Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome

作者：Sanghyeon Park, Soyeon Kim, Beomsu Kim, Dan Say Kim, Jaeyoung Kim, Yeeun Ahn, Hyejin Kim, Minku Song, Injeong Shim, Sang‐Hyuk Jung, Chamlee Cho, Soo‐Hyun Lim, Sanghoon Hong, 조현빈, Akl C. Fahed, Pradeep Natarajan, Patrick T. Ellinor, Ali Torkamani, Woong‐Yang Park, Tae Yang Yu, Woojae Myung, Hong‐Hee Won · 发表于：Nature Genetics · 年份：2024 · DOI：10.1038/s41588-024-01933-1 · 被引用次数：64 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、Liver Disease Diagnosis and Treatment

Metabolic syndrome (MetS) is a complex hereditary condition comprising various metabolic traits as risk factors. Although the genetics of individual MetS components have been investigated actively through large-scale genome-wide association studies, the conjoint genetic architecture has not been fully elucidated. Here, we performed the largest multivariate genome-wide association study of MetS in Europe (nobserved = 4,947,860) by leveraging genetic correlation between MetS components. We identified 1,307 genetic loci associated with MetS that were enriched primarily in brain tissues. Using transcriptomic data, we identified 11 genes associated strongly with MetS. Our phenome-wide association and Mendelian randomization analyses highlighted associations of MetS with diverse diseases beyond cardiometabolic diseases. Polygenic risk score analysis demonstrated better discrimination of MetS and predictive power in European and East Asian populations. Altogether, our findings will guide future studies aimed at elucidating the genetic architecture of MetS. Large-scale multivariate analyses across populations of European ancestry identify risk loci for the metabolic syndrome, improving polygenic prediction models and highlighting associations with diverse traits beyond cardiometabolic diseases.