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Maldonado BL, Piqué DG, Kaplan RC, Claw KG, Gignoux CR. Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations. J Community Genet 2023; 14:543-553. [PMID: 37962783 PMCID: PMC10725387 DOI: 10.1007/s12687-023-00686-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 10/18/2023] [Indexed: 11/15/2023] Open
Abstract
Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably.
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Affiliation(s)
- Betzaida L Maldonado
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
| | - Daniel G Piqué
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Section of Genetics and Metabolism, Department of Pediatrics, Children's Hospital Colorado, Aurora, CO, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Katrina G Claw
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher R Gignoux
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Chen Y, Du X, Kuppa A, Feitosa MF, Bielak LF, O'Connell JR, Musani SK, Guo X, Kahali B, Chen VL, Smith AV, Ryan KA, Eirksdottir G, Allison MA, Bowden DW, Budoff MJ, Carr JJ, Chen YDI, Taylor KD, Oliveri A, Correa A, Crudup BF, Kardia SLR, Mosley TH, Norris JM, Terry JG, Rotter JI, Wagenknecht LE, Halligan BD, Young KA, Hokanson JE, Washko GR, Gudnason V, Province MA, Peyser PA, Palmer ND, Speliotes EK. Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease. Nat Genet 2023; 55:1640-1650. [PMID: 37709864 PMCID: PMC10918428 DOI: 10.1038/s41588-023-01497-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/07/2023] [Indexed: 09/16/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.
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Affiliation(s)
- Yanhua Chen
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Xiaomeng Du
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Annapurna Kuppa
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey R O'Connell
- Department of Endocrinology, Diabetes and Nutrition, University of Maryland - Baltimore, Baltimore, MD, USA
| | - Solomon K Musani
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 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
| | - Bratati Kahali
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
| | - Vincent L Chen
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Kathleen A Ryan
- Department of Endocrinology, Diabetes and Nutrition, University of Maryland - Baltimore, Baltimore, MD, USA
| | | | - Matthew A Allison
- Department of Family Medicine, University of California San Diego, San Diego, CA, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Matthew J Budoff
- Department of Internal Medicine, Lundquist Institute at Harbor-UCLA, Torrance, CA, USA
| | - John Jeffrey Carr
- Department of Radiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yii-Der I Chen
- 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
| | - Kent D Taylor
- 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
| | - Antonino Oliveri
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Breland F Crudup
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - James G Terry
- Department of Radiology, Vanderbilt University School of Medicine, 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
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Brian D Halligan
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kendra A Young
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - George R Washko
- Department of Medicine, Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Elizabeth K Speliotes
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, Arruda ALDSV, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Lithgart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Metspalu A, Mo H, Morris AD, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. medRxiv 2023:2023.03.31.23287839. [PMID: 37034649 PMCID: PMC10081410 DOI: 10.1101/2023.03.31.23287839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing City, China
| | - Kim M. Lorenz
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza de S. V. Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S. K. Lee
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H. Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E. Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P. Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississsauga, Mississauga, ON, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S. Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J. Y. Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Institute of Data Science, Korea University, Seoul, South Korea
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Anny H. Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K. Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Linda S. Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A. Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S. Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S. Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A. Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, 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
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, 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
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K. Das
- Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H. Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P. Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S. Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S. Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C. Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A. Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jost B. Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Fouad R. Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - 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, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N. Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G. Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A. Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Severance Biomedical Science Institute and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R. Lee
- USC-Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Lithgart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre For Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E. Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A. Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D. Morris
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Jerry L. Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R. Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians Universität München, Munich, Germany
| | - Fraser J. Pirie
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G. Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig Maximilians Universität München, Munich, Germany
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C. Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B. van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R. Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S. Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | | | | | | | | | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A. Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L. Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, US
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, 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 Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M. Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C. Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Wayne H. H. Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M. Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Lund University Diabetes Centre, Malmö, Sweden
| | - Giriraj R. Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Michèle M. Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Deceased
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S. Kooner
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J. Wareham
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Mark O. Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, 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
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Joshua C. Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E. Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S. Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F. Voight
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
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Okita T, Kita S, Fukuda S, Fukuoka K, Kawada-Horitani E, Iioka M, Nakamura Y, Fujishima Y, Nishizawa H, Kawamori D, Matsuoka TA, Norikazu M, Shimomura I. Soluble T-cadherin promotes pancreatic β-cell proliferation by upregulating Notch signaling. iScience 2022; 25:105404. [DOI: 10.1016/j.isci.2022.105404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
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Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee JJ, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Paré G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng CY, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers JC. Identification of genetic effects underlying type 2 diabetes in South Asian and European populations. Commun Biol 2022; 5:329. [PMID: 35393509 PMCID: PMC8991226 DOI: 10.1038/s42003-022-03248-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/08/2022] [Indexed: 02/08/2023] Open
Abstract
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
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Affiliation(s)
- Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Hong Kiat Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Katharina Schmid
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, 85764, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, 85748, Garching bei München, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Lin Tong
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Meraj Ahmad
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Jung-Jin Lee
- Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, 01003, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Md Tariqul Islam
- U Chicago Research Bangladesh, House#4, Road#2b, Sector#4, Uttara, Dhaka, 1230, Bangladesh
| | - Farzana Jasmine
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Anuradhani Kasturiratne
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Muhammad Kibriya
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Mohammad Shahriar
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Miao Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - James C Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - K Radha Mani
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Marijana Vujkovic
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ananda R Wickremasinghe
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Habibul Ahsan
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Donald W Bowden
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
| | - Giriraj R Chandak
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- JSS Academy of Health Education of Research, Mysuru, India
- Science and Engineering Research Board, Department of Science and Technology, Ministry of Science and technology, Government of India, New Delhi, India
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK.
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - John C Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
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Lubberding AF, Juhl CR, Skovhøj EZ, Kanters JK, Mandrup‐Poulsen T, Torekov SS. Celebrities in the heart, strangers in the pancreatic beta cell: Voltage-gated potassium channels K v 7.1 and K v 11.1 bridge long QT syndrome with hyperinsulinaemia as well as type 2 diabetes. Acta Physiol (Oxf) 2022; 234:e13781. [PMID: 34990074 PMCID: PMC9286829 DOI: 10.1111/apha.13781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 12/20/2021] [Accepted: 01/02/2022] [Indexed: 12/13/2022]
Abstract
Voltage‐gated potassium (Kv) channels play an important role in the repolarization of a variety of excitable tissues, including in the cardiomyocyte and the pancreatic beta cell. Recently, individuals carrying loss‐of‐function (LoF) mutations in KCNQ1, encoding Kv7.1, and KCNH2 (hERG), encoding Kv11.1, were found to exhibit post‐prandial hyperinsulinaemia and episodes of hypoglycaemia. These LoF mutations also cause the cardiac disorder long QT syndrome (LQTS), which can be aggravated by hypoglycaemia. Interestingly, patients with LQTS also have a higher burden of diabetes compared to the background population, an apparent paradox in relation to the hyperinsulinaemic phenotype, and KCNQ1 has been identified as a type 2 diabetes risk gene. This review article summarizes the involvement of delayed rectifier K+ channels in pancreatic beta cell function, with emphasis on Kv7.1 and Kv11.1, using the cardiomyocyte for context. The functional and clinical consequences of LoF mutations and polymorphisms in these channels on blood glucose homeostasis are explored using evidence from pre‐clinical, clinical and genome‐wide association studies, thereby evaluating the link between LQTS, hyperinsulinaemia and type 2 diabetes.
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Affiliation(s)
- Anniek F. Lubberding
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Christian R. Juhl
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Emil Z. Skovhøj
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Jørgen K. Kanters
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Thomas Mandrup‐Poulsen
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Signe S. Torekov
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
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8
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Cong S, Yao X, Xie L, Yan J, Shen L. Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Front Genet 2022; 12:782953. [PMID: 35237294 PMCID: PMC8884108 DOI: 10.3389/fgene.2021.782953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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Affiliation(s)
- Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Linhui Xie
- Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Nikolaev G, Robeva R, Konakchieva R. Membrane Melatonin Receptors Activated Cell Signaling in Physiology and Disease. Int J Mol Sci 2021; 23:ijms23010471. [PMID: 35008896 PMCID: PMC8745360 DOI: 10.3390/ijms23010471] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/28/2021] [Accepted: 12/29/2021] [Indexed: 02/07/2023] Open
Abstract
The pineal hormone melatonin has attracted great scientific interest since its discovery in 1958. Despite the enormous number of basic and clinical studies the exact role of melatonin in respect to human physiology remains elusive. In humans, two high-affinity receptors for melatonin, MT1 and MT2, belonging to the family of G protein-coupled receptors (GPCRs) have been cloned and identified. The two receptor types activate Gi proteins and MT2 couples additionally to Gq proteins to modulate intracellular events. The individual effects of MT1 and MT2 receptor activation in a variety of cells are complemented by their ability to form homo- and heterodimers, the functional relevance of which is yet to be confirmed. Recently, several melatonin receptor genetic polymorphisms were discovered and implicated in pathology-for instance in type 2 diabetes, autoimmune disease, and cancer. The circadian patterns of melatonin secretion, its pleiotropic effects depending on cell type and condition, and the already demonstrated cross-talks of melatonin receptors with other signal transduction pathways further contribute to the perplexity of research on the role of the pineal hormone in humans. In this review we try to summarize the current knowledge on the membrane melatonin receptor activated cell signaling in physiology and pathology and their relevance to certain disease conditions including cancer.
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Affiliation(s)
- Georgi Nikolaev
- Faculty of Biology, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria;
- Correspondence:
| | - Ralitsa Robeva
- Department of Endocrinology, Faculty of Medicine, Medical University, 1431 Sofia, Bulgaria;
| | - Rossitza Konakchieva
- Faculty of Biology, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria;
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Polikowsky HG, Shaw DM, Petty LE, Chen HH, Pruett DG, Linklater JP, Viljoen KZ, Beilby JM, Highland HM, Levitt B, Avery CL, Mullan Harris K, Jones RM, Below JE, Kraft SJ. Population-based genetic effects for developmental stuttering. HGG Adv 2021; 3:100073. [PMID: 35047858 PMCID: PMC8756529 DOI: 10.1016/j.xhgg.2021.100073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022] Open
Abstract
Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = -5.576, p = 2.46 × 10-8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10-6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.
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Affiliation(s)
- Hannah G. Polikowsky
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Douglas M. Shaw
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren E. Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dillon G. Pruett
- Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | | | | | - Janet M. Beilby
- Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Heather M. Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brandt Levitt
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Robin M. Jones
- Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA,Corresponding author
| | - Shelly Jo Kraft
- Communication Sciences and Disorders, Wayne State University, Detroit, MI, USA,Corresponding author
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11
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Das SK, Ainsworth HC, Dimitrov L, Okut H, Comeau ME, Sharma N, Ng MCY, Norris JM, Chen YDI, Wagenknecht LE, Bowden DW, Hsu FC, Taylor KD, Langefeld CD, Palmer ND. Metabolomic architecture of obesity implicates metabolonic lactone sulfate in cardiometabolic disease. Mol Metab 2021; 54:101342. [PMID: 34563731 PMCID: PMC8640864 DOI: 10.1016/j.molmet.2021.101342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 09/17/2021] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE Identify and characterize circulating metabolite profiles associated with adiposity to inform precision medicine. METHODS Untargeted plasma metabolomic profiles in the Insulin Resistance Atherosclerosis Family Study (IRASFS) Mexican American cohort (n = 1108) were analyzed for association with anthropometric (body mass index, BMI; waist circumference, WC; waist-to-hip ratio, WHR) and computed tomography measures (visceral adipose tissue, VAT; subcutaneous adipose tissue, SAT; visceral-to-subcutaneous ratio, VSR) of adiposity. Genetic data, inclusive of genome-wide array-based genotyping, whole exome sequencing (WES) and whole genome sequencing (WGS), were evaluated to identify the genetic contributors. Phenotypic and genetic association signals were replicated across ancestries. Transcriptomic data were analyzed to explore the relationship between genetic and metabolomic data. RESULTS A partially characterized metabolite, tentatively named metabolonic lactone sulfate (X-12063), was consistently associated with BMI, WC, WHR, VAT, and SAT in IRASFS Mexican Americans (PMA <2.02 × 10-27). Trait associations were replicated in IRASFS African Americans (PAA < 1.12 × 10-07). Expanded analyses revealed associations with multiple phenotypic measures of cardiometabolic health, e.g. insulin sensitivity (SI), triglycerides (TG), diastolic blood pressure (DBP) and plasminogen activator inhibitor-1 (PAI-1) in both ancestries. Metabolonic lactone sulfate levels were heritable (h2 > 0.47), and a significant genetic signal at the ZSCAN25/CYP3A5 locus (PMA = 9.00 × 10-41, PAA = 2.31 × 10-10) was observed, highlighting a putative functional variant (rs776746, CYP3A5∗3). Transcriptomic analysis in the African American Genetics of Metabolism and Expression (AAGMEx) cohort supported the association of CYP3A5 with metabolonic lactone sulfate levels (PFDR = 6.64 × 10-07). CONCLUSIONS Variant rs776746 is associated with a decrease in the transcript levels of CYP3A5, which in turn is associated with increased metabolonic lactone sulfate levels and poor cardiometabolic health.
