Metabomatching
Metabomatching is a procedure to identify compounds from spectral features that jointly associate with genotypes.
- Software [Not available yet (2016/10/24)]
The simplest method to run metabomatching is to use the metabomatching docker image. With a working docker installation (get docker), run
docker pull metabomatching/metabomatching:pre01
to download the metabomatching container. The download may take some time, as the container includes a full Linux OS, octave to run metabomatching, and inkscape to convert metabomatching figures from SVG to PDF. To run metabomatching on the included sample pseudospectrum, run
docker run -i -v <absolute path to results directory>:/mm-ps metabomatching/metabomatching:pre01
which will run metabomatching, and write results to <absolute path to results directory>. To run metabomatching on your own pseudospectra, please refer to the documentation. For a system running either Matlab or octave, metabomatching can also be obtained from GitHub
https://github.com/rrueedi/metabomatching.git
- Examples
The procedure was in applied in the metabolome-genome wide association studies on the CoLaus cohort PLoS Genetics 2014, 10(2): e1004132 and SHIP cohort PLoS Genet 11(9): e1005487
For inquiries please contact Sven Bergmann.
Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links
- 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.
