ArticleNature genetics2024
Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome.
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Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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47citing papers in PubMed, 3 pooled it
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycaemic traits during pregnancy reveals limited evidence of pregnancy-specific genetic effects.Nature communications · 2026Pooled it
- Cross-ancestry analysis of gestational diabetes mellitus identifies novel loci, drug targets and biological pathways.Frontiers in endocrinology · 2026Pooled it
- Sex-stratified genome-wide association meta-analysis of major depressive disorder.Nature communications · 2025Pooled it
- Polygenic Risk Scores Show Distinct Genetic Architectures of IBD-Associated Comorbidities.Digestive diseases and sciences · 2026Article
- Disorder-specific and shared genetic architecture underlying schizophrenia and bipolar disorder.medRxiv : the preprint server for health sciences · 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
- New Genetic Associations Between Alzheimer's Disease and Its Key Risk Factors.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2026Article
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- Effects of BACE2 polymorphisms on verbal memory in the general population.European archives of psychiatry and clinical neuroscience · 2026Article
- Genomic structural equation modeling elucidates the genetic mechanisms underlying allostatic load.Comprehensive psychoneuroendocrinology · 2026Article
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- The genetics of fibromyalgia and its relationships to psychiatric and medical traits.Nature communications · 2026Article
- Can Polygenic Risk Scores for Metabolic Syndrome Contribute to Long-Term Syndrome Development in First-Episode Psychosis Patients? A Naturalistic 21-Year Follow-Up Study.Schizophrenia bulletin · 2026Article
- The Use of Population Isolates to Identify Metabolic Syndrome's Genetic Aetiology.Molecular genetics & genomic medicine · 2026Review
- Multi-Level Latent Variable Models for Coheritability Analysis in Electronic Health Records.Statistics in medicine · 2026Article
- Identifying genetic contributions of 6q21 loci and PPAR pathway to antipsychotic-induced metabolic syndrome: a Sex-Stratified Multi-Omics study.Molecular psychiatry · 2026Article
- Article
- Metabolic disease variants rewire gene regulation through disruption of DNA G-quadruplex structures.Genome biology · 2026Article
- Multivariate genetic analysis reveals three distinct pathological dimensions in musculoskeletal disorders.Nature communications · 2026Article
- Dissecting pleiotropy to gain mechanistic insights into human disease.Nature reviews. Genetics · 2026Review
Corrections and comments
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Authors and funding
22 authors.
Sanghyeon ParkDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Soyeon KimAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2798-2257
Beomsu KimDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Dan Say KimDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Jaeyoung KimDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.ORCID http://orcid.org/0000-0001-6983-7907
Yeeun AhnDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.ORCID http://orcid.org/0000-0002-8584-5322
Hyejin KimDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Minku SongDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Injeong ShimDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.ORCID http://orcid.org/0000-0002-1001-0945
Sang-Hyuk JungDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Chamlee ChoDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.ORCID http://orcid.org/0009-0002-3155-1932
Soohyun LimDepartment of Integrative Biotechnology, Sungkyunkwan University, Suwon, South Korea.
Sanghoon HongDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Hyeonbin JoDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Akl C FahedDepartment of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.
Pradeep NatarajanDepartment of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.
Patrick T EllinorDepartment of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2067-0533
Ali TorkamaniScripps Research Translational Institute, Scripps Research, La Jolla, CA, USA.ORCID http://orcid.org/0000-0003-0232-8053
Woong-Yang ParkDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Tae Yang YuDepartment of Medicine, Division of Endocrinology and Metabolism, Wonkwang Medical Center, Wonkwang University School of Medicine, Iksan, South Korea.ORCID http://orcid.org/0000-0003-0893-592X
Woojae Myung *Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea. wmyung@snu.ac.kr.ORCID http://orcid.org/0000-0001-9985-2032
Hong-Hee Won *Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea. wonhh@skku.edu.ORCID http://orcid.org/0000-0001-5719-0552
Funding
Integrating genomic and nongenomic risk for coronary artery diseaseK08HL161448 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed · 2022 to 2026
$751kNational Research Foundation of Korea (NRF) NRF-2021R1A2C4001779National Research Foundation of Korea (NRF) NRF-2022R1A2C2009998NHLBI NIH HHS K08 HL161448
Abstract
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
Indexed as
Genetic Predisposition to DiseaseGenome-Wide Association StudyMetabolic SyndromeMultifactorial InheritancePolymorphism, Single NucleotideEuropeFemaleGenomicsHumansMaleMendelian Randomization AnalysisMultivariate AnalysisRisk Factors
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