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Carrasco-Zanini J, Wheeler E, Uluvar B, Kerrison N, Koprulu M, Wareham NJ, Pietzner M, Langenberg C. Mapping biological influences on the human plasma proteome beyond the genome. Nat Metab 2024:10.1038/s42255-024-01133-5. [PMID: 39327534 DOI: 10.1038/s42255-024-01133-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 08/23/2024] [Indexed: 09/28/2024]
Abstract
Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.
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Affiliation(s)
- Julia Carrasco-Zanini
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nicola Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
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2
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Ye W, Shen B, Tang Q, Fang C, Wang L, Xie L, He Q. Identification of a novel immune infiltration-related gene signature, MCEMP1, for coronary artery disease. PeerJ 2024; 12:e18135. [PMID: 39346078 PMCID: PMC11438437 DOI: 10.7717/peerj.18135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/29/2024] [Indexed: 10/01/2024] Open
Abstract
Background This study aims to identify a novel gene signature for coronary artery disease (CAD), explore the role of immune cell infiltration in CAD pathogenesis, and assess the cell function of mast cell-expressed membrane protein 1 (MCEMP1) in human umbilical vein endothelial cells (HUVECs) treated with oxidized low-density lipoprotein (ox-LDL). Methods To identify differentially expressed genes (DEGs) of CAD, datasets GSE24519 and GSE61145 were downloaded from the Gene Expression Omnibus (GEO) database using the R "limma" package with p < 0.05 and |log2 FC| > 1. Gene ontology (GO) and pathway analyses were conducted to determine the biological functions of DEGs. Hub genes were identified using support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO). The expression levels of these hub genes in CAD were validated using the GSE113079 dataset. CIBERSORT program was used to quantify the proportion of immune cell infiltration. Western blot assay and qRT-PCR were used to detect the expression of hub genes in ox-LDL-treated HUVECs to validate the bioinformatics results. Knockdown interference sequences for MCEMP1 were synthesized, and cell proliferation and apoptosis were examined using a CCK8 kit and Muse® Cell Analyzer, respectively. The concentrations of IL-1β, IL-6, and TNF-α were measured with respective enzyme-linked immunosorbent assay (ELISA) kits. Results A total of 73 DEGs (four down-regulated genes and 69 up-regulated genes) were identified in the metadata (GSE24519 and GSE61145) cohort. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results indicated that these DEGs might be associated with the regulation of platelet aggregation, defense response or response to bacterium, NF-kappa B signaling pathway, and lipid and atherosclerosis. Using SVM-RFE and LASSO, seven hub genes were obtained from the metadata. The upregulated expression of DIRC2 and MCEMP1 in CAD was confirmed in the GSE113079 dataset and in ox-LDL-treated HUVECs. The associations between the two hub genes (DIRC2 and MCEMP1) and the 22 types of immune cell infiltrates in CAD were found. MCEMP1 knockdown accelerated cell proliferation and suppressed cell apoptosis for ox-LDL-treated HUVECs. Additionally, MCEMP1 knockdown appeared to decrease the expression of inflammatory factors IL-1β, IL-6, and TNF-α. Conclusions The results of this study indicate that MCEMP1 may play an important role in CAD pathophysiology.
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Affiliation(s)
- Wei Ye
- Department of Neonatology, Renmin Hospital of Wuhan University, Wuhan, China
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Metabolic and Chronic Diseases, Wuhan, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Shen
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Metabolic and Chronic Diseases, Wuhan, China
| | - Qizhu Tang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Metabolic and Chronic Diseases, Wuhan, China
| | - Chengzhi Fang
- Department of Neonatology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lei Wang
- Department of Cardiology, HanChuan Hospital, Hanchuan, China
| | - Lili Xie
- Department of Neonatology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qi He
- Department of Neonatology, Renmin Hospital of Wuhan University, Wuhan, China
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3
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Kumar N, Yang ML, Sun P, Hunker KL, Li J, Jia J, Fan F, Wang J, Ning X, Gao W, Xu M, Zhang J, Chang L, Chen YE, Huo Y, Zhang Y, Ganesh SK. Genetic variation in CCDC93 is associated with elevated central systolic blood pressure, impaired arterial relaxation, and mitochondrial dysfunction. PLoS Genet 2024; 20:e1011151. [PMID: 39250516 PMCID: PMC11421807 DOI: 10.1371/journal.pgen.1011151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/24/2024] [Accepted: 01/23/2024] [Indexed: 09/11/2024] Open
Abstract
Genetic studies of blood pressure (BP) traits to date have been performed on conventional measures by brachial cuff sphygmomanometer for systolic BP (SBP) and diastolic BP, integrating several physiologic occurrences. Genetic associations with central SBP (cSBP) have not been well-studied. Genetic discovery studies of BP have been most often performed in European-ancestry samples. Here, we investigated genetic associations with cSBP in a Chinese population and functionally validated the impact of a novel associated coiled-coil domain containing 93 (CCDC93) gene on BP regulation. An exome-wide association study (EWAS) was performed using a mixed linear model of non-invasive cSBP and peripheral BP traits in a Han Chinese population (N = 5,954) from Beijing, China genotyped with a customized Illumina ExomeChip array. We identified four SNP-trait associations with three SNPs, including two novel associations (rs2165468-SBP and rs33975708-cSBP). rs33975708 is a coding variant in the CCDC93 gene, c.535C>T, p.Arg179Cys (MAF = 0.15%), and was associated with increased cSBP (β = 29.3 mmHg, P = 1.23x10-7). CRISPR/Cas9 genome editing was used to model the effect of Ccdc93 loss in mice. Homozygous Ccdc93 deletion was lethal prior to day 10.5 of embryonic development. Ccdc93+/- heterozygous mice were viable and morphologically normal, with 1.3-fold lower aortic Ccdc93 protein expression (P = 0.0041) and elevated SBP as compared to littermate Ccdc93+/+ controls (110±8 mmHg vs 125±10 mmHg, P = 0.016). Wire myography of Ccdc93+/- aortae showed impaired acetylcholine-induced relaxation and enhanced phenylephrine-induced contraction. RNA-Seq transcriptome analysis of Ccdc93+/- mouse thoracic aortae identified significantly enriched pathways altered in fatty acid metabolism and mitochondrial metabolism. Plasma free fatty acid levels were elevated in Ccdc93+/- mice (96±7mM vs 124±13mM, P = 0.0031) and aortic mitochondrial dysfunction was observed through aberrant Parkin and Nix protein expression. Together, our genetic and functional studies support a novel role of CCDC93 in the regulation of BP through its effects on vascular mitochondrial function and endothelial function.
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Affiliation(s)
- Nitin Kumar
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Min-Lee Yang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Pengfei Sun
- Department of Cardiology, Peking University First hospital, Beijing, China
- Department of Cardiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Kristina L. Hunker
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Jianping Li
- Department of Cardiology, Peking University First hospital, Beijing, China
| | - Jia Jia
- Department of Cardiology, Peking University First hospital, Beijing, China
| | - Fangfang Fan
- Department of Cardiology, Peking University First hospital, Beijing, China
| | - Jinghua Wang
- Laboratory of Epidemiology, Tianjin Neurological Institute, Tianjin, China
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xianjia Ning
- Laboratory of Epidemiology, Tianjin Neurological Institute, Tianjin, China
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Wei Gao
- Department of Cardiology, Peking University Third hospital, Beijing, China
| | - Ming Xu
- Department of Cardiology, Peking University Third hospital, Beijing, China
| | - Jifeng Zhang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Lin Chang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Y. Eugene Chen
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Yong Huo
- Department of Cardiology, Peking University First hospital, Beijing, China
| | - Yan Zhang
- Department of Cardiology, Peking University First hospital, Beijing, China
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing, China
- Hypertension Precision Diagnosis and Treatment Research Center, Peking University First Hospital, Beijing, China
| | - Santhi K. Ganesh
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
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4
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Grilo LF, Zimmerman KD, Puppala S, Chan J, Huber HF, Li G, Jadhav AYL, Wang B, Li C, Clarke GD, Register TC, Oliveira PJ, Nathanielsz PW, Olivier M, Pereira SP, Cox LA. Cardiac Molecular Analysis Reveals Aging-Associated Metabolic Alterations Promoting Glycosaminoglycans Accumulation via Hexosamine Biosynthetic Pathway. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2309211. [PMID: 39119859 DOI: 10.1002/advs.202309211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 07/17/2024] [Indexed: 08/10/2024]
Abstract
Age is a prominent risk factor for cardiometabolic disease, often leading to heart structural and functional changes. However, precise molecular mechanisms underlying cardiac remodeling and dysfunction exclusively resulting from physiological aging remain elusive. Previous research demonstrated age-related functional alterations in baboons, analogous to humans. The goal of this study is to identify early cardiac molecular alterations preceding functional adaptations, shedding light on the regulation of age-associated changes. Unbiased transcriptomics of left ventricle samples are performed from female baboons aged 7.5-22.1 years (human equivalent ≈30-88 years). Weighted-gene correlation network and pathway enrichment analyses are performed, with histological validation. Modules of transcripts negatively correlated with age implicated declined metabolism-oxidative phosphorylation, tricarboxylic acid cycle, glycolysis, and fatty-acid β-oxidation. Transcripts positively correlated with age suggested a metabolic shift toward glucose-dependent anabolic pathways, including hexosamine biosynthetic pathway (HBP). This shift is associated with increased glycosaminoglycan synthesis, modification, precursor synthesis via HBP, and extracellular matrix accumulation, verified histologically. Upregulated extracellular matrix-induced signaling coincided with glycosaminoglycan accumulation, followed by cardiac hypertrophy-related pathways. Overall, these findings revealed a transcriptional shift in metabolism favoring glycosaminoglycan accumulation through HBP before cardiac hypertrophy. Unveiling this metabolic shift provides potential targets for age-related cardiac diseases, offering novel insights into early age-related mechanisms.
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Affiliation(s)
- Luís F Grilo
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, 3060, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, 3060, Portugal
- Institute for Interdisciplinary Research, PDBEB - Doctoral Programme in Experimental Biology and Biomedicine, University of Coimbra, Coimbra, 3060, Portugal
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
| | - Kip D Zimmerman
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Sobha Puppala
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Jeannie Chan
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Hillary F Huber
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, 78245, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
| | - Avinash Y L Jadhav
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
| | - Benlian Wang
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
| | - Cun Li
- Texas Pregnancy & Life-Course Health Research Center, Department of Animal Science, University of Wyoming, Laramie, WY, 82071, USA
| | - Geoffrey D Clarke
- Department of Radiology, University of Texas Health Science Center, San Antonio, TX, 78229, USA
| | - Thomas C Register
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Comparative Medicine, Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Paulo J Oliveira
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, 3060, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, 3060, Portugal
| | - Peter W Nathanielsz
- Texas Pregnancy & Life-Course Health Research Center, Department of Animal Science, University of Wyoming, Laramie, WY, 82071, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Susana P Pereira
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, 3060, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, 3060, Portugal
- Laboratory of Metabolism and Exercise (LaMetEx), Research Centre in Physical Activity, Health and Leisure (CIAFEL), Laboratory for Integrative and Translational Research in Population Health (ITR), Faculty of Sports, University of Porto, Porto, 4050, Portugal
| | - Laura A Cox
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, 27157, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, 78245, USA
- Section on Comparative Medicine, Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
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5
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Kelly J, Xu X, Eales JM, Keavney B, Berzuini C, Tomaszewski M, Guo H. Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC. BMC Med Res Methodol 2024; 24:168. [PMID: 39095705 PMCID: PMC11295895 DOI: 10.1186/s12874-024-02229-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/23/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.
