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Kim Y, Shim SC. Wolves Trapped in the NETs–The Pathogenesis of Lupus Nephritis. JOURNAL OF RHEUMATIC DISEASES 2018. [DOI: 10.4078/jrd.2018.25.2.81] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Young Kim
- Division of Internal Medicine, Daejeon Veterans Hospital, Daejeon, Korea
| | - Seung Cheol Shim
- Division of Rheumatology, Department of Internal Medicine, Daejeon Rheumatoid and Degenerative Arthritis Center, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Korea
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Aleksandrova K, Mozaffarian D, Pischon T. Addressing the Perfect Storm: Biomarkers in Obesity and Pathophysiology of Cardiometabolic Risk. Clin Chem 2018; 64:142-153. [DOI: 10.1373/clinchem.2017.275172] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/25/2017] [Indexed: 02/06/2023]
Abstract
AbstractBACKGROUNDThe worldwide rise of obesity has provoked intensified research to better understand its pathophysiology as a means for disease prevention. Several biomarkers that may reflect various pathophysiological pathways that link obesity and cardiometabolic diseases have been identified over the past decades.CONTENTWe summarize research evidence regarding the role of established and novel obesity-related biomarkers, focusing on recent epidemiological evidence for detrimental associations with cardiometabolic diseases including obesity-related cancer. The reviewed biomarkers include biomarkers of glucose–insulin homeostasis (insulin, insulin-like growth factors, and C-peptide), adipose tissue biomarkers (adiponectin, omentin, apelin, leptin, resistin, and fatty-acid-binding protein-4), inflammatory biomarkers (C-reactive protein, interleukin 6, tumor necrosis factor α), and omics-based biomarkers (metabolites and microRNAs).SUMMARYAlthough the evidence for many classical obesity biomarkers, including adiponectin and C-reactive protein (CRP), in disease etiology has been initially promising, the evidence for a causal role in humans remains limited. Further, there has been little demonstrated ability to improve disease prediction beyond classical risk factors. In the era of “precision medicine,” there is an increasing interest in novel biomarkers, and the extended list of potentially promising biomarkers, such as adipokines, cytokines, metabolites, and microRNAs, implicated in obesity may bring new promise for improved, personalized prevention. To further evaluate the role of obesity-related biomarkers as etiological and early-disease-prediction targets, well-designed studies are needed to evaluate temporal associations, replicate findings, and test clinical utility of novel biomarkers. In particular, studies to determine the therapeutic implications of novel biomarkers beyond established metabolic risk factors are highly warranted.
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Affiliation(s)
- Krasimira Aleksandrova
- Nutrition, Immunity and Metabolism Start-up Lab, Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | | | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Berlin, Germany
- MDC/BIH Biobank, Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Berlin Institute of Health (BIH), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
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From Discovery to Translation: Characterization of C-Mannosyltryptophan and Pseudouridine as Markers of Kidney Function. Sci Rep 2017; 7:17400. [PMID: 29234020 PMCID: PMC5727198 DOI: 10.1038/s41598-017-17107-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/21/2017] [Indexed: 01/15/2023] Open
Abstract
Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 µmol/L and 3.39/4.30 µmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 µmol/L and 39.7/33.9 µmol/mmol creatinine. Median (25th, 75th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.
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Rueedi R, Mallol R, Raffler J, Lamparter D, Friedrich N, Vollenweider P, Waeber G, Kastenmüller G, Kutalik Z, Bergmann S. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput Biol 2017; 13:e1005839. [PMID: 29194434 PMCID: PMC5711027 DOI: 10.1371/journal.pcbi.1005839] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 10/23/2017] [Indexed: 01/06/2023] Open
Abstract
A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
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Affiliation(s)
- Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Roger Mallol
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - David Lamparter
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site, Greifswald, Germany
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gérard Waeber
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- * E-mail:
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Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies. Am J Epidemiol 2017; 186:1084-1096. [PMID: 29106475 PMCID: PMC5860146 DOI: 10.1093/aje/kwx016] [Citation(s) in RCA: 305] [Impact Index Per Article: 43.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 01/19/2017] [Indexed: 12/13/2022] Open
Abstract
Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
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Affiliation(s)
- Peter Würtz
- Correspondence to Dr. Peter Würtz, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: ); or Dr. Mika Ala-Korpela, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: )
| | | | | | | | | | - Mika Ala-Korpela
- Correspondence to Dr. Peter Würtz, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: ); or Dr. Mika Ala-Korpela, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: )
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McMahon GM, Hwang SJ, Clish CB, Tin A, Yang Q, Larson MG, Rhee EP, Li M, Levy D, O'Donnell CJ, Coresh J, Young JH, Gerszten RE, Fox CS. Urinary metabolites along with common and rare genetic variations are associated with incident chronic kidney disease. Kidney Int 2017; 91:1426-1435. [DOI: 10.1016/j.kint.2017.01.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 12/04/2016] [Accepted: 01/05/2017] [Indexed: 10/20/2022]
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Abstract
SLE is a chronic inflammatory disease that affects the kidneys in about 50% of patients. Lupus nephritis is a major risk factor for overall morbidity and mortality in SLE, and despite potent anti-inflammatory and immunosuppressive therapies still ends in CKD or ESRD for too many patients. This review highlights recent updates in our understanding of disease epidemiology, genetics, pathogenesis, and treatment in an effort to establish a framework for lupus nephritis management that is patient-specific and oriented toward maintaining long-term kidney function in patients with lupus.
