ArticleBriefings in bioinformatics2024
Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative Roles of Functional Foods, Microbiotics, Nutrigenetics, and Nutrigenomics in Managing Type 2 Diabetes and Obesity.Nutrients · 2025Pooled it
- Beyond BMI: Genome-Wide Associations of Visceral Fat, Fat Mass, and Fat-Free Mass in Eastern Asian Populations.Obesity (Silver Spring, Md.) · 2026Article
- ADRB3, LEPR, FTO and PPARG variants contribute to obesity-related metabolic syndrome in Brazilian adults: a polygenic risk score approach.International journal of obesity (2005) · 2026Article
- Current Trends and Perspectives on Obesity and Metabolic Dysfunction-Associated Steatohepatitis in East Asia.Journal of obesity & metabolic syndrome · 2026Review
- Genetic architecture of obesity and advances in precision pharmacotherapy: a comprehensive review.Acta biochimica Polonica · 2026Review
- Association of Multiple-trait Polygenic Risk Score with Obesity and Cardiometabolic Diseases in Korean Population.Genomics, proteomics & bioinformatics · 2025Article
- Beyond the BMI Paradox: Unraveling the Cellular and Molecular Determinants of Metabolic Health in Obesity.Biomolecules · 2025Review
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Authors and funding
11 authors.
Funding
Abstract
East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.
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