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Affiliation(s)
- Jack Kelly
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
| | - Xiaoguang Xu
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - James M Eales
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
- Division of Cardiology and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Carlo Berzuini
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK
- Manchester Heart Centre and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Hui Guo
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
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Tschiderer L, Bakker MK, Gill D, Burgess S, Willeit P, Ruigrok YM, Peters SAE. Sex differences in risk factor relationships with subarachnoid haemorrhage and intracranial aneurysms: A Mendelian randomization study. Eur Stroke J 2024:23969873241265224. [PMID: 39081091 PMCID: PMC7616166 DOI: 10.1177/23969873241265224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND The prevalence of intracranial aneurysms (IAs) and incidence of aneurysmal subarachnoid haemorrhage (aSAH) is higher in women than in men. Although several cardiometabolic and lifestyle factors have been related to the risk of IAs or aSAH, it is unclear whether there are sex differences in causal relationships of these risk factors. AIMS The aim of this study was to determine sex differences in causal relationships between cardiometabolic and lifestyle factors and risk of aSAH and IA. METHODS We conducted a sex-specific two-sample Mendelian randomization study using summary-level data from genome-wide association studies. We analysed low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], triglycerides, non-HDL-C, total cholesterol, fasting glucose, systolic and diastolic blood pressure, smoking initiation, and alcohol use as exposures, and aSAH and IA (i.e. aSAH and unruptured IA combined) as outcomes. RESULTS We found statistically significant sex differences in the relationship between genetically proxied non-HDL-C and aSAH risk, with odds ratios (ORs) of 0.72 (95% confidence interval 0.58, 0.88) in women and 1.01 (0.77, 1.31) in men (p-value for sex difference 0.044). Moreover, genetic liability to smoking initiation was related to a statistically significantly higher risk of aSAH in men compared to women (p-value for sex difference 0.007) with ORs of 3.81 (1.93, 7.52) and 1.12 (0.63, 1.99), respectively, and to a statistically significantly higher IA risk in men compared to women (p-value for sex difference 0.036) with ORs of 3.58 (2.04, 6.27) and 1.61 (0.98, 2.64), respectively. In addition, higher genetically proxied systolic and diastolic blood pressure were related to a higher risk of aSAH and IA in both women and men. CONCLUSIONS Higher genetically proxied non-HDL-C was related to a lower risk of aSAH in women compared to men. Moreover, genetic liability to smoking initiation was associated with a higher risk for aSAH and IA in men compared to women. These findings may help improve understanding of sex differences in the development of aSAH and IA.
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Affiliation(s)
- Lena Tschiderer
- Institute of Health Economics; Medical University of Innsbruck, Innsbruck, Austria
| | - Mark K Bakker
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Peter Willeit
- Institute of Health Economics; Medical University of Innsbruck, Innsbruck, Austria
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Ynte M Ruigrok
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands
| | - Sanne AE Peters
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- The George Institute for Global Health, School of Public Health, Imperial College London, London, United Kingdom
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
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7
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Huang D, Shang W, Xu M, Wan Q, Zhang J, Tang X, Shen Y, Wang Y, Yu Y. Genome-Wide Methylation Analysis Reveals a KCNK3-Prominent Causal Cascade on Hypertension. Circ Res 2024; 135:e76-e93. [PMID: 38841840 DOI: 10.1161/circresaha.124.324455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Despite advances in understanding hypertension's genetic structure, how noncoding genetic variants influence it remains unclear. Studying their interaction with DNA methylation is crucial to deciphering this complex disease's genetic mechanisms. METHODS We investigated the genetic and epigenetic interplay in hypertension using whole-genome bisulfite sequencing. Methylation profiling in 918 males revealed allele-specific methylation and methylation quantitative trait loci. We engineered rs1275988T/C mutant mice using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9), bred them for homozygosity, and subjected them to a high-salt diet. Telemetry captured their cardiovascular metrics. Protein-DNA interactions were elucidated using DNA pull-downs, mass spectrometry, and Western blots. A wire myograph assessed vascular function, and analysis of the Kcnk3 gene methylation highlighted the mutation's role in hypertension. RESULTS We discovered that DNA methylation-associated genetic effects, especially in non-cytosine-phosphate-guanine (non-CpG) island and noncoding distal regulatory regions, significantly contribute to hypertension predisposition. We identified distinct methylation quantitative trait locus patterns in the hypertensive population and observed that the onset of hypertension is influenced by the transmission of genetic effects through the demethylation process. By evidence-driven prioritization and in vivo experiments, we unearthed rs1275988 in a cell type-specific enhancer as a notable hypertension causal variant, intensifying hypertension through the modulation of local DNA methylation and consequential alterations in Kcnk3 gene expression and vascular remodeling. When exposed to a high-salt diet, mice with the rs1275988C/C genotype exhibited exacerbated hypertension and significant vascular remodeling, underscored by increased aortic wall thickness. The C allele of rs1275988 was associated with elevated DNA methylation levels, driving down the expression of the Kcnk3 gene by attenuating Nr2f2 (nuclear receptor subfamily 2 group F member 2) binding at the enhancer locus. CONCLUSIONS Our research reveals new insights into the complex interplay between genetic variations and DNA methylation in hypertension. We underscore hypomethylation's potential in hypertension onset and identify rs1275988 as a causal variant in vascular remodeling. This work advances our understanding of hypertension's molecular mechanisms and encourages personalized health care strategies.
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Affiliation(s)
- Dandan Huang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammatory Biology, Center for Cardiovascular Diseases, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, China (D.H., W.S., M.X., Y.S., Y.Y.)
- School of Food Science and Technology, Jiangnan University, Wuxi, China (D.H.)
| | - Wenlong Shang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammatory Biology, Center for Cardiovascular Diseases, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, China (D.H., W.S., M.X., Y.S., Y.Y.)
| | - Mengtong Xu
- Department of Pharmacology, Tianjin Key Laboratory of Inflammatory Biology, Center for Cardiovascular Diseases, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, China (D.H., W.S., M.X., Y.S., Y.Y.)
| | - Qiangyou Wan
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine (Q.W.)
| | - Jin Zhang
- Department of Cardiovascular Medicine, Research Center for Hypertension Management and Prevention in Community, State Key Laboratory of Medical Genomics, Shanghai Key Laboratory of Hypertension, Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (J.Z., X.T., Y.W.)
| | - Xiaofeng Tang
- Department of Cardiovascular Medicine, Research Center for Hypertension Management and Prevention in Community, State Key Laboratory of Medical Genomics, Shanghai Key Laboratory of Hypertension, Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (J.Z., X.T., Y.W.)
| | - Yujun Shen
- Department of Pharmacology, Tianjin Key Laboratory of Inflammatory Biology, Center for Cardiovascular Diseases, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, China (D.H., W.S., M.X., Y.S., Y.Y.)
| | - Yan Wang
- Department of Cardiovascular Medicine, Research Center for Hypertension Management and Prevention in Community, State Key Laboratory of Medical Genomics, Shanghai Key Laboratory of Hypertension, Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (J.Z., X.T., Y.W.)
| | - Ying Yu
- Department of Pharmacology, Tianjin Key Laboratory of Inflammatory Biology, Center for Cardiovascular Diseases, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, China (D.H., W.S., M.X., Y.S., Y.Y.)
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8
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Tschiderer L, Bakker MK, Gill D, Burgess S, Willeit P, Ruigrok YM, Peters SAE. Sex differences in risk factor relationships with subarachnoid haemorrhage and intracranial aneurysms: A Mendelian Randomisation study. Eur J Prev Cardiol 2024; 31:zwae175.095. [PMID: 38989054 PMCID: PMC7616166 DOI: 10.1093/eurjpc/zwae175.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Background The prevalence of intracranial aneurysms (IAs) and incidence of aneurysmal subarachnoid haemorrhage (aSAH) is higher in women than in men. Although several cardiometabolic and lifestyle factors have been related to the risk of IAs or aSAH, it is unclear whether there are sex differences in causal relationships of these risk factors. Aims The aim of this study was to determine sex differences in causal relationships between cardiometabolic and lifestyle factors and risk of aSAH and IA. Methods We conducted a sex-specific two-sample Mendelian randomisation study using summary-level data from genome-wide association studies. We analysed low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], triglycerides, non-HDL-C, total cholesterol, fasting glucose, systolic and diastolic blood pressure, smoking initiation, and alcohol use as exposures, and aSAH and IA (i.e., aSAH and unruptured IA combined) as outcomes. Results We found statistically significant sex differences in the relationship between genetically proxied non-HDL-C and aSAH risk, with odds ratios (ORs) of 0.72 (95% confidence interval 0.58, 0.88) in women and 1.01 (0.77, 1.31) in men (P-value for sex difference 0.044). Moreover, genetic liability to smoking initiation was related to a statistically significantly higher risk of aSAH in men compared to women (P-value for sex difference 0.007) with ORs of 3.81 (1.93, 7.52) and 1.12 (0.63, 1.99), respectively, and to a statistically significantly higher IA risk in men compared to women (P-value for sex difference 0.036) with ORs of 3.58 (2.04, 6.27) and 1.61 (0.98, 2.64), respectively. In addition, higher genetically proxied systolic and diastolic blood pressure were related to a higher risk of aSAH and IA in both women and men. Conclusions Higher genetically proxied non-HDL-C was related to a lower risk of aSAH in women compared to men. Moreover, genetic liability to smoking initiation was associated with a higher risk for aSAH and IA in men compared to women. These findings may help improve understanding of sex differences in the development of aSAH and IA.
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Affiliation(s)
- Lena Tschiderer
- Institute of Health Economics; Medical University of Innsbruck, Innsbruck, Austria
| | - Mark K Bakker
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Peter Willeit
- Institute of Health Economics; Medical University of Innsbruck, Innsbruck, Austria
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Ynte M Ruigrok
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands
| | - Sanne AE Peters
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- The George Institute for Global Health, School of Public Health, Imperial College London, London, United Kingdom
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
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9
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MacCarthy G, Pazoki R. Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank. J Clin Med 2024; 13:2955. [PMID: 38792496 PMCID: PMC11122671 DOI: 10.3390/jcm13102955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.
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Affiliation(s)
- Gideon MacCarthy
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Raha Pazoki
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Campus, Norfolk Place, Imperial College London, London W2 1PG, UK
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10
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Keaton JM, Kamali Z, Xie T, Vaez A, Williams A, Goleva SB, Ani A, Evangelou E, Hellwege JN, Yengo L, Young WJ, Traylor M, Giri A, Zheng Z, Zeng J, Chasman DI, Morris AP, Caulfield MJ, Hwang SJ, Kooner JS, Conen D, Attia JR, Morrison AC, Loos RJF, Kristiansson K, Schmidt R, Hicks AA, Pramstaller PP, Nelson CP, Samani NJ, Risch L, Gyllensten U, Melander O, Riese H, Wilson JF, Campbell H, Rich SS, Psaty BM, Lu Y, Rotter JI, Guo X, Rice KM, Vollenweider P, Sundström J, Langenberg C, Tobin MD, Giedraitis V, Luan J, Tuomilehto J, Kutalik Z, Ripatti S, Salomaa V, Girotto G, Trompet S, Jukema JW, van der Harst P, Ridker PM, Giulianini F, Vitart V, Goel A, Watkins H, Harris SE, Deary IJ, van der Most PJ, Oldehinkel AJ, Keavney BD, Hayward C, Campbell A, Boehnke M, Scott LJ, Boutin T, Mamasoula C, Järvelin MR, Peters A, Gieger C, Lakatta EG, Cucca F, Hui J, Knekt P, Enroth S, De Borst MH, Polašek O, Concas MP, Catamo E, Cocca M, Li-Gao R, Hofer E, Schmidt H, Spedicati B, Waldenberger M, Strachan DP, Laan M, Teumer A, Dörr M, Gudnason V, Cook JP, Ruggiero D, Kolcic I, Boerwinkle E, Traglia M, Lehtimäki T, Raitakari OT, Johnson AD, Newton-Cheh C, Brown MJ, Dominiczak AF, Sever PJ, Poulter N, Chambers JC, Elosua R, Siscovick D, Esko T, Metspalu A, Strawbridge RJ, Laakso M, Hamsten A, Hottenga JJ, de Geus E, Morris AD, Palmer CNA, Nolte IM, Milaneschi Y, Marten J, Wright A, Zeggini E, Howson JMM, O'Donnell CJ, Spector T, Nalls MA, Simonsick EM, Liu Y, van Duijn CM, Butterworth AS, Danesh JN, Menni C, Wareham NJ, Khaw KT, Sun YV, Wilson PWF, Cho K, Visscher PM, Denny JC, Levy D, Edwards TL, Munroe PB, Snieder H, Warren HR. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nat Genet 2024; 56:778-791. [PMID: 38689001 PMCID: PMC11096100 DOI: 10.1038/s41588-024-01714-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/11/2024] [Indexed: 05/02/2024]
Abstract
Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.
