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Suhre, K. ; Raffler, J. ; Kastenmüller, G.

Biochemical insights from population studies with genetics and metabolomics.

Arch. Biochem. Biophys. 589, 168-176 (2016)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Genome-wide association studies with concentrations of hundreds of small molecules in samples collected from thousands of individuals (mGWAS) access otherwise inaccessible natural genetic experiments and their influence on the metabolic capacities of the human body. By sampling the natural metabolic and genetic variability that is present in the general population, mGWAS identified over 150 associations between genetic variants and variation in the metabolic composition of human body fluids. Many of these genetic variants were found to be located in enzyme or transporter coding genes, whose functions match the biochemical nature of the associated metabolites. Associations identified by mGWAS can reveal novel biochemical knowledge, such as the function of uncharacterized genes, the biochemical identity of small molecules, and the structure of entire biochemical pathways. Here we review findings of recent mGWAS and discuss concrete examples of how their results can be interpreted in a biochemical context. We describe online resources that are available for mining mGWAS results. In this context, we present two concepts that also find more general applications in the field of metabolomics: strengthening of associations by looking at ratios between metabolite pairs and reconstruction of metabolic pathways by Gaussian graphical modeling.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Genetic Variation ; Genome-wide Association Study ; Metabolic Individuality ; Metabolomics ; Partial Correlation
ISSN (print) / ISBN 0003-9861
e-ISSN 1096-0384
Journal Archives of Biochemistry and Biophysics
Quellenangaben Volume: 589, Issue: , Pages: 168-176 Article Number: , Supplement: ,
Publisher Elsevier
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Bioinformatics and Systems Biology (IBIS)