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Affiliation(s)
- Swapan K Das
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Hannah C Ainsworth
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Latchezar Dimitrov
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Hayrettin Okut
- Office of Research, University of Kansas Medical Center, Wichita, Kansas, USA
| | - Mary E Comeau
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Neeraj Sharma
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Maggie C Y Ng
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Yii-der I Chen
- 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
| | - Lynne E Wagenknecht
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Kent D Taylor
- 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
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
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12
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Burt O, Johnston KJA, Graham N, Cullen B, Lyall DM, Lyall LM, Pell JP, Ward J, Smith DJ, Strawbridge RJ. Genetic Variation in the ASTN2 Locus in Cardiovascular, Metabolic and Psychiatric Traits: Evidence for Pleiotropy Rather Than Shared Biology. Genes (Basel) 2021; 12:1194. [PMID: 34440368 DOI: 10.3390/genes12081194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 11/24/2022] Open
Abstract
Background: The link between cardiometabolic and psychiatric illness has long been attributed to human behaviour, however recent research highlights shared biological mechanisms. The ASTN2 locus has been previously implicated in psychiatric and cardiometabolic traits, therefore this study aimed to systematically investigate the genetic architecture of ASTN2 in relation to a wide range of relevant traits. Methods: Baseline questionnaire, assessment and genetic data of 402111 unrelated white British ancestry individuals from the UK Biobank was analysed. Genetic association analyses were conducted using PLINK 1.07, assuming an additive genetic model and adjusting for age, sex, genotyping chip, and population structure. Conditional analyses and linkage disequilibrium assessment were used to determine whether cardiometabolic and psychiatric signals were independent. Results: Associations between genetic variants in the ASTN2 locus and blood pressure, total and central obesity, neuroticism, anhedonia and mood instability were identified. All analyses support the independence of the cardiometabolic traits from the psychiatric traits. In silico analyses provide support for the central obesity signal acting through ASTN2, however most of the other signals are likely acting through other genes in the locus. Conclusions: Our systematic analysis demonstrates that ASTN2 has pleiotropic effects on cardiometabolic and psychiatric traits, rather than contributing to shared pathology.
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13
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Lim WY, Lee H, Cho YS. Identification of genetic variants for blood insulin level in sex-stratified Korean population and evaluation of the causal relationship between blood insulin level and polycystic ovary syndrome. Genes Genomics 2021; 43:1105-1117. [PMID: 34304350 DOI: 10.1007/s13258-021-01134-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 06/24/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Blood insulin level is an important risk factor for numerous disorders. Individual blood insulin level is known to be substantially influenced by genetic factors. Several genetic association studies identified a number of genetic variants for blood insulin level, but none of them was from a sex-stratified population. OBJECTIVE This study aimed to identify male- and female-specific genetic variants related to blood insulin level and to evaluate the causal relationship between blood insulin level and polycystic ovary syndrome (PCOS) that is likely caused by high insulin in Korean women. METHODS A genome-wide association study was conducted to identify genetic variants influencing blood insulin level in males (N = 4183) and females (N = 4659) in the Korean population. Two-sample Mendelian randomization (MR) analysis was used to investigate the causal effects of the insulin variants identified from GWAS on PCOS in Korean women. Genetic association data for PCOS were obtained from a PCOS study cohort (946 cases, 976 controls) in Ewha Womans University Hospital. RESULTS GWAS linear regression analysis identified 13 female-specific SNPs and 13 male-specific SNPs showing suggestive associations (P < 10-5) with blood insulin level. The results from two-sample MR analysis using the GWAS variants for PCOS indicated that genetically determined insulin level was not associated with the risk of PCOS in Korean women. CONCLUSION This study identified sex-specific genetic variants showing associations with insulin for the first time in East Asian populations. In addition, MR analysis using variants discovered from Korean women revealed that genetically determined high level of insulin is not the cause of PCOS.
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Affiliation(s)
- Woo Young Lim
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, 24252, Republic of Korea
| | - Hyejin Lee
- Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Republic of Korea
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, 24252, Republic of Korea.
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14
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Kita S, Shimomura I. Stimulation of exosome biogenesis by adiponectin, a circulating factor secreted from adipocytes. J Biochem 2021; 169:173-179. [PMID: 32979268 DOI: 10.1093/jb/mvaa105] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 08/25/2020] [Indexed: 01/08/2023] Open
Abstract
Adiponectin is an adipocyte-derived circulating factor that protects various organs and tissues. Such a pleiotropic action mechanism has not yet been fully explained. Clinically important multimer adiponectin existing in serum bound to cells expressing T-cadherin, a glycosylphosphatidylinositol-anchored cadherin, but not to the cells expressing other known receptors, AdipoRs or calreticulin. Adiponectin bound to the cell-surface, accumulated inside of multivesicular bodies through T-cadherin, and increased exosome biogenesis and secretion from the cells. Such increased exosome production accompanied the reduction of cellular ceramides in endothelial cells and mouse aorta, and enhanced skeletal muscle regeneration. Significantly lower plasma exosome levels were found in mice genetically deficient in either adiponectin or T-cadherin. Therapeutic effects of mesenchymal stem cells (MSCs) for a pressure overload-induced heart failure in mice required the presence of adiponectin in plasma, T-cadherin expression and exosome biogenesis in MSCs themselves, accompanying an increase of plasma exosomes. Essentially all organs seem to have MSCs and/or their related somatic stem cells expressing T-cadherin. Our recent studies suggested the importance of exosome-stimulation by multimer adiponectin in its well-known pleiotropic organ protections.
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Affiliation(s)
- Shunbun Kita
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, 2-2 Suita, Osaka 565-0871, Japan.,Department of Adipose Management, Graduate School of Medicine, Osaka University, 2-2 Suita, Osaka 565-0871, Japan
| | - Iichiro Shimomura
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, 2-2 Suita, Osaka 565-0871, Japan
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15
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Abstract
PURPOSE OF REVIEW Recent large-scale multiancestry efforts has contributed to our knowledge of the hereditary basis of type 2 diabetes (T2D). The present review will summarize findings of the genetic basis of T2D in African Americans, a population group with a disproportionate burden of this disease. RECENT FINDINGS To date, >400 risk genetic variants have been found to be associated with the risk of T2D across populations of different ancestries. Although these findings are based on primarily European-ancestry populations, most of the identified loci show similar associations in African Americans. Ancestry-specific analyses including genome-wide associations studies (GWAS) in African Americans, Africans; as well as admixture mapping scans in African Americans have identified additional risk variants and genomic loci associate with the risk of T2D. These efforts have also uncovered new genetic links between low birth weight and T2D. In particular, admixture mapping approaches have identified a shared genetic ancestry component of both phenotypic traits in African Americans. SUMMARY Recent findings have helped us to better understand the genetic basis of T2D in African Americans. Of particular interest are new genetic discoveries linking low birth weight and T2D, two conditions with a much higher prevalence in African Americans compared to U.S. whites. Continuing work, including large-scale sequencing efforts would add to our knowledge of the genetic architecture of T2D in African Americans, as well as genetic links with other conditions.
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Affiliation(s)
- Edward A Ruiz-Narváez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
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16
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Arikoglu H, Erkoc-Kaya D, Ipekci SH, Gokturk F, Iscioglu F, Korez MK, Baldane S, Gonen MS. Type 2 diabetes is associated with the MTNR1B gene, a genetic bridge between circadian rhythm and glucose metabolism, in a Turkish population. Mol Biol Rep 2021; 48:4181-4189. [PMID: 34117605 DOI: 10.1007/s11033-021-06431-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/21/2021] [Indexed: 01/06/2023]
Abstract
Type 2 diabetes (T2D) is a complicated public health problem in Turkey as well as worldwide. Genome-wide approaches have been guiding in very challenging situations, such as the elucidation of genetic variations underlying complex diseases such as T2D. Despite intensive studies worldwide, few studies have determined the genetic susceptibility to T2D in Turkish populations. In this study, we investigated the effect of genes that are strongly associated with T2D in genome-wide association (GWA) studies, including MTNR1B, CDKAL1, THADA, ADAMTS9 and ENPP1, on T2D and its characteristic traits in a Turkish population. In 824 nonobese individuals (454 T2D patients and 370 healthy individuals), prominent variants of these GWA genes were genotyped by real-time PCR using the LightSNiP Genotyping Assay System. The SNP rs1387153 C/T, which is located 28 kb upstream of the MTNR1B gene, was significantly associated with T2D and fasting blood glucose levels (P < 0.05). The intronic SNP rs10830963 C/G in the MTNR1B gene was not associated with T2D, but it was associated with fasting blood glucose, HbA1C and LDL levels (P < 0.05). The other important GWA loci investigated in our study were not found to be associated with T2D or its traits. Only the SNP rs1044498 (A/C variation) in the ENPP1 gene was determined to be related to fasting blood glucose (P < 0.05). Our study suggests, consistent with the literature, that the MTNR1B locus, which has a prominent role in glucose regulation, is associated with T2D development by affecting blood glucose levels in our population.
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Affiliation(s)
- Hilal Arikoglu
- Department of Medical Biology, Faculty of Medicine, Selcuk University, Konya, Turkey.
| | - Dudu Erkoc-Kaya
- Department of Medical Biology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Suleyman Hilmi Ipekci
- Department of Endocrinology and Metabolic Diseases, Hisar Hospital Intercontinental, Istanbul, Turkey
| | - Fatma Gokturk
- Department of Medical Biology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Funda Iscioglu
- Department of Statistics, Faculty of Science, Ege University, Izmir, Turkey
| | - Muslu Kazim Korez
- Department of Biostatistics, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Suleyman Baldane
- Department of Endocrinology and Metabolic Diseases, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Mustafa Sait Gonen
- Department of Endocrinology and Metabolic Diseases, Faculty of Cerrahpasa Medicine, Istanbul University, Istanbul, Turkey
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17
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Fukuda S, Kita S, Miyashita K, Iioka M, Murai J, Nakamura T, Nishizawa H, Fujishima Y, Morinaga J, Oike Y, Maeda N, Shimomura I. Identification and Clinical Associations of 3 Forms of Circulating T-cadherin in Human Serum. J Clin Endocrinol Metab 2021; 106:1333-1344. [PMID: 33539522 PMCID: PMC8063249 DOI: 10.1210/clinem/dgab066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT T-cadherin (T-cad) is a glycosylphosphatidylinositol (GPI)-anchored cadherin that mediates adiponectin to induce exosome biogenesis and secretion, protect cardiovascular tissues, promote muscle regeneration, and stimulate therapeutic heart protection by transplanted mesenchymal stem cells. CDH13, the gene locus of T-cad, affects plasma adiponectin levels most strongly, in addition to affecting cardiovascular disease risk and glucose homeostasis. Recently, it has been suggested that T-cad exists in human serum, although the details are still unclear. OBJECTIVE To validate the existence of T-cad forms in human serum and investigate the association with clinical parameters of type 2 diabetes patients. METHODS Using newly developed monoclonal antibodies against T-cad, pooled human serum was analyzed, and novel T-cad enzyme-linked immunosorbent assays (ELISAs) were developed. The serum T-cad concentrations of 183 Japanese type 2 diabetes patients were measured in a cross-sectional observational study. The main outcome measure was the existence of soluble T-cad in human serum. RESULTS There were 3 forms of soluble T-cad: a 130-kDa form with a prodomain, a 100-kDa mature form, and a 30-kDa prodomain in human serum. Using newly developed ELISAs to measure them simultaneously, we found that the 130-kDa form of T-cad positively correlated with plasma adiponectin (r = 0.28, P < .001), although a physiological interaction with adiponectin was not observed in serum. The unique 30-kDa prodomain was associated with several clinical parameters in diabetes patients. CONCLUSION We identified 3 novel forms of soluble T-cad. Their importance as disease markers and/or biomarkers of adiponectin function and the possible bioactivity of the respective molecules require further investigation.
