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Nat Commun. 2015 Jun 12;6:7208. doi: 10.1038/ncomms8208.

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

Author information

1
Department of Biological Psychology, VU University Amsterdam, van der Boechorststraat 1, Amsterdam 1081 BT, The Netherlands.
2
The EMGO+ Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, Amsterdam 1081 BT, The Netherlands.
3
Neuroscience Campus Amsterdam, De Boelelaan 1085, Amsterdam 1081 HV, The Netherlands.
4
BBMRINL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), S4-P, Postbus 9600, Leiden 2300 RC, The Netherlands.
5
Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.
6
Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, VUmc, A.J. Ernststraat 1187, Amsterdam 1081 HL, The Netherlands.
7
Department of Molecular Epidemiology, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands.
8
Netherlands Consortium for Healthy Aging, Leiden University Medical Center, Leiden, The Netherlands.
9
Department of Twin Research and Genetic Epidemiology, King's College London, Westminster Bridge Road, London SE1 7EH, UK.
10
Estonian Genome Center, University of Tartu, 23b Riia Street, Tartu 51010, Estonia.
11
Department of Human Genetics, Leiden University Medical Center, S4-P, PO Box 9600, Leiden 2300 RC, The Netherlands.
12
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, P.O. Box 2040, Rotterdam 3000 CA, The Netherlands.
13
Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, 300 Longwood Ave, Boston MA02115, Massachusetts, USA.
14
Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 2142, USA.
15
Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, NRB 0330, Boston MA 02115, Massachusetts, USA.
16
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane 4006, Australia.
17
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands.
18
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.
19
Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Ingolstädter Landstraße1, Neuherberg 85764, Germany.
20
German Center for Diabetes Research at Helmholtz Zentrum München, Ingolstädter Landstr. 1, Neuherberg 85764, Germany.
21
Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan 85350, Germany.
22
Department of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, Leiden 2300 RC, The Netherlands.
23
Department of Genetics, CB50, UniversityMedical Center Groningen, University of Groningen, P.O. Box 30001, Groningen 9700 RB, The Netherlands.
24
Statistical and Genomic Epidemiology, Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Ave, Kelvin Grove QLD 4059, Queensland, Australia.
25
Department of Psychiatry, VU University Medical Center, A.J. Ernststraat 1187, Amsterdam 1081 HL, The Netherlands.
26
Department of Endocrinology, Leiden University Medical Center, S4-P, PO Box 9600, Leiden 2300 RC, The Netherlands.
27
Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich 81377, Germany.
28
Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.
29
Hannover Unified Biobank, Hannover Medical School, Hannover 30625, Germany.
30
Institute for Human Genetics, Hannover Medical School, Carl-Neuberg-Strasse 1, Hannover 30625, Germany.
31
Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried 82152, Germany.
32
Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), PO Box 24144, Education City-Qatar Foundation, Doha, Qatar.
33
Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
34
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK.
35
Oxford National Institute for Health Research Biomedical Research Centre, The Joint Research Office, Block 60, Churchill Hospital, Old Road, Headington OX3 7LE, UK.
36
Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.
#
Contributed equally

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 < 1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N = 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

PMID:
26068415
PMCID:
PMC4745136
DOI:
10.1038/ncomms8208
[Indexed for MEDLINE]
Free PMC Article

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