http://www2.unil.ch/cbg/index.php?title=Metabolomics&feed=atom&action=historyMetabolomics - Revision history2024-03-29T12:15:34ZRevision history for this page on the wikiMediaWiki 1.31.12http://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=5306&oldid=prevWikiSysop at 14:18, 18 December 20172017-12-18T14:18:16Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:18, 18 December 2017</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Clearly genetic effect <del class="diffchange diffchange-inline">start </del>off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Clearly <ins class="diffchange diffchange-inline">any </ins>genetic effect <ins class="diffchange diffchange-inline">starts </ins>off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In our first paper "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in [PLoS Genetics http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132]) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In our first paper "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in [PLoS Genetics http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132]) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td></tr>
</table>WikiSysophttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=5305&oldid=prevWikiSysop at 14:17, 18 December 20172017-12-18T14:17:49Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:17, 18 December 2017</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Clearly genetic effect start off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Clearly genetic effect start off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In our <del class="diffchange diffchange-inline">[</del>first paper <del class="diffchange diffchange-inline">| </del>http://journals.plos.org/<del class="diffchange diffchange-inline">ploscompbiol</del>/article?id=10.1371/journal.<del class="diffchange diffchange-inline">pcbi</del>.<del class="diffchange diffchange-inline">1005839</del>] <del class="diffchange diffchange-inline">"Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in PLoS Genetics</del>) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In our first paper <ins class="diffchange diffchange-inline">"Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in [PLoS Genetics </ins>http://journals.plos.org/<ins class="diffchange diffchange-inline">plosgenetics</ins>/article?id=10.1371/journal.<ins class="diffchange diffchange-inline">pgen</ins>.<ins class="diffchange diffchange-inline">1004132</ins>]) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in PLoS Comp Bio) we elaborated on our [[Metabomatching]] method. Our main point is that metabolome-wide genome-wide association studies  typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for  an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.  </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in <ins class="diffchange diffchange-inline">[</ins>PLoS Comp Bio <ins class="diffchange diffchange-inline">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839]</ins>) we elaborated on our [[Metabomatching]] method. Our main point is that metabolome-wide genome-wide association studies  typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for  an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <a href="http://www2.unil.ch/cbg/index.php?title=Metabomatching">metabomatching</a> page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <a href="http://www2.unil.ch/cbg/index.php?title=Metabomatching">metabomatching</a> page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td></tr>
</table>WikiSysophttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=5304&oldid=prevWikiSysop at 14:15, 18 December 20172017-12-18T14:15:36Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:15, 18 December 2017</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Clearly genetic effect start off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Clearly genetic effect start off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In our [first paper http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839] <del class="diffchange diffchange-inline">on relating genotypes to metabolites and human disease </del>"Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in PLoS Genetics) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In our [first paper <ins class="diffchange diffchange-inline">| </ins>http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839] "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in PLoS Genetics) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in PLoS Comp Bio) we elaborated on our [[Metabomatching]] method. Our main point is that metabolome-wide genome-wide association studies  typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for  an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in PLoS Comp Bio) we elaborated on our [[Metabomatching]] method. Our main point is that metabolome-wide genome-wide association studies  typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for  an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.  </div></td></tr>