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Affiliation(s)
- Shiro Fukuda
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Adipose Management, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Shunbun Kita
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Adipose Management, Graduate School of Medicine, Osaka University, Osaka, Japan
- Correspondence: Shunbun Kita, PhD, Osaka University, Suita, Osaka Japan.
| | | | - Masahito Iioka
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jun Murai
- Department of Diabetes and Endocrinology, Kawasaki Hospital, Kobe, Japan
| | - Tadashi Nakamura
- Department of Diabetes and Endocrinology, Kawasaki Hospital, Kobe, Japan
| | - Hitoshi Nishizawa
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuya Fujishima
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jun Morinaga
- Department of Molecular Genetics, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Yuichi Oike
- Department of Molecular Genetics, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Norikazu Maeda
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Metabolism and Atherosclerosis, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Iichiro Shimomura
- Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
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18
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Liu Y, Yu Z, Sun H. Treatment Effect of Type 2 Diabetes Patients in Outpatient Department Based on Blockchain Electronic Mobile Medical App. J Healthc Eng 2021; 2021:6693810. [PMID: 33728034 DOI: 10.1155/2021/6693810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/21/2021] [Accepted: 02/18/2021] [Indexed: 12/13/2022]
Abstract
As the pace of people's lives accelerates, there are more and more diabetic patients. This research mainly explores the treatment effect of type 2 diabetic patients based on blockchain electronic mobile medical app. Considering that it is more realistic to adopt an off-chain storage solution, the blockchain-based medical data sharing platform in this study adopts an off-chain storage solution. Only key information is stored in the blockchain network, and all medical data will be in the cloud space. For storage, cloud storage uses Aliyun's OSS storage service, which can be expanded infinitely. The cloud operation module is responsible for all operations that interact with cloud storage. The chain code can call the cloud operation module to upload the user's encrypted medical data and user ID to Alibaba Cloud's OSS. The chain code will return the storage address of the medical data and the authorized access address is sent to the blockchain network for consensus on the chain. The message processing module provides information processing functions such as chat information processing, APP use reminders, and health tips. The indicator recording module includes indicator recording functions including 6 indicators of blood sugar, medication, diet, weight, exercise, and sleep. The main function of the indicator analysis module is to display the curve trends of the 6 indicators recorded by the patient in three days, one week, and one month. Comparing the change range of the mean value of glycosylated hemoglobin at the beginning and end of the two groups of patients, it can be found that the change range of glycosylated hemoglobin in the intervention group is −6.04%, while the change range of the control group is only −3.26%. The impact of the mobile medical app designed in this study will indeed be reflected in the patient's blood sugar control and help patients to better control blood sugar.
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19
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Mathkar PP, Chen X, Sulovari A, Li D. Characterization of Hepatitis B Virus Integrations Identified in Hepatocellular Carcinoma Genomes. Viruses 2021; 13:v13020245. [PMID: 33557409 PMCID: PMC7915589 DOI: 10.3390/v13020245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/31/2021] [Accepted: 02/02/2021] [Indexed: 12/19/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Almost half of HCC cases are associated with hepatitis B virus (HBV) infections, which often lead to HBV sequence integrations in the human genome. Accurate identification of HBV integration sites at a single nucleotide resolution is critical for developing a better understanding of the cancer genome landscape and of the disease itself. Here, we performed further analyses and characterization of HBV integrations identified by our recently reported VIcaller platform in recurrent or known HCC genes (such as TERT, MLL4, and CCNE1) as well as non-recurrent cancer-related genes (such as CSMD2, NKD2, and RHOU). Our pathway enrichment analysis revealed multiple pathways involving the alcohol dehydrogenase 4 gene, such as the metabolism pathways of retinol, tyrosine, and fatty acid. Further analysis of the HBV integration sites revealed distinct patterns involving the integration upper breakpoints, integrated genome lengths, and integration allele fractions between tumor and normal tissues. Our analysis also implies that the VIcaller method has diagnostic potential through discovering novel clonal integrations in cancer-related genes. In conclusion, although VIcaller is a hypothesis free virome-wide approach, it can still be applied to accurately identify genome-wide integration events of a specific candidate virus and their integration allele fractions.
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Affiliation(s)
- Pranav P. Mathkar
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA; (P.P.M.); (A.S.)
| | - Xun Chen
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA; (P.P.M.); (A.S.)
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto 606-8501, Japan
- Correspondence: (X.C.); (D.L.)
| | - Arvis Sulovari
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA; (P.P.M.); (A.S.)
- Cajal Neuroscience Inc., Seattle, WA 98102, USA
| | - Dawei Li
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA; (P.P.M.); (A.S.)
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA
- Correspondence: (X.C.); (D.L.)
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20
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Cirelli T, Nepomuceno R, Goveia JM, Orrico SRP, Cirelli JA, Theodoro LH, Barros SP, Scarel-Caminaga RM. Association of type 2 diabetes mellitus and periodontal disease susceptibility with genome-wide association-identified risk variants in a Southeastern Brazilian population. Clin Oral Investig 2021; 25:3873-3892. [PMID: 33392810 DOI: 10.1007/s00784-020-03717-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Genome-wide association studies (GWAS) and literature have identified polymorphisms in the KCNJ11, HNF1A, IRS1, TCF7L2, CDKAL1, CDKN2B, RPSAP52, GPR45 HHEX, IL18, and RUNX2 genes associated with type 2 diabetes mellitus (T2DM) and/or periodontitis (P) in diverse populations, and we sought to evaluate them as genetic risk variants for these diseases in the Brazilian population. MATERIAL AND METHODS Periodontal, glycemic, and lipid data were obtained from 931 individuals divided into: control (n = 334), periodontitis (P; n = 358), and periodontitis associated with T2DM (P + T2DM; n = 239). After genotyping, associations between polymorphisms and pathologies were tested by multiple logistic and linear regressions, adjusting for age, sex, and smoking habits. RESULTS Considering the studied subjects, the increased risk to develop periodontitis in the periodontitis P + T2DM group was found for HNF1A-rs7957197-TA, CDKAL1-rs7754840-CG, RPSAP52-rs1531343-GC, TCF7L2-rs7903146-TT, and CDKN2B-rs7018475-GG. The association of these genetic variants for TCF7L2 and CDKN2B was confirmed for female, never smokers, and poorly controlled P + T2DM. CDKN2B-rs7018475 was associated with worse glycemic condition and periodontal parameters. CONCLUSION These five reported genetic variants were associated in the studied Southeastern Brazilian population as genetic risk variants of periodontitis and T2DM associated to periodontitis as comorbidity. Gene-phenotype associations with sex and smoking habits and the CDKN2B-rs7018475 with the poor glycemic control and more severe periodontal conditions should be further investigated. CLINICAL RELEVANCE Polymorphisms in the CDKAL1-rs7754840, HNF1A-rs7957197, RPSAP52-rs1531343, TCF7L2-rs7903146, and CDKN2B-rs7018475 might predispose to periodontitis and T2DM associated with periodontitis. These findings may be useful in public health genomics and future advanced clinical practice, since genetic carriage can be measured before disease onset, being of potential great benefit for treatment planning and prognosis in early disease stages.
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Affiliation(s)
- Thamiris Cirelli
- Department of Diagnosis and Surgery, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil.,Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil
| | - Rafael Nepomuceno
- Department of Diagnosis and Surgery, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil.,Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil
| | - Jéssica Marina Goveia
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil
| | - Silvana R P Orrico
- Department of Diagnosis and Surgery, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil.,Union of the Colleges of the Great Lakes (UNILAGO), São José do Rio Preto, SP, Brazil
| | - Joni A Cirelli
- Department of Diagnosis and Surgery, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil
| | - Letícia Helena Theodoro
- Department of Surgery and Integrated Clinic, São Paulo State University - UNESP, School of Dentistry at Araçatuba, Araçatuba, SP, Brazil
| | - Silvana P Barros
- Department of Comprehensive Oral Health - Periodontology, University of North Carolina at Chapel Hill - UNC, School of Dentistry, Chapel Hill, NC, USA
| | - Raquel M Scarel-Caminaga
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, São Paulo State University - UNESP, School of Dentistry at Araraquara, Araraquara, SP, Brazil.
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21
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Sorlí JV, Barragán R, Coltell O, Portolés O, Pascual EC, Ortega-Azorín C, González JI, Estruch R, Saiz C, Pérez-Fidalgo A, Ordovas JM, Corella D. Chronological Age Interacts with the Circadian Melatonin Receptor 1B Gene Variation, Determining Fasting Glucose Concentrations in Mediterranean Populations. Additional Analyses on Type-2 Diabetes Risk. Nutrients 2020; 12:nu12113323. [PMID: 33138317 PMCID: PMC7692445 DOI: 10.3390/nu12113323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/21/2020] [Accepted: 10/24/2020] [Indexed: 12/25/2022] Open
Abstract
Gene-age interactions have not been systematically investigated on metabolic phenotypes and this modulation will be key for a better understanding of the temporal regulation in nutrigenomics. Taking into account that aging is typically associated with both impairment of the circadian system and a decrease in melatonin secretion, we focused on the melatonin receptor 1B (MTNR1B)-rs10830963 C>G variant that has been associated with fasting glucose concentrations, gestational diabetes, and type-2 diabetes. Therefore, our main aim was to investigate whether the association between the MTNR1B-rs10830963 polymorphism and fasting glucose is age dependent. Our secondary aims were to analyze the polymorphism association with type-2 diabetes and explore the gene-pregnancies interactions on the later type-2 diabetes risk. Three Mediterranean cohorts (n = 2823) were analyzed. First, a cross-sectional study in the discovery cohort consisting of 1378 participants (aged 18 to 80 years; mean age 41 years) from the general population was carried out. To validate and extend the results, two replication cohorts consisting of elderly individuals were studied. In the discovery cohort, we observed a strong gene-age interaction (p = 0.001), determining fasting glucose in such a way that the increasing effect of the risk G-allele was much greater in young (p = 5.9 × 10-10) than in elderly participants (p = 0.805). Consistently, the association of the MTNR1B-rs10830963 polymorphism with fasting glucose concentrations in the two replication cohorts (mean age over 65 years) did not reach statistical significance (p > 0.05 for both). However, in the elderly cohorts, significant associations between the polymorphism and type-2 diabetes at baseline were found. Moreover, in one of the cohorts, we obtained a statistically significant interaction between the MTNR1B polymorphism and the number of pregnancies, retrospectively assessed, on the type-2 diabetes risk. In conclusion, the association of the MTNR1B-rs10830963 polymorphism with fasting glucose is age-dependent, having a greater effect in younger people. However, in elderly subjects, associations of the polymorphism with type-2 diabetes were observed and our exploratory analysis suggested a modulatory effect of the number of past pregnancies on the future type-2 diabetes genetic risk.
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Affiliation(s)
- Jose V. Sorlí
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
| | - Rocío Barragán
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
- Department of Medicine, Sleep Center of Excellence, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oscar Coltell
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
- Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castellón, Spain
| | - Olga Portolés
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
| | - Eva C. Pascual
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
| | - Carolina Ortega-Azorín
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
| | - José I. González
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
- Department of Internal Medicine, Hospital Clinic, Institut d’Investigació Biomèdica August Pi i Sunyer (IDIBAPS), University of Barcelona, Villarroel, 170, 08036 Barcelona, Spain
| | - Carmen Saiz
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
| | - Alejandro Pérez-Fidalgo
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Cáncer, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Jose M. Ordovas
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111, USA;
- Precision Nutrition and Obesity Program, IMDEA Alimentación, 28049 Madrid, Spain
| | - Dolores Corella
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain; (J.V.S.); (R.B.); (O.P.); (E.C.P.); (C.O.-A.); (J.I.G.); (C.S.); (A.P.-F.)
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain; (O.C.); (R.E.)
- Correspondence: ; Tel.: +34-96-386-4800
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Huang B, Wang YK, Qin LY, Wei Q, Liu N, Jiang M, Yu HP, Yu XY. A functional polymorphism rs10830963 in melatonin receptor 1B associated with the risk of gestational diabetes mellitus. Biosci Rep 2019; 39:BSR20190744. [PMID: 31808503 DOI: 10.1042/BSR20190744] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 11/16/2019] [Accepted: 12/04/2019] [Indexed: 12/14/2022] Open
Abstract
The melatonin receptor 1B (MTNR1B) polymorphism rs10830963 C>G has been reported to be associated with the risk of gestational diabetes mellitus (GDM) with inconsistent results. To clarify the effect of the polymorphism on the risk of GDM, a meta-analysis therefore was performed. Pooled OR with its corresponding 95%CI was used to estimate the strength of the association. Totally 14 eligible studies with a number of 5033 GDM patients and 5614 controls were included in this meta-analysis. Results indicated that the variant G allele was significantly associated with an increased GDM risk (CG vs. CC: OR = 1.25, 95% CI = 1.11−1.40, P < 0.001; GG vs. CC: OR = 1.78, 95% CI = 1.45−2.19, P < 0.001; G vs. C: OR = 1.33, 95% CI = 1.21−1.47, P < 0.001). In the stratified analysis by ethnicity, similar results were found in Asians (CG vs. CC: OR = 1.15, 95%CI = 1.02−1.28, P = 0.020; GG vs. CC: OR = 1.52, 95% CI = 1.23−1.89, P < 0.001; G vs. C: OR = 1.23, 95% CI = 1.10−1.37, P < 0.001) and in Caucasians (CG vs. CC: OR = 1.40, 95% CI = 1.16−1.70, P < 0.001; GG vs. CC: OR = 2.21, 95% CI = 1.54−3.17, P < 0.001; G vs. C: OR = 1.47, 95% CI = 1.24−1.73, P < 0.001). FPRP and TSA analyses confirmed findings support that the rs10830963 G allele increases the risk of GDM, and further functional experimental studies are warranted to explore and clarify the potential mechanism.