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Affiliation(s)
- Jacob M Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Ariel Williams
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Slavina B Goleva
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Ioannina, Greece
| | - Jacklyn N Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - William J Young
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Matthew Traylor
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Ayush Giri
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Daniel I Chasman
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Shih-Jen Hwang
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Jaspal S Kooner
- National Heart and Lung Institute, Imperial College London, London, UK
| | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - John R Attia
- Faculty of Health and Medicine, University of Newcastle, New Lambton Heights, Newcastle, New South Wales, Australia
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- University of Lübeck, Lübeck, Germany
| | - Peter P Pramstaller
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- University of Lübeck, Lübeck, Germany
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Lorenz Risch
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, Triesen, Liechtenstein
- Department of Laboratory Medicine, Dr. Risch Anstalt, Vaduz, Liechtenstein
| | - Ulf Gyllensten
- Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Olle Melander
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Harriette Riese
- Interdisciplinary Center Psychopathology and Emotional Regulation (ICPE), Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Yingchang Lu
- Vanderbilt Genetic Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Johan Sundström
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, UK
- Leicester NIHR Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Jaakko Tuomilehto
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Zoltan Kutalik
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Giorgia Girotto
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Stella Trompet
- Department Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Paul M Ridker
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Franco Giulianini
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sarah E Harris
- Lothian Birth Cohorts Group, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts Group, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Michael Boehnke
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Scott
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Thibaud Boutin
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | | | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Epidemiologie, Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Ludwig-Maximilians-Universität München, Neuherberg, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Edward G Lakatta
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Francesco Cucca
- Institute of Genetic and Biomedical Research, National Research Council (CNR), Monserrato, Italy
| | - Jennie Hui
- Busselton Population Medical Research Institute, Perth, Western Australia, Australia
- School of Population and Global Health, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Paul Knekt
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Stefan Enroth
- Department of Immunology, Genetics, and Pathology, Biomedical Center, Science for Life Laboratory (SciLifeLab) Uppsala, Uppsala University, Uppsala, Sweden
| | - Martin H De Borst
- Department of Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Eulalia Catamo
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Massimiliano Cocca
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - David P Strachan
- Population Health Sciences Institute St George's, University of London, London, UK
| | - Maris Laan
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Kopavogur, Iceland
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Daniela Ruggiero
- IRCCS Neuromed, Pozzilli, Italy
- Institute of Genetics and Biophysics - 'A. Buzzati-Traverso', National Research Council of Italy, Naples, Italy
| | - Ivana Kolcic
- Algebra University College, Zagreb, Croatia
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Andrew D Johnson
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Christopher Newton-Cheh
- Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Morris J Brown
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Anna F Dominiczak
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Peter J Sever
- International Centre for Circulatory Health, Imperial College London, London, UK
| | - Neil Poulter
- School of Public Health, Imperial College London, London, UK
| | - John C Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Roberto Elosua
- Hospital del Mar Research Institute (IMIM), Barcelona, Spain
- CIBER Enfermedades Cardiovasculares (CIBERCV), Barcelona, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia (UVic-UCC), Vic, Spain
| | | | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Health Data Research UK, Glasgow, UK
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Anders Hamsten
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Eco de Geus
- Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Amsterdam, the Netherlands
| | - Andrew D Morris
- Data Science, University of Edinburgh, Edinburgh, UK
- Health Data Research UK, London, UK
| | - Colin N A Palmer
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yuri Milaneschi
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jonathan Marten
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Alan Wright
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Joanna M M Howson
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tim Spector
- Department of Twin Research, King's College London, London, UK
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias, NIA/NINDS, NIH, Bethesda, MD, USA
- Laboratory of Neurogenetics, NIA, NIH, Bethesda, MD, USA
- DataTecnica LLC, Washington, DC, USA
| | - Eleanor M Simonsick
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yongmei Liu
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - John N Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Human Genetics, The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, London, UK
| | | | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
- VA Atlanta Healthcare System, Decatur, GA, USA
| | - Peter W F Wilson
- Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Joshua C Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Levy
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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11
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Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Wade KH, Thomas LF, Wolford BN, Wisløff U, Gudbjartsson DF, Holm H, Stefansson K, Brumpton BM, Hveem K. Polygenic risk scores associate with blood pressure traits across the lifespan. Eur J Prev Cardiol 2024; 31:644-654. [PMID: 38007706 PMCID: PMC11025038 DOI: 10.1093/eurjpc/zwad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 10/18/2023] [Accepted: 11/02/2023] [Indexed: 11/28/2023]
Abstract
AIMS Hypertension is a major modifiable cause of morbidity and mortality that affects over 1 billion people worldwide. Blood pressure (BP) traits have a strong genetic component that can be quantified with polygenic risk scores (PRSs). To date, the performance of BP PRSs has mainly been assessed in adults, and less is known about polygenic hypertension risk in childhood. METHODS AND RESULTS Multiple PRSs for systolic BP (SBP), diastolic BP (DBP), and pulse pressure were developed using either genome-wide significant weights, pruning and thresholding, or Bayesian regression. Among 87 total PRSs, the top performer for each trait was applied in independent cohorts of children and adult to assess genotype-phenotype associations and disease risk across the lifespan. Differences between those with low (1st decile), average (2nd-9th decile), and high (10th decile) PRS emerge in the first years of life and are maintained throughout adulthood. These diverging BP trajectories also seem to affect cardiovascular and renal disease risk, with increased risk observed among those in the top decile and reduced risk among those in the bottom decile of the polygenic risk distribution compared with the rest of the population. CONCLUSION Genetic risk factors are associated with BP traits across the lifespan, beginning in the first years of life. Given the importance of exposure time in disease pathogenesis and the early rise in BP levels among those genetically susceptible, PRSs may help identify high-risk individuals prior to hypertension onset, facilitate primordial prevention, and reduce the burden of this public health challenge.
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Affiliation(s)
- Karsten Øvretveit
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Emma M L Ingeström
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Michail Spitieris
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, Bristol BS8 1TH, UK
- Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK
| | - Laurent F Thomas
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Brooke N Wolford
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ben M Brumpton
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Innovation and Research, St. Olavs Hospital, Trondheim, Norway
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12
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Zhang J. Hereditary causes of hypertension due to increased sodium transport. Curr Opin Pediatr 2024; 36:211-218. [PMID: 37909881 DOI: 10.1097/mop.0000000000001304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
PURPOSE OF REVIEW Hypertension, commonly known as high blood pressure, is a widespread health condition affecting a large number of individuals across the globe. Although lifestyle choices and environmental factors are known to have a significant impact on its development, there is growing recognition of the influence of genetic factors in the pathogenesis of hypertension. This review specifically focuses on the hereditary causes of hypertension that are associated with increased sodium transport through the thiazide-sensitive NaCl cotransporter (NCC) or amiloride-sensitive epithelial sodium channel (ENaC), crucial mechanisms involved in regulating blood pressure in the kidneys. By examining genetic mutations and signaling molecules linked to the dysregulation of sodium transport, this review aims to deepen our understanding of the hereditary causes of hypertension and shed light on potential therapeutic targets. RECENT FINDINGS Liddle syndrome (LS) is a genetic disorder that typically manifests early in life and is characterized by hypertension, hypokalemic metabolic alkalosis, hyporeninemia, and suppressed aldosterone secretion. This condition is primarily caused by gain-of-function mutations in ENaC. In contrast, Pseudohypoaldosteronism type II (PHAII) is marked by hyperkalemia and hypertension, alongside other clinical features such as hyperchloremia, metabolic acidosis, and suppressed plasma renin levels. PHAII results from overactivations of NCC, brought about by gain-of-function mutations in its upstream signaling molecules, including WNK1 (with no lysine (K) 1), WNK4, Kelch-like 3 (KLHL3), and cullin3 (CUL3). SUMMARY NCC and ENaC are integral components, and their malfunctions lead to disorders like LS and PHAII, hereditary causes of hypertension. Current treatments for LS involve ENaC blockers (e.g., triamterene and amiloride) in conjunction with low-sodium diets, effectively normalizing blood pressure and potassium levels. In PHAII, thiazide diuretics, which inhibit NCC, are the mainstay treatment, albeit with some limitations and potential side effects. Ongoing research in developing alternative treatments, including small molecules targeting key regulators, holds promise for more effective and tailored hypertension solutions.
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Affiliation(s)
- Jinwei Zhang
- Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian
- State Key Laboratory of Chemical Biology, Research Center of Chemical Kinomics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Institute of Biomedical and Clinical Sciences, Medical School, Faculty of Health and Life Sciences, University of Exeter, Hatherly Laboratories, Exeter, UK
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13
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Chia SPS, Pang JKS, Soh BS. Current RNA strategies in treating cardiovascular diseases. Mol Ther 2024; 32:580-608. [PMID: 38291757 PMCID: PMC10928165 DOI: 10.1016/j.ymthe.2024.01.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/22/2023] [Accepted: 01/23/2024] [Indexed: 02/01/2024] Open
Abstract
Cardiovascular disease (CVD) continues to impose a significant global health burden, necessitating the exploration of innovative treatment strategies. Ribonucleic acid (RNA)-based therapeutics have emerged as a promising avenue to address the complex molecular mechanisms underlying CVD pathogenesis. We present a comprehensive review of the current state of RNA therapeutics in the context of CVD, focusing on the diverse modalities that bring about transient or permanent modifications by targeting the different stages of the molecular biology central dogma. Considering the immense potential of RNA therapeutics, we have identified common gene targets that could serve as potential interventions for prevalent Mendelian CVD caused by single gene mutations, as well as acquired CVDs developed over time due to various factors. These gene targets offer opportunities to develop RNA-based treatments tailored to specific genetic and molecular pathways, presenting a novel and precise approach to address the complex pathogenesis of both types of cardiovascular conditions. Additionally, we discuss the challenges and opportunities associated with delivery strategies to achieve targeted delivery of RNA therapeutics to the cardiovascular system. This review highlights the immense potential of RNA-based interventions as a novel and precise approach to combat CVD, paving the way for future advancements in cardiovascular therapeutics.
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Affiliation(s)
- Shirley Pei Shan Chia
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore; Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558, Singapore
| | - Jeremy Kah Sheng Pang
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Boon-Seng Soh
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore; Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558, Singapore.
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14
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Yang ML, Xu C, Gupte T, Hoffmann TJ, Iribarren C, Zhou X, Ganesh SK. Sex-specific genetic architecture of blood pressure. Nat Med 2024; 30:818-828. [PMID: 38459180 DOI: 10.1038/s41591-024-02858-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/05/2024] [Indexed: 03/10/2024]
Abstract
The genetic and genomic basis of sex differences in blood pressure (BP) traits remain unstudied at scale. Here, we conducted sex-stratified and combined-sex genome-wide association studies of BP traits using the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Among associated loci, 412 were female-specific (Pfemale ≤ 5 × 10-8; Pmale > 5 × 10-8) and 142 were male-specific (Pmale ≤ 5 × 10-8; Pfemale > 5 × 10-8); these sex-specific loci were enriched for hormone-related transcription factors, in particular, estrogen receptor 1. Analyses of gene-by-sex interactions and sexually dimorphic effects identified four genomic regions, showing female-specific associations with diastolic BP or pulse pressure, including the chromosome 13q34-COL4A1/COL4A2 locus. Notably, female-specific pulse pressure-associated loci exhibited enriched acetylated histone H3 Lys27 modifications in arterial tissues and a female-specific association with fibromuscular dysplasia, a female-biased vascular disease; colocalization signals included Chr13q34: COL4A1/COL4A2, Chr9p21: CDKN2B-AS1 and Chr4q32.1: MAP9 regions. Sex-specific and sex-biased polygenic associations of BP traits were associated with multiple cardiovascular traits. These findings suggest potentially clinically significant and BP sex-specific pleiotropic effects on cardiovascular diseases.
