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Human metabolic individuality in biomedical and pharmaceutical research


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.

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Figure 1: Genetic basis of human metabolic individuality and its overlap with loci of biomedical and pharmaceutical interest.
Figure 2: Experimental evidence for SLC16A9 (MCT9) as a carnitine efflux transporter.


  1. 1

    Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)

    ADS  CAS  Article  Google Scholar 

  2. 2

    Newgard, C. B. & Attie, A. D. Getting biological about the genetics of diabetes. Nature Med. 16, 388–391 (2010)

    CAS  Article  Google Scholar 

  3. 3

    Illig, T. et al. A genome-wide perspective of genetic variation in human metabolism. Nature Genet. 42, 137–141 (2010)

    CAS  Article  Google Scholar 

  4. 4

    Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008)

    Article  Google Scholar 

  5. 5

    Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009)

    CAS  Article  Google Scholar 

  6. 6

    Ohta, T. et al. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol. Pathol. 37, 521–535 (2009)

    CAS  Article  Google Scholar 

  7. 7

    Suhre, K. et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 5, e13953 (2010)

    ADS  Article  Google Scholar 

  8. 8

    Altmaier, E. et al. Bioinformatics analysis of targeted metabolomics—uncovering old and new tales of diabetic mice under medication. Endocrinology 149, 3478–3489 (2008)

    CAS  Article  Google Scholar 

  9. 9

    Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009)

    Article  Google Scholar 

  10. 10

    Chambers, J. C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nature Genet. 42, 373–375 (2010)

    CAS  Article  Google Scholar 

  11. 11

    Köttgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nature Genet. 42, 376–384 (2010)

    Article  Google Scholar 

  12. 12

    Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genet. 42, 105–116 (2010)

    CAS  Article  Google Scholar 

  13. 13

    Aulchenko, Y. S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nature Genet. 41, 47–55 (2009)

    CAS  Article  Google Scholar 

  14. 14

    Panneerselvam, K. & Freeze, H. H. Mannose enters mammalian cells using a specific transporter that is insensitive to glucose. J. Biol. Chem. 271, 9417–9421 (1996)

    CAS  Article  Google Scholar 

  15. 15

    Taguchi, T. et al. Hepatic glycogen breakdown is implicated in the maintenance of plasma mannose concentration. Am. J. Physiol. Endocrinol. Metab. 288, E534–E540 (2005)

    CAS  Article  Google Scholar 

  16. 16

    Blombaeck, B., Blombaeck, M., Edman, P. & Hessel, B. Amino-acid sequence and the occurrence of phosphorus in human fibrinopeptides. Nature 193, 833–834 (1962)

    ADS  CAS  Article  Google Scholar 

  17. 17

    Martin, S. C., Ekman, P., Forsberg, P. O. & Ersmark, H. Increased phosphate content of fibrinogen in vivo correlates with alteration in fibrinogen behaviour. Thromb. Res. 68, 467–473 (1992)

    CAS  Article  Google Scholar 

  18. 18

    Yuan, X. et al. Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes. Am. J. Hum. Genet. 83, 520–528 (2008)

    CAS  Article  Google Scholar 

  19. 19

    Tregouet, D. A. et al. Common susceptibility alleles are unlikely to contribute as strongly as the FV and ABO loci to VTE risk: results from a GWAS approach. Blood 113, 5298–5303 (2009)

    CAS  Article  Google Scholar 

  20. 20

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010)

    ADS  CAS  Article  Google Scholar 

  21. 21

    Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nature Genet. 43, 333–338 (2011)

    CAS  Article  Google Scholar 

  22. 22

    Döring, A. et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nature Genet. 40, 430–436 (2008)

    Article  Google Scholar 

  23. 23

    Caulfield, M. J. et al. SLC2A9 is a high-capacity urate transporter in humans. PLoS Med. 5, e197 (2008)

    Article  Google Scholar 

  24. 24

    Klein, T. E. et al. Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenomics J. 1, 167–170 (2001)

    CAS  Article  Google Scholar 

  25. 25

    Deeken, J. F. et al. A pharmacogenetic study of docetaxel and thalidomide in patients with castration-resistant prostate cancer using the DMET genotyping platform. Pharmacogenomics J. 10, 191–199 (2010)

    CAS  Article  Google Scholar 

  26. 26

    Lankisch, T. O. et al. Gilbert’s Syndrome and irinotecan toxicity: combination with UDP-glucuronosyltransferase 1A7 variants increases risk. Cancer Epidemiol. Biomarkers Prev. 17, 695–701 (2008)

    CAS  Article  Google Scholar 

  27. 27

    Huang, R. S. et al. A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proc. Natl Acad. Sci. USA 104, 9758–9763 (2007)

    ADS  CAS  Article  Google Scholar 

  28. 28

    Chen, Y. et al. Effect of genetic variation in the organic cation transporter 2 on the renal elimination of metformin. Pharmacogenet. Genomics 19, 497–504 (2009)

    Article  Google Scholar 

  29. 29

    Shu, Y. et al. Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics. Clin. Pharmacol. Ther. 83, 273–280 (2008)

