Jinyeon Jo, Nayoung Ha, Yunmi Ji, Ahra Do, Je Hyun Seo, Bumjo Oh, Sungkyoung Choi, Eun Kyung Choe, Woojoo Lee, Jang Won Son, Sungho Won
{"title":"韩国人肥胖的遗传决定因素:探索全基因组关联和多基因风险评分。","authors":"Jinyeon Jo, Nayoung Ha, Yunmi Ji, Ahra Do, Je Hyun Seo, Bumjo Oh, Sungkyoung Choi, Eun Kyung Choe, Woojoo Lee, Jang Won Son, Sungho Won","doi":"10.1093/bib/bbae389","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11359806/pdf/","citationCount":"0","resultStr":"{\"title\":\"Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores.\",\"authors\":\"Jinyeon Jo, Nayoung Ha, Yunmi Ji, Ahra Do, Je Hyun Seo, Bumjo Oh, Sungkyoung Choi, Eun Kyung Choe, Woojoo Lee, Jang Won Son, Sungho Won\",\"doi\":\"10.1093/bib/bbae389\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":9209,\"journal\":{\"name\":\"Briefings in bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11359806/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Briefings in bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bib/bbae389\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbae389","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores.
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.
期刊介绍:
Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data.
The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.