Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information.
Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable a...
Main Authors: | , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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Series: | PLoS Genetics |
Online Access: | http://europepmc.org/articles/PMC3475673?pdf=render |
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author | Jan Krumsiek Karsten Suhre Anne M Evans Matthew W Mitchell Robert P Mohney Michael V Milburn Brigitte Wägele Werner Römisch-Margl Thomas Illig Jerzy Adamski Christian Gieger Fabian J Theis Gabi Kastenmüller |
author_facet | Jan Krumsiek Karsten Suhre Anne M Evans Matthew W Mitchell Robert P Mohney Michael V Milburn Brigitte Wägele Werner Römisch-Margl Thomas Illig Jerzy Adamski Christian Gieger Fabian J Theis Gabi Kastenmüller |
author_sort | Jan Krumsiek |
collection | DOAJ |
description | Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms. |
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issn | 1553-7390 1553-7404 |
language | English |
last_indexed | 2024-04-12T10:30:22Z |
publishDate | 2012-01-01 |
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series | PLoS Genetics |
spelling | doaj.art-5e16ddf58c2244ba8fbb7aa973fa87812022-12-22T03:36:51ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042012-01-01810e100300510.1371/journal.pgen.1003005Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information.Jan KrumsiekKarsten SuhreAnne M EvansMatthew W MitchellRobert P MohneyMichael V MilburnBrigitte WägeleWerner Römisch-MarglThomas IlligJerzy AdamskiChristian GiegerFabian J TheisGabi KastenmüllerRecent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.http://europepmc.org/articles/PMC3475673?pdf=render |
spellingShingle | Jan Krumsiek Karsten Suhre Anne M Evans Matthew W Mitchell Robert P Mohney Michael V Milburn Brigitte Wägele Werner Römisch-Margl Thomas Illig Jerzy Adamski Christian Gieger Fabian J Theis Gabi Kastenmüller Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genetics |
title | Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. |
title_full | Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. |
title_fullStr | Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. |
title_full_unstemmed | Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. |
title_short | Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. |
title_sort | mining the unknown a systems approach to metabolite identification combining genetic and metabolic information |
url | http://europepmc.org/articles/PMC3475673?pdf=render |
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