Metabomatching

Metabomatching is a method to identify compounds from spectral features that jointly associate with genotypes.

The method has been developed in the context of our publication "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" that was accepted for publication in PLoS Genetics on 10.2.2013.

This page will provide additional information on Metabomatching and its software implementation. For inquiries please contact [user:Sven].

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

• Authors

Rico Rueedi1,2,♣, Mirko Ledda3,♣, Andrew W. Nicholls4, Reza M. Salek5,6, Pedro Marques-Vidal7, Edgard Morya8,9, Koichi Sameshima10, Ivan Montoliu11, Laeticia Da Silva11, Sebastiano Collino11, François-Pierre Martin11, Serge Rezzi11, Christoph Steinbeck5, Dawn M. Waterworth12, Gérard Waeber13, Peter Vollenweider13, Jacques S. Beckmann1,2,14, Johannes Le Coutre3,15, Vincent Mooser16, Sven Bergmann1,2,♠,*, Ulrich K. Genick3,♠, and Zoltán Kutalik1,2,7,♠ 1 Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland 2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland 3 Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland 4 Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Park Road, Ware, Herts SG12 0DP, UK 5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom 6 Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2 1GA, UK 7 Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), 1010 Lausanne, Switzerland, and University of Lausanne 8 Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio Libanes Hospital, São Paulo, Brazil 9 Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil 10 Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil 11 Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland. 12 Medical Genetics, GlaxoSmithKline, Philadelphia, PA, USA 13 Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland 14 Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland 15 Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan 16 Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland ♣,♠ These authors contributed equally to this work. Correspondence to be addressed to: Sven Bergmann, email: sven.bergmann@unil.ch

• 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 1H-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. 56 of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to 11 gene-metabolite associations, 6 of which had been previously identified in the urine metabolome and 3 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.

• Author summary

The concentrations of small molecules, known as metabolites, are subject to tight regulation in all organisms. Collectively, the metabolite concentrations make up the metabolome, which differs amongst individuals as a function of their environment and genetic makeup. In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants that correlate with any of the measured metabolome features in a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates the expert or computational identification of the metabolite whose concentration is most likely affected by the genetic variant at hand. Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.