Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genome-wide association study identifies multiple loci influencing human serum metabolite levels

Abstract

Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10−10) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The heritability estimates and proportion of variance explained for all traits.
Figure 2: Overall summary of basic metabolism, key constituents of the NMR-measurable serum metabolome and associated genetic loci.

Similar content being viewed by others

References

  1. Schlessinger, B.S., Wilson, F.H. Jr. & Milch, L.J. Serum parameters as discriminators between normal and coronary groups. Circulation 19, 265–268 (1959).

    Article  CAS  PubMed  Google Scholar 

  2. Stumvoll, M., Goldstein, B.J. & van Haeften, T.W. Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365, 1333–1346 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, T.J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Dunn, W.B., Broadhurst, D.I., Atherton, H.J., Goodacre, R. & Griffin, J.L. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem. Soc. Rev. 40, 387–426 (2011).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  11. Soeters, P.B. & Fischer, J.E. Insulin, glucagon, aminoacid imbalance, and hepatic encephalopathy. Lancet 2, 880–882 (1976).

    Article  CAS  PubMed  Google Scholar 

  12. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  13. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  14. Surakka, I. et al. Founder population–specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging. Genome Res. 20, 1344–1351 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Natt, E., Kida, K., Odievre, M., Di Rocco, M. & Scherer, G. Point mutations in the tyrosine aminotransferase gene in tyrosinemia type II. Proc. Natl. Acad. Sci. USA 89, 9297–9301 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Orinska, Z. et al. TLR3-induced activation of mast cells modulates CD8+ T-cell recruitment. Blood 106, 978–987 (2005).

    Article  CAS  PubMed  Google Scholar 

  18. Marone, G., Galli, S.J. & Kitamura, Y. Probing the roles of mast cells and basophils in natural and acquired immunity, physiology and disease. Trends Immunol. 23, 425–427 (2002).

    Article  PubMed  Google Scholar 

  19. Cross, L.J., Heaney, L.G. & Ennis, M. Histamine release from human bronchoalveolar lavage mast cells by neurokinin A and bradykinin. Inflamm. Res. 46, 306–309 (1997).

    Article  CAS  PubMed  Google Scholar 

  20. Bouatia-Naji, N. et al. A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science 320, 1085–1088 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Prokopenko, I. et al. Variants in MTNR1B influence fasting glucose levels. Nat. Genet. 41, 77–81 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Shin, Y., Vaziri, N.D., Willekes, N., Kim, C.H. & Joles, J.A. Effects of gender on hepatic HMG-CoA reductase, cholesterol 7α-hydroxylase, and LDL receptor in hereditary analbuminemia. Am. J. Physiol. Endocrinol. Metab. 289, E993–E998 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Rosipal, S., Debreova, M. & Rosipal, R. A speculation about hypercholesterolemia in congenital analbuminemia. Am. J. Med. 119, 181–182 (2006).

    Article  PubMed  Google Scholar 

  24. Koot, B.G., Houwen, R., Pot, D.J. & Nauta, J. Congenital analbuminaemia: biochemical and clinical implications. A case report and literature review. Eur. J. Pediatr. 163, 664–670 (2004).

    PubMed  Google Scholar 

  25. Zhao, M. et al. FcγRIIB inhibits the development of atherosclerosis in low-density lipoprotein receptor–deficient mice. J. Immunol. 184, 2253–2260 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Hernández-Vargas, P. et al. Fcγ receptor deficiency confers protection against atherosclerosis in apolipoprotein E knockout mice. Circ. Res. 99, 1188–1196 (2006).

    Article  PubMed  Google Scholar 

  27. Cohen, P.T. Protein phosphatase 1—targeted in many directions. J. Cell Sci. 115, 241–256 (2002).

    CAS  PubMed  Google Scholar 

  28. Chasman, D.I. et al. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 5, e1000730 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Bergstrom, J.D. & Reitz, R.C. Studies on carnitine palmitoyl transferase: the similar nature of CPTi (inner form) and CPTo (outer form). Arch. Biochem. Biophys. 204, 71–79 (1980).

    Article  CAS  PubMed  Google Scholar 

  31. Willer, C.J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kathiresan, S. et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 40, 189–197 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wallace, C. et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am. J. Hum. Genet. 82, 139–149 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhang, S. et al. Genetic and environmental contributions to phenotypic components of metabolic syndrome: a population-based twin study. Obesity (Silver Spring) 17, 1581–1587 (2009).

    Article  CAS  Google Scholar 

  35. Isaacs, A. et al. Heritabilities, apolipoprotein E, and effects of inbreeding on plasma lipids in a genetically isolated population: the Erasmus Rucphen Family Study. Eur. J. Epidemiol. 22, 99–105 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Mägi, R. & Morris, A.P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Soininen, P. et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134, 1781–1785 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2007).

