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Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Author

Listed:
  • Harmen H. M. Draisma

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research
    Neuroscience Campus Amsterdam)

  • René Pool

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research
    BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4))

  • Michael Kobl

    (Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health)

  • Rick Jansen

    (VU University Medical Center, Neuroscience Campus Amsterdam, VUmc)

  • Ann-Kristin Petersen

    (Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health)

  • Anika A. M. Vaarhorst

    (BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4)
    Leiden University Medical Center
    Netherlands Consortium for Healthy Aging, Leiden University Medical Center)

  • Idil Yet

    (King's College London)

  • Toomas Haller

    (Estonian Genome Center, University of Tartu)

  • Ayşe Demirkan

    (Leiden University Medical Center
    Genetic Epidemiology Unit, Erasmus Medical Center)

  • Tõnu Esko

    (Estonian Genome Center, University of Tartu
    Boston Children's Hospital
    Medical and Population Genetics Program, Broad Institute of MIT and Harvard
    Harvard Medical School)

  • Gu Zhu

    (QIMR Berghofer Medical Research Institute)

  • Stefan Böhringer

    (Leiden University Medical Center)

  • Marian Beekman

    (Leiden University Medical Center)

  • Jan Bert van Klinken

    (Leiden University Medical Center)

  • Werner Römisch-Margl

    (Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München)

  • Cornelia Prehn

    (Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München)

  • Jerzy Adamski

    (Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München
    German Center for Diabetes Research at Helmholtz Zentrum München
    Lehrstuhl für Experimentelle Genetik, Technische Universität München)

  • Anton J. M. de Craen

    (Leiden University Medical Center)

  • Elisabeth M. van Leeuwen

    (Genetic Epidemiology Unit, Erasmus Medical Center)

  • Najaf Amin

    (Genetic Epidemiology Unit, Erasmus Medical Center)

  • Harish Dharuri

    (Leiden University Medical Center)

  • Harm-Jan Westra

    (CB50, University Medical Center Groningen, University of Groningen)

  • Lude Franke

    (CB50, University Medical Center Groningen, University of Groningen)

  • Eco J. C. de Geus

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research)

  • Jouke Jan Hottenga

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research)

  • Gonneke Willemsen

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research)

  • Anjali K. Henders

    (QIMR Berghofer Medical Research Institute)

  • Grant W. Montgomery

    (QIMR Berghofer Medical Research Institute)

  • Dale R. Nyholt

    (QIMR Berghofer Medical Research Institute
    Statistical and Genomic Epidemiology, Institute of Health and Biomedical Innovation, Queensland University of Technology)

  • John B. Whitfield

    (QIMR Berghofer Medical Research Institute)

  • Brenda W. Penninx

    (The EMGO+ Institute for Health and Care Research
    VU University Medical Center)

  • Tim D. Spector

    (King's College London)

  • Andres Metspalu

    (Estonian Genome Center, University of Tartu)

  • P. Eline Slagboom

    (BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4)
    Leiden University Medical Center)

  • Ko Willems van Dijk

    (Leiden University Medical Center
    Leiden University Medical Center)

  • Peter A. C. ‘t Hoen

    (Leiden University Medical Center)

  • Konstantin Strauch

    (Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
    Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität)

  • Nicholas G. Martin

    (QIMR Berghofer Medical Research Institute)

  • Gert-Jan B. van Ommen

    (Leiden University Medical Center)

  • Thomas Illig

    (Research Unit of Molecular Epidemiology, Helmholtz Zentrum München
    Hannover Unified Biobank, Hannover Medical School
    Institute for Human Genetics, Hannover Medical School)

  • Jordana T. Bell

    (King's College London)

  • Massimo Mangino

    (King's College London)

  • Karsten Suhre

    (Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München
    Faculty of Biology, Ludwig-Maximilians-Universität
    Weill Cornell Medical College in Qatar (WCMC-Q))

  • Mark I. McCarthy

    (Wellcome Trust Centre for Human Genetics, University of Oxford
    Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford
    Oxford National Institute for Health Research Biomedical Research Centre, The Joint Research Office, Block 60, Churchill Hospital)

  • Christian Gieger

    (Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
    Research Unit of Molecular Epidemiology, Helmholtz Zentrum München
    Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health)

  • Aaron Isaacs

    (Genetic Epidemiology Unit, Erasmus Medical Center)

  • Cornelia M. van Duijn

    (BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4)
    Netherlands Consortium for Healthy Aging, Leiden University Medical Center
    Genetic Epidemiology Unit, Erasmus Medical Center)

  • Dorret I. Boomsma

    (VU University Amsterdam
    The EMGO+ Institute for Health and Care Research
    BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4))

Abstract

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P

Suggested Citation

  • Harmen H. M. Draisma & René Pool & Michael Kobl & Rick Jansen & Ann-Kristin Petersen & Anika A. M. Vaarhorst & Idil Yet & Toomas Haller & Ayşe Demirkan & Tõnu Esko & Gu Zhu & Stefan Böhringer & Marian, 2015. "Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8208
    DOI: 10.1038/ncomms8208
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