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Affiliation(s)
- Min-Lee Yang
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chang Xu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Trisha Gupte
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Thomas J Hoffmann
- Department of Epidemiology & Biostatistics, and Institute for Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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15
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 182] [Impact Index Per Article: 182.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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16
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Tsare EPG, Klapa MI, Moschonas NK. Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis. Hum Genomics 2024; 18:15. [PMID: 38326862 DOI: 10.1186/s40246-023-00565-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/12/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS The implemented workflow could be used for other multifactorial diseases.
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Affiliation(s)
- Evridiki-Pandora G Tsare
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
| | - Nicholas K Moschonas
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece.
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
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17
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Nebert DW. Gene-Environment Interactions: My Unique Journey. Annu Rev Pharmacol Toxicol 2024; 64:1-26. [PMID: 37788491 DOI: 10.1146/annurev-pharmtox-022323-082311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
I am deeply honored to be invited to write this scientific autobiography. As a physician-scientist, pediatrician, molecular biologist, and geneticist, I have authored/coauthored more than 600 publications in the fields of clinical medicine, biochemistry, biophysics, pharmacology, drug metabolism, toxicology, molecular biology, cancer, standardized gene nomenclature, developmental toxicology and teratogenesis, mouse genetics, human genetics, and evolutionary genomics. Looking back, I think my career can be divided into four distinct research areas, which I summarize mostly chronologically in this article: (a) discovery and characterization of the AHR/CYP1 axis, (b) pharmacogenomics and genetic prediction of response to drugs and other environmental toxicants, (c) standardized drug-metabolizing gene nomenclature based on evolutionary divergence, and (d) discovery and characterization of the SLC39A8 gene encoding the ZIP8 metal cation influx transporter. Collectively, all four topics embrace gene-environment interactions, hence the title of my autobiography.
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Affiliation(s)
- Daniel W Nebert
- Department of Environmental and Public Health Sciences and Center for Environmental Genetics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Department of Pediatrics and Molecular Developmental Biology, Division of Human Genetics, Cincinnati Children's Hospital, Cincinnati, Ohio, USA;
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18
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Ahles A, Engelhardt S. Genetic Variants of Adrenoceptors. Handb Exp Pharmacol 2024; 285:27-54. [PMID: 37578621 DOI: 10.1007/164_2023_676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Adrenoceptors are class A G-protein-coupled receptors grouped into three families (α1-, α2-, and β-adrenoceptors), each one including three members. All nine corresponding adrenoceptor genes display genetic variation in their coding and adjacent non-coding genomic region. Coding variants, i.e., nucleotide exchanges within the transcribed and translated receptor sequence, may result in a difference in amino acid sequence thus altering receptor function and signaling. Such variants have been intensely studied in vitro in overexpression systems and addressed in candidate-gene studies for distinct clinical parameters. In recent years, large cohorts were analyzed in genome-wide association studies (GWAS), where variants are detected as significant in context with specific traits. These studies identified two of the in-depth characterized 18 coding variants in adrenoceptors as repeatedly statistically significant genetic risk factors - p.Arg389Gly in the β1- and p.Thr164Ile in the β2-adrenoceptor, along with 56 variants in the non-coding regions adjacent to the adrenoceptor gene loci, the functional role of which is largely unknown at present. This chapter summarizes current knowledge on the two coding variants in adrenoceptors that have been consistently validated in GWAS and provides a prospective overview on the numerous non-coding variants more recently attributed to adrenoceptor gene loci.
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Affiliation(s)
- Andrea Ahles
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany
| | - Stefan Engelhardt
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
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19
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk alters the penetrance of monogenic kidney disease. Nat Commun 2023; 14:8318. [PMID: 38097619 PMCID: PMC10721887 DOI: 10.1038/s41467-023-43878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Jordan G Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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20
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Grilo LF, Zimmerman KD, Puppala S, Chan J, Huber HF, Li G, Jadhav AYL, Wang B, Li C, Clarke GD, Register TC, Oliveira PJ, Nathanielsz PW, Olivier M, Pereira SP, Cox LA. Cardiac Molecular Analysis Reveals Aging-Associated Metabolic Alterations Promoting Glycosaminoglycans Accumulation Via Hexosamine Biosynthetic Pathway. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.17.567640. [PMID: 38014295 PMCID: PMC10680868 DOI: 10.1101/2023.11.17.567640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Age is a prominent risk factor for cardiometabolic disease, and often leads to heart structural and functional changes. However, precise molecular mechanisms underlying cardiac remodeling and dysfunction resulting from physiological aging per se remain elusive. Understanding these mechanisms requires biological models with optimal translation to humans. Previous research demonstrated that baboons undergo age-related reduction in ejection fraction and increased heart sphericity, mirroring changes observed in humans. The goal of this study was to identify early cardiac molecular alterations that precede functional adaptations, shedding light on the regulation of age-associated changes. We performed unbiased transcriptomics of left ventricle (LV) samples from female baboons aged 7.5-22.1 years (human equivalent ~30-88 years). Weighted-gene correlation network and pathway enrichment analyses were performed to identify potential age-associated mechanisms in LV, with histological validation. Myocardial modules of transcripts negatively associated with age were primarily enriched for cardiac metabolism, including oxidative phosphorylation, tricarboxylic acid cycle, glycolysis, and fatty-acid β-oxidation. Transcripts positively correlated with age suggest upregulation of glucose uptake, pentose phosphate pathway, and hexosamine biosynthetic pathway (HBP), indicating a metabolic shift towards glucose-dependent anabolic pathways. Upregulation of HBP commonly results in increased glycosaminoglycan precursor synthesis. Transcripts involved in glycosaminoglycan synthesis, modification, and intermediate metabolism were also upregulated in older animals, while glycosaminoglycan degradation transcripts were downregulated with age. These alterations would promote glycosaminoglycan accumulation, which was verified histologically. Upregulation of extracellular matrix (ECM)-induced signaling pathways temporally coincided with glycosaminoglycan accumulation. We found a subsequent upregulation of cardiac hypertrophy-related pathways and an increase in cardiomyocyte width. Overall, our findings revealed a transcriptional shift in metabolism from catabolic to anabolic pathways that leads to ECM glycosaminoglycan accumulation through HBP prior to upregulation of transcripts of cardiac hypertrophy-related pathways. This study illuminates cellular mechanisms that precede development of cardiac hypertrophy, providing novel potential targets to remediate age-related cardiac diseases.
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Affiliation(s)
- Luís F. Grilo
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Portugal
- University of Coimbra, Institute for Interdisciplinary Research, PDBEB - Doctoral Programme in Experimental Biology and Biomedicine
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Kip D. Zimmerman
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Sobha Puppala
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jeannie Chan
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Hillary F. Huber
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Avinash Y. L. Jadhav
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Benlian Wang
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Cun Li
- Texas Pregnancy & Life-Course Health Research Center, Department of Animal Science, University of Wyoming, Laramie, Wyoming, USA
| | - Geoffrey D. Clarke
- Department of Radiology, University of Texas Health Science Center, San Antonio, Texas
| | - Thomas C. Register
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Comparative Medicine, Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Paulo J. Oliveira
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Portugal
| | - Peter W. Nathanielsz
- Texas Pregnancy & Life-Course Health Research Center, Department of Animal Science, University of Wyoming, Laramie, Wyoming, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Susana P. Pereira
- CNC-UC, Center for Neuroscience and Cell Biology, University of Coimbra, Portugal
- CIBB, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Portugal
- Laboratory of Metabolism and Exercise (LaMetEx), Research Centre in Physical Activity, Health and Leisure (CIAFEL), Laboratory for Integrative and Translational Research in Population Health (ITR), Faculty of Sports, University of Porto, Porto, Portugal
| | - Laura A. Cox
- Center for Precision Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
- Section on Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
- Section on Comparative Medicine, Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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21
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D’Urso S, Hwang LD. New Insights into Polygenic Score-Lifestyle Interactions for Cardiometabolic Risk Factors from Genome-Wide Interaction Analyses. Nutrients 2023; 15:4815. [PMID: 38004209 PMCID: PMC10675788 DOI: 10.3390/nu15224815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
The relationship between lifestyles and cardiometabolic outcomes varies between individuals. In 382,275 UK Biobank Europeans, we investigate how lifestyles interact with polygenic scores (PGS) of cardiometabolic risk factors. We identify six interactions (PGS for body mass index with meat diet, physical activity, sedentary behaviour and insomnia; PGS for high-density lipoprotein cholesterol with sedentary behaviour; PGS for triglycerides with meat diet) in multivariable linear regression models including an interaction term and show stronger associations between lifestyles and cardiometabolic risk factors among individuals with high PGSs than those with low PGSs. Genome-wide interaction analyses pinpoint three genetic variants (FTO rs72805613 for BMI; CETP rs56228609 for high-density lipoprotein cholesterol; TRIB2 rs4336630 for triglycerides; PInteraction < 5 × 10-8). The associations between lifestyles and cardiometabolic risk factors differ between individuals grouped by the genotype of these variants, with the degree of differences being similar to that between individuals with high and low values for the corresponding PGSs. This study demonstrates that associations between lifestyles and cardiometabolic risk factors can differ between individuals based upon their genetic profiles. It further suggests that genetic variants with interaction effects contribute more to such differences compared to those without interaction effects, which has potential implications for developing PGSs for personalised intervention.
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Affiliation(s)
| | - Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4067, Australia;
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22
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Forsyth L, Aman A, Cullen B, Graham N, Lyall DM, Lyall LM, Pell JP, Ward J, Smith DJ, Strawbridge RJ. Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank. J Affect Disord 2023; 339:943-953. [PMID: 37487843 DOI: 10.1016/j.jad.2023.07.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/28/2023] [Accepted: 07/08/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND People with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct. METHODS In UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated "white British" participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals. RESULTS Significant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner. LIMITATIONS Restricting analyses in non-"white British" individuals to the phenotypes significant in the "white British" sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse. CONCLUSIONS Genetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.
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Affiliation(s)
- Lewis Forsyth
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Alisha Aman
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Nicholas Graham
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Laura M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Joey Ward
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK
| | - Daniel J Smith
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh E10 5HF, UK
| | - Rona J Strawbridge
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; Health Data Research, Glasgow G12 8RZ, UK; Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm 171 76, Sweden.
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23
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van Duijvenboden S, Ramírez J, Young WJ, Olczak KJ, Ahmed F, Alhammadi MJAY, Bell CG, Morris AP, Munroe PB. Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension. Am J Hum Genet 2023; 110:1718-1734. [PMID: 37683633 PMCID: PMC10577090 DOI: 10.1016/j.ajhg.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.
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Affiliation(s)
- Stefan van Duijvenboden
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK; Institute of Cardiovascular Science, University College London, London, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julia Ramírez
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK; Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain; Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Zaragoza, Spain
| | - William J Young
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK; Barts Heart Centre, St Bartholomew's Hospital, EC1A 7BE London, UK
| | - Kaya J Olczak
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK
| | - Farah Ahmed
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK
| | | | - Christopher G Bell
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK; National Institute of Health and Care Research, Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ London, UK; National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, EC1M 6BQ London, UK.
