. Here, we performed a multiple-trait GWAS using data from the Korean Genome and Epidemiology Study. Using the Korea Biobank Array (K-Chip), we analyzed a discovery cohort from the Ansan and Ansung studies (1490 cases and 3856 controls) and validated the findings in CAVAS (3620 cases and 4461 controls) and HEXA (15,257 cases and 41,807 controls) cohorts. We jointly modeled six MetS-related traits using the complementary multivariate frameworks Multiple Phenotype Association Tests and Genome-wide Efficient Mixed Model Association, alongside a baseline logistic regression. Whereas logistic regression detected 2 APOA5 variants (rs662799 and rs2075291), the multivariate analyses identified 79 significant single-nucleotide polymorphisms; all were replicated in the HEXA cohort. Functional annotation prioritized 27 high-risk variants mapping to 12 genes, whereas pathway analysis (DAVID) implicated eight Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways related to lipid metabolism. Our findings demonstrate that multivariate analysis substantially improves the identification of pleiotropic susceptibility loci for MetS in the Korean population and indicates candidate genes for early detection and management.