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Orthofer M, Valsesia A, Mägi R, Wang QP, Kaczanowska J, Kozieradzki I, Leopoldi A, Cikes D, Zopf LM, Tretiakov EO, Demetz E, Hilbe R, Boehm A, Ticevic M, Nõukas M, Jais A, Spirk K, Clark T, Amann S, Lepamets M, Neumayr C, Arnold C, Dou Z, Kuhn V, Novatchkova M, Cronin SJF, Tietge UJF, Müller S, Pospisilik JA, Nagy V, Hui CC, Lazovic J, Esterbauer H, Hagelkruys A, Tancevski I, Kiefer FW, Harkany T, Haubensak W, Neely GG, Metspalu A, Hager J, Gheldof N, Penninger JM. Identification of ALK in Thinness. Cell 2020; 181:1246-1262.e22. [PMID: 32442405 DOI: 10.1016/j.cell.2020.04.034] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 01/28/2020] [Accepted: 04/20/2020] [Indexed: 12/25/2022]
Abstract
There is considerable inter-individual variability in susceptibility to weight gain despite an equally obesogenic environment in large parts of the world. Whereas many studies have focused on identifying the genetic susceptibility to obesity, we performed a GWAS on metabolically healthy thin individuals (lowest 6th percentile of the population-wide BMI spectrum) in a uniquely phenotyped Estonian cohort. We discovered anaplastic lymphoma kinase (ALK) as a candidate thinness gene. In Drosophila, RNAi mediated knockdown of Alk led to decreased triglyceride levels. In mice, genetic deletion of Alk resulted in thin animals with marked resistance to diet- and leptin-mutation-induced obesity. Mechanistically, we found that ALK expression in hypothalamic neurons controls energy expenditure via sympathetic control of adipose tissue lipolysis. Our genetic and mechanistic experiments identify ALK as a thinness gene, which is involved in the resistance to weight gain.
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Affiliation(s)
- Michael Orthofer
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Armand Valsesia
- Metabolic Phenotyping, Nestlé Research, EPFL Innovation Park, Lausanne 1015, Switzerland
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Qiao-Ping Wang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China
| | | | - Ivona Kozieradzki
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Alexandra Leopoldi
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Domagoj Cikes
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Lydia M Zopf
- Vienna BioCenter Core Facilities GmbH (VBCF), Vienna 1030, Austria
| | - Evgenii O Tretiakov
- Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Spitalgasse 4, Vienna 1090, Austria
| | - Egon Demetz
- Department of Internal Medicine II, Innsbruck Medical University, Innsbruck 6020, Austria
| | - Richard Hilbe
- Department of Internal Medicine II, Innsbruck Medical University, Innsbruck 6020, Austria
| | - Anna Boehm
- Department of Internal Medicine II, Innsbruck Medical University, Innsbruck 6020, Austria
| | - Melita Ticevic
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Margit Nõukas
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Alexander Jais
- Department of Laboratory Medicine, Medical University of Vienna, Vienna 1090, Austria
| | - Katrin Spirk
- Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna 1090, Austria
| | - Teleri Clark
- Dr. John and Anne Chong Lab for Functional Genomics, Charles Perkins Centre, Centenary Institute, and School of Life and Environmental Sciences, University of Sydney, Camperdown, NSW 2006, Australia
| | - Sabine Amann
- Department of Laboratory Medicine, Medical University of Vienna, Vienna 1090, Austria
| | - Maarja Lepamets
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | | | - Cosmas Arnold
- IMP, Institute of Molecular Pathology, Vienna 1030, Austria
| | - Zhengchao Dou
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Volker Kuhn
- Department of Internal Medicine II, Innsbruck Medical University, Innsbruck 6020, Austria
| | | | - Shane J F Cronin
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Uwe J F Tietge
- Division of Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institute, 141 52 Huddinge, Sweden; Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital, 141 86 Stockholm, Sweden
| | - Simone Müller
- Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria
| | - J Andrew Pospisilik
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Vanja Nagy
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, 1090 Vienna, Austria
| | - Chi-Chung Hui
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jelena Lazovic
- Vienna BioCenter Core Facilities GmbH (VBCF), Vienna 1030, Austria
| | - Harald Esterbauer
- Department of Laboratory Medicine, Medical University of Vienna, Vienna 1090, Austria
| | - Astrid Hagelkruys
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
| | - Ivan Tancevski
- Department of Internal Medicine II, Innsbruck Medical University, Innsbruck 6020, Austria
| | - Florian W Kiefer
- Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna 1090, Austria
| | - Tibor Harkany
- Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Spitalgasse 4, Vienna 1090, Austria; Section for Chemical Neurotransmission, Department of Neuroscience, Biomedicum 7D, Solnavägen 9, 17165 Solna, Sweden
| | - Wulf Haubensak
- IMP, Institute of Molecular Pathology, Vienna 1030, Austria
| | - G Gregory Neely
- Dr. John and Anne Chong Lab for Functional Genomics, Charles Perkins Centre, Centenary Institute, and School of Life and Environmental Sciences, University of Sydney, Camperdown, NSW 2006, Australia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Jorg Hager
- Metabolic Phenotyping, Nestlé Research, EPFL Innovation Park, Lausanne 1015, Switzerland.
| | - Nele Gheldof
- Metabolic Phenotyping, Nestlé Research, EPFL Innovation Park, Lausanne 1015, Switzerland.
| | - Josef M Penninger
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria; Department of Medical Genetics, Life Science Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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Abstract
Adipose tissue plays important roles in regulating whole-body energy metabolism through its storage function in white adipocytes and its dissipating function in brown and beige adipocytes. Adipose tissue also produces a variety of secreted factors called adipocytokines, including leptin and adiponectin. Furthermore, recent studies have suggested the important roles of extracellular vesicles of endosomal origin termed exosomes, which are secreted from adipocytes and other cells in adipose tissue and influence whole-body glucose and lipid metabolism. Adiponectin is known to be a pleiotropic organ-protective protein that is exclusively produced by adipocytes and decreased in obesity. Adiponectin accumulates in tissues such as heart, muscle, and vascular endothelium through binding with T-cadherin, a glycosylphosphatidylinositol-anchored (GPI-anchored) cadherin. Recently, adiponectin was found to enhance exosome biogenesis and secretion, leading to a decrease in cellular ceramides, excess of which is known to cause insulin resistance and cardiovascular disease phenotypes. These findings support the hypothesis that adipose tissue metabolism systemically regulates exosome production and whole-body metabolism through exosomes. This review focuses on intra-adipose and interorgan communication by exosomes, adiponectin-stimulated exosome production, and their dysregulation in metabolic diseases.
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Affiliation(s)
- Shunbun Kita
- Department of Metabolic Medicine.,Department of Adipose Management, and
| | - Norikazu Maeda
- Department of Metabolic Medicine.,Department of Metabolism and Atherosclerosis, Graduate School of Medicine, Osaka University, Osaka, Japan
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25
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Goodarzi MO, Palmer ND, Cui J, Guo X, Chen YDI, Taylor KD, Raffel LJ, Wagenknecht LE, Buchanan TA, Hsueh WA, Rotter JI. Classification of Type 2 Diabetes Genetic Variants and a Novel Genetic Risk Score Association With Insulin Clearance. J Clin Endocrinol Metab 2020; 105:dgz198. [PMID: 31714576 PMCID: PMC7059988 DOI: 10.1210/clinem/dgz198] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 11/11/2019] [Indexed: 12/16/2022]
Abstract
CONTEXT Genome-wide association studies have identified more than 450 single nucleotide polymorphisms (SNPs) for type 2 diabetes (T2D). OBJECTIVE To facilitate use of these SNPs in future genetic risk score (GRS)-based analyses, we aimed to classify the SNPs based on physiology. We also sought to validate GRS associations with insulin-related traits in deeply phenotyped Mexican Americans. DESIGN, SETTING, AND PARTICIPANTS A total of 457 T2D SNPs from the literature were assigned physiologic function based on association studies and cluster analyses. All SNPs (All-GRS), beta-cell (BC-GRS), insulin resistance (IR-GRS), lipodystrophy (Lipo-GRS), and body mass index plus lipids (B + L-GRS) were evaluated for association with diabetes and indices of insulin secretion (from oral glucose tolerance test), insulin sensitivity and insulin clearance (from euglycemic clamp), and adiposity and lipid markers in 1587 Mexican Americans. RESULTS Of the 457 SNPs, 52 were classified as BC, 30 as IR, 12 as Lipo, 12 as B + L, whereas physiologic function of 351 was undefined. All-GRS was strongly associated with T2D. Among nondiabetic Mexican Americans, BC-GRS was associated with reduced insulinogenic index, IR-GRS was associated with reduced insulin sensitivity, and Lipo-GRS was associated with reduced adiposity. B + L-GRS was associated with increased insulin clearance. The latter did not replicate in an independent cohort wherein insulin clearance was assessed by a different method. CONCLUSIONS Supporting their utility, BC-GRS, IR-GRS, and Lipo-GRS, based on SNPs discovered largely in Europeans, exhibited expected associations in Mexican Americans. The novel association of B + L-GRS with insulin clearance suggests that impaired ability to reduce insulin clearance in compensation for IR may play a role in the pathogenesis of T2D. Whether this applies to other ethnic groups remains to be determined.
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Affiliation(s)
- Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, US
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, US
| | - Jinrui Cui
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, US
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, US
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, US
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, US
| | - Leslie J Raffel
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, US
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, US
| | - Thomas A Buchanan
- Department of Physiology and Biophysics and Department of Medicine, Keck School of Medicine of USC, Los Angeles, California, US
| | - Willa A Hsueh
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, US
| | - Jerome I Rotter
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, US
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26
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Yelmen B, Mondal M, Marnetto D, Pathak AK, Montinaro F, Gallego Romero I, Kivisild T, Metspalu M, Pagani L. Ancestry-Specific Analyses Reveal Differential Demographic Histories and Opposite Selective Pressures in Modern South Asian Populations. Mol Biol Evol 2020; 36:1628-1642. [PMID: 30952160 PMCID: PMC6657728 DOI: 10.1093/molbev/msz037] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Genetic variation in contemporary South Asian populations follows a northwest to southeast decreasing cline of shared West Eurasian ancestry. A growing body of ancient DNA evidence is being used to build increasingly more realistic models of demographic changes in the last few thousand years. Through high-quality modern genomes, these models can be tested for gene and genome level deviations. Using local ancestry deconvolution and masking, we reconstructed population-specific surrogates of the two main ancestral components for more than 500 samples from 25 South Asian populations and showed our approach to be robust via coalescent simulations. Our f3 and f4 statistics–based estimates reveal that the reconstructed haplotypes are good proxies for the source populations that admixed in the area and point to complex interpopulation relationships within the West Eurasian component, compatible with multiple waves of arrival, as opposed to a simpler one wave scenario. Our approach also provides reliable local haplotypes for future downstream analyses. As one such example, the local ancestry deconvolution in South Asians reveals opposite selective pressures on two pigmentation genes (SLC45A2 and SLC24A5) that are common or fixed in West Eurasians, suggesting post-admixture purifying and positive selection signals, respectively.
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Affiliation(s)
- Burak Yelmen
- Institute of Genomics, University of Tartu, Tartu, Estonia.,Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mayukh Mondal
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Ajai K Pathak
- Institute of Genomics, University of Tartu, Tartu, Estonia.,Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Francesco Montinaro
- Institute of Genomics, University of Tartu, Tartu, Estonia.,Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Irene Gallego Romero
- Melbourne Integrative Genomics and School of BioSciences, University of Melbourne, Parkville, Australia
| | - Toomas Kivisild
- Institute of Genomics, University of Tartu, Tartu, Estonia.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Mait Metspalu
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Luca Pagani
- Institute of Genomics, University of Tartu, Tartu, Estonia.,APE Lab, Department of Biology, University of Padova, Padova, Italy
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27
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Xu Z, Xie C, Xia L, Yuan Y, Zhu H, Huang X, Li C, Tao Y, Qu X, Zhang F, Zhang Z. Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder. Transl Psychiatry 2020; 10:30. [PMID: 32066657 PMCID: PMC7026085 DOI: 10.1038/s41398-020-0689-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 10/23/2019] [Accepted: 11/06/2019] [Indexed: 02/08/2023] Open
Abstract
In order to determine the role of single nucleotide variants (SNVs) in modulating antidepressant response, we conducted a study, consisting of 929 major depressive disorder (MDD) patients, who were treated with antidepressant drugs (drug-only) or in combination with a repetitive transcranial magnetic stimulation (plus-rTMS), followed by targeted exome sequencing analysis. We found that the "plus-rTMS" patients presented a more effective response to the treatment when compared to the 'drug-only' group. Our data firstly demonstrated that the SNV burden had a significant impact on the antidepressant response presented in the "drug-only" group, but was limited in the "plus-rTMS" group. Further, after controlling for overall SNV burden, seven single nucleotide polymorphisms (SNPs) at five loci, IL1A, GNA15, PPP2CB, PLA2G4C, and GBA, were identified as affecting the antidepressant response at genome-wide significance (P < 5 × 10-08). Additional multiple variants achieved a level of correction for multiple testing, including GNA11, also shown as a strong signal for MDD risk. Our study showed some promising evidence on genetic variants that could be used as individualized therapeutic guides for MDD patients.
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Affiliation(s)
- Zhi Xu
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Chunming Xie
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Lu Xia
- Global Clinical and Translational Research Institute, Bethesda, MD 20814 USA
| | - Yonggui Yuan
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Hong Zhu
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Xiaofa Huang
- grid.263826.b0000 0004 1761 0489The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009 Nanjing, Jiangsu China
| | - Caihua Li
- Center for Genetics and Genomics Analysis, Genesky Biotechnologies, Inc, 201203 Shanghai, China
| | - Yu Tao
- Center for Genetics and Genomics Analysis, Genesky Biotechnologies, Inc, 201203 Shanghai, China
| | - Xiaoxiao Qu
- Genesky Diagnostics, Inc., BioBay, SIP, 215123 Jiangsu, China
| | - Fengyu Zhang
- Global Clinical and Translational Research Institute, Bethesda, MD, 20814, USA.
| | - Zhijun Zhang
- The Department of Neurology and Psychiatry of Affiliated ZhongDa Hospital, and Medical School of Southeast University, 210009, Nanjing, Jiangsu, China. .,Global Clinical and Translational Research Institute, Bethesda, MD, 20814, USA. .,The Institute of Neuropsychiatry, the Key Laboratory of Development Genes and Human Diseases, the Ministry of Education and Institute of Life Sciences of Southeast University, 210096, Nanjing, Jiangsu, China.
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28
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Hebbar P, Abu-Farha M, Alkayal F, Nizam R, Elkum N, Melhem M, John SE, Channanath A, Abubaker J, Bennakhi A, Al-Ozairi E, Tuomilehto J, Pitkaniemi J, Alsmadi O, Al-Mulla F, Thanaraj TA. Genome-wide association study identifies novel risk variants from RPS6KA1, CADPS, VARS, and DHX58 for fasting plasma glucose in Arab population. Sci Rep 2020; 10:152. [PMID: 31932636 DOI: 10.1038/s41598-019-57072-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 12/20/2019] [Indexed: 12/14/2022] Open
Abstract
Consanguineous populations of the Arabian Peninsula, which has seen an uncontrolled rise in type 2 diabetes incidence, are underrepresented in global studies on diabetes genetics. We performed a genome-wide association study on the quantitative trait of fasting plasma glucose (FPG) in unrelated Arab individuals from Kuwait (discovery-cohort:n = 1,353; replication-cohort:n = 1,196). Genome-wide genotyping in discovery phase was performed for 632,375 markers from Illumina HumanOmniExpress Beadchip; and top-associating markers were replicated using candidate genotyping. Genetic models based on additive and recessive transmission modes were used in statistical tests for associations in discovery phase, replication phase, and meta-analysis that combines data from both the phases. A genome-wide significant association with high FPG was found at rs1002487 (RPS6KA1) (p-discovery = 1.64E-08, p-replication = 3.71E-04, p-combined = 5.72E-11; β-discovery = 8.315; β-replication = 3.442; β-combined = 6.551). Further, three suggestive associations (p-values < 8.2E-06) with high FPG were observed at rs487321 (CADPS), rs707927 (VARS and 2Kb upstream of VWA7), and rs12600570 (DHX58); the first two markers reached genome-wide significance in the combined analysis (p-combined = 1.83E-12 and 3.07E-09, respectively). Significant interactions of diabetes traits (serum triglycerides, FPG, and glycated hemoglobin) with homeostatic model assessment of insulin resistance were identified for genotypes heterozygous or homozygous for the risk allele. Literature reports support the involvement of these gene loci in type 2 diabetes etiology.
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29
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Cao M, Zhang L, Chen T, Shi A, Xie K, Li Z, Xu J, Chen Z, Ji C, Wen J. Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population. Front Endocrinol (Lausanne) 2020; 11:247. [PMID: 32390949 PMCID: PMC7188786 DOI: 10.3389/fendo.2020.00247] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 04/03/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction: New genetic variants associated with susceptibility to obesity and metabolic diseases have been discovered in recent genome-wide association (GWA) studies. The aim of this study was to investigate the association of theses risk variants with gestational diabetes mellitus (GDM). Methods: We performed a case-control study including 964 unrelated pregnant women with GDM and 1,021 pregnant women with normal glucose tolerance (as controls). A total of 33 genetic variants confirmed by GWA studies for obesity and metabolic diseases were selected and measured. Results: We observed that FTO rs1121980 and KCNQ1 rs163182 conferred a decreased GDM risk in the dominant and additive model [additive model: OR (95% CI) = 0.79 (0.67-0.94), P = 0.007 for rs1121980; OR(95%CI) = 0.84 (0.73-0.96), P = 0.009 for rs163182], whereas MC4R rs12970134 and PROX1 rs340841 conferred an increased GDM risk in the dominant, recessive, and additive model [additive model: OR(95%CI) = 1.25 (1.07-1.46), P = 0.006 for rs12970134; OR(95%CI) = 1.22 (1.07-1.39), P = 0.002 for rs340841). With the increasing number of risk alleles of the four significant SNPs, GDM risk was significantly increased in a dose-dependent manner (Ptrend < 0.001). And the significant positive associations between the weighted genetic risk score and risk of GDM persisted. Further function annotation indicated that these four SNPs may fall on the functional elements of human pancreatic islets. The genotype-phenotype associations indicated that these SNPs may contribute to GDM by affecting the expression levels of their nearby or distant genes. Conclusion: Our study suggests that FTO rs1121980, KCNQ1 rs163182, MC4R rs12970134, and PROX1 rs340841 may be markers for susceptibility to GDM in a Chinese population.
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Affiliation(s)
- Minkai Cao
- Department of Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Le Zhang
- Department of Neonatology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, Wuxi, China
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
| | - Aiwu Shi
- Department of MICU, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
| | - Kaipeng Xie
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
| | - Zhengying Li
- Department of Neonatology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, Wuxi, China
| | - Jianjuan Xu
- Department of Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Zhong Chen
- Department of Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
- *Correspondence: Zhong Chen
| | - Chenbo Ji
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
- Chenbo Ji
| | - Juan Wen
- Nanjing Maternity and Child Health Care Institute, Nanjing Maternity and Child Health Care Hospital, Women's Hospital of Nanjing Medical University, Nanjing, China
- Juan Wen
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Franck M, de Toro-Martín J, Guénard F, Rudkowska I, Lemieux S, Lamarche B, Couture P, Vohl MC. Prevention of Potential Adverse Metabolic Effects of a Supplementation with Omega-3 Fatty Acids Using a Genetic Score Approach. Lifestyle Genom 2019; 13:32-42. [PMID: 31779001 DOI: 10.1159/000504022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 10/07/2019] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION The consumption of long-chain omega-3 polyunsaturated fatty acids (n-3 PUFA) has been reported to have beneficial health effects, notably, by reducing plasma triglyceride levels. Nonetheless, a concomitant decrease in insulin sensitivity has also been observed, but is highly variable among subjects. Herein, we aimed to determine the importance of the genetic background in the interindividual variability of the insulin sensitivity response following an n-3 PUFA supplementation. METHODS A total of 210 participants completed a 6-week n-3 PUFA supplementation with 5 g/day of fish oil (providing 1.9-2.2 g of eicosapentaenoic acid + 1.1 g of docosahexaenoic acid). Insulin resistance was estimated by the homeostatic model assessment (HOMA-IR), and participants were further classified as high-risk or low-risk depending on their HOMA-IR change following the n-3 PUFA supplementation, as compared to pre-supplementation values. Genome-wide genotyping data were obtained for 138 participants using HumanOmni-5-Quad BeadChips containing 4,301,331 single nucleotide polymorphisms. A genome-wide association analysis (GWAS) was carried out between high-risk and low-risk participants. The population study was split into training (60%) and testing (40%) datasets to assess the predictive accuracy of a genetic risk score (GRS) constructed by summing the number of risk alleles. RESULTS Following the n-3 PUFA supplementation, 32 participants had increased HOMA-IR as compared to initial values and were classified as high risk (23.2%), whereas remaining subjects were classified as low risk (n = 106, 76.8%). A total of 8 loci had frequency differences between high-risk and low-risk participants at a suggestive GWAS association threshold (p value <1 × 10-5). After applying 10-fold cross validation, the GRS showed a significant association with the risk of increased HOMA-IR in the testing dataset (OR = 3.16 [95% CI, 1.85-7.14]), with a predictive accuracy of 0.85, and explained 40% of variation in HOMA-IR change. CONCLUSIONS These results suggest that the genetic background has a relevant role in the interindividual variability observed in the insulin sensitivity response following an n-3 PUFA supplementation. Subjects being at risk of insulin sensitivity lowering following an n-3 PUFA supplementation may be identified using genetic-based precision nutrition approaches.
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Affiliation(s)
- Maximilien Franck
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,School of Nutrition, Laval University, Quebec City, Québec, Canada
| | - Juan de Toro-Martín
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,School of Nutrition, Laval University, Quebec City, Québec, Canada
| | - Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,School of Nutrition, Laval University, Quebec City, Québec, Canada
| | - Iwona Rudkowska
- Department of Kinesiology, Laval University, Quebec City, Québec, Canada.,Endocrinology and Nephrology Unit, CHU de Quebec Research Center, Quebec City, Québec, Canada
| | - Simone Lemieux
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,School of Nutrition, Laval University, Quebec City, Québec, Canada
| | - Benoît Lamarche
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,School of Nutrition, Laval University, Quebec City, Québec, Canada
| | - Patrick Couture
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada.,Endocrinology and Nephrology Unit, CHU de Quebec Research Center, Quebec City, Québec, Canada
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec City, Québec, Canada, .,School of Nutrition, Laval University, Quebec City, Québec, Canada,
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Young KA, Palmer ND, Fingerlin TE, Langefeld CD, Norris JM, Wang N, Xiang AH, Guo X, Williams AH, Chen YDI, Taylor KD, Rotter JI, Raffel LJ, Goodarzi MO, Watanabe RM, Wagenknecht LE. Genome-Wide Association Study Identifies Loci for Liver Enzyme Concentrations in Mexican Americans: The GUARDIAN Consortium. Obesity (Silver Spring) 2019; 27:1331-1337. [PMID: 31219225 PMCID: PMC6656610 DOI: 10.1002/oby.22527] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 04/18/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Populations of Mexican American ancestry are at an increased risk for nonalcoholic fatty liver disease. The objective of this study was to determine whether loci in known and novel genes were associated with variation in aspartate aminotransferase (AST) (n = 3,644), alanine aminotransferase (ALT) (n = 3,595), and gamma-glutamyl transferase (GGT) (n = 1,577) levels by conducting the first genome-wide association study (GWAS) of liver enzymes, which commonly measure liver function, in individuals of Mexican American ancestry. METHODS Levels of AST, ALT, and GGT were determined by enzymatic colorimetric assays. A multi-cohort GWAS of individuals of Mexican American ancestry was performed. Single-nucleotide polymorphisms (SNP) were tested for association with liver outcomes by multivariable linear regression using an additive genetic model. Association analyses were conducted separately in each cohort, followed by a nonparametric meta-analysis. RESULTS In the PNPLA3 gene, rs4823173 (P = 3.44 × 10-10 ), rs2896019 (P = 7.29 × 10-9 ), and rs2281135 (P = 8.73 × 10-9 ) were significantly associated with AST levels. Although not genome-wide significant, these same SNPs were the top hits for ALT (P = 7.12 × 10-8 , P = 1.98 × 10-7 , and P = 1.81 × 10-7 , respectively). The strong correlation (r2 = 1.0) for these SNPs indicated a single hit in the PNPLA3 gene. No genome-wide significant associations were found for GGT. CONCLUSIONS PNPLA3, a locus previously identified with ALT, AST, and nonalcoholic fatty liver disease in European and Japanese GWAS, is also associated with liver enzymes in populations of Mexican American ancestry.