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24
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Yasmin, O’Shaughnessy KM. Genetic Markers Regulating Blood Pressure in Extreme Discordant Sib Pairs. Genes (Basel) 2023; 14:1862. [PMID: 37895212 PMCID: PMC10606487 DOI: 10.3390/genes14101862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023] Open
Abstract
Genome-wide scans performed in affected sib pairs have revealed small and often inconsistent clues to the loci responsible for the inherited components of hypertension. Since blood pressure is a quantitative trait regulated by many loci, two siblings at opposite extremes of the blood pressure distribution are more likely to have inherited different alleles at any given locus. Hence, we investigated an extreme discordant sib pair strategy to analyse markers from two previous loci of interest: (1) the Gordons syndrome locus that includes the WNK4 gene and (2) the ROMK locus identified in our first genome-wide scan. For this study, 24 sib pairs with strong family histories of essential hypertension were selected from the top and bottom 10% of the blood pressure distribution and genotyped for highly polymorphic microsatellite markers on chromosomes 11 and 17. The mean age of the population was 39.8 ± 7.8 years. A significant inverse correlation was found between the squared difference in pulse pressure and the number of alleles shared by IBD between the siblings for the DS11925 marker (r = -0.44, p = 0.031), systolic pressure and chromosome 17 markers (D17S250: r = -0.42, p = 0.040; D17S799 (r = -0.51, p = 0.011), and this relationship persisted after correcting for age and gender. Markers on chromosome 17 (D17S250, D17S928 and D17S1301) and 11 (D11S1999) also correlated with diastolic pressure. These results illustrate the successful use of discordant sib pair analysis to detect linkage within relatively small numbers of pedigrees with hypertension. Further analysis of this cohort may be valuable in complementing findings from the large genome wide scans in affected sib pairs.
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Affiliation(s)
- Yasmin
- Experimental Medicine & Immunotherapeutics Division, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
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25
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Dhande IS, Zhu Y, Joshi AS, Hicks MJ, Braun MC, Doris PA. Polygenic genetic variation affecting antibody formation underlies hypertensive renal injury in the stroke-prone spontaneously hypertensive rat. Am J Physiol Renal Physiol 2023; 325:F317-F327. [PMID: 37439198 PMCID: PMC10511163 DOI: 10.1152/ajprenal.00058.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/14/2023] Open
Abstract
During development of the spontaneously hypertensive rat (SHR), several distinct but closely related lines were generated. Most lines are resistant to hypertensive renal disease. However, the SHR-A3 line (stroke-prone SHR) experiences end-organ injury (EOI) and provides a model of injury susceptibility that can be used to uncover genetic causation. In the present study, we generated a congenic line in which three distinct disease loci in SHR-A3 are concurrently replaced with homologous loci from an injury-resistant SHR line (SHR-B2). Verification that all three loci were homozygously replaced in this triple congenic line [SHR-A3(Trip B2)] while the genetic background of SHR-A3 was fully retained was obtained by whole genome sequencing. Congenic genome substitution was without effect on systolic blood pressure [198.9 ± 3.34 mmHg, mean ± SE, SHR-A3(Trip B2) = 194.7 ± 2.55 mmHg]. Measures of renal injury (albuminuria, histological injury scores, and urinary biomarker levels) were reduced in SHR-A3(Trip B2) animals, even though only 4.5 Mbases of the 2.8 Gbases of the SHR-B2 genome (0.16% of the genome) was transferred into the congenic line. The gene content of the three congenic loci and the functional effects of gene polymorphism within suggest a role of immunoglobulin in EOI pathogenesis. To prove the role of antibodies in EOI in SHR-A3, we generated an SHR-A3 line in which expression from the immunoglobulin heavy chain gene was knocked out (SHR-A3-IGHKO). Animals in the SHR-A3-IGHKO line lack B cells and immunoglobulin, but the hypertensive phenotype is not affected. Renal injury, however, was reduced in this line, confirming a pathogenic role for immunoglobulin in hypertensive EOI in this model of heritable risk.NEW & NOTEWORTHY Here, we used a polygenic animal model of hypertensive renal disease to show that genetic variation affecting antibody formation underlies hypertensive renal disease. We proved the genetic thesis by generating an immunoglobulin knockout in the susceptible animal model.
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Affiliation(s)
- Isha S Dhande
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States
| | - Yaming Zhu
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States
| | - Aniket S Joshi
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States
| | - M John Hicks
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas, United States
| | - Michael C Braun
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, United States
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, United States
| | - Peter A Doris
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States
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26
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Oliveros W, Delfosse K, Lato DF, Kiriakopulos K, Mokhtaridoost M, Said A, McMurray BJ, Browning JW, Mattioli K, Meng G, Ellis J, Mital S, Melé M, Maass PG. Systematic characterization of regulatory variants of blood pressure genes. CELL GENOMICS 2023; 3:100330. [PMID: 37492106 PMCID: PMC10363820 DOI: 10.1016/j.xgen.2023.100330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/29/2023] [Accepted: 04/28/2023] [Indexed: 07/27/2023]
Abstract
High blood pressure (BP) is the major risk factor for cardiovascular disease. Genome-wide association studies have identified genetic variants for BP, but functional insights into causality and related molecular mechanisms lag behind. We functionally characterize 4,608 genetic variants in linkage with 135 BP loci in vascular smooth muscle cells and cardiomyocytes by massively parallel reporter assays. High densities of regulatory variants at BP loci (i.e., ULK4, MAP4, CFDP1, PDE5A) indicate that multiple variants drive genetic association. Regulatory variants are enriched in repeats, alter cardiovascular-related transcription factor motifs, and spatially converge with genes controlling specific cardiovascular pathways. Using heuristic scoring, we define likely causal variants, and CRISPR prime editing finally determines causal variants for KCNK9, SFXN2, and PCGF6, which are candidates for developing high BP. Our systems-level approach provides a catalog of functionally relevant variants and their genomic architecture in two trait-relevant cell lines for a better understanding of BP gene regulation.
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Affiliation(s)
- Winona Oliveros
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Catalonia, Spain
| | - Kate Delfosse
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Daniella F. Lato
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Katerina Kiriakopulos
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Milad Mokhtaridoost
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Abdelrahman Said
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Brandon J. McMurray
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Jared W.L. Browning
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Kaia Mattioli
- Division of Genetics, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Guoliang Meng
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - James Ellis
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Seema Mital
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Ted Rogers Centre for Heart Research, Toronto, ON M5G 1X8, Canada
- Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 0A4, Canada
| | - Marta Melé
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Catalonia, Spain
| | - Philipp G. Maass
- Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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27
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Hellwege JN, Stallings SC, Piekos JA, Jasper EA, Aronoff DM, Edwards TL, Velez Edwards DR. Association of genetically-predicted placental gene expression with adult blood pressure traits. J Hypertens 2023; 41:1024-1032. [PMID: 37016918 PMCID: PMC10287061 DOI: 10.1097/hjh.0000000000003427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
OBJECTIVE Blood pressure is a complex, polygenic trait, and the need to identify prehypertensive risks and new gene targets for blood pressure control therapies or prevention continues. We hypothesize a developmental origins model of blood pressure traits through the life course where the placenta is a conduit mediating genomic and nongenomic transmission of disease risk. Genetic control of placental gene expression has recently been described through expression quantitative trait loci (eQTL) studies which have identified associations with childhood phenotypes. METHODS We conducted a transcriptome-wide gene expression analysis estimating the predicted gene expression of placental tissue in adult individuals with genome-wide association study (GWAS) blood pressure summary statistics. We constructed predicted expression models of 15 154 genes from reference placenta eQTL data and investigated whether genetically-predicted gene expression in placental tissue is associated with blood pressure traits using published GWAS summary statistics. Functional annotation of significant genes was generated using FUMA. RESULTS We identified 18, 9, and 21 genes where predicted expression in placenta was significantly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP), respectively. There were 14 gene-tissue associations (13 unique genes) significant only in placenta. CONCLUSIONS In this meta-analysis using S-PrediXcan and GWAS summary statistics, the predicted expression in placenta of 48 genes was statistically significantly associated with blood pressure traits. Notable findings included the association of FGFR1 expression with increased SBP and PP. This evidence of gene expression variation in placenta preceding the onset of adult blood pressure phenotypes is an example of extreme preclinical biological changes which may benefit from intervention.
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Affiliation(s)
- Jacklyn N Hellwege
- Department of Medicine, Division of Genetic Medicine
- Vanderbilt Genetics Institute
| | - Sarah C Stallings
- Department of Medicine, Division of Genetic Medicine
- Vanderbilt Genetics Institute
| | - Jacqueline A Piekos
- Vanderbilt Genetics Institute
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
| | - Elizabeth A Jasper
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
| | - David M Aronoff
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Todd L Edwards
- Vanderbilt Genetics Institute
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Shang L, Zhao W, Wang YZ, Li Z, Choi JJ, Kho M, Mosley TH, Kardia SLR, Smith JA, Zhou X. meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans. Nat Commun 2023; 14:2711. [PMID: 37169753 PMCID: PMC10175543 DOI: 10.1038/s41467-023-37961-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 04/07/2023] [Indexed: 05/13/2023] Open
Abstract
Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.
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Affiliation(s)
- Lulu Shang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yi Zhe Wang
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Zheng Li
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jerome J Choi
- Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53726, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, 39126, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk affects the penetrance of monogenic kidney disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.07.23289614. [PMID: 37214819 PMCID: PMC10197721 DOI: 10.1101/2023.05.07.23289614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. Most forms of monogenic kidney diseases have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Methods Using the UK Biobank (N=469,835 participants) and the All of Us (N=98,622 participants) datasets, we examined two most common forms of monogenic kidney disorders, autosomal dominant polycystic kidney disease (ADPKD) caused by deleterious variants in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN caused by deleterious variants in COL4A3, COL4A4, or COL4A5 genes). We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 or kidney failure) and controls (eGFR >90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The effects of the genome-wide polygenic score (GPS) for CKD were tested in monogenic variant carriers and non-carriers using logistic regression controlling for age, sex, diabetes, and genetic ancestry. Results As expected, the carriers of known pathogenic and rare predicted loss-of-function variants in PKD1 or PKD2 had a high risk of CKD (ORmeta=17.1, 95% CI: 11.1-26.4, P=1.8E-37). The GPS was comparably predictive of CKD in both ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55-3.37, P=2.6E-05) and non-carriers (ORmeta=1.72 per SD, 95% CI=1.69-1.76, P< E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN variant carriers (ORmeta=1.78, 95% CI=1.22-2.58, P=2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68-1.73 P Conclusions Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by differences in polygenic risk profiles. Accounting for polygenic factors has the potential to improve risk stratification in monogenic kidney disease and may have implications for genetic counseling.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Ning Shang
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Jordan G. Nestor
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C. Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Ali G. Gharavi
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Krzysztof Kiryluk
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
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Ivanova T, Churnosova M, Abramova M, Ponomarenko I, Reshetnikov E, Aristova I, Sorokina I, Churnosov M. Risk Effects of rs1799945 Polymorphism of the HFE Gene and Intergenic Interactions of GWAS-Significant Loci for Arterial Hypertension in the Caucasian Population of Central Russia. Int J Mol Sci 2023; 24:ijms24098309. [PMID: 37176017 PMCID: PMC10179076 DOI: 10.3390/ijms24098309] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this case-control replicative study was to investigate the link between GWAS-impact for arterial hypertension (AH) and/or blood pressure (BP) gene polymorphisms and AH risk in Russian subjects (Caucasian population of Central Russia). AH (n = 939) and control (n = 466) cohorts were examined for ten GWAS AH/BP risk loci. The genotypes/alleles of these SNP and their combinations (SNP-SNP interactions) were tested for their association with the AH development using a logistic regression statistical procedure. The genotype GG of the SNP rs1799945 (C/G) HFE was strongly linked with an increased AH risk (ORrecGG = 2.53; 95%CIrecGG1.03-6.23; ppermGG = 0.045). The seven SNPs such as rs1173771 (G/A) AC026703.1, rs1799945 (C/G) HFE, rs805303 (G/A) BAG6, rs932764 (A/G) PLCE1, rs4387287 (C/A) OBFC1, rs7302981 (G/A) CERS5, rs167479 (T/G) RGL3, out of ten regarded loci, were related with AH within eight SNP-SNP interaction models (<0.001 ≤ pperm-interaction ≤ 0.047). Three polymorphisms such as rs8068318 (T/C) TBX2, rs633185 (C/G) ARHGAP42, and rs2681472 (A/G) ATP2B1 were not linked with AH. The pairwise rs805303 (G/A) BAG6-rs7302981 (G/A) CERS5 combination was a priority in determining the susceptibility to AH (included in six out of eight SNP-SNP interaction models [75%] and described 0.82% AH entropy). AH-associated variants are conjecturally functional for 101 genes involved in processes related to the immune system (major histocompatibility complex protein, processing/presentation of antigens, immune system process regulation, etc.). In conclusion, the rs1799945 polymorphism of the HFE gene and intergenic interactions of BAG6, CERS5, AC026703.1, HFE, PLCE1, OBFC1, RGL3 have been linked with AH risky in the Caucasian population of Central Russia.