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Affiliation(s)
- Salem Almaani
- Department of Internal Medicine, The Ohio State Wexner Medical Center, Columbus, Ohio
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Brennan L. Use of metabotyping for optimal nutrition. Curr Opin Biotechnol 2017; 44:35-38. [PMID: 27835796 DOI: 10.1016/j.copbio.2016.10.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 10/22/2016] [Indexed: 11/25/2022]
Abstract
In recent years there has been general agreement that dietary advice needs to be tailored to the individual and that we need to move from a one size fits all approach. Evidence has emerged that personalising dietary advice results in improved dietary behaviours. Concomitant with this there has been an increase in the application of developing technologies such as metabolomics to nutrition studies. The concept of the metabotype has emerged and set to play a key role in the development and delivery or personalised nutrition. The term metabotype refers to a group of individuals with similar metabolic profiles. This review gives an overview of the potential role of this approach in delivering optimal nutrition advice.
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Affiliation(s)
- Lorraine Brennan
- UCD School of Agriculture and Food Science, UCD Institute of Food and Health, Belfield, UCD, Dublin 4, Ireland; Institute of Health and Society, Human Nutrition Research Centre, Newcastle University, Newcastle upon Tyne, UK.
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59
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Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Segura Lepe MP, O’Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Mägi R, Stanton A, Connell J, Bakker SJL, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JMM, Tobin MD, Munroe PB, Ehret GB, Wain LV, Barnes MR, Tzoulaki I, Caulfield MJ, Elliott P. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 2017; 49:403-415. [PMID: 28135244 PMCID: PMC5972004 DOI: 10.1038/ng.3768] [Citation(s) in RCA: 382] [Impact Index Per Article: 54.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 12/14/2016] [Indexed: 11/21/2022]
Abstract
Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure-raising genetic variants on future cardiovascular disease risk.
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Affiliation(s)
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Claudia P Cabrera
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - He Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Meixia Ren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Borbala Mifsud
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Praveen Surendran
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Chunyu Liu
- Population Sciences Branch, National Heart Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Boston University School of Public Health, Boston, MA, USA
- National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis MO, USA
| | - Fotios Drenos
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
- Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, Rayne Building, University College London, London, WC1E 6JF, UK
| | - Marie Loh
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan Marten
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Marcelo P Segura Lepe
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- Bayer Pharma AG, Berlin, Germany
| | - Paul F O’Reilly
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joanne Knight
- Data Science Institute, Lancester University, Lancaster, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - M Abdullah Said
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - David Porteous
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Maris Alver
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Neil Poulter
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ron T Gansevoort
- Department of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Alice Stanton
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - John Connell
- Ninewells Hospital & Medical School, University of Dundee, Dundee, UK
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Denis C Shields
- School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Simon Thom
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Morris Brown
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Tõnu Esko
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, USA
- Centre for Non-Communicable Diseases, Karachi, Pakistan
- Department of Public Health and Primary Care, University of Cambridge, UK
| | - Rajiv Chowdhury
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Aravinda Chakravarti
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher Newton-Cheh
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Cecilia M Lindgren
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- The Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7BN, UK
| | - Daniel Levy
- Population Sciences Branch, National Heart Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Jaspal S Kooner
- Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK
- National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Division of Medicine, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Division of Medicine, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Leicester, UK
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Joanna M M Howson
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Georg B Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Cardiology, Department of Medicine, Geneva University Hospital, Geneva, Switzerland
| | - Louise V Wain
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Michael R Barnes
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Mark J Caulfield
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
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Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun 2017; 8:14357. [PMID: 28240269 PMCID: PMC5333359 DOI: 10.1038/ncomms14357] [Citation(s) in RCA: 357] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 12/16/2016] [Indexed: 12/29/2022] Open
Abstract
Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications. Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.
