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

Autor: Park S; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea., Kim S; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA., Kim B; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea., Kim DS; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Kim J; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea., Ahn Y; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea., Kim H; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Song M; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Shim I; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Jung SH; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Cho C; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea., Lim S; Department of Integrative Biotechnology, Sungkyunkwan University, Suwon, South Korea., Hong S; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Jo H; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea., Fahed AC; Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA.; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA., Natarajan P; Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA.; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA., Ellinor PT; Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.; Department of Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department of Medicine, Harvard Medical School, Boston, MA, USA.; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA., Torkamani A; Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA., Park WY; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.; Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea., Yu TY; Department of Medicine, Division of Endocrinology and Metabolism, Wonkwang Medical Center, Wonkwang University School of Medicine, Iksan, South Korea., Myung W; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea. wmyung@snu.ac.kr.; Department of Neuropsychiatry, College of Medicine, Seoul National University, Seoul, South Korea. wmyung@snu.ac.kr., Won HH; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea. wonhh@skku.edu.; Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. wonhh@skku.edu.
Jazyk: angličtina
Zdroj: Nature genetics [Nat Genet] 2024 Nov; Vol. 56 (11), pp. 2380-2391. Date of Electronic Publication: 2024 Sep 30.
DOI: 10.1038/s41588-024-01933-1
Abstrakt: 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 (n observed  = 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: