Difference between revisions of "Metabolomics"
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We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility. | We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility. | ||
| − | + | Clearly any genetic effect starts 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. | |
| − | ---- | + | 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. |
| − | + | In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in [PLoS Comp Bio http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839]) 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. | |
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[[Category:Bulletins]] | [[Category:Bulletins]] | ||
<newstitle>Augmenting genomics through metabolomics</newstitle> | <newstitle>Augmenting genomics through metabolomics</newstitle> | ||
<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> | <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> | ||
Latest revision as of 15:18, 18 December 2017
We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.
Clearly any genetic effect starts 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.
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.
In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in [PLoS Comp Bio http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839]) 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.
