Skip to main content

Mapping Metabolomic Quantitative Trait Loci (mQTL): A Link Between Metabolome-Wide Association Studies and Systems Biology

  • Chapter
  • First Online:
Genetics Meets Metabolomics

Abstract

The comprehensive understanding of metabolism in health and disease has reached a new level with the introduction of Metabolome-Wide Association Studies. Quantitative Trait Locus mapping of metabolic phenotypes (mQTL) is a powerful approach to unravel the genetic component associated with metabolic profiles, and typically identifies genes associated with metabolic markers of disease. In this chapter we describe the various stages of the mQTL mapping strategy, which typically involves a genetically heterogeneous cohort, modern genotyping platforms, hypothesis-free metabolic profiling using high-throughput nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS), generating up to 20,000 metabolic traits per sample and the statistical tools required to map these traits onto the genome of this experimental population. Finally we describe network and systems biology strategies enhancing the biological interpretation of haplotype – metabotype association networks derived from mQTL studies for a better understanding of pathophysiological mechanisms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dumas ME, Maibaum EC, Teague C et al (2006) Assessment of analytical reproducibility of 1 H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 78(7):2199–2208

    Article  PubMed  CAS  Google Scholar 

  2. Holmes E, Loo RL, Stamler J et al (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453(7193):396–400

    Article  PubMed  CAS  Google Scholar 

  3. Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17(7):388–391

    Article  PubMed  CAS  Google Scholar 

  4. Dixon AL, Liang L, Moffatt MF et al (2007) A genome-wide association study of global gene expression. Nat Genet 39(10):1202–1207

    Article  PubMed  CAS  Google Scholar 

  5. Schadt EE, Monks SA, Drake TA et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422(6929):297–302

    Article  PubMed  CAS  Google Scholar 

  6. Klose J, Nock C, Herrmann M et al (2002) Genetic analysis of the mouse brain proteome. Nat Genet 30(4):385–393

    Article  PubMed  CAS  Google Scholar 

  7. Schauer N, Semel Y, Roessner U et al (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat Biotechnol 24(4):447–454

    Article  PubMed  CAS  Google Scholar 

  8. Keurentjes JJ, Fu J, de Vos CH et al (2006) The genetics of plant metabolism. Nat Genet 38(7):842–849

    Article  PubMed  CAS  Google Scholar 

  9. Dumas ME, Wilder SP, Bihoreau MT et al (2007) Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet 39(5):666–672

    Article  PubMed  CAS  Google Scholar 

  10. Gieger C, Geistlinger L, Altmaier E et al (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4(11):e1000282

    Article  PubMed  Google Scholar 

  11. Illig T, Gieger C, Zhai G et al (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42(2):137–141

    Article  PubMed  CAS  Google Scholar 

  12. Suhre K, Wallaschofski H, Raffler J et al (2011) A genome-wide association study of metabolic traits in human urine. Nat Genet 43:565–569

    Article  PubMed  CAS  Google Scholar 

  13. Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19(7):889–890

    Article  PubMed  CAS  Google Scholar 

  14. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575

    Article  PubMed  CAS  Google Scholar 

  15. Rapp JP, Wang SM, Dene H (1989) A genetic polymorphism in the renin gene of Dahl rats cosegregates with blood pressure. Science 243(4890):542–544

    Article  PubMed  CAS  Google Scholar 

  16. Gauguier D, Froguel P, Parent V et al (1996) Chromosomal mapping of genetic loci associated with non-insulin dependent diabetes in the GK rat. Nat Genet 12(1):38–43

    Article  PubMed  CAS  Google Scholar 

  17. Johannesson M, Lopez-Aumatell R, Stridh P et al (2009) A resource for the simultaneous high-resolution mapping of multiple quantitative trait loci in rats: the NIH heterogeneous stock. Genome Res 19(1):150–158

    Article  PubMed  CAS  Google Scholar 

  18. Valdar W, Solberg LC, Gauguier D et al (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38(8):879–887

    Article  PubMed  CAS  Google Scholar 

  19. Collins SC, Wallis RH, Wilder SP et al (2006) Mapping diabetes QTL in an intercross derived from a congenic strain of the Brown Norway and Goto-Kakizaki rats. Mamm Genome 17(6):538–547

    Article  PubMed  Google Scholar 

  20. Beckonert O, Coen M, Keun HC et al (2010) High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc 5(6):1019–1032

    Article  PubMed  CAS  Google Scholar 

  21. Beckonert O, Keun HC, Ebbels TM et al (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2(11):2692–2703

    Article  PubMed  CAS  Google Scholar 

  22. Want EJ, Wilson ID, Gika H et al (2010) Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 5(6):1005–1018

    Article  PubMed  CAS  Google Scholar 

  23. Blaise BJ, Giacomotto J, Elena B et al (2007) Metabotyping of Caenorhabditis elegans reveals latent phenotypes. Proc Natl Acad Sci USA 104(50):19808–19812

    Article  PubMed  CAS  Google Scholar 

  24. Blaise BJ, Giacomotto J, Triba MN et al (2009) Metabolic profiling strategy of Caenorhabditis elegans by whole-organism nuclear magnetic resonance. J Proteome Res 8(5):2542–2550

    Article  PubMed  CAS  Google Scholar 

  25. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787

    Article  PubMed  CAS  Google Scholar 

  26. Cloarec O, Dumas ME, Trygg J et al (2005) Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1 H NMR spectroscopic metabonomic studies. Anal Chem 77(2):517–526