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Affiliation(s)
- Tatiana Ivanova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Abramova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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Ivanova T, Churnosova M, Abramova M, Plotnikov D, Ponomarenko I, Reshetnikov E, Aristova I, Sorokina I, Churnosov M. Sex-Specific Features of the Correlation between GWAS-Noticeable Polymorphisms and Hypertension in Europeans of Russia. Int J Mol Sci 2023; 24:ijms24097799. [PMID: 37175507 PMCID: PMC10178435 DOI: 10.3390/ijms24097799] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of the study was directed at studying the sex-specific features of the correlation between genome-wide association studies (GWAS)-noticeable polymorphisms and hypertension (HTN). In two groups of European subjects of Russia (n = 1405 in total), such as men (n = 821 in total: n = 564 HTN, n = 257 control) and women (n = 584 in total: n = 375 HTN, n = 209 control), the distribution of ten specially selected polymorphisms (they have confirmed associations of GWAS level with blood pressure (BP) parameters and/or HTN in Europeans) has been considered. The list of studied loci was as follows: (PLCE1) rs932764 A > G, (AC026703.1) rs1173771 G > A, (CERS5) rs7302981 G > A, (HFE) rs1799945 C > G, (OBFC1) rs4387287 C > A, (BAG6) rs805303 G > A, (RGL3) rs167479 T > G, (ARHGAP42) rs633185 C > G, (TBX2) rs8068318 T > C, and (ATP2B1) rs2681472 A > G. The contribution of individual loci and their inter-locus interactions to the HTN susceptibility with bioinformatic interpretation of associative links was evaluated separately in men's and women's cohorts. The men-women differences in involvement in the disease of the BP/HTN-associated GWAS SNPs were detected. Among women, the HTN risk has been associated with HFE rs1799945 C > G (genotype GG was risky; ORGG = 11.15 ppermGG = 0.014) and inter-locus interactions of all 10 examined SNPs as part of 26 intergenic interactions models. In men, the polymorphism BAG6 rs805303 G > A (genotype AA was protective; ORAA = 0.30 ppermAA = 0.0008) and inter-SNPs interactions of eight loci in only seven models have been founded as HTN-correlated. HTN-linked loci and strongly linked SNPs were characterized by pronounced polyvector functionality in both men and women, but at the same time, signaling pathways of HTN-linked genes/SNPs in women and men were similar and were represented mainly by immune mechanisms. As a result, the present study has demonstrated a more pronounced contribution of BP/HTN-associated GWAS SNPs to the HTN susceptibility (due to weightier intergenic interactions) in European women than in men.
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Affiliation(s)
- Tatiana Ivanova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Abramova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Denis Plotnikov
- Genetic Epidemiology Lab, Kazan State Medical University, 420012 Kazan, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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Del Pinto R, Landi L, Grassi G, Marco Sforza N, Cairo F, Citterio F, Paolantoni G, D'aiuto F, Ferri C, Monaco A, Pietropaoli D. Hypertension and periodontitis: A joint report by the Italian society of hypertension (SIIA) and the Italian society of periodontology and implantology (SIdP). Oral Dis 2023; 29:803-814. [PMID: 34561934 DOI: 10.1111/odi.14009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 02/07/2023]
Abstract
An accumulating body of evidence supports an independent association between high blood pressure (BP) and periodontitis, possibly mediated by low-grade inflammation. This joint report by the Italian Society of Hypertension (SIIA) and the Italian Society of Periodontology and Implantology (SIdP) working group on Hypertension and Periodontitis (Hy-Per Group) provides a review of the evidence on this topic encompassing epidemiology, biological plausibility, relevance, magnitude, and treatment management. Consensus recommendations are provided for health professionals on how to manage BP in individuals showing signs of poor oral health. In summary, (1) large epidemiological studies highlight that individuals with periodontal diseases have increased risk for high/uncontrolled BP independent of confounders; (2) mechanistically, low-grade inflammation might have a causal role in the association; (3) BP profile and control might benefit from periodontal treatment in pre-hypertensive and hypertensive individuals; (4) oral health status should be evaluated as a potential risk factor for high/uncontrolled BP, and effective oral care should be included as an adjunct lifestyle measure during hypertension management. Further research is needed to optimize BP management in individuals with poor oral health.
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Affiliation(s)
- Rita Del Pinto
- Unit of Internal Medicine and Nephrology, Department of Life, Health and Environmental Sciences, Center for Hypertension and Cardiovascular Prevention, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
| | | | - Guido Grassi
- Department of Medicine and Surgery, Clinica Medica, University of Milano-Bicocca, Milan, Italy
| | | | - Francesco Cairo
- Research Unit in Periodontology and Periodontal Medicine, Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy
| | - Filippo Citterio
- Department of Surgical Sciences, C.I.R. Dental School, University of Turin, Turin, Italy
| | | | - Francesco D'aiuto
- Periodontology Unit, UCL Eastman Dental Institute and Hospital, University College London, London, UK
| | - Claudio Ferri
- Unit of Internal Medicine and Nephrology, Department of Life, Health and Environmental Sciences, Center for Hypertension and Cardiovascular Prevention, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
| | - Annalisa Monaco
- Unit of Oral Diseases, Prevention and Translational Research, Department of Life, Health and Environmental Sciences, Dental Clinic, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
| | - Davide Pietropaoli
- Unit of Oral Diseases, Prevention and Translational Research, Department of Life, Health and Environmental Sciences, Dental Clinic, San Salvatore Hospital, University of L'Aquila, L'Aquila, Italy
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1446] [Impact Index Per Article: 1446.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Limonova AS, Ershova AI, Kiseleva AV, Ramensky VE, Vyatkin YV, Kutsenko VA, Meshkov AN, Drapkina OM. Assessment of polygenic risk of hypertension. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2023. [DOI: 10.15829/1728-8800-2022-3464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Hypertension (HTN) is a leading risk factor for the development of cardiovascular diseases. In recent decades, the rapid development of genetic tests, in particular genome-wide association study (GWAS), has made it possible to identify hundreds of nucleotide sequence variants associated with the development of HTN. One approach to improve the predictive power of genetic testing is to combine information about many nucleotide sequence variants into a single risk assessment system, often referred to as a genetic risk score. Within the framework of this review, the most significant publications on the study of the genetic risk score for HTN will be considered, and the features of their development and application will be discussed.
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Affiliation(s)
- A. S. Limonova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. I. Ershova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Medical Research Center for Therapy and Preventive Medicine
| | - V. E. Ramensky
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - Yu. V. Vyatkin
- National Medical Research Center for Therapy and Preventive Medicine; Novosibirsk National Research State University
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Faculty of Mechanics and Mathematics, Lomonosov Moscow State University
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Pirogov Russian National Research Medical University
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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mRNA Metabolism and Hypertension. Biomedicines 2023; 11:biomedicines11010118. [PMID: 36672629 PMCID: PMC9855994 DOI: 10.3390/biomedicines11010118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/28/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Hypertension is the most frequent cardiovascular risk factor all over the world. It remains a leading contributor to the risk of cardiovascular events and death. In the year 2015, about 1.5 billion of adult people worldwide had hypertension (as defined by office systolic blood pressure ≥ 140 mmHg or office diastolic blood pressure ≥ 90 mmHg). Moreover, the number of hypertensive patients with age ranging from 30 to 79 years doubled in the last 30 years (from 317 million men and 331 million women in the year 1990 to 652 million men and 626 million women in 2019) despite stable age-standardized prevalence worldwide. Despite such impressive growth, the proportion of controlled hypertension is very low. A better understanding of the pathogenesis of hypertension may contribute to the development of innovative therapeutic strategies. In this context, alterations of the messenger RNA metabolism have been recently evaluated as contributors to the pathogenesis of hypertension, and pharmacological modulation of RNA metabolism is under investigation as potential and novel therapeutic armamentarium in hypertension.
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Bakker MK, van Straten T, Chong M, Paré G, Gill D, Ruigrok YM. Anti-Epileptic Drug Target Perturbation and Intracranial Aneurysm Risk: Mendelian Randomization and Colocalization Study. Stroke 2023; 54:208-216. [PMID: 36300369 PMCID: PMC9794136 DOI: 10.1161/strokeaha.122.040598] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND In a genome-wide association study of intracranial aneurysms (IA), enrichment was found between genes associated with IA and genes encoding targets of effective anti-epileptic drugs. Our aim was to assess if this pleiotropy is driven by shared disease mechanisms that could potentially highlight a treatment strategy for IA. METHODS Using 2-sample inverse-variance weighted Mendelian randomization and genetic colocalization analyses we assessed: (1) if epilepsy liability in general affects IA risk, and (2) whether changes in gene- and protein-expression levels of anti-epileptic drug targets in blood and arterial tissue may causally affect IA risk. RESULTS We found no overall effect of epilepsy liability on IA. Expression of 21 genes and 13 proteins corresponding to anti-epileptic drug targets supported a causal effect (P<0.05) on IA risk. Of those genes and proteins, genetic variants affecting CNNM2 levels showed strong evidence for colocalization with IA risk (posterior probability>70%). Higher CNNM2 levels in arterial tissue were associated with increased IA risk (odds ratio, 3.02; [95% CI, 2.32-3.94]; P=3.39×10-16). CNNM2 expression was best proxied by rs11191580. The magnitude of the effect of this variant was greater than would be expected if systemic blood pressure was the sole IA-causing mechanism in this locus. CONCLUSIONS CNNM2 is a driver of the pleiotropy between IA and anti-epileptic drug targets. Administration of the anti-epileptic drugs phenytoin, valproic acid, or carbamazepine may be expected to decrease CNNM2 levels and therefore subsequently decrease IA risk. CNNM2 is therefore an important target to investigate further for its role in the pathogenesis of IA.
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Affiliation(s)
- Mark K. Bakker
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands (M.K.B., T.v.S., Y.M.R.)
| | - Tijmen van Straten
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands (M.K.B., T.v.S., Y.M.R.)
| | - Michael Chong
- Population Health Research Institute; Thrombosis and Atherosclerosis Research Institute; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario (M.C., G.P.)
| | - Guillaume Paré
- Population Health Research Institute; Thrombosis and Atherosclerosis Research Institute; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario (M.C., G.P.)
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom (D.G.)
| | - Ynte M. Ruigrok
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, the Netherlands (M.K.B., T.v.S., Y.M.R.)
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Gholami M, Zoughi M, Hasanzad M, Larijani B, Amoli MM. Haplotypic variants of COVID-19 related genes are associated with blood pressure and metabolites levels. J Med Virol 2023; 95:e28355. [PMID: 36443248 PMCID: PMC9877746 DOI: 10.1002/jmv.28355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 12/03/2022]
Abstract
The genetic association of coronavirus disease 2019 (COVID-19) with its complications has not been fully understood. This study aimed to identify variants and haplotypes of candidate genes implicated in COVID-19 related traits by combining the literature review and pathway analysis. To explore such genes, the protein-protein interactions and relevant pathways of COVID-19-associated genes were assessed. A number of variants on candidate genes were identified from Genome-wide association studies (GWASs) which were associated with COVID-19 related traits (p ˂ 10-6 ). Haplotypic blocks were assessed using haplotypic structures among the 1000 Genomes Project (r2 ≥ 0.8, D' ≥ 0.8). Further functional analyses were performed on the selected variants. The results demonstrated that a group of variants in ACE and AGT genes were significantly correlated with COVID-19 related traits. Three haplotypes were identified to be involved in the blood metabolites levels and the development of blood pressure. Functional analyses revealed that most GWAS index variants were expression quantitative trait loci and had transcription factor binding sites, exonic splicing enhancers or silencer activities. Furthermore, the proxy haplotype variants, rs4316, rs4353, rs4359, and three variants, namely rs2493133, rs2478543, and rs5051, were associated with blood metabolite and systolic blood pressure, respectively. These variants exerted more regulatory effects compared with other GWAS variants. The present study indicates that the genetic variants and candidate haplotypes of COVID-19 related genes are associated with blood pressure and blood metabolites. However, further observational studies are warranted to confirm these results.
