Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $18.75 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.
For TwinsUK, we thank the Genotyping Facilities at the Wellcome Trust Sanger Institute and the Center for Inherited Disease Research (CIDR)/US National Institutes of Health (NIH) for SNP genotyping. The KORA Study Group consists of A. Peters (speaker), J. Heinrich, R. Holle, R. Leidl, C. Meisinger, K. Strauch and their coworkers, who are responsible for the design and implementation of the KORA studies. For KORA, we thank P. Lichtner, G. Eckstein, G. Fischer, T. Strom, the Helmholtz Zentrum München genotyping staff and the field staff of the MONICA/KORA Augsburg studies. We also thank G. Fischer (KORA) and G. Surdulescu (TwinsUK) for sample handling and H. Chavez (KORA) and D. Hodgkiss (TwinsUK) for sample shipment. We are grateful to the MuTHER investigators for the transcriptomic data. Finally, we wish to express our appreciation to all study participants of the TwinsUK and KORA studies for donating their blood and time.
Part of this work was funded by Pfizer Worldwide Research and Development. For TwinsUK, the study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and King's College London. T.D.S. is the holder of a European Research Council (ERC) Advanced Principal Investigator award. SNP genotyping was performed by the Wellcome Trust Sanger Institute and the National Eye Institute via NIH/CIDR. The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform and the MONICA Augsburg studies were initiated and financed by the Helmholtz Zentrum München National Research Center for Environmental Health, which is 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). The German National Genome Research Network financed part of this work (NGFNPlus 01GS0823). Computing resources have been made available by the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (HLRB project h1231) and by the DEISA Extreme Computing Initiative (project PHAGEDA). Part of this research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. S.-Y.S. is supported by a Post-Doctoral Research Fellowship from the Oak Foundation. F.J.T. is supported by an ERC starting grant (LatentCauses). J.K. is supported by the German Research Foundation (SPP 1395, InKoMBio) and by a grant from the German Helmholtz Postdoctoral Programme. K.S. is supported by Biomedical Research Program funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. C.G. is supported by the European Union's Seventh Framework project MIMOmics (FP7-Health-F5-2012-305280) and by the Russian Foundation for Basic Research (RFBR)-Helmholtz research group program. N.S. is supported by the Wellcome Trust (grants WT098051 and WT091310) and by the European Commission (EUFP7 EPIGENESYS grant 257082 and BLUEPRINT grant HEALTH-F5-2011-282510). J.B.R. and V.F. are supported by the Canadian Institutes of Health Research, Fonds du Recherche du Science Québec and the Québec Consortium for Drug Discovery.
The Pfizer colleagues dedicate this manuscript to the memory of our friend Phoebe Roberts, whose passion for text mining, molecular biology and drug discovery contributed to the identification of causal genes in this research and to our work in general.
Integrated supplementary information
Supplementary Tables 1–14