PLoS Genetics (Feb 2014)

Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links.

  • Rico Rueedi,
  • Mirko Ledda,
  • Andrew W Nicholls,
  • Reza M Salek,
  • Pedro Marques-Vidal,
  • Edgard Morya,
  • Koichi Sameshima,
  • Ivan Montoliu,
  • Laeticia Da Silva,
  • Sebastiano Collino,
  • François-Pierre Martin,
  • Serge Rezzi,
  • Christoph Steinbeck,
  • Dawn M Waterworth,
  • Gérard Waeber,
  • Peter Vollenweider,
  • Jacques S Beckmann,
  • Johannes Le Coutre,
  • Vincent Mooser,
  • Sven Bergmann,
  • Ulrich K Genick,
  • Zoltán Kutalik

DOI
https://doi.org/10.1371/journal.pgen.1004132
Journal volume & issue
Vol. 10, no. 2
p. e1004132

Abstract

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Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on (1)H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10(-8)) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10(-44)) and lysine (rs8101881, P = 1.2×10(-33)), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.