Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Acknowledgements We acknowledge the contributions of P. Lichtner, G. Eckstein, G. Fischer, T. Strom and all other members of the Helmholtz Zentrum München genotyping staff in generating the SNP data set, as well as all members of field staff who were involved in the planning and conduct of the MONICA (Monitoring trends and determinants on cardiovascular diseases) and KORA (Kooperative Gesundheitsforschung in der Region Augsburg) studies. The KORA group consists of H. E. Wichmann (speaker), A. Peters, R. Holle, J. John, C.M., T.I. and their co-workers, who are responsible for the design and conduct of the KORA studies. For TwinsUK, we thank the staff from the genotyping facilities at the Wellcome Trust Sanger Institute for sample preparation, quality control and genotyping. G. Fischer (KORA) and G. Surdulescu (TwinsUK) selected the samples; sample handling and shipment was organized by H. Chavez (KORA) and D. Hodgkiss (TwinsUK); and U. Goebel (Helmholtz) provided administrative support. Special thanks go to D. Garcia-West for his role in facilitating this study. We are grateful to the CARDIoGRAM investigators for access to their data set. Finally, we thank all study participants of the KORA and the TwinsUK studies for donating their blood and time. The KORA research platform and the MONICA studies were initiated and financed by the Helmholtz Zentrum München, National Research Center for Environmental Health, funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria. This study was supported by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). Part of this work was financed by the German National Genome Research Network (NGFNPlus: 01GS0823). Computing resources were made available by the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (HLRB project h1231) and the DEISA Extreme Computing Initiative (project MeMGenA). Part of this research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. The TwinsUK study was funded by the Wellcome Trust; the European Community’s Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F2-2008-201865-GEFOS and (FP7/2007-2013); and the FP-5 GenomEUtwin Project (QLG2-CT-2002-01254). The study also receives support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London. T.D.S. is an NIHR Senior Investigator. The project also received support from a Biotechnology and Biological Sciences Research Council (BBSRC) project grant (G20234). Both studies received support from ENGAGE project grant agreement HEALTH-F4-2007-201413. N.J.S. holds a British Heart Foundation Chair, is an NIHR Senior Investigator and is supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease. The authors acknowledge the funding and support of the National Eye Institute via an NIH/CIDR genotyping project (PI: T. Young). Genotyping was also performed by CIDR as part of an NEI/NIH project grant. D.M. received support from the Early Career Researcher Scheme at Oxford Brookes University. J.R. is supported by DFG Graduiertenkolleg ‘GRK 1563, Regulation and Evolution of Cellular Systems’ (RECESS); E.A., by BMBF grant 0315494A (project SysMBo); W.R.-M., by BMBF grant 03IS2061B (project Gani_Med); and B.W., by Era-Net grant 0315442A (project PathoGenoMics). A.K. is supported by the Emmy Noether Programme of the German Research Foundation (DFG grant KO-3598/2-1) and F.K., by grants from the ‘Genomics of Lipid-associated Disorders (GOLD)’ of the Austrian Genome Research Programme (GEN-AU). N.S. is supported by the Wellcome Trust (core grant number 091746/Z/10/Z).
This file contains Supplementary Table 2a, which contains the KORA.best.ratios data set and Supplementary Table 2b, which contains the TwinsUK.best.ratios data set. These file were replace on 12 September 2011 as the previous versions seen online had corrupted.