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

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 untarget...

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Vydáno v:	PLoS computational biology Ročník 13; číslo 12; s. e1005839
Hlavní autoři:	Rueedi, Rico, Mallol, Roger, Raffler, Johannes, Lamparter, David, Friedrich, Nele, Vollenweider, Peter, Waeber, Gérard, Kastenmüller, Gabi, Kutalik, Zoltán, Bergmann, Sven
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.12.2017 Public Library of Science (PLoS)
Témata:	Bioinformatics Biology Biology and Life Sciences Databases, Genetic Environmental health Funding Genetic aspects Genetic diversity Genetic variance Genome-wide association studies Genome-Wide Association Study - methods Genomes Hospitals Humans Identification Identification and classification Internal medicine Magnetic resonance spectroscopy Magnetic Resonance Spectroscopy - methods Medicine and Health Sciences Metabolism Metabolites Metabolomics Metabolomics - methods Methods NMR NMR spectroscopy Nuclear magnetic resonance Nuclear magnetic resonance spectroscopy Physical Sciences Preventive medicine Research and analysis methods Ships Software Studies Supervision Urine Wavelet transforms
ISSN:	1553-7358, 1553-734X, 1553-7358
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1005839