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Arch Biochem Biophys. 2016 Jan 1;589:168-76. doi: 10.1016/j.abb.2015.09.023. Epub 2015 Oct 9.

Biochemical insights from population studies with genetics and metabolomics.

Author information

  • 1Department of Physiology and Biophysics, Weill Cornell Medical College - Qatar, Doha, Qatar; Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. Electronic address: karsten@suhre.fr.
  • 2Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
  • 3Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany.

Abstract

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.

KEYWORDS:

Genetic variation; Genome-wide association study; Metabolic individuality; Metabolomics; Partial correlation

PMID:
26432701
DOI:
10.1016/j.abb.2015.09.023
[PubMed - indexed for MEDLINE]
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