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61
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Scerri TS, Quaglieri A, Cai C, Zernant J, Matsunami N, Baird L, Scheppke L, Bonelli R, Yannuzzi LA, Friedlander M, Egan CA, Fruttiger M, Leppert M, Allikmets R, Bahlo M. Genome-wide analyses identify common variants associated with macular telangiectasia type 2. Nat Genet 2017; 49:559-567. [DOI: 10.1038/ng.3799] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 01/31/2017] [Indexed: 02/07/2023]
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Zych K, Snoek BL, Elvin M, Rodriguez M, Van der Velde KJ, Arends D, Westra HJ, Swertz MA, Poulin G, Kammenga JE, Breitling R, Jansen RC, Li Y. reGenotyper: Detecting mislabeled samples in genetic data. PLoS One 2017; 12:e0171324. [PMID: 28192439 PMCID: PMC5305221 DOI: 10.1371/journal.pone.0171324] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 01/19/2017] [Indexed: 12/11/2022] Open
Abstract
In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis.
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Affiliation(s)
- Konrad Zych
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
| | - Basten L. Snoek
- Laboratory of Nematology, Wageningen University, Wageningen, The Netherlands
| | - Mark Elvin
- Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom
| | - Miriam Rodriguez
- Laboratory of Nematology, Wageningen University, Wageningen, The Netherlands
| | - K. Joeri Van der Velde
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Danny Arends
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Morris A. Swertz
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Gino Poulin
- Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom
| | - Jan E. Kammenga
- Laboratory of Nematology, Wageningen University, Wageningen, The Netherlands
| | - Rainer Breitling
- Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom
| | - Ritsert C. Jansen
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
| | - Yang Li
- Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
- * E-mail:
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63
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Lee S, Wen H, An YJ, Cha JW, Ko YJ, Hyberts SG, Park S. Carbon Isotopomer Analysis with Non-Unifom Sampling HSQC NMR for Cell Extract and Live Cell Metabolomics Studies. Anal Chem 2016; 89:1078-1085. [DOI: 10.1021/acs.analchem.6b02107] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Sujin Lee
- Natural
Product Research Institute, College of Pharmacy, Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
| | - He Wen
- Natural
Product Research Institute, College of Pharmacy, Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
- Department
of Biochemistry and Molecular Biology, School of Medicine, Shenzhen University, Shenzhen 518060, China
| | - Yong Jin An
- Natural
Product Research Institute, College of Pharmacy, Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
| | - Jin Wook Cha
- Natural
Product Research Institute, College of Pharmacy, Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
- Natural
Constituents Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea
| | - Yoon-Joo Ko
- National
Center for Inter-University Research Facilities (NCIRF), Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
| | - Sven G. Hyberts
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Sunghyouk Park
- Natural
Product Research Institute, College of Pharmacy, Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742, Korea
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Yu B, de Vries PS, Metcalf GA, Wang Z, Feofanova EV, Liu X, Muzny DM, Wagenknecht LE, Gibbs RA, Morrison AC, Boerwinkle E. Whole genome sequence analysis of serum amino acid levels. Genome Biol 2016; 17:237. [PMID: 27884205 PMCID: PMC5123402 DOI: 10.1186/s13059-016-1106-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Accepted: 11/10/2016] [Indexed: 02/08/2023] Open
Abstract
Background Blood levels of amino acids are important biomarkers of disease and are influenced by synthesis, protein degradation, and gene–environment interactions. Whole genome sequence analysis of amino acid levels may establish a paradigm for analyzing quantitative risk factors. Results In a discovery cohort of 1872 African Americans and a replication cohort of 1552 European Americans we sequenced exons and whole genomes and measured serum levels of 70 amino acids. Rare and low-frequency variants (minor allele frequency ≤5%) were analyzed by three types of aggregating motifs defined by gene exons, regulatory regions, or genome-wide sliding windows. Common variants (minor allele frequency >5%) were analyzed individually. Over all four analysis strategies, 14 gene–amino acid associations were identified and replicated. The 14 loci accounted for an average of 1.8% of the variance in amino acid levels, which ranged from 0.4 to 9.7%. Among the identified locus–amino acid pairs, four are novel and six have been reported to underlie known Mendelian conditions. These results suggest that there may be substantial genetic effects on amino acid levels in the general population that may underlie inborn errors of metabolism. We also identify a predicted promoter variant in AGA (the gene that encodes aspartylglucosaminidase) that is significantly associated with asparagine levels, with an effect that is independent of any observed coding variants. Conclusions These data provide insights into genetic influences on circulating amino acid levels by integrating -omic technologies in a multi-ethnic population. The results also help establish a paradigm for whole genome sequence analysis of quantitative traits. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1106-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bing Yu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ginger A Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Zhe Wang
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Elena V Feofanova
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoming Liu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Donna Marie Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA. .,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
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65
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1H nuclear magnetic resonance (NMR)-based serum metabolomics of human gallbladder inflammation. Inflamm Res 2016; 66:97-105. [DOI: 10.1007/s00011-016-0998-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 10/05/2016] [Accepted: 10/13/2016] [Indexed: 12/13/2022] Open
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Fitó M, Melander O, Martínez JA, Toledo E, Carpéné C, Corella D. Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention. Int J Mol Sci 2016; 17:E1469. [PMID: 27598147 PMCID: PMC5037747 DOI: 10.3390/ijms17091469] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 08/08/2016] [Accepted: 08/26/2016] [Indexed: 12/17/2022] Open
Abstract
Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.
