Format

Send to

Choose Destination
PLoS Comput Biol. 2017 Dec 1;13(12):e1005839. doi: 10.1371/journal.pcbi.1005839. eCollection 2017 Dec.

Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy.

Author information

1
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
2
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
3
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
4
Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.
5
German Centre for Cardiovascular Research (DZHK), Partner site, Greifswald, Germany.
6
Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
7
German Center for Diabetes Research, Neuherberg, Germany.
8
Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
9
Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.

Abstract

A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.

PMID:
29194434
PMCID:
PMC5711027
DOI:
10.1371/journal.pcbi.1005839
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center