Combining GWAS & Gaussian graphical modeling with metabolomics to predict the identity of unknown metabolites.

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Genetic variation in the human genome has been recently linked to differences in individual metabolite levels by combining GWAS studies and metabolomic tools. A considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. Krumsiek et al. developed a systems-level approach that combines GWAS studies and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. They applied their method to original data of 517 metabolic traits (225/517 are unknowns) and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. They report seven previously unknown genotype–metabotype associations. Moreover, by combining knowledge of metabolic networks, and knowledge-based pathway information, with the deduced genetic associations, Krumsiek et al. also derived testable hypotheses on the biochemical identities of 106 unknown metabolites. They experimentally confirmed nine concrete predictions as a proof of principle. Finally, Krumsiek et al. show that their method can be used for the functional interpretation of previous metabolomic biomarker studies on liver detoxification, hypertension, and insulin resistance.

Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. Krumsiek et al. PLoS Genet. 2012 Oct;8(10) PMID: 23093944

posted by Yannis Trakadis, MD

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