  42. Inouye, M. et al. An immune response network associated with blood lipid levels. PLoS Genet. 6, e1001113 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ding, J. et al. Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals. Am. J. Hum. Genet. 87, 779–789 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all the Finnish volunteers who participated in the studies. We thank the IT Center for Science and the technology center of the Institute for Molecular Medicine Finland for providing the computational facilities required in this study. The expert technical assistance for statistical analyses provided by A. Vikman, I. Lisinen, V. Aalto and the Genotyping Facilities at the Wellcome Trust Sanger Institute are gratefully acknowledged. The study was supported through funds from The European Community's Seventh Framework Programme (FP7/2007-2013), the BioSHaRE Consortium (261433), the Sigrid Juselius Foundation (251217 to S.R.), the Academy of Finland (137870 to P.S. and 135973 to P.W.), the Responding to Public Health Challenges Research Programme of the Academy of Finland (129269 to M.J.S., 129429 to M.A.-K., 129322 to M.P. and 139635 to V.S.), the Academy of Finland Center of Excellence in Complex Disease Genetics (213506 and 129680 to A.P., J. Kaprio, L.P., K.S. and S.R.), the Finnish Foundation for Cardiovascular Research (to M.J.S., M.A.-K., M.P., S.R. and K.H.P), the Jenny and Antti Wihuri Foundation (to A.J.K.), the Instrumentarium Science Foundation (to T.T. and P.W.), the Finnish Cultural Foundation (to T.T. and T.L.), an Aalto University School of Science and Technology researcher training scholarship (to T.T.) and the Wellcome Trust (098051 to A.P.). The Young Finns Study has been financially supported by the Academy of Finland (126925, 121584, 124282, 129378 (Salve), 117787 (Gendi) and 41071 (Skidi)), the Social Insurance Institution of Finland, the Turku University Foundation, the Yrjö Jahnsson Foundation, the Emil Aaltonen Foundation (to T.L.), the Medical Research Fund of Tampere University Hospital, the Turku University Hospital Medical Fund, the Juho Vainio Foundation, the Finnish Foundation for Cardiovascular Research (to T.L.) and the Tampere Tuberculosis Foundation (to T.L. and M.K.). The Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland (120386, 125876 and 126775 to J.E.), the Finnish Diabetes Research Society, the Novo Nordisk Foundation, the European Science Foundation (EuroSTRESS), the Wellcome Trust (89061/Z/09/Z and 089062/Z/09/Z), the Samfundet Folkhälsan and the Finska Läkaresällskapet. The FINRISK/DILGOM study was supported by the Academy of Finland (118081). Data collection for FinnTwin12 and FinnTwin16 were supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (AA-12502, AA-09203 and AA-08315 to R.J.R. and AA-15416 to D.M.D.) and the Academy of Finland (100499, 205585, 118555 and 141054 (Skidi-Kids) to J. Kaprio). The Finnish Twin cohorts are also supported by the Novo Nordisk Foundation, the Diabetes Research Foundation, Biomedicum Helsinki and Helsinki University Central Hospital grants (all to K.H.P.). NFBC1966 received financial support from the Academy of Finland (104781, 120315, 129269, 1114194, 139900 and SALVE to M.-R.J. and Center of Excellence in Complex Disease Genetics to L.P.), University Hospital Oulu, Biocenter, University of Oulu (75617 to M.-R.J. and M.J.S.), the European Commission EURO-BLCS Framework 5 award (QLG1-CT-2000-01643 to M.-R.J.), the US National Heart, Lung, and Blood Institute (NHLBI) (5R01HL087679), the US National Institute of Mental Health (NIMH) (1RL1MH083268), European Network for Genetic and Genomic Epidemiology (ENGAGE) (HEALTH-F4-2007-201413 to L.P. and M.-R.J.), the MRC UK (G0500539, G0600705 and PrevMetSyn/Salve to M.-R.J.) and the Wellcome Trust (GR069224).

Author information

Authors and Affiliations

Authors

Contributions

Experiments were designed by L.P., M.P., M.A.-K., A.P. and S.R. Statistical analyses were performed by J. Kettunen, T.T., A.O.-A., E.T. and L.-P.L. Materials and/or analysis tools were contributed by J. Kettunen, T.T., A.-P.S., P.S., A.J.K., P.W., K.S., D.M.D., R.J.R., M.J.S., J.V., M.K., T.L., K.H.P., M.I.M., A.J., J.E., O.T.R., V.S., J. Kaprio, M.-R.J., N.B.F., M.A.-K., A.P. and S.R. The manuscript was written by J. Kettunen, T.T., A.J.K., M.I., N.B.F., M.A.-K., A.P. and S.R. All authors reviewed the manuscript.

Corresponding author

Correspondence to Samuli Ripatti.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Table 1–6 and 8, Supplementary Figures 1–4 and Supplementary Note (PDF 3440 kb)

Supplementary Table 7

All metabolite associations P < 2.31×10−10 (XLSX 2480 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kettunen, J., Tukiainen, T., Sarin, AP. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44, 269–276 (2012). https://doi.org/10.1038/ng.1073

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.1073

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research