</table>WikiSysophttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=5303&oldid=prevWikiSysop at 14:14, 18 December 20172017-12-18T14:14:32Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:14, 18 December 2017</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">A particular interest lies in small molecules underlying metabolism</del>, <del class="diffchange diffchange-inline">whose concentration can be measured quantitatively in body fluids, like blood and urine</del>. <del class="diffchange diffchange-inline">In our recent article "Genome</del>-<del class="diffchange diffchange-inline">wide association study </del>of <del class="diffchange diffchange-inline">metabolic traits reveals novel gene-metabolite-disease links" that was accepted for publication in PLoS Genetics on 10</del>.<del class="diffchange diffchange-inline">2</del>.<del class="diffchange diffchange-inline">2013 we studied such data derived </del>from the ''Cohorte Lausannoise''<del class="diffchange diffchange-inline">. Below is more information on this publication </del>and <del class="diffchange diffchange-inline">details on the our method can be found [[Metabomatching|here]]. For inquiries please contact '''[[User:Sven|Sven Bergmann]]'''</del>.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Clearly genetic effect start off at the molecular level</ins>, <ins class="diffchange diffchange-inline">e.g</ins>. <ins class="diffchange diffchange-inline">by impacting gene</ins>-<ins class="diffchange diffchange-inline">expression which may then modulate the concentrations </ins>of <ins class="diffchange diffchange-inline">other small molecules</ins>. <ins class="diffchange diffchange-inline">In order to trace these effects we need molecular measurements</ins>. <ins class="diffchange diffchange-inline">Indeed, for samples </ins>from the ''Cohorte Lausannoise'' <ins class="diffchange diffchange-inline">we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine </ins>and <ins class="diffchange diffchange-inline">serum samples</ins>.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>----</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">In our [first paper http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839] on relating genotypes to metabolites and human disease "Genome</ins>-<ins class="diffchange diffchange-inline">wide association study of metabolic traits reveals novel gene</ins>-<ins class="diffchange diffchange-inline">metabolite</ins>-<ins class="diffchange diffchange-inline">disease links" (published in PLoS Genetics) we reported results from a metabolome</ins>- <ins class="diffchange diffchange-inline">and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health. </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">'''Genome-wide </del>association <del class="diffchange diffchange-inline">study of metabolic traits reveals novel gene-metabolite-disease links'''</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">In second paper "Metabomatching: Using genetic </ins>association <ins class="diffchange diffchange-inline">to identify metabolites in proton NMR spectroscopy" </ins>(<ins class="diffchange diffchange-inline">published in PLoS Comp Bio</ins>) <ins class="diffchange diffchange-inline">we elaborated on our [[Metabomatching]] method</ins>. <ins class="diffchange diffchange-inline">Our main point is </ins>that metabolome-<ins class="diffchange diffchange-inline">wide </ins>genome-wide association <ins class="diffchange diffchange-inline">studies  typically follow an acquire-identify</ins>-<ins class="diffchange diffchange-inline">associate procedure: </ins>metabolome <ins class="diffchange diffchange-inline">data are acquired experimentally</ins>, <ins class="diffchange diffchange-inline">metabolites are </ins>identified in the <ins class="diffchange diffchange-inline">experimental data </ins>and <ins class="diffchange diffchange-inline">their concentrations quantified</ins>, and the <ins class="diffchange diffchange-inline">metabolite concentrations are tested for association </ins>with <ins class="diffchange diffchange-inline">genetic variants</ins>. <ins class="diffchange diffchange-inline">We provide </ins>a <ins class="diffchange diffchange-inline">method </ins>for <ins class="diffchange diffchange-inline"> an untargeted approach</ins>, which <ins class="diffchange diffchange-inline">follows an acquire-associate-identify procedure: </ins>the <ins class="diffchange diffchange-inline">experimental data </ins>are <ins class="diffchange diffchange-inline">binned into </ins>metabolome <ins class="diffchange diffchange-inline">features</ins>, and <ins class="diffchange diffchange-inline">the features tested directly for </ins>genetic <ins class="diffchange diffchange-inline">association</ins>. <ins class="diffchange diffchange-inline">Our method relies on the fact </ins>that <ins class="diffchange diffchange-inline">when </ins>the <ins class="diffchange diffchange-inline">metabolome is </ins>measured <ins class="diffchange diffchange-inline">by proton NMR spectroscopy, genetically associated </ins>features <ins class="diffchange diffchange-inline">tend to correspond to peaks </ins>in the <ins class="diffchange diffchange-inline">NMR spectrum </ins>of the <ins class="diffchange diffchange-inline">underlying metabolites</ins>. <ins class="diffchange diffchange-inline">This inherent property </ins>of the <ins class="diffchange diffchange-inline">untargeted approach acts as a </ins>genetic <ins class="diffchange diffchange-inline">spiking which informs </ins>on the <ins class="diffchange diffchange-inline">identities of involved metabolites</ins>. <ins class="diffchange diffchange-inline">Metabomatching is a method that uses genetic spiking information to identify </ins>the metabolite <ins class="diffchange diffchange-inline">candidates</ins>, <ins class="diffchange diffchange-inline">listed </ins>in <ins class="diffchange diffchange-inline">a spectral database</ins>, <ins class="diffchange diffchange-inline">most likely to underlie observed feature associations</ins>.  </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">*'''Authors'''</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Rico Rueedi1,2,♣, Mirko Ledda3,♣, Andrew W. Nicholls4, Reza M. Salek5,6, Pedro Marques-Vidal7, Edgard Morya8,9, Koichi Sameshima10, Ivan Montoliu11, Laeticia Da Silva11, Sebastiano Collino11, François-Pierre Martin11, Serge Rezzi11, Christoph Steinbeck5, Dawn M. Waterworth12, Gérard Waeber13, Peter Vollenweider13, Jacques S. Beckmann1,2,14, Johannes Le Coutre3,15, Vincent Mooser16, Sven Bergmann1,2,♠,*, Ulrich K. Genick3,♠, and Zoltán Kutalik1,2,7,♠</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">1 Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">3 Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">4 Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Park Road, Ware, Herts SG12 0DP, UK</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">6 Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2 1GA, UK</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">7 Institute of Social and Preventive Medicine </del>(<del class="diffchange diffchange-inline">IUMSP</del>)<del class="diffchange diffchange-inline">, Centre Hospitalier Universitaire Vaudois (CHUV), 1010 Lausanne, Switzerland, and University of Lausanne</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">8 Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio </del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Libanes Hospital, São Paulo, Brazil</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">9 Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">10 Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">11 Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland</del>.</div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">12 Medical Genetics, GlaxoSmithKline, Philadelphia, PA, USA </del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">13 Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">14 Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">15 Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">16 Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">♣,♠ These authors contributed equally to this work.</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Correspondence to be addressed to: Sven Bergmann, email: sven.bergmann@unil.ch</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">*'''Abstract'''</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Metabolic traits are molecular phenotypes </del>that <del class="diffchange diffchange-inline">can drive clinical phenotypes and may predict disease progression. Here we report results from a </del>metabolome- <del class="diffchange diffchange-inline">and </del>genome-wide association <del class="diffchange diffchange-inline">study on 1H</del>-<del class="diffchange diffchange-inline">NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10 8) and independent associations between single nucleotide polymorphisms (SNP) and </del>metabolome <del class="diffchange diffchange-inline">features. 56 of these associations replicated in the TasteSensomics cohort</del>, <del class="diffchange diffchange-inline">comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to 11 gene-metabolite associations, 6 of which had been previously </del>identified in the <del class="diffchange diffchange-inline">urine metabolome </del>and <del class="diffchange diffchange-inline">3 in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602</del>, <del class="diffchange diffchange-inline">P=6.9×10-44) </del>and <del class="diffchange diffchange-inline">lysine (rs8101881, P=1.2×10-33), respectively. Fine-mapping of </del>the <del class="diffchange diffchange-inline">first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated </del>with <del class="diffchange diffchange-inline">Crohn’s disease</del>. <del class="diffchange diffchange-inline">This implicates fucose as </del>a <del class="diffchange diffchange-inline">potential prognostic disease marker, </del>for <del class="diffchange diffchange-inline">which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene</del>, <del class="diffchange diffchange-inline">rare mutations of </del>which <del class="diffchange diffchange-inline">have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate </del>the <del class="diffchange diffchange-inline">potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">*'''Author summary'''</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">The concentrations of small molecules, known as metabolites, </del>are <del class="diffchange diffchange-inline">subject to tight regulation in all organisms. Collectively, the metabolite concentrations make up the </del>metabolome, <del class="diffchange diffchange-inline">which differs amongst individuals as a function of their environment </del>and genetic <del class="diffchange diffchange-inline">makeup</del>.  </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants </del>that <del class="diffchange diffchange-inline">correlate with any of </del>the measured <del class="diffchange diffchange-inline">metabolome </del>features in <del class="diffchange diffchange-inline">a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates </del>the <del class="diffchange diffchange-inline">expert or computational identification </del>of the <del class="diffchange diffchange-inline">metabolite whose concentration is most likely affected by the genetic variant at hand</del>.  </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Our study replicated many </del>of the <del class="diffchange diffchange-inline">previously reported genetically driven variations in human metabolism and revealed two new striking examples of </del>genetic <del class="diffchange diffchange-inline">variations with a sizeable effect </del>on the <del class="diffchange diffchange-inline">urine metabolome</del>. <del class="diffchange diffchange-inline">Interestingly, in these two gene-metabolite pairs both </del>the <del class="diffchange diffchange-inline">gene and the affected </del>metabolite <del class="diffchange diffchange-inline">are related to human diseases – Crohn’s disease in the first case</del>, <del class="diffchange diffchange-inline">and kidney disease </del>in <del class="diffchange diffchange-inline">the second. This highlights the connection between genetic predispositions</del>, <del class="diffchange diffchange-inline">affected metabolites, and human health</del>.  </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <a href="http://www2.unil.ch/cbg/index.php?title=Metabomatching">metabomatching</a> page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <a href="http://www2.unil.ch/cbg/index.php?title=Metabomatching">metabomatching</a> page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td></tr>
</table>WikiSysophttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3870&oldid=prevRico at 14:14, 21 February 20142014-02-21T14:14:55Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
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<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:14, 21 February 2014</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l39" >Line 39:</td>
<td colspan="2" class="diff-lineno">Line 39:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <del class="diffchange diffchange-inline">[[</del>Metabomatching<del class="diffchange diffchange-inline">]] </del>page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <ins class="diffchange diffchange-inline"><a href="http://www2.unil.ch/cbg/index.php?title=</ins>Metabomatching<ins class="diffchange diffchange-inline">">metabomatching</a> </ins>page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser></div></td></tr>
</table>Ricohttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3869&oldid=prevRico at 14:13, 21 February 20142014-02-21T14:13:08Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:13, 21 February 2014</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l39" >Line 39:</td>
<td colspan="2" class="diff-lineno">Line 39:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><newstitle>Augmenting genomics through metabolomics</newstitle>     </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide study (MWGWAS) on the <del class="diffchange diffchange-inline">[</del>http://www.colaus.ch CoLaus<del class="diffchange diffchange-inline">] </del>cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the [[Metabomatching]] page. The paper has been published in <del class="diffchange diffchange-inline">[</del>http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132 PLOS Genetics<del class="diffchange diffchange-inline">]</del><date>21 Feb 2014 — 09:00</date> </teaser></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide <ins class="diffchange diffchange-inline">association </ins>study (MWGWAS) on the <ins class="diffchange diffchange-inline"><a href="</ins>http://www.colaus.ch<ins class="diffchange diffchange-inline">"></ins>CoLaus<ins class="diffchange diffchange-inline"></a> </ins>cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the [[Metabomatching]] page. The paper has been published in <ins class="diffchange diffchange-inline"><a href="</ins>http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132<ins class="diffchange diffchange-inline">"></ins>PLOS Genetics<ins class="diffchange diffchange-inline"></a></ins><date>21 Feb 2014 — 09:00</date> </teaser></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"> </del></div></td><td colspan="2"> </td></tr>
</table>Ricohttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3868&oldid=prevRico at 14:11, 21 February 20142014-02-21T14:11:34Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:11, 21 February 2014</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l37" >Line 37:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]  </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Bulletins]]</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><newstitle>Augmenting genomics through metabolomics</newstitle>    </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide study (MWGWAS) on the [http://www.colaus.ch CoLaus] cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the [[Metabomatching]] page. The paper has been published in [http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132 PLOS Genetics]<date>21 Feb 2014 — 09:00</date> </teaser></div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><teaser> In a metabolome-wide genome-wide study (MWGWAS) on the [http://www.colaus.ch CoLaus] cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the [[Metabomatching]] page. The paper has been published in [http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132 PLOS Genetics]<date>21 Feb 2014 — 09:00</date> </teaser></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
</table>Ricohttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3867&oldid=prevRico at 14:09, 21 February 20142014-02-21T14:09:09Z<p></p>