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Affiliation(s)
- Kendra A Young
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, USA
| | - Nicholette D Palmer
- Department of Biochemistry, School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Tasha E Fingerlin
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, USA
- Center for Genes, Environment, and Health, National Jewish Health, Denver, Colorado, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences, School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, USA
| | - Nan Wang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anny H Xiang
- Research and Evaluation Branch, Kaiser Permanente of Southern California, Pasadena, California, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Adrienne H Williams
- Department of Biostatistical Sciences, School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Leslie J Raffel
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mark O Goodarzi
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA
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Sharma NK, Chuang Key CC, Civelek M, Wabitsch M, Comeau ME, Langefeld CD, Parks JS, Das SK. Genetic Regulation of Enoyl-CoA Hydratase Domain-Containing 3 in Adipose Tissue Determines Insulin Sensitivity in African Americans and Europeans. Diabetes 2019; 68:1508-1522. [PMID: 31010960 PMCID: PMC6609988 DOI: 10.2337/db18-1229] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 04/03/2019] [Indexed: 12/17/2022]
Abstract
Insulin resistance (IR) is a harbinger of type 2 diabetes (T2D) and partly determined by genetic factors. However, genetically regulated mechanisms of IR remain poorly understood. Using gene expression, genotype, and insulin sensitivity data from the African American Genetics of Metabolism and Expression (AAGMEx) cohort, we performed transcript-wide correlation and expression quantitative trait loci (eQTL) analyses to identify IR-correlated cis-regulated transcripts (cis-eGenes) in adipose tissue. These IR-correlated cis-eGenes were tested in the European ancestry individuals in the Metabolic Syndrome in Men (METSIM) cohort for trans-ethnic replication. Comparison of Matsuda index-correlated transcripts in AAGMEx with the METSIM study identified significant correlation of 3,849 transcripts, with concordant direction of effect for 97.5% of the transcripts. cis-eQTL for 587 Matsuda index-correlated genes were identified in both cohorts. Enoyl-CoA hydratase domain-containing 3 (ECHDC3) was the top-ranked Matsuda index-correlated cis-eGene. Expression levels of ECHDC3 were positively correlated with Matsuda index, and regulated by cis-eQTL, rs34844369 being the top cis-eSNP in AAGMEx. Silencing of ECHDC3 in adipocytes significantly reduced insulin-stimulated glucose uptake and Akt Ser473 phosphorylation. RNA sequencing analysis identified 691 differentially expressed genes in ECHDC3-knockdown adipocytes, which were enriched in γ-linolenate biosynthesis, and known IR genes. Thus, our studies elucidated genetic regulatory mechanisms of IR and identified genes and pathways in adipose tissue that are mechanistically involved in IR.
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Affiliation(s)
- Neeraj K Sharma
- Section of Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - Chia-Chi Chuang Key
- Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - Mete Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, VA
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, University Medical Center Ulm, Ulm, Germany
| | - Mary E Comeau
- Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC
| | - Carl D Langefeld
- Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC
| | - John S Parks
- Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
| | - Swapan K Das
- Section of Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC
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Allum F, Hedman ÅK, Shao X, Cheung WA, Vijay J, Guénard F, Kwan T, Simon MM, Ge B, Moura C, Boulier E, Rönnblom L, Bernatsky S, Lathrop M, McCarthy MI, Deloukas P, Tchernof A, Pastinen T, Vohl MC, Grundberg E. Dissecting features of epigenetic variants underlying cardiometabolic risk using full-resolution epigenome profiling in regulatory elements. Nat Commun 2019; 10:1209. [PMID: 30872577 DOI: 10.1038/s41467-019-09184-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 02/25/2019] [Indexed: 12/16/2022] Open
Abstract
Sparse profiling of CpG methylation in blood by microarrays has identified epigenetic links to common diseases. Here we apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (Ntotal~400), providing high-resolution methylation profiling (>1.3 M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits. Obesity and related metabolic complications represent an important health burden. Here the authors carry out a methylC-capture sequencing-based epigenome-wide association study to link circulating plasma lipid levels, CpG methylation and cardiometabolic risk across adipose and blood tissues.
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Mannino GC, Andreozzi F, Sesti G. Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine. Diabetes Metab Res Rev 2019; 35:e3109. [PMID: 30515958 PMCID: PMC6590177 DOI: 10.1002/dmrr.3109] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 12/11/2022]
Abstract
Type 2 diabetes mellitus (T2DM) is a chronic disease that has reached the levels of a global epidemic. In order to achieve optimal glucose control, it is often necessary to rely on combination therapy of multiple drugs or insulin because uncontrolled glucose levels result in T2DM progression and enhanced risk of complications and mortality. Several antihyperglycemic agents have been developed over time, and T2DM pharmacotherapy should be prescribed based on suitability for the individual patient's characteristics. Pharmacogenetics is the branch of genetics that investigates how our genome influences individual responses to drugs, therapeutic outcomes, and incidence of adverse effects. In this review, we evaluated the pharmacogenetic evidences currently available in the literature, and we identified the top informative genetic variants associated with response to the most common anti-diabetic drugs: metformin, DPP-4 inhibitors/GLP1R agonists, thiazolidinediones, and sulfonylureas/meglitinides. Overall, we found 40 polymorphisms for each drug class in a total of 71 loci, and we examined the possibility of encouraging genetic screening of these variants/loci in order to critically implement decision-making about the therapeutic approach through precision medicine strategies. It is possible then to anticipate that when the clinical practice will take advantage of the genetic information of the diabetic patients, this will provide a useful resource for the prevention of T2DM progression, enabling the identification of the precise drug that is most likely to be effective and safe for each patient and the reduction of the economic impact on a global scale.
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Affiliation(s)
- Gaia Chiara Mannino
- Department of Medical and Surgical SciencesUniversity Magna Graecia of CatanzaroCatanzaroItaly
| | - Francesco Andreozzi
- Department of Medical and Surgical SciencesUniversity Magna Graecia of CatanzaroCatanzaroItaly
| | - Giorgio Sesti
- Department of Medical and Surgical SciencesUniversity Magna Graecia of CatanzaroCatanzaroItaly
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Joshi HJ, Hansen L, Narimatsu Y, Freeze HH, Henrissat B, Bennett E, Wandall HH, Clausen H, Schjoldager KT. Glycosyltransferase genes that cause monogenic congenital disorders of glycosylation are distinct from glycosyltransferase genes associated with complex diseases. Glycobiology 2018; 28:284-294. [PMID: 29579191 DOI: 10.1093/glycob/cwy015] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Indexed: 12/12/2022] Open
Abstract
Glycosylation of proteins, lipids and proteoglycans in human cells involves at least 167 identified glycosyltransferases (GTfs), and these orchestrate the biosynthesis of diverse types of glycoconjugates and glycan structures. Mutations in this part of the genome-the GTf-genome-cause more than 58 rare, monogenic congenital disorders of glycosylation (CDGs). They are also statistically associated with a large number of complex phenotypes, diseases or predispositions to complex diseases based on Genome-Wide Association Studies (GWAS). CDGs are extremely rare and often with severe medical consequences. In contrast, GWAS are likely to identify more common genetic variations and generally involve less severe and distinct traits. We recently confirmed that structural defects in GTf genes are extremely rare, which seemed at odds with the large number of GWAS pointing to GTf-genes. To resolve this issue, we surveyed the GTf-genome for reported CDGs and GWAS candidates; we found little overlap between the two groups of genes. Moreover, GTf-genes implicated by CDG or GWAS appear to constitute different classes with respect to their: (i) predicted roles in glycosylation pathways; (ii) potential for partial redundancy by closely homologous genes; and (iii) transcriptional regulation as evaluated by RNAseq data. Our analysis suggest that more complex traits are caused by dysregulation rather than structural deficiency of GTfs, which suggests that some glycosylation reactions may be predicted to be under tight regulation for fine-tuning of important biological functions.
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Affiliation(s)
- Hiren J Joshi
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Lars Hansen
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Yoshiki Narimatsu
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Hudson H Freeze
- Human Genetics Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Bernard Henrissat
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark.,Architecture et Fonction des Macromolécules Biologiques, Centre National de la Recherche Scientifique (CNRS), Aix-Marseille University, F-13288 Marseille, France
| | - Eric Bennett
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Hans H Wandall
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Henrik Clausen
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
| | - Katrine T Schjoldager
- Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
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36
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Huang JV, Cardenas A, Colicino E, Schooling CM, Rifas-Shiman SL, Agha G, Zheng Y, Hou L, Just AC, Litonjua AA, DeMeo DL, Lin X, Oken E, Hivert MF, Baccarelli AA. DNA methylation in blood as a mediator of the association of mid-childhood body mass index with cardio-metabolic risk score in early adolescence. Epigenetics 2018; 13:1072-1087. [PMID: 30412002 PMCID: PMC6342073 DOI: 10.1080/15592294.2018.1543503] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 09/20/2018] [Accepted: 10/22/2018] [Indexed: 12/16/2022] Open
Abstract
Obesity is associated with higher cardio-metabolic risk even in childhood and adolescence; whether this association is mediated by epigenetic mechanisms remains unclear. We examined the extent to which mid-childhood body mass index (BMI) z-score (median age 7.7 years) was associated with cardio-metabolic risk score in early adolescence (median age 12.9 years) via mid-childhood DNA methylation among 265 children in the Project Viva. We measured DNA methylation in leukocytes using the Infinium Human Methylation450K BeadChip. We assessed mediation CpG-by-CpG using epigenome-wide association analyses, high-dimensional mediation analysis, and natural effect models. We observed mediation by mid-childhood DNA methylation at 6 CpGs for the association between mid-childhood BMI z-score and cardio-metabolic risk score in early adolescence in the high-dimensional mediation analysis (accounting for 10% of the total effect) and in the natural effect model (β = 0.04, P = 3.2e-2, accounting for 13% of the total effect). The natural direct effect of BMI z-score on cardio-metabolic risk score was still evident (β = 0.27, P = 1.1e-25). We also observed mediation by mid-childhood DNA methylation at 5 CpGs that was in the opposite direction from the total effect (natural effect model: β = -0.04, P = 2.0e-2). Mediation in different directions implies a complex role of DNA methylation in the association between BMI and cardio-metabolic risk and needs further investigation. Future studies with larger sample size and greater variability in cardio-metabolic risk will further help elucidate the role of DNA methylation for cardio-metabolic risk.
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Affiliation(s)
- Jian V. Huang
- Department of Environmental Health Sciences, Columbia Mailman School of Public Health, NY, NY, USA
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Andres Cardenas
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Elena Colicino
- Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - C. Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Graduate School of Public Health and Health Policy, City University of New York, New York, USA
| | - Sheryl L. Rifas-Shiman
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Golareh Agha
- Department of Environmental Health Sciences, Columbia Mailman School of Public Health, NY, NY, USA
| | - Yinan Zheng
- Center for Population Epigenetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Allan C. Just
- Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Augusto A. Litonjua
- Division of Pediatric Pulmonary Medicine, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Andrea A. Baccarelli
- Department of Environmental Health Sciences, Columbia Mailman School of Public Health, NY, NY, USA
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Abstract
Efficient and accurate protein translation is essential to producing insulin in pancreatic β-cells. Transfer RNA (tRNA) is known as the key component of the protein translational machinery. Interestingly, tRNA contains a wide variety of chemical modifications, which are posttranscriptionally catalysed by tRNA modifying enzymes. Recent advances in genome-sequencing technology have unveiled a number of genetic variations that are associated with the development of type 2 diabetes (T2D). Some of these mutations are located in the genes of tRNA modifying enzymes. Using cellular and animal models, it has been showed that dysregulation of tRNA modification impairs protein translation in pancreatic β-cells and leads to aberrant insulin production. In this review, we discuss the recent findings in the molecular functions of tRNA modifications and their involvement in the development of T2D.
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Affiliation(s)
- Fan-Yan Wei
- Department of Molecular Physiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Kazuhito Tomizawa
- Department of Molecular Physiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Uribe-Salazar JM, Palmer JR, Haddad SA, Rosenberg L, Ruiz-Narváez EA. Admixture mapping and fine-mapping of type 2 diabetes susceptibility loci in African American women. J Hum Genet 2018; 63:1109-17. [PMID: 30135545 DOI: 10.1038/s10038-018-0503-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 07/18/2018] [Accepted: 08/13/2018] [Indexed: 12/12/2022]
Abstract
African American women are disproportionately affected by type 2 diabetes. Genetic factors may explain part of the excess risk. More than 100 genetic variants have been associated with risk of type 2 diabetes, but most studies have been conducted in white populations. Two genome-wide association studies (GWAS) in African Americans have identified three novel genetic variants only. We conducted admixture mapping using 2,918 ancestral informative markers in 2,632 cases of type 2 diabetes and 2,596 controls nested in the ongoing Black Women’s Health Study cohort, with the goal of identifying genomic loci with local African ancestry associated with type 2 diabetes. In addition, we performed replication analysis of 71 previously identified index SNPs, and fine-mapped those genetic loci to identify better or new genetic variants associated with type 2 diabetes in African Americans. We found that individual African ancestry was associated with higher risk of type 2 diabetes. In addition, we identified two genomic regions, 3q26 and 12q23, with excess of African ancestry associated with higher risk of type 2 diabetes. Lastly, we replicated 8 out of 71 index SNPs from previous GWAS, including, for the first time in African Americans, the X-linked rs5945326 SNP near the DUSP9 gene. In addition, our fine-mapping efforts suggest independent signals at five loci. Our detailed analysis identified two genomic regions associated with risk of type 2 diabetes, and showed that many genetic risk variants are shared across ancestries.
