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PLoS Genet. 2014 Feb 20;10(2):e1004132. doi: 10.1371/journal.pgen.1004132. eCollection 2014 Feb.

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

Author information

1
Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
2
Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland.
3
Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Ware, Herts, United Kingdom.
4
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom ; Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom.
5
Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Lausanne, Switzerland.
6
Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio, Libanes Hospital, São Paulo, Brazil ; Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil.
7
Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.
8
Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland.
9
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
10
Medical Genetics, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America.
11
Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
12
Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland ; Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
13
Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland ; Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan.
14
Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
15
Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland ; Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Lausanne, Switzerland.

Abstract

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.

PMID:
24586186
PMCID:
PMC3930510
DOI:
10.1371/journal.pgen.1004132
[Indexed for MEDLINE]
Free PMC Article

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