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Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information
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نویسنده
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krumsiek j. ,suhre k. ,evans a.m. ,mitchell m.w. ,mohney r.p. ,milburn m.v. ,wägele b. ,römisch-margl w. ,illig t. ,adamski j. ,gieger c. ,theis f.j. ,kastenmüller g.
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منبع
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plos genetics - 2012 - دوره : 8 - شماره : 10
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چکیده
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Recent genome-wide association studies (gwas) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. a strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. however,a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. the identification of these unknown metabolites is still a demanding and intricate task,limiting their usability as functional markers of metabolic processes. as a consequence,previous gwas largely ignored unknown metabolites as metabolic traits for the analysis. here we present a systems-level approach that combines genome-wide association analysis and gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. we apply our method to original data of 517 metabolic traits,of which 225 are unknowns,and genotyping information on 655,658 genetic variants,measured in 1,768 human blood samples. we report previously undescribed genotype-metabotype associations for six distinct gene loci (slc22a2,comt,cyp3a5,cyp2c18,gba3,ugt3a1) and one locus not related to any known gene (rs12413935). overlaying the inferred genetic associations,metabolic networks,and knowledge-based pathway information,we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. as a proof of principle,we experimentally confirm nine concrete predictions. we demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification,hypertension,and insulin resistance. our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms. © 2012 krumsiek et al.
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آدرس
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institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg, Germany, institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg,germany,department of physiology and biophysics,weill cornell medical college in qatar,education city,qatar foundation,doha, Qatar, metabolon,research triangle park,nc, United States, metabolon,research triangle park,nc, United States, metabolon,research triangle park,nc, United States, metabolon,research triangle park,nc, United States, institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg,germany,department of genome-oriented bioinformatics,life and food science center weihenstephan,technische universität münchen,freising, Germany, institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg, Germany, research unit of molecular epidemiology,helmholtz zentrum münchen,neuherberg,germany,biobank of the hanover medical school,hanover medical school,hanover, Germany, institute of experimental genetics,genome analysis center,helmholtz zentrum münchen,neuherberg,germany,lehrstuhl für experimentelle genetik,technische universität münchen,freising-weihenstephan, Germany, institute of epidemiology,helmholtz zentrum münchen,neuherberg, Germany, institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg,germany,department of mathematics,technische universität münchen,garching, Germany, institute of bioinformatics and systems biology,helmholtz zentrum münchen,neuherberg, Germany
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Authors
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