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Affiliation(s)
- Morteza Gholami
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular‐Cellular Sciences InstituteTehran University of Medical SciencesTehranIran,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Marziyeh Zoughi
- Metabolomics and genomics research center endocrinology and metabolism molecular‐cellular sciences instituteTehran University of medical sciencesTehranIran
| | - Mandana Hasanzad
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
| | - Mahsa M. Amoli
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular‐Cellular Sciences InstituteTehran University of Medical SciencesTehranIran
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38
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Abramova M, Churnosova M, Efremova O, Aristova I, Reshetnikov E, Polonikov A, Churnosov M, Ponomarenko I. Effects of Pre-Pregnancy Overweight/Obesity on the Pattern of Association of Hypertension Susceptibility Genes with Preeclampsia. Life (Basel) 2022; 12:life12122018. [PMID: 36556383 PMCID: PMC9784908 DOI: 10.3390/life12122018] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/21/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
The aim of this study was to explore the effects of pre-pregnancy overweight/obesity on the pattern of association of hypertension susceptibility genes with preeclampsia (PE). Ten single-nucleotide polymorphisms (SNPs) of the 10 genome-wide association studies (GWAS)-significant hypertension/blood pressure (BP) candidate genes were genotyped in 950 pregnant women divided into two cohorts according to their pre-pregnancy body mass index (preBMI): preBMI ≥ 25 (162 with PE and 159 control) and preBMI < 25 (290 with PE and 339 control). The PLINK software package was utilized to study the association (analyzed four genetic models using logistic regression). The functionality of PE-correlated loci was analyzed by performing an in silico database analysis. Two SNP hypertension/BP genes, rs805303 BAG6 (OR: 0.36−0.66) and rs167479 RGL3 (OR: 1.86), in subjects with preBMI ≥ 25 were associated with PE. No association between the studied SNPs and PE in the preBMI < 25 group was determined. Further analysis showed that two PE-associated SNPs are functional (have weighty eQTL, sQTL, regulatory, and missense values) and could be potentially implicated in PE development. In conclusion, this study was the first to discover the modifying influence of overweight/obesity on the pattern of association of GWAS-significant hypertension/BP susceptibility genes with PE: these genes are linked with PE in preBMI ≥ 25 pregnant women and are not PE-involved in the preBMI < 25 group.
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Affiliation(s)
- Maria Abramova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Olesya Efremova
- Department of Medical Genetics, Kharkiv National Medical University, 61022 Kharkov, Ukraine
- Grishchenko Clinic of Reproductive Medicine, 61052 Kharkov, Ukraine
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Alexey Polonikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
- Department of Biology, Medical Genetics and Ecology and Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
- Correspondence:
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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39
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Wang Y, Chen H, Peloso GM, DeStefano AL, Dupuis J. Exploiting family history in aggregation unit-based genetic association tests. Eur J Hum Genet 2022; 30:1355-1362. [PMID: 34690355 PMCID: PMC9712547 DOI: 10.1038/s41431-021-00980-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022] Open
Abstract
The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.
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Affiliation(s)
- Yanbing Wang
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA.
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Gina M Peloso
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Anita L DeStefano
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Massachusetts, MA, 02215, USA.
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40
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Staplin N, Herrington WG, Murgia F, Ibrahim M, Bull KR, Judge PK, Ng SYA, Turner M, Zhu D, Emberson J, Landray MJ, Baigent C, Haynes R, Hopewell JC. Determining the Relationship Between Blood Pressure, Kidney Function, and Chronic Kidney Disease: Insights From Genetic Epidemiology. Hypertension 2022; 79:2671-2681. [PMID: 36082669 PMCID: PMC9640248 DOI: 10.1161/hypertensionaha.122.19354] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND It is well established that decreased kidney function can increase blood pressure (BP), but it is unproven whether moderately elevated BP causes chronic kidney disease (CKD) or glomerular hyperfiltration. METHODS 311 119 White British UK Biobank participants were included in logistic regression analyses to estimate the odds of CKD (defined as long-term kidney replacement therapy, estimated glomerular filtration rate [eGFR]< 60mL/min/1.73m2, or urinary albumin:creatinine ratio ≥3 mg/mmol) associated with higher genetically predicted BP using genetic risk scores comprising 219 systolic and 223 diastolic BP loci. Analyses estimating associations with clinical categories of eGFR and urinary albumin:creatinine ratio were also conducted, with an eGFR ≥120 mL (min·1.73m2) considered evidence of glomerular hyperfiltration. RESULTS 21 623 participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. 1828 participants had an eGFR ≥120 mL/min/1.73m2. Each genetically predicted 10 mmHg higher systolic BP and 5 mmHg higher diastolic BP were associated with a 37% (95% CI, 1.29-1.45) and 19% (1.14-1.25) higher odds of CKD, respectively. Associations were evident for both the reduced eGFR and albuminuria components of the CKD outcome. The odds of hyperfiltration (versus an eGFR ≥60 and <90 mL/min/1.73m2 were 49% higher (95% CI, 1.21-1.84) for each genetically predicted 10 mmHg higher systolic BP. Associations with CKD and hyperfiltration were similar irrespective of preexisting diabetes, vascular disease, or different levels of adiposity. CONCLUSIONS In this general population, genetic epidemiological evidence supports a causal role of life-long differences in BP for decreased kidney function, glomerular hyperfiltration, and albuminuria. Physiological autoregulation may not afford complete renal protection against the moderate BP elevations.
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Affiliation(s)
- Natalie Staplin
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - William G Herrington
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Health Data Research UK (W.G.H., M.J.L.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Federico Murgia
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Maysson Ibrahim
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Katherine R Bull
- Nuffield Department of Medicine (K.R.B.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Parminder K Judge
- Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Sarah Y A Ng
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Michael Turner
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Doreen Zhu
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Jonathan Emberson
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Martin J Landray
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom.,Health Data Research UK (W.G.H., M.J.L.), University of Oxford, Oxford, United Kingdom.,National Institute for Health Research Oxford Biomedical Research Centre (M.J.L.), University of Oxford, Oxford, United Kingdom
| | - Colin Baigent
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom
| | - Richard Haynes
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom (W.G.H., K.R.B., P.K.J., M.T., D.Z., R.H.)
| | - Jemma C Hopewell
- Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health (NDPH), United Kingdom (N.S., W.G.H., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.).,Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH (N.S., W.G.H., F.M., M.I., P.J., S.Y.A.N., M.T., D.Z., J.E., M.J.L., C.B., R.H., J.C.H.), University of Oxford, Oxford, United Kingdom.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (N.S., F.M., M.I., J.E., M.J.L., J.C.H.), University of Oxford, Oxford, United Kingdom
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Hay R, Cullen B, Graham N, Lyall DM, Aman A, Pell JP, Ward J, Smith DJ, Strawbridge RJ. Genetic analysis of the PCSK9 locus in psychological, psychiatric, metabolic and cardiovascular traits in UK Biobank. Eur J Hum Genet 2022; 30:1380-1390. [PMID: 35501368 PMCID: PMC9712543 DOI: 10.1038/s41431-022-01107-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/11/2022] [Accepted: 04/12/2022] [Indexed: 11/09/2022] Open
Abstract
The association between severe mental illness (SMI) and cardiovascular and metabolic disease (CMD) is poorly understood. PCSK9 is expressed in systems critical to both SMI and CMD and influences lipid homeostasis and brain function. We systematically investigated relationships between genetic variation within the PCSK9 locus and risk for both CMD and SMI. UK Biobank recruited ~500,000 volunteers and assessed a wide range of SMI and CMD phenotypes. We used genetic data from white British ancestry individuals of UK Biobank. Genetic association analyses were conducted in PLINK, with statistical significance defined by the number of independent SNPs. Conditional analyses and linkage disequilibrium assessed the independence of SNPs and the presence of multiple signals. Two genetic risk scores of lipid-lowering alleles were calculated and used as proxies for putative lipid-lowering effects of PCSK9. PCSK9 variants were associated with central adiposity, venous thrombosis embolism, systolic blood pressure, mood instability, and neuroticism (all p < 1.16 × 10-4). No secondary signals were identified. Conditional analyses and high linkage disequilibrium (r2 = 0.98) indicated that mood instability and central obesity may share a genetic signal. Genetic risk scores suggested that the lipid-lowering effects of PCSK9 may be causal for greater mood instability and higher neuroticism. This is the first study to implicate the PCSK9 locus in mood-disorder symptoms and related traits, as well as the shared pathology of SMI and CMD. PCSK9 effects on mood may occur via lipid-lowering mechanisms. Further work is needed to understand whether repurposing PCSK9-targeting therapies might improve SMI symptoms and prevent CMD.
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Affiliation(s)
- Rachel Hay
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Breda Cullen
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Nicholas Graham
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Donald M Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Alisha Aman
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jill P Pell
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.
- Health Data Research UK, Glasgow, UK.
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden.
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42
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Abramova MY, Ponomarenko IV, Churnosov MI. The Polymorphic Locus rs167479 of the RGL3 Gene Is Associated with the Risk of Severe Preeclampsia. RUSS J GENET+ 2022. [DOI: 10.1134/s102279542212002x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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43
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Hoffmann TJ, Lu M, Oni-Orisan A, Lee C, Risch N, Iribarren C. A large genome-wide association study of QT interval length utilizing electronic health records. Genetics 2022; 222:iyac157. [PMID: 36271874 PMCID: PMC9713425 DOI: 10.1093/genetics/iyac157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/22/2022] [Indexed: 12/13/2022] Open
Abstract
QT interval length is an important risk factor for adverse cardiovascular outcomes; however, the genetic architecture of QT interval remains incompletely understood. We conducted a genome-wide association study of 76,995 ancestrally diverse Kaiser Permanente Northern California members enrolled in the Genetic Epidemiology Research on Adult Health and Aging cohort using 448,517 longitudinal QT interval measurements, uncovering 9 novel variants, most replicating in 40,537 individuals in the UK Biobank and Population Architecture using Genomics and Epidemiology studies. A meta-analysis of all 3 cohorts (n = 117,532) uncovered an additional 19 novel variants. Conditional analysis identified 15 additional variants, 3 of which were novel. Little, if any, difference was seen when adjusting for putative QT interval lengthening medications genome-wide. Using multiple measurements in Genetic Epidemiology Research on Adult Health and Aging increased variance explained by 163%, and we show that the ≈6 measurements in Genetic Epidemiology Research on Adult Health and Aging was equivalent to a 2.4× increase in sample size of a design with a single measurement. The array heritability was estimated at ≈17%, approximately half of our estimate of 36% from family correlations. Heritability enrichment was estimated highest and most significant in cardiovascular tissue (enrichment 7.2, 95% CI = 5.7-8.7, P = 2.1e-10), and many of the novel variants included expression quantitative trait loci in heart and other relevant tissues. Comparing our results to other cardiac function traits, it appears that QT interval has a multifactorial genetic etiology.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Meng Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Akinyemi Oni-Orisan
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA 94143, USA
| | - Catherine Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Neil Risch
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
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44
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Churnosov M, Abramova M, Reshetnikov E, Lyashenko IV, Efremova O, Churnosova M, Ponomarenko I. Polymorphisms of hypertension susceptibility genes as a risk factors of preeclampsia in the Caucasian population of central Russia. Placenta 2022; 129:51-61. [PMID: 36219912 DOI: 10.1016/j.placenta.2022.09.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/18/2022] [Accepted: 09/14/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The study was designed to assess the effects of hypertension (HT) susceptibility genes polymorphisms in the development of preeclampsia (PE) in Caucasians from Central Russia. METHODS PE patients (n = 452) and women control group (n = 498) were genotyped for 10 polymorphisms of HT/blood pressure (BP) susceptibility genes (according to the previously published GWAS in Caucasian populations) including AC026703.1 (rs1173771), HFE (rs1799945), BAG6 (rs805303), PLCE1 (rs932764), OBFC1 (rs4387287), ARHGAP42 (rs633185), CERS5 (rs7302981), ATP2B1 (rs2681472), TBX2 (rs8068318) and RGL3 (rs167479). A logistic regression method was applied to search for associations between SNPs and PE. The relationship between SNP-SNP interactions and PE risk was analyzed by performing MB-MDR. RESULTS The rs1799945 gene in HFE significantly independently increased the risk of developing PE (OR = 2.24) and rs805303 in BAG6 was associated with a reduced risk in the occurrence of PE (OR = 0.55-0.78). Among the 10 SNPs examined, nine SNPs were associated with PEs within the 10 most significant SNP-SNP interaction models. Loci rs7302981 CERS5, rs805303 BAG6 and rs932764 PLCE1 contributed to the largest number of epistatic models (50% or more). DISCUSSION The present study is the first to report an association between polymorphisms of HT/BP susceptibility genes important for GWAS and the risk of PE in Caucasians from Central Russia. Our pathway-based functional annotation of the PE risk variants highlights the potential regulatory function (epigenetic/eQTL/sQTL/non-synonymous) that nine genetic risk markers and their 115 highly correlated variants exert on 155 genes. The study shows that these genes may function cooperatively in key signaling pathways in PE biology.
