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A genome-wide perspective of genetic variation in human metabolism

Abstract

Serum metabolite concentrations provide a direct readout of biological processes in the human body, and they are associated with disorders such as cardiovascular and metabolic diseases. We present a genome-wide association study (GWAS) of 163 metabolic traits measured in human blood from 1,809 participants from the KORA population, with replication in 422 participants of the TwinsUK cohort. For eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH and SLC16A9), the genetic variant is located in or near genes encoding enzymes or solute carriers whose functions match the associating metabolic traits. In our study, the use of metabolite concentration ratios as proxies for enzymatic reaction rates reduced the variance and yielded robust statistical associations with P values ranging from 3 × 10−24 to 6.5 × 10−179. These loci explained 5.6%–36.3% of the observed variance in metabolite concentrations. For several loci, associations with clinically relevant parameters have been reported previously.

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Figure 1: Manhattan plot of the strength of association with metabolite concentrations (above, data points with P < 10−7 are plotted in red) and concentration ratios (below, data points with P < 10−9 are plotted in red), based on association with 1,029 samples (step 1 of discovery stage).
Figure 2: A systemic view of genetic variation in human metabolism, as identified in this study.

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Acknowledgements

The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform and the MONICA (Monitoring trends and determinants on cardiovascular diseases) 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. Part of this work was financed by the German National Genome Research Network (NGFNPlus: 01GS0823) and by grants from the 'Genomics of Lipid-associated Disorders–GOLD' of the 'Austrian Genome Research Programme GEN-AU'. Computing resources have been 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 PHAGEDA). 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, European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F2-2008-201865-GEFOS and (FP7/2007-2013), ENGAGE project grant agreement HEALTH-F4-2007-201413 and the FP-5 GenomEUtwin Project (QLG2-CT-2002-01254). The study also received support from the Department of Health via the UK 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.). The project also received support from a UK Biotechnology and Biological Sciences Research Council (BBSRC) project grant (G20234). We acknowledge the funding and support of the US National Eye Institute (NEI) via a US National Institutes of Health (NIH) and Center for Inherited Disease Research (CIDR) genotyping project (principal investigator T. Young). 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, T. Halex and A. Sabunchi to the metabolomics measurements, and of all members of the field staffs who were involved in the planning and conduct of the MONICA and KORA Augsburg studies. The KORA group consists of H.-E.W. (speaker), A. Peters, C. Meisinger, T.I., R. Holle, J. John and their co-workers who are responsible for the design and conduct of the KORA studies. For the TwinsUK study, we thank the staff from the Genotyping Facilities at the Wellcome Trust Sanger Institute for sample preparation, quality control and genotyping led by L. Peltonen and P. Deloukas, Le Centre National de Génotypage (France), led by M. Lathrop, for genotyping, Duke University, North Carolina, USA, led by D. Goldstein, for genotyping and the Finnish Institute of Molecular Medicine, Finnish Genome Center, University of Helsinki, led by A. Palotie. Genotyping was also performed by the CIDR as part of an NEI and NIH project grant. Finally, we thank all participants of the KORA and the TwinsUK studies.

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Authors and Affiliations

Authors

Contributions

T.I., T.D.S., J.A. and K.S. designed the experiment; T.I., C.G., T.M. and H.-E.W. contributed genetics data and analysis from the KORA study; G.Z., B.S.K., N.S. and T.D.S. contributed genetics data and analysis from the TwinsUK study; W.R.-M., R.W.-S., C.P., G.K., H.-W.M., M.H.d.A., T.D.S., J.A. and K.S. contributed to the metabolomics experiments; C.G., G.Z., E.A. and K.S. analyzed the data; C.G., F.K., N.S. and K.S. wrote the manuscript; all authors contributed their critical reviews of the manuscript during its preparation.

Corresponding author

Correspondence to Karsten Suhre.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 3–4, Supplementary Figures 1–3 and Supplementary Note (PDF 1128 kb)

Supplementary Table 1

Association data for all SNPs associated with metabolite traits (XLS 2377 kb)

Supplementary Table 2

Association data for 50 strongest associations with metabolite traits (XLS 327 kb)

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Illig, T., Gieger, C., Zhai, G. et al. A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42, 137–141 (2010). https://doi.org/10.1038/ng.507

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