    CAS  Article  Google Scholar 

  30. 30

    The SEARCH Collaborative Group SLCO1B1 variants and statin-induced myopathy—a genomewide study. N. Engl. J. Med. 359, 789–799 (2008)

    Article  Google Scholar 

  31. 31

    Davies, N. J. et al. AKR1C isoforms represent a novel cellular target for jasmonates alongside their mitochondrial-mediated effects. Cancer Res. 69, 4769–4775 (2009)

    CAS  Article  Google Scholar 

  32. 32

    Sanna, S. et al. Common variants in the SLCO1B3 locus are associated with bilirubin levels and unconjugated hyperbilirubinemia. Hum. Mol. Genet. 18, 2711–2718 (2009)

    CAS  Article  Google Scholar 

  33. 33

    Johnson, A. D. et al. Genome-wide association meta-analysis for total serum bilirubin levels. Hum. Mol. Genet. 18, 2700–2710 (2009)

    CAS  Article  Google Scholar 

  34. 34

    Kolz, M. et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet. 5, e1000504 (2009)

    Article  Google Scholar 

  35. 35

    Zhai, G. et al. Eight common genetic variants associated with serum DHEAS levels suggest a key role in ageing mechanisms. PLoS Genet. 7, e1002025 (2011)

    CAS  Article  Google Scholar 

  36. 36

    Mootha, V. K. & Hirschhorn, J. N. Inborn variation in metabolism. Nature Genet. 42, 97–98 (2010)

    CAS  Article  Google Scholar 

  37. 37

    Meredith, D. & Christian, H. C. The SLC16 monocaboxylate transporter family. Xenobiotica 38, 1072–1106 (2008)

    CAS  Article  Google Scholar 

  38. 38

    Koepsell, H. & Endou, H. The SLC22 drug transporter family. Pflugers Arch. 447, 666–676 (2004)

    CAS  Article  Google Scholar 

  39. 39

    Wichmann, H. E., Gieger, C. & Illig, T. KORA-gen—resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67 (Suppl 1). 26–30 (2005)

    Article  Google Scholar 

  40. 40

    Andrew, T. et al. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res. 4, 464–477 (2001)

    CAS  Article  Google Scholar 

  41. 41

    Lawton, K. A. et al. Analysis of the adult human plasma metabolome. Pharmacogenomics 9, 383–397 (2008)

    CAS  Article  Google Scholar 

  42. 42

    Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009)

    ADS  CAS  Article  Google Scholar 

  43. 43

    Soranzo, N. et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nature Genet. 41, 1182–1190 (2009)

    CAS  Article  Google Scholar 

  44. 44

    Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009)

    Article  Google Scholar 

  45. 45

    Richards, J. B. et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371, 1505–1512 (2008)

    CAS  Article  Google Scholar 

  46. 46

    Soranzo, N. et al. Meta-analysis of genome-wide scans for human adult stature identifies novel loci and associations with measures of skeletal frame size. PLoS Genet. 5, e1000445 (2009)

    Article  Google Scholar 

  47. 47

    Teo, Y. Y. et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23, 2741–2746 (2007)

    CAS  Article  Google Scholar 

  48. 48

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)

    CAS  Article  Google Scholar 

  49. 49

    Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nature Genet. 30, 97–101 (2002)

    CAS  Article  Google Scholar 

  50. 50

    Meredith, D. Site-directed mutation of arginine 282 to glutamate uncouples the movement of peptides and protons by the rabbit proton-peptide cotransporter PepT1. J. Biol. Chem. 279, 15795–15798 (2004)

    CAS  Article  Google Scholar 

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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).

Author information





Designed the study: J.A., C.G., T.I., D.M., N.S. and K.S. Conducted the experiments: D.M., M.V.M. and R.P.M. Analysed the data: J.A., E.A., C.G., G.K., A.K., F.K., C.M., D.M., A.-K.P., C.P., J.R., J.S.R., W.R.-M., S.-Y.S., K.S. and B.W. Provided material, data or analysis tools: the CARDIoGRAM consortium, P.D., J.E., E.G., C.J.H., M.H.d.A., T.I., M.M., T.M., H.-W.M., N.J.S., K.S.S., T.D.S., H.-E.W. and G.Z. Wrote the paper: C.G., N.S. and K.S. All authors read the paper and contributed to its final form.

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Correspondence to Karsten Suhre or Nicole Soranzo.

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Competing interests

M.V.M. and R.P.M. are employees of Metabolon Inc.

Additional information

A list of authors and their affiliations appears in Supplementary Information.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-8 (see separate files for Supplementary Tables 2A and 2B), Supplementary References, a listing of the CARDIoGRAM consortium and funding and Supplementary Figures 1-4. (PDF 4723 kb)

Supplementary Data

This file contains Supplementary Table 2a, which contains the data set and Supplementary Table 2b, which contains the data set. These file were replace on 12 September 2011 as the previous versions seen online had corrupted. (ZIP 47844 kb)

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Suhre, K., Shin, SY., Petersen, AK. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).

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