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Affiliation(s)
- Montserrat Fitó
- Cardiovascular Risk and Nutrition Research (REGICOR Group), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
| | - Olle Melander
- Department of Clinical Sciences, Lund University, 22100 Lund, Sweden.
- Department of Internal Medicine, Skåne University Hospital, 22241 Lund, Sweden.
| | - José Alfredo Martínez
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Nutrition and Food Sciences, University of Navarra, 31009 Pamplona, Spain.
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, University of Navarra, 31009 Pamplona, Spain.
| | - Christian Carpéné
- INSERM U1048, Institute of Metabolic and Cardiovascular Diseases (I2MC), Rangueil Hospital, 31442 Toulouse, France.
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, University of Valencia, 46010 Valencia, Spain.
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Molloy A, Pangilinan F, Mills J, Shane B, O’Neill M, McGaughey D, Velkova A, Abaan H, Ueland P, McNulty H, Ward M, Strain J, Cunningham C, Casey M, Cropp C, Kim Y, Bailey-Wilson J, Wilson A, Brody L. A Common Polymorphism in HIBCH Influences Methylmalonic Acid Concentrations in Blood Independently of Cobalamin. Am J Hum Genet 2016; 98:869-882. [PMID: 27132595 DOI: 10.1016/j.ajhg.2016.03.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 03/08/2016] [Indexed: 12/20/2022] Open
Abstract
Methylmalonic acid (MMA) is a by-product of propionic acid metabolism through the vitamin B12 (cobalamin)-dependent enzyme methylmalonyl CoA mutase. Elevated MMA concentrations are a hallmark of several inborn errors of metabolism and indicators of cobalamin deficiency in older persons. In a genome-wide analysis of 2,210 healthy young Irish adults (median age 22 years) we identified a strong association of plasma MMA with SNPs in 3-hydroxyisobutyryl-CoA hydrolase (HIBCH, p = 8.42 × 10(-89)) and acyl-CoA synthetase family member 3 (ACSF3, p = 3.48 × 10(-19)). These loci accounted for 12% of the variance in MMA concentration. The most strongly associated SNP (HIBCH rs291466; c:2T>C) causes a missense change of the initiator methionine codon (minor-allele frequency = 0.43) to threonine. Surprisingly, the resulting variant, p.Met1?, is associated with increased expression of HIBCH mRNA and encoded protein. These homozygotes had, on average, 46% higher MMA concentrations than methionine-encoding homozygotes in young adults with generally low MMA concentrations (0.17 [0.14-0.21] μmol/L; median [25(th)-75(th) quartile]). The association between MMA levels and HIBCH rs291466 was highly significant in a replication cohort of 1,481 older individuals (median age 79 years) with elevated plasma MMA concentrations (0.34 [0.24-0.51] μmol/L; p = 4.0 × 10(-26)). In a longitudinal study of 185 pregnant women and their newborns, the association of this SNP remained significant across the gestational trimesters and in newborns. HIBCH is unique to valine catabolism. Studies evaluating flux through the valine catabolic pathway in humans should account for these variants. Furthermore, this SNP could help resolve equivocal clinical tests where plasma MMA values have been used to diagnose cobalamin deficiency.
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Simonds NI, Ghazarian AA, Pimentel CB, Schully SD, Ellison GL, Gillanders EM, Mechanic LE. Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know? Genet Epidemiol 2016; 40:356-65. [PMID: 27061572 DOI: 10.1002/gepi.21967] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 01/07/2016] [Accepted: 02/11/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature. METHODS To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed. RESULTS A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest. CONCLUSION Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings.
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Affiliation(s)
- Naoko I Simonds
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Armen A Ghazarian
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Camilla B Pimentel
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Sheri D Schully
- Office of Disease Prevention, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Gary L Ellison
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Elizabeth M Gillanders
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Leah E Mechanic
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, United States of America
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