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Affiliation(s)
- Mikhail Churnosov
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia.
| | - Maria Abramova
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Evgeny Reshetnikov
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Igor V Lyashenko
- Belgorod State National Research University, Department of English Philology and Cross-cultural Communication, Belgorod, Russia
| | - Olesya Efremova
- Kharkiv National Medical University, Department of Medical Genetics, Kharkov, Ukraine; Grishchenko Clinic of Reproductive Medicine, Kharkov, Ukraine
| | - Maria Churnosova
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
| | - Irina Ponomarenko
- Belgorod State National Research University, Department of Medical Biological Disciplines, Belgorod, Russia
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Ma J, Chen X. Advances in pathogenesis and treatment of essential hypertension. Front Cardiovasc Med 2022; 9:1003852. [PMID: 36312252 PMCID: PMC9616110 DOI: 10.3389/fcvm.2022.1003852] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/26/2022] [Indexed: 11/23/2022] Open
Abstract
Hypertension is a significant risk factor for cardiovascular and cerebrovascular diseases and the leading cause of premature death worldwide. However, the pathogenesis of the hypertension, especially essential hypertension, is complex and requires in-depth studies. Recently, new findings about essential hypertension have emerged, and these may provide important theoretical bases and therapeutic tools to break through the existing bottleneck of essential hypertension. In this review, we demonstrated important advances in the different pathogenesis areas of essential hypertension, and highlighted new treatments proposed in these areas, hoping to provide insight for the prevention and treatment of the essential hypertension.
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Portilla-Fernandez E, Klarin D, Hwang SJ, Biggs ML, Bis JC, Weiss S, Rospleszcz S, Natarajan P, Hoffmann U, Rogers IS, Truong QA, Völker U, Dörr M, Bülow R, Criqui MH, Allison M, Ganesh SK, Yao J, Waldenberger M, Bamberg F, Rice KM, Essers J, Kapteijn DMC, van der Laan SW, de Knegt RJ, Ghanbari M, Felix JF, Ikram MA, Kavousi M, Uitterlinden AG, Roks AJM, Danser AHJ, Tsao PS, Damrauer SM, Guo X, Rotter JI, Psaty BM, Kathiresan S, Völzke H, Peters A, Johnson C, Strauch K, Meitinger T, O’Donnell CJ, Dehghan A. Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study. Hum Mol Genet 2022; 31:3566-3579. [PMID: 35234888 PMCID: PMC9558840 DOI: 10.1093/hmg/ddac051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 11/13/2022] Open
Abstract
Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.
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Affiliation(s)
- Eliana Portilla-Fernandez
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Derek Klarin
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, NHLBI/NIH, Bethesda MD, USA
- National Heart Lung and Blood Institute's Intramural Research Program's Framingham Heart Study, Framingham, MA, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Stefan Weiss
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Susanne Rospleszcz
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ian S Rogers
- Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Quynh A Truong
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Michael H Criqui
- Department of Family Medicine, University of California, San Diego, CA, USA
| | - Matthew Allison
- Department of Family Medicine, University of California, San Diego, CA, USA
| | - Santhi K Ganesh
- Department of Internal Medicine and Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jeroen Essers
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiation Oncology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniek M C Kapteijn
- Laboratory of Experimental Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sander W van der Laan
- Laboratory of Clinical Chemistry & Hematology, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Rob J de Knegt
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Janine F Felix
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Anton J M Roks
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - A H Jan Danser
- Department of Internal Medicine, Division of Vascular Medicine and Pharmacology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Philip S Tsao
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Henry Völzke
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Craig Johnson
- Collaborative Health Studies Coordinating Center, Department of Biostatistics in the School of Public Health, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Meitinger
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Human Genetics, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
| | - Christopher J O’Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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Genomic basis of insularity and ecological divergence in barn owls (Tyto alba) of the Canary Islands. Heredity (Edinb) 2022; 129:281-294. [PMID: 36175501 PMCID: PMC9613907 DOI: 10.1038/s41437-022-00562-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 11/14/2022] Open
Abstract
Islands, and the particular organisms that populate them, have long fascinated biologists. Due to their isolation, islands offer unique opportunities to study the effect of neutral and adaptive mechanisms in determining genomic and phenotypical divergence. In the Canary Islands, an archipelago rich in endemics, the barn owl (Tyto alba), present in all the islands, is thought to have diverged into a subspecies (T. a. gracilirostris) on the eastern ones, Fuerteventura and Lanzarote. Taking advantage of 40 whole-genomes and modern population genomics tools, we provide the first look at the origin and genetic makeup of barn owls of this archipelago. We show that the Canaries hold diverse, long-standing and monophyletic populations with a neat distinction of gene pools from the different islands. Using a new method, less sensitive to structure than classical FST, to detect regions involved in local adaptation to insular environments, we identified a haplotype-like region likely under selection in all Canaries individuals and genes in this region suggest morphological adaptations to insularity. In the eastern islands, where the subspecies is present, genomic traces of selection pinpoint signs of adapted body proportions and blood pressure, consistent with the smaller size of this population living in a hot arid climate. In turn, genomic regions under selection in the western barn owls from Tenerife showed an enrichment in genes linked to hypoxia, a potential response to inhabiting a small island with a marked altitudinal gradient. Our results illustrate the interplay of neutral and adaptive forces in shaping divergence and early onset speciation.
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Le Floch E, Cosentino T, Larsen CK, Beuschlein F, Reincke M, Amar L, Rossi GP, De Sousa K, Baron S, Chantalat S, Saintpierre B, Lenzini L, Frouin A, Giscos-Douriez I, Ferey M, Abdellatif AB, Meatchi T, Empana JP, Jouven X, Gieger C, Waldenberger M, Peters A, Cusi D, Salvi E, Meneton P, Touvier M, Deschasaux M, Druesne-Pecollo N, Boulkroun S, Fernandes-Rosa FL, Deleuze JF, Jeunemaitre X, Zennaro MC. Identification of risk loci for primary aldosteronism in genome-wide association studies. Nat Commun 2022; 13:5198. [PMID: 36057693 PMCID: PMC9440917 DOI: 10.1038/s41467-022-32896-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/23/2022] [Indexed: 11/23/2022] Open
Abstract
Primary aldosteronism affects up to 10% of hypertensive patients and is responsible for treatment resistance and increased cardiovascular risk. Here we perform a genome-wide association study in a discovery cohort of 562 cases and 950 controls and identify three main loci on chromosomes 1, 13 and X; associations on chromosome 1 and 13 are replicated in a second cohort and confirmed by a meta-analysis involving 1162 cases and 3296 controls. The association on chromosome 13 is specific to men and stronger in bilateral adrenal hyperplasia than aldosterone producing adenoma. Candidate genes located within the two loci, CASZ1 and RXFP2, are expressed in human and mouse adrenals in different cell clusters. Their overexpression in adrenocortical cells suppresses mineralocorticoid output under basal and stimulated conditions, without affecting cortisol biosynthesis. Our study identifies the first risk loci for primary aldosteronism and highlights new mechanisms for the development of aldosterone excess.
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Affiliation(s)
- Edith Le Floch
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | | | - Casper K Larsen
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | - Felix Beuschlein
- Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, 80336, Munich, Germany
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich (USZ) und Universität Zürich (UZH), Zürich, Switzerland
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, 80336, Munich, Germany
| | - Laurence Amar
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France
| | - Gian-Paolo Rossi
- DMCS 'G. Patrassi' University of Padova Medical School, University Hospital, 35126, Padova, Italy
| | - Kelly De Sousa
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | - Stéphanie Baron
- Université Paris Cité, F-75006, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie, Paris, France
| | - Sophie Chantalat
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Benjamin Saintpierre
- Université Paris Cité, Institut Cochin, Genom'IC platform, INSERM, CNRS, 75014, Paris, France
| | - Livia Lenzini
- DMCS 'G. Patrassi' University of Padova Medical School, University Hospital, 35126, Padova, Italy
| | - Arthur Frouin
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | | | - Matthis Ferey
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
| | | | - Tchao Meatchi
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service d'Anatomie Pathologique, Paris, France
| | | | - Xavier Jouven
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Cardiologie, Paris, France
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Daniele Cusi
- Institute of Biomedical Technologies National Research Council of Italy, Milan, Italy
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Pierre Meneton
- UMR_1142, INSERM, Sorbonne Université, Université Paris 13, Paris, France
| | - Mathilde Touvier
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | - Mélanie Deschasaux
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | - Nathalie Druesne-Pecollo
- Sorbonne Paris Nord University, INSERM U1153, INRAe U1125, CNAM, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center - Université Paris Cité (CRESS), 93017, Bobigny, France
| | | | | | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine, Institut de biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
| | - Xavier Jeunemaitre
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France
| | - Maria-Christina Zennaro
- Université Paris Cité, Inserm, PARCC, F-75015, Paris, France.
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France.
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Life-Course Associations between Blood Pressure-Related Polygenic Risk Scores and Hypertension in the Bogalusa Heart Study. Genes (Basel) 2022; 13:genes13081473. [PMID: 36011384 PMCID: PMC9408577 DOI: 10.3390/genes13081473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/30/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic information may help to identify individuals at increased risk for hypertension in early life, prior to the manifestation of elevated blood pressure (BP) values. We examined 369 Black and 832 White Bogalusa Heart Study (BHS) participants recruited in childhood and followed for approximately 37 years. The multi-ancestry genome-wide polygenic risk scores (PRSs) for systolic BP (SBP), diastolic BP (DBP), and hypertension were tested for an association with incident hypertension and stage 2 hypertension using Cox proportional hazards models. Race-stratified analyses were adjusted for baseline age, age2, sex, body mass index, genetic principal components, and BP. In Black participants, each standard deviation increase in SBP and DBP PRS conferred a 38% (p = 0.009) and 22% (p = 0.02) increased risk of hypertension and a 74% (p < 0.001) and 50% (p < 0.001) increased risk of stage 2 hypertension, respectively, while no association was observed with the hypertension PRSs. In Whites, each standard deviation increase in SBP, DBP, and hypertension PRS conferred a 24% (p < 0.05), 29% (p = 0.01), and 25% (p < 0.001) increased risk of hypertension, and a 27% (p = 0.08), 29% (0.01), and 42% (p < 0.001) increased risk of stage 2 hypertension, respectively. The addition of BP PRSs to the covariable-only models generally improved the C-statistics (p < 0.05). Multi-ancestry BP PRSs demonstrate the utility of genomic information in the early life prediction of hypertension.
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