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Ramsey LB, Pounds S, Cheng C, Cao X, Yang W, Smith C, Karol SE, Liu C, Panetta JC, Inaba H, Rubnitz JE, Metzger ML, Ribeiro RC, Sandlund JT, Jeha S, Pui CH, Evans WE, Relling MV. Genetics of pleiotropic effects of dexamethasone. Pharmacogenet Genomics 2017; 27:294-302. [PMID: 28628558 DOI: 10.1097/FPC.0000000000000293] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Glucocorticoids such as dexamethasone have pleiotropic effects, including desired antileukemic, anti-inflammatory, or immunosuppressive effects, and undesired metabolic or toxic effects. The most serious adverse effects of dexamethasone among patients with acute lymphoblastic leukemia are osteonecrosis and thrombosis. To identify inherited genomic variation involved in these severe adverse effects, we carried out genome-wide association studies (GWAS) by analyzing 14 pleiotropic glucocorticoid phenotypes in 391 patients with acute lymphoblastic leukemia. PATIENTS AND METHODS We used the Projection Onto the Most Interesting Statistical Evidence integrative analysis technique to identify genetic variants associated with pleiotropic dexamethasone phenotypes, stratifying for age, sex, race, and treatment, and compared the results with conventional single-phenotype GWAS. The phenotypes were osteonecrosis, central nervous system toxicity, hyperglycemia, hypokalemia, thrombosis, dexamethasone exposure, BMI, growth trajectory, and levels of cortisol, albumin, and asparaginase antibodies, and changes in cholesterol, triglycerides, and low-density lipoproteins after dexamethasone. RESULTS The integrative analysis identified more pleiotropic single nucleotide polymorphism variants (P=1.46×10(-215), and these variants were more likely to be in gene-regulatory regions (P=1.22×10(-6)) than traditional single-phenotype GWAS. The integrative analysis yielded genomic variants (rs2243057 and rs6453253) in F2RL1, a receptor that functions in hemostasis, thrombosis, and inflammation, which were associated with pleiotropic effects, including osteonecrosis and thrombosis, and were in regulatory gene regions. CONCLUSION The integrative pleiotropic analysis identified risk variants for osteonecrosis and thrombosis not identified by single-phenotype analysis that may have importance for patients with underlying sensitivity to multiple dexamethasone adverse effects.
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Mercader JM, Florez JC. The Genetic Basis of Type 2 Diabetes in Hispanics and Latin Americans: Challenges and Opportunities. Front Public Health 2017; 5:329. [PMID: 29376044 PMCID: PMC5763127 DOI: 10.3389/fpubh.2017.00329] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 11/22/2017] [Indexed: 12/29/2022] Open
Abstract
Type 2 diabetes (T2D) affects 415 million people worldwide, and has a much higher prevalence in Hispanics (16.9%), compared to non-Hispanic whites (10.2%). Genome-wide association studies and whole-genome and whole-exome sequencing studies have discovered more than 100 genetic regions associated with modified risk for T2D. However, the identified genetic factors explain a very small fraction of the estimated heritability. Until recently, little attention has been put in studying other non European populations that suffer from a higher burden of T2D, such as Hispanics/Latinos. In the past few years, genetic studies in Hispanic populations have started to provide new insights into the genetic architecture of T2D in this ancestry group. Of note, several genetic variants that are absent or very rare in non-Hispanic populations but more common in Hispanics have shown from moderate to strong association with T2D and have provided new insights into the biology of T2D, which may be ultimately useful for developing novel therapeutic strategies applicable to all populations. Studying diverse populations can also improve the ability to find the causal variants in known T2D loci by a multi-ancestry fine-mapping approach, which leverages the different patterns of linkage disequilibrium between the causal and the ascertained genetic variants. In this mini-review, we summarize the main genetic findings discovered in Hispanics and discuss the limitations and challenges of performing genetic studies in these populations. Finally, we present possible next steps to make studies in Latino populations more valuable in providing a deeper understanding of T2D and anticipate their future application to the development of predictive and preventive medicine and personalized therapies.
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Affiliation(s)
- Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, United States.,Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, United States.,Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
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Avilés-Santa ML, Colón-Ramos U, Lindberg NM, Mattei J, Pasquel FJ, Pérez CM. From Sea to Shining Sea and the Great Plains to Patagonia: A Review on Current Knowledge of Diabetes Mellitus in Hispanics/Latinos in the US and Latin America. Front Endocrinol (Lausanne) 2017; 8:298. [PMID: 29176960 PMCID: PMC5687125 DOI: 10.3389/fendo.2017.00298] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/16/2017] [Indexed: 12/13/2022] Open
Abstract
The past two decades have witnessed many advances in the prevention, treatment, and control of diabetes mellitus (DM) and its complications. Increased screening has led to a greater recognition of type 2 diabetes mellitus (type 2 DM) and prediabetes; however, Hispanics/Latinos, the largest minority group in the US, have not fully benefited from these advances. The Hispanic/Latino population is highly diverse in ancestries, birth places, cultures, languages, and socioeconomic backgrounds, and it populates most of the Western Hemisphere. In the US, the prevalence of DM varies among Hispanic/Latino heritage groups, being higher among Mexicans, Puerto Ricans, and Dominicans, and lower among South Americans. The risk and prevalence of diabetes among Hispanics/Latinos are significantly higher than in non-Hispanic Whites, and nearly 40% of Hispanics/Latinos with diabetes have not been formally diagnosed. Despite these striking facts, the representation of Hispanics/Latinos in pharmacological and non-pharmacological clinical trials has been suboptimal, while the prevalence of diabetes in these populations continues to rise. This review will focus on the epidemiology, etiology and prevention of type 2 DM in populations of Latin American origin. We will set the stage by defining the terms Hispanic, Latino, and Latin American, explaining the challenges identifying Hispanics/Latinos in the scientific literature and databases, describing the epidemiology of diabetes-including type 2 DM and gestational diabetes mellitus (GDM)-and cardiovascular risk factors in Hispanics/Latinos in the US and Latin America, and discussing trends, and commonalities and differences across studies and populations, including methodology to ascertain diabetes. We will discuss studies on mechanisms of disease, and research on prevention of type 2 DM in Hispanics/Latinos, including women with GDM, youth and adults; and finalize with a discussion on lessons learned and opportunities to enhance research, and, consequently, clinical care oriented toward preventing type 2 DM in Hispanics/Latinos in the US and Latin America.
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Affiliation(s)
- M. Larissa Avilés-Santa
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD, United States
| | - Uriyoán Colón-Ramos
- Department of Global Health, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
| | - Nangel M. Lindberg
- Kaiser Permanente Center for Health Research, Portland, OR, United States
| | - Josiemer Mattei
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, United States
| | - Francisco J. Pasquel
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Cynthia M. Pérez
- University of Puerto Rico Graduate School of Public Health, San Juan, Puerto Rico
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Abstract
PURPOSE OF REVIEW The purpose of this review was to summarize and reflect on advances over the past decade in human genetic and metabolomic discovery with particular focus on their contributions to type 2 diabetes (T2D) risk prediction. RECENT FINDINGS In the past 10 years, a combination of advances in genotyping efficiency, metabolomic profiling, bioinformatics approaches, and international collaboration have moved T2D genetics and metabolomics from a state of frustration to an abundance of new knowledge. Efforts to control and prevent T2D have failed to stop this global epidemic. New approaches are needed, and although neither genetic nor metabolomic profiling yet have a clear clinical role, the rapid pace of accumulating knowledge offers the possibility for "multi-omic" prediction to improve health.
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Affiliation(s)
- Jordi Merino
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
| | - Miriam S Udler
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02115, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
| | - Aaron Leong
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
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Manosroi W, Tan JW, Rariy CM, Sun B, Goodarzi MO, Saxena AR, Williams JS, Pojoga LH, Lasky-Su J, Cui J, Guo X, Taylor KD, Chen YDI, Xiang AH, Hsueh WA, Raffel LJ, Buchanan TA, Rotter JI, Williams GH, Seely EW. The Association of Estrogen Receptor-β Gene Variation With Salt-Sensitive Blood Pressure. J Clin Endocrinol Metab 2017; 102:4124-4135. [PMID: 28938457 PMCID: PMC5673274 DOI: 10.1210/jc.2017-00957] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 08/29/2017] [Indexed: 11/19/2022]
Abstract
CONTEXT Hypertension in young women is uncommon compared with young men and older women. Estrogen appears to protect most women against hypertension, with incidence increasing after menopause. Because some premenopausal women develop hypertension, estrogen may play a different role in these women. Genetic variations in the estrogen receptor (ER) are associated with cardiovascular disease. ER-β, encoded by ESR2, is the ER predominantly expressed in vascular smooth muscle. OBJECTIVE To determine an association of single nucleotide polymorphisms in ESR2 with salt sensitivity of blood pressure (SSBP) and estrogen status in women. METHODS Candidate gene association study with ESR2 and SSBP conducted in normotensive and hypertensive women and men in two cohorts: International Hypertensive Pathotype (HyperPATH) (n = 584) (discovery) and Mexican American Hypertension-Insulin Resistance Study (n = 662) (validation). Single nucleotide polymorphisms in ESR1 (ER-α) were also analyzed. Analysis conducted in younger (<51 years, premenopausal, "estrogen-replete") and older women (≥51 years, postmenopausal, "estrogen-deplete"). Men were analyzed to control for aging. RESULTS Multivariate analyses of HyperPATH data between variants of ESR2 and SSBP documented that ESR2 rs10144225 minor (risk) allele carriers had a significantly positive association with SSBP driven by estrogen-replete women (β = +4.4 mm Hg per risk allele, P = 0.004). Findings were confirmed in Hypertension Insulin-Resistance Study premenopausal women. HyperPATH cohort analyses revealed risk allele carriers vs noncarriers had increased aldosterone/renin ratios. No associations were detected with ESR1. CONCLUSIONS The variation at rs10144225 in ESR2 was associated with SSBP in premenopausal women (estrogen-replete) and not in men or postmenopausal women (estrogen-deplete). Inappropriate aldosterone levels on a liberal salt diet may mediate the SSBP.
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Affiliation(s)
- Worapaka Manosroi
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Division of Endocrinology and Metabolism, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Jia Wei Tan
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Cell and Molecular Biology Laboratory, Department of Cellular Biology and Pharmacology, Faculty of Medicine and Health Sciences, UCSI University, Cheras 56000, Kuala Lumpur, Malaysia
| | - Chevon M. Rariy
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Bei Sun
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California 90048
| | - Aditi R. Saxena
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jonathan S. Williams
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Luminita H. Pojoga
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jessica Lasky-Su
- Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115
| | - Jinrui Cui
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California 90048
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California 90502
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California 90502
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California 90502
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California 90502
| | - Yii-Der I. Chen
- Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California 90502
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California 90502
| | - Anny H. Xiang
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California 91101
| | - Willa A. Hsueh
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California 91101
- Division of Endocrinology, Diabetes and Metabolism and Diabetes and Metabolism Research Center, The Ohio State University, Columbus, Ohio 43210
| | - Leslie J. Raffel
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, California 92868
| | - Thomas A. Buchanan
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, California 90089
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California 90502
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, California 90502
| | - Gordon H. Williams
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Ellen W. Seely
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115
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Wheeler E, Marenne G, Barroso I. Genetic aetiology of glycaemic traits: approaches and insights. Hum Mol Genet 2017; 26:R172-R184. [PMID: 28977447 PMCID: PMC5886471 DOI: 10.1093/hmg/ddx293] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 07/18/2017] [Accepted: 07/21/2017] [Indexed: 12/17/2022] Open
Abstract
Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.
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Affiliation(s)
- Eleanor Wheeler
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Gaëlle Marenne
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
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Ward-Kavanagh LK, Lin WW, Šedý JR, Ware CF. The TNF Receptor Superfamily in Co-stimulating and Co-inhibitory Responses. Immunity 2017; 44:1005-19. [PMID: 27192566 DOI: 10.1016/j.immuni.2016.04.019] [Citation(s) in RCA: 264] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Indexed: 02/08/2023]
Abstract
Cytokines related to tumor necrosis factor (TNF) provide a communication network essential for coordinating multiple cell types into an effective host defense system against pathogens and malignant cells. The pathways controlled by the TNF superfamily differentiate both innate and adaptive immune cells and modulate stromal cells into microenvironments conducive to host defenses. Members of the TNF receptor superfamily activate diverse cellular functions from the production of type 1 interferons to the modulation of survival of antigen-activated T cells. Here, we focus attention on the subset of TNF superfamily receptors encoded in the immune response locus in chromosomal region 1p36. Recent studies have revealed that these receptors use diverse mechanisms to either co-stimulate or restrict immune responses. Translation of the fundamental mechanisms of TNF superfamily is leading to the design of therapeutics that can alter pathogenic processes in several autoimmune diseases or promote immunity to tumors.
