Evidence map›Paper›PMID 42472903›Full record

ArticleFunctional & integrative genomics2026

Integrative exome sequencing and multi-omics analysis elucidates the regulatory role of rare variants in metabolic syndrome.

Cheng-Xi Wu, Jin Sang, Qian Xu, Ya Zhan, Shan-Juan Yuan, Yuan-Yuan Ge, Xiu-Juan Yu, Xiao Zheng, Xiao-Feng Wang, Xian-Mei He and 4 more

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Article in Functional & integrative genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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14 authors.

Cheng-Xi WuDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Jin SangDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Qian XuDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Ya ZhanDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Shan-Juan YuanDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Yuan-Yuan GeDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China.
Xiu-Juan YuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Xiao ZhengCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Xiao-Feng WangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Xian-Mei HeCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Chao-Jie LiuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Yuan LuoDepartment of Orthopedics, Taicang Affiliated Hospital of Soochow University, SuZhou Medical College of Soochow University, Suzhou, 215400, Jiangsu, PR China.
Lei ZhangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, PR China.
Yu-Fang PeiDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Moe Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Jiangsu, PR China. ypei@suda.edu.cn.

Funding

National Natural Science Foundation of China 32170670
6 · The paper itself

Abstract

Metabolic syndrome (MetS) is a cluster of interconnected cardiometabolic risk factors with a substantial but incompletely characterized genetic basis. To identify novel genetic determinants, particularly rare variants, we here conducted a large-scale exome-wide association study in 367,188 individuals from the UK Biobank (78,300 cases and 288,888 controls). Single-variant analysis identified 74 independent loci, three of which correspond to rare variants in CARMIL2, ACTR6, and PLA2G12A without prior documented MetS association. Furthermore, gene-based burden testing pinpointed three key genes: ABCA1, APOC3, and PDE3B. Integrative follow-up analyses - including colocalization across molecular traits (mRNA, protein, microbiota) and association testing with proteomic/metabolic biomarkers - collectively suggest that these rare variants influence MetS susceptibility through coordinated effects on protein expression, metabolite concentrations, and microbial abundance. Importantly, ABCA1, APOC3, and PDE3B are established or promising drug targets for related metabolic disorders. In summary, our study not only elucidates the significant contribution of rare variants to MetS susceptibility, distinct from common variant signals, but also highlights the central role of lipid metabolism pathways, thereby nominating plausible targets for therapeutic intervention. Clinical trial number:Not applicable.

Indexed as

Metabolic SyndromeApolipoprotein C-IIIATP Binding Cassette Transporter 1Cyclic Nucleotide Phosphodiesterases, Type 3Exome SequencingGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipid MetabolismMultiomicsPolymorphism, Single NucleotideABCA1 protein, humanApolipoprotein C-IIIATP Binding Cassette Transporter 1Cyclic Nucleotide Phosphodiesterases, Type 3ColocalizationDrug targetExome-wide association studyLipid metabolismMetabolic syndromeRare variants

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.