A genome-wide perspective of genetic variation in human metabolism

Journal name:
Nature Genetics
Volume:
42,
Pages:
137–141
Year published:
DOI:
doi:10.1038/ng.507
Received
Accepted
Published online

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.

At a glance

Figures

  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 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).

    For each SNP, only the metabolic trait with the lowest P value of association is shown; thus, multiple dots indicate that several SNPs support the association at that locus.

  2. A systemic view of genetic variation in human metabolism, as identified in this study.
    Figure 2: A systemic view of genetic variation in human metabolism, as identified in this study.

    Eight of nine replicated genetic polymorphisms (beige) and also four of five suggestive loci (gray) are located in or near genes encoding enzymes that are central to the different processes in human lipid metabolism, including β-oxidation (ACADS, ACADM and ACADL), polyunsaturated fatty acid biosynthesis (FADS1 and ELOVL2), fatty acid synthesis (SCD), breakdown of fats and proteins to energy (ETFDH) and biosynthesis of phospholipids (SPTLC3). Two SNPs are located in or near genes encoding carrier proteins (SLC22A4 and SLC16A9), and two SNPs involve enzymes that are related to amino acid metabolism (PHGDH and CPS1). Only for two genetic variants does the attribution of a metabolic function remain elusive (PLEKHH1 and SYNE2). For each locus, the most strongly associating single metabolite is indicated in red.

References

  1. Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).
  2. Altmaier, E. et al. Bioinformatics analysis of targeted metabolomics—uncovering old and new tales of diabetic mice under medication. Endocrinology 149, 34783489 (2008).
  3. Köttgen, A. et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. 41, 712717 (2009).
  4. 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, 189197 (2008).
  5. Willer, C.J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161169 (2008).
  6. Aulchenko, Y.S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 41, 4755 (2009).
  7. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 5665 (2009).
  8. Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 3546 (2009).
  9. Hindorff, L.A., Junkins, H.A., Mehta, J.P. & Manolio, T.A. A Catalog of Published Genome-Wide Association Studies (Office of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, accessed 14 April 2009). <http://www.genome.gov/26525384>.
  10. Vaxillaire, M. et al. The common P446L polymorphism in GCKR inversely modulates fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective general French population. Diabetes 57, 22532257 (2008).
  11. Prokopenko, I. et al. Variants in MTNR1B influence fasting glucose levels. Nat. Genet. 41, 7781 (2009).
  12. Gross, E. et al. Strong association of a common dihydropyrimidine dehydrogenase gene polymorphism with fluoropyrimidine-related toxicity in cancer patients. PLoS One 3, e4003 (2008).
  13. Chen, Y. et al. Thermolabile phenotype of carnitine palmitoyltransferase II variations as a predisposing factor for influenza-associated encephalopathy. FEBS Lett. 579, 20402044 (2005).
  14. Kido, H., Kinoshita, M., Mizuguchi, H. & Takahashi, N. Method of diagnosing the risk of thermolabile phenotype diseases by using gene. Japanese patent PCT/JP2005/021294 (2007).
  15. 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), S26S30 (2005).
  16. 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, 906913 (2007).
  17. Ramsay, S.L., Stoeggl, W.M., Weinberger, K.M., Graber, A. & Guggenbichler, W. Apparatus and method for analyzing a metabolite profile. US Patent 2007/0004044 (2007).
  18. Weinberger, K.M. Metabolomics in diagnosing metabolic diseases. Ther. Umsch. 65, 487491 (2008).
  19. Weinberger, K.M. & Graber, A. Using comprehensive metabolomics to identify novel biomarkers. Screening Trends in Drug Discovery 6, 4245 (2005).
  20. Wenk, M.R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594610 (2005).
  21. Wang-Sattler, R. et al. Metabolic profiling reveals distinct variations linked to nicotine consumption in humans—first results from the KORA study. PLoS One 3, e3863 (2008).
  22. Andrew, T. et al. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res. 4, 464477 (2001).
  23. Brookes, K.J., Chen, W., Xu, X., Taylor, E. & Asherson, P. Association of fatty acid desaturase genes with attention-deficit/hyperactivity disorder. Biol. Psychiatry 60, 10531061 (2006).
  24. Caspi, A. et al. Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism. Proc. Natl. Acad. Sci. USA 104, 1886018865 (2007).
  25. Tanaka, T. et al. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS Genet. 5, e1000338 (2009).
  26. Gregersen, N. et al. Identification of four new mutations in the short-chain acyl-CoA dehydrogenase (SCAD) gene in two patients: one of the variant alleles, 511C→T, is present at an unexpectedly high frequency in the general population, as was the case for 625G→A, together conferring susceptibility to ethylmalonic aciduria. Hum. Mol. Genet. 7, 619627 (1998).
  27. Nagan, N. et al. The frequency of short-chain acyl-CoA dehydrogenase gene variants in the US population and correlation with the C4-acylcarnitine concentration in newborn blood spots. Mol. Genet. Metab. 78, 239246 (2003).

Download references

Author information

  1. These authors contributed equally to this work.

    • Thomas Illig &
    • Christian Gieger

Affiliations

  1. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

    • Thomas Illig,
    • Christian Gieger,
    • Rui Wang-Sattler &
    • H-Erich Wichmann
  2. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

    • Guangju Zhai,
    • Bernet S Kato,
    • Nicole Soranzo &
    • Tim D Spector
  3. Institute of Bioinformatics and Systems Biology, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

    • Werner Römisch-Margl,
    • Elisabeth Altmaier,
    • Gabi Kastenmüller,
    • Hans-Werner Mewes &
    • Karsten Suhre
  4. Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

    • Cornelia Prehn,
    • Martin Hrabé de Angelis &
    • Jerzy Adamski
  5. Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany.

    • Elisabeth Altmaier &
    • Karsten Suhre
  6. Department of Genome Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany.

    • Hans-Werner Mewes
  7. Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

    • Thomas Meitinger
  8. Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.

    • Thomas Meitinger
  9. Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany.

    • Martin Hrabé de Angelis &
    • Jerzy Adamski
  10. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.

    • Florian Kronenberg
  11. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.

    • Nicole Soranzo
  12. Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Grosshadern, Munich, Germany.

    • H-Erich Wichmann

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:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (1M)

    Supplementary Tables 3–4, Supplementary Figures 1–3 and Supplementary Note

Excel files

  1. Supplementary Table 1 (2M)

    Association data for all SNPs associated with metabolite traits

  2. Supplementary Table 2 (328K)

    Association data for 50 strongest associations with metabolite traits

Additional data