    Article  PubMed  CAS  Google Scholar 

  27. Veselkov KA, Lindon JC, Ebbels TM et al (2009) Recursive segment-wise peak alignment of biological (1)h NMR spectra for improved metabolic biomarker recovery. Anal Chem 81(1):56–66

    Article  PubMed  CAS  Google Scholar 

  28. Blaise BJ, Shintu L, Elena B, Emsley L, Dumas ME, Toulhoat P (2009) Statistical recoupling prior to significance testing in nuclear magnetic resonance based metabonomics. Anal Chem 81(15):6242–6251

    Article  PubMed  CAS  Google Scholar 

  29. Dumas ME, Debrauwer L, Beyet L et al (2002) Analyzing the physiological signature of anabolic steroids in cattle urine using pyrolysis/metastable atom bombardment mass spectrometry and pattern recognition. Anal Chem 74(20):5393–5404

    Article  PubMed  CAS  Google Scholar 

  30. Fonville JM, Richards SE, Barton RH et al (2010) The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J Chemom 24(11–12):636–649

    Article  CAS  Google Scholar 

  31. Cloarec O, Dumas ME, Craig A et al (2005) Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1 H NMR data sets. Anal Chem 77(5):1282–1289

    Article  PubMed  CAS  Google Scholar 

  32. Dumas ME, Canlet C, Debrauwer L, Martin P, Paris A (2005) Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis. J Proteome Res 4(5):1485–1492

    Article  PubMed  CAS  Google Scholar 

  33. Crockford DJ, Holmes E, Lindon JC et al (2006) Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal Chem 78(2):363–371

    Article  PubMed  CAS  Google Scholar 

  34. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM (2006) Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 78(13):4430–4442

    Article  PubMed  CAS  Google Scholar 

  35. Dumas ME, Canlet C, Andre F, Vercauteren J, Paris A (2002) Metabonomic assessment of physiological disruptions using 1 H-13C HMBC-NMR spectroscopy combined with pattern recognition procedures performed on filtered variables. Anal Chem 74(10):2261–2273

    Article  PubMed  CAS  Google Scholar 

  36. Gauguier D, Samani N (2002) Approaches to the analysis of complex quantitative phenotypes and marker map construction based on the analysis of rat models of hypertension. Methods Mol Biol 195:225–251

    PubMed  CAS  Google Scholar 

  37. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69(4):315–324

    Article  PubMed  CAS  Google Scholar 

  38. Barton NH, Keightley PD (2002) Understanding quantitative genetic variation. Nat Rev Genet 3(1):11–21

    Article  PubMed  CAS  Google Scholar 

  39. Doerge RW (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3(1):43–52

    Article  PubMed  CAS  Google Scholar 

  40. Mackay TF, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10(8):565–577

    Article  PubMed  CAS  Google Scholar 

  41. Doerge RW, Churchill GA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142(1):285–294

    PubMed  CAS  Google Scholar 

  42. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113

    Article  PubMed  CAS  Google Scholar 

  43. Fell DA, Wagner A (2000) The small world of metabolism. Nat Biotechnol 18(11):1121–1122

    Article  PubMed  CAS  Google Scholar 

  44. Beyer A, Bandyopadhyay S, Ideker T (2007) Integrating physical and genetic maps: from genomes to interaction networks. Nat Rev Genet 8(9):699–710

    Article  PubMed  CAS  Google Scholar 

  45. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  PubMed  CAS  Google Scholar 

  46. Pontoizeau C, Fearnside JF, Navratil V et al (2011) Broad-ranging natural metabotype variation drives physiological plasticity in healthy control inbred rat strains. J Proteome Res 10(4):1675–1689

    Article  PubMed  CAS  Google Scholar 

  47. Xia J, Wishart DS (2010) MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 38:W71–W77

    Article  PubMed  CAS  Google Scholar 

  48. Chagoyen M, Pazos F (2011) MBRole: enrichment analysis of metabolomic data. Bioinformatics 27(5):730–731

    Article  PubMed  CAS  Google Scholar 

  49. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545–15550

    Article  PubMed  CAS  Google Scholar 

  50. Arita M (2004) The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci USA 101(6):1543–1547

    Article  PubMed  CAS  Google Scholar 

  51. Blaise BJ, Navratil V, Domange C et al (2010) Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy. J Proteome Res 9(9):4513–4520

    Article  PubMed  CAS  Google Scholar 

  52. Uetz P, Giot L, Cagney G et al (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–627

    Article  PubMed  CAS  Google Scholar 

  53. Davidovic L, Navratil V, Bonaccorso CM et al (2011) A metabolomic and systems biology perspective on the brain of the fragile X syndrome mouse model. Genome Res 21:2190–2202

    Google Scholar 

  54. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  55. Dumas ME, Barton RH, Toye A et al (2006) Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc Natl Acad Sci USA 103(33):12511–12516

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge support from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N° HEALTH-F4-2010-241504 (EURATRANS), the ANR (mQTL ANR-08-GENO-030-02) and the Fondation pour la Recherche Médicale. Marc-Emmanuel Dumas holds a Young Investigator Award from Agence Nationale de la Recherche (ANR-07-JCJC-0042-01) Dominique Gauguier holds a Wellcome Trust Senior Fellowship in basic biomedical science (057733).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marc-Emmanuel Dumas Ph.D., M.Eng., M.Sc., B.Eng., B.Sc. .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Dumas, ME., Gauguier, D. (2012). Mapping Metabolomic Quantitative Trait Loci (mQTL): A Link Between Metabolome-Wide Association Studies and Systems Biology. In: Suhre, K. (eds) Genetics Meets Metabolomics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1689-0_14

Download citation

Publish with us

Policies and ethics