<table class="diff diff-contentalign-left" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:09, 21 February 2014</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l36" >Line 36:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants that correlate with any of the measured metabolome features in a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates the expert or computational identification of the metabolite whose concentration is most likely affected by the genetic variant at hand.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants that correlate with any of the measured metabolome features in a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates the expert or computational identification of the metabolite whose concentration is most likely affected by the genetic variant at hand.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[Category:Bulletins]] </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><teaser> In a metabolome-wide genome-wide study (MWGWAS) on the [http://www.colaus.ch CoLaus] cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the [[Metabomatching]] page. The paper has been published in [http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132 PLOS Genetics]<date>21 Feb 2014 — 09:00</date> </teaser></ins></div></td></tr>
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</table>Ricohttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3846&oldid=prevSven at 14:33, 29 January 20142014-01-29T14:33:03Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 14:33, 29 January 2014</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Page </del>in <del class="diffchange diffchange-inline">construction</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">We are interested </ins>in <ins class="diffchange diffchange-inline">how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">A particular interest lies in small molecules underlying metabolism, whose concentration can be measured quantitatively in body fluids, like blood and urine. In our recent article "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" that was accepted for publication in PLoS Genetics on 10.2.2013 we studied such data derived from the ''Cohorte Lausannoise''. Below is more information on this publication and details on the our method can be found [[Metabomatching|here]]. For inquiries please contact '''[[User:Sven|Sven Bergmann]]'''.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">----</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">'''Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">*'''Authors'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Rico Rueedi1,2,♣, Mirko Ledda3,♣, Andrew W. Nicholls4, Reza M. Salek5,6, Pedro Marques-Vidal7, Edgard Morya8,9, Koichi Sameshima10, Ivan Montoliu11, Laeticia Da Silva11, Sebastiano Collino11, François-Pierre Martin11, Serge Rezzi11, Christoph Steinbeck5, Dawn M. Waterworth12, Gérard Waeber13, Peter Vollenweider13, Jacques S. Beckmann1,2,14, Johannes Le Coutre3,15, Vincent Mooser16, Sven Bergmann1,2,♠,*, Ulrich K. Genick3,♠, and Zoltán Kutalik1,2,7,♠</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">1 Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">3 Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">4 Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Park Road, Ware, Herts SG12 0DP, UK</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">6 Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2 1GA, UK</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">7 Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), 1010 Lausanne, Switzerland, and University of Lausanne</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">8 Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Libanes Hospital, São Paulo, Brazil</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">9 Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">10 Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">11 Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">12 Medical Genetics, GlaxoSmithKline, Philadelphia, PA, USA </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">13 Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">14 Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">15 Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">16 Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">♣,♠ These authors contributed equally to this work.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Correspondence to be addressed to: Sven Bergmann, email: sven.bergmann@unil.ch</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">*'''Abstract'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10 8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. 56 of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to 11 gene-metabolite associations, 6 of which had been previously identified in the urine metabolome and 3 in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P=6.9×10-44) and lysine (rs8101881, P=1.2×10-33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn’s disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">*'''Author summary'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">The concentrations of small molecules, known as metabolites, are subject to tight regulation in all organisms. Collectively, the metabolite concentrations make up the metabolome, which differs amongst individuals as a function of their environment and genetic makeup. </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants that correlate with any of the measured metabolome features in a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates the expert or computational identification of the metabolite whose concentration is most likely affected by the genetic variant at hand. </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health. </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"> </ins></div></td></tr>
</table>Svenhttp://www2.unil.ch/cbg/index.php?title=Metabolomics&diff=3813&oldid=prevMicha at 10:52, 19 December 20132013-12-19T10:52:40Z<p></p>
<p><b>New page</b></p><div>Page in construction</div>Micha