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Affiliation(s)
- Lindsay K Ward-Kavanagh
- Infectious and Inflammatory Diseases Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Wai Wai Lin
- Infectious and Inflammatory Diseases Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - John R Šedý
- Infectious and Inflammatory Diseases Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Carl F Ware
- Infectious and Inflammatory Diseases Center, Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
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Wang L, Norris ET, Jordan IK. Human Retrotransposon Insertion Polymorphisms Are Associated with Health and Disease via Gene Regulatory Phenotypes. Front Microbiol 2017; 8:1418. [PMID: 28824558 PMCID: PMC5539088 DOI: 10.3389/fmicb.2017.01418] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 07/13/2017] [Indexed: 11/18/2022] Open
Abstract
The human genome hosts several active families of transposable elements (TEs), including the Alu, LINE-1, and SVA retrotransposons that are mobilized via reverse transcription of RNA intermediates. We evaluated how insertion polymorphisms generated by human retrotransposon activity may be related to common health and disease phenotypes that have been previously interrogated through genome-wide association studies (GWAS). To address this question, we performed a genome-wide screen for retrotransposon polymorphism disease associations that are linked to TE induced gene regulatory changes. Our screen first identified polymorphic retrotransposon insertions found in linkage disequilibrium (LD) with single nucleotide polymorphisms that were previously associated with common complex diseases by GWAS. We further narrowed this set of candidate disease associated retrotransposon polymorphisms by identifying insertions that are located within tissue-specific enhancer elements. We then performed expression quantitative trait loci analysis on the remaining set of candidates in order to identify polymorphic retrotransposon insertions that are associated with gene expression changes in B-cells of the human immune system. This progressive and stringent screen yielded a list of six retrotransposon insertions as the strongest candidates for TE polymorphisms that lead to disease via enhancer-mediated changes in gene regulation. For example, we found an SVA insertion within a cell-type specific enhancer located in the second intron of the B4GALT1 gene. B4GALT1 encodes a glycosyltransferase that functions in the glycosylation of the Immunoglobulin G (IgG) antibody in such a way as to convert its activity from pro- to anti-inflammatory. The disruption of the B4GALT1 enhancer by the SVA insertion is associated with down-regulation of the gene in B-cells, which would serve to keep the IgG molecule in a pro-inflammatory state. Consistent with this idea, the B4GALT1 enhancer SVA insertion is linked to a genomic region implicated by GWAS in both inflammatory conditions and autoimmune diseases, such as systemic lupus erythematosus and Crohn’s disease. We explore this example and the other cases uncovered by our genome-wide screen in an effort to illuminate how retrotransposon insertion polymorphisms can impact human health and disease by causing changes in gene expression.
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Affiliation(s)
- Lu Wang
- School of Biological Sciences, Georgia Institute of Technology, AtlantaGA, United States.,PanAmerican Bioinformatics InstituteCali, Colombia.,Applied Bioinformatics Laboratory, AtlantaGA, United States
| | - Emily T Norris
- School of Biological Sciences, Georgia Institute of Technology, AtlantaGA, United States.,PanAmerican Bioinformatics InstituteCali, Colombia.,Applied Bioinformatics Laboratory, AtlantaGA, United States
| | - I K Jordan
- School of Biological Sciences, Georgia Institute of Technology, AtlantaGA, United States.,PanAmerican Bioinformatics InstituteCali, Colombia.,Applied Bioinformatics Laboratory, AtlantaGA, United States
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Wood AR, Jonsson A, Jackson AU, Wang N, van Leewen N, Palmer ND, Kobes S, Deelen J, Boquete-Vilarino L, Paananen J, Stančáková A, Boomsma DI, de Geus EJC, Eekhoff EMW, Fritsche A, Kramer M, Nijpels G, Simonis-Bik A, van Haeften TW, Mahajan A, Boehnke M, Bergman RN, Tuomilehto J, Collins FS, Mohlke KL, Banasik K, Groves CJ, McCarthy MI, Pearson ER, Natali A, Mari A, Buchanan TA, Taylor KD, Xiang AH, Gjesing AP, Grarup N, Eiberg H, Pedersen O, Chen YD, Laakso M, Norris JM, Smith U, Wagenknecht LE, Baier L, Bowden DW, Hansen T, Walker M, Watanabe RM, 't Hart LM, Hanson RL, Frayling TM. A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants. Diabetes 2017; 66:2296-2309. [PMID: 28490609 PMCID: PMC5521867 DOI: 10.2337/db16-1452] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 05/02/2017] [Indexed: 01/19/2023]
Abstract
Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose-raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose-raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide-based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.
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Affiliation(s)
- Andrew R Wood
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Anna Jonsson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
| | - Nan Wang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Nienke van Leewen
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Joris Deelen
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Lorena Boquete-Vilarino
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Jussi Paananen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Alena Stančáková
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Elisabeth M W Eekhoff
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Andreas Fritsche
- Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Department of Internal Medicine IV, University of Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich, University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD e.V.), Tübingen, Germany
| | - Mark Kramer
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Giel Nijpels
- EMGO+ Institute for Health and Care Research, VU University Medical Center, Department of General Practice, Amsterdam, the Netherlands
| | - Annemarie Simonis-Bik
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Timon W van Haeften
- Department of Internal Medicine, Utrecht University Medical Center, Utrecht, the Netherlands
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
| | - Richard N Bergman
- Diabetes & Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Jaakko Tuomilehto
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Dasman Diabetes Institute, Dasman, Kuwait
- Department of Clinical Neurosciences and Preventive Medicine, Danube University Krems, Krems, Austria
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Karina Banasik
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, U.K
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, U.K
- Oxford Biomedical Research Centre, National Institute for Health Research, Churchill Hospital, Oxford, U.K
| | | | - Andrea Natali
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, Italy
| | - Thomas A Buchanan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
- Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA
| | - Anny H Xiang
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans Eiberg
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yii-Derr Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO
| | - Ulf Smith
- Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Leslie Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle, U.K.
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Leen M 't Hart
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
- Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Timothy M Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K.
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48
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Abstract
Metabotropic pyrimidine and purine nucleotide receptors (P2Y receptors) are expressed in virtually all cells with implications in very diverse biological functions, including the well-established platelet aggregation (P2Y12), but also immune regulation and inflammation. The classical P2Y receptors bind nucleotides and are encoded by eight genes with limited sequence homology, while phylogenetically related receptors (e.g., P2Y12-like) recognize lipids and peptides, but also nucleotide derivatives. Growing lines of evidence suggest an important function of P2Y receptors in immune cell differentiation and maturation, migration, and cell apoptosis. Here, we give a perspective on the P2Y receptors' molecular structure and physiological importance in immune cells, as well as the related diseases and P2Y-targeting therapies. Extensive research is being undertaken to find modulators of P2Y receptors and uncover their physiological roles. We anticipate the medical applications of P2Y modulators and their immune relevance.
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Affiliation(s)
- Diana Le Duc
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Angela Schulz
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Vera Lede
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Annelie Schulze
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Doreen Thor
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
| | - Antje Brüser
- Rudolf Schönheimer Institute of Biochemistry, Molecular Biochemistry, University of Leipzig, Leipzig, Germany
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49
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Guénard F, Tchernof A, Deshaies Y, Biron S, Lescelleur O, Biertho L, Marceau S, Pérusse L, Vohl MC. Genetic regulation of differentially methylated genes in visceral adipose tissue of severely obese men discordant for the metabolic syndrome. Transl Res 2017; 184:1-11.e2. [PMID: 28219716 DOI: 10.1016/j.trsl.2017.01.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 12/19/2016] [Accepted: 01/24/2017] [Indexed: 01/01/2023]
Abstract
A genetic influence on methylation levels has been reported and methylation quantitative trait loci (meQTL) have been identified in various tissues. The contribution of genetic and epigenetic factors in the development of the metabolic syndrome (MetS) has also been noted. To pinpoint candidate genes for testing the association of SNPs with MetS and its components, we aimed to evaluate the contribution of genetic variations to differentially methylated CpG sites in severely obese men discordant for MetS. A genome-wide differential methylation analysis was conducted in visceral adipose tissue (VAT) of 31 severely obese men discordant for MetS (16 with and 15 without MetS) and identified ∼17,800 variable CpG sites. The genome-wide association study conducted to identify the SNPs (meQTL) associated with methylation levels at variable CpG sites revealed 2292 significant associations (P < 2.22 × 10-11) involving 2182 unique meQTLs regulating the methylation levels of 174 variable CpG sites. Two meQTLs disrupting CpG sites located within the collagen-encoding COL11A2 gene were tested for associations with MetS and its components in a cohort of 3021 obese individuals. Rare alleles of these meQTLs showed association with plasma fasting glucose levels. Further analysis conducted on these meQTL suggested a biological impact mediated through the disruption of transcription factor (TF)-binding sites based on the prediction of TF-binding affinities. The current study identified meQTL in the VAT of severely obese men and revealed associations of two COL11A2 meQTL with fasting glucose levels.
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Affiliation(s)
- Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Québec, Canada; School of Nutrition, Laval University, Québec, Canada
| | - André Tchernof
- School of Nutrition, Laval University, Québec, Canada; Québec Heart and Lung Institute, Québec, Canada
| | - Yves Deshaies
- Québec Heart and Lung Institute, Québec, Canada; Department of Medicine, Laval University, Québec, Canada
| | - Simon Biron
- Department of Surgery, Laval University, Québec, Canada
| | | | | | - Simon Marceau
- Department of Surgery, Laval University, Québec, Canada
| | - Louis Pérusse
- Institute of Nutrition and Functional Foods (INAF), Québec, Canada; Department of Kinesiology, Laval University, Québec, Canada
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Québec, Canada; School of Nutrition, Laval University, Québec, Canada.
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50
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Gao C, Hsu FC, Dimitrov LM, Okut H, Chen YDI, Taylor KD, Rotter JI, Langefeld CD, Bowden DW, Palmer ND. A genome-wide linkage and association analysis of imputed insertions and deletions with cardiometabolic phenotypes in Mexican Americans: The Insulin Resistance Atherosclerosis Family Study. Genet Epidemiol 2017; 41:353-362. [PMID: 28378447 DOI: 10.1002/gepi.22042] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 12/23/2016] [Accepted: 02/04/2017] [Indexed: 11/09/2022]
Abstract
Insertions and deletions (INDELs) represent a significant fraction of interindividual variation in the human genome yet their contribution to phenotypes is poorly understood. To confirm the quality of imputed INDELs and investigate their roles in mediating cardiometabolic phenotypes, genome-wide association and linkage analyses were performed for 15 phenotypes with 1,273,952 imputed INDELs in 1,024 Mexican-origin Americans. Imputation quality was validated using whole exome sequencing with an average kappa of 0.93 in common INDELs (minor allele frequencies [MAFs] ≥ 5%). Association analysis revealed one genome-wide significant association signal for the cholesterylester transfer protein gene (CETP) with high-density lipoprotein levels (rs36229491, P = 3.06 × 10-12 ); linkage analysis identified two peaks with logarithm of the odds (LOD) > 5 (rs60560566, LOD = 5.36 with insulin sensitivity (SI ) and rs5825825, LOD = 5.11 with adiponectin levels). Suggestive overlapping signals between linkage and association were observed: rs59849892 in the WSC domain containing 2 gene (WSCD2) was associated and nominally linked with SI (P = 1.17 × 10-7 , LOD = 1.99). This gene has been implicated in glucose metabolism in human islet cell expression studies. In addition, rs201606363 was linked and nominally associated with low-density lipoprotein (P = 4.73 × 10-4 , LOD = 3.67), apolipoprotein B (P = 1.39 × 10-3 , LOD = 4.64), and total cholesterol (P = 1.35 × 10-2 , LOD = 3.80) levels. rs201606363 is an intronic variant of the UBE2F-SCLY (where UBE2F is ubiquitin-conjugating enzyme E2F and SCLY is selenocysteine lyase) fusion gene that may regulate cholesterol through selenium metabolism. In conclusion, these results confirm the feasibility of imputing INDELs from array-based single nucleotide polymorphism (SNP) genotypes. Analysis of these variants using association and linkage replicated previously identified SNP signals and identified multiple novel INDEL signals. These results support the inclusion of INDELs into genetic studies to more fully interrogate the spectrum of genetic variation.
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Affiliation(s)
- Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Fang-Chi Hsu
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Latchezar M Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Hayrettin Okut
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America.,Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America.,Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America.,Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Carl D Langefeld
- Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Nicholette D Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
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