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      Genome-wide association studies with metabolomics

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      Author(s): 1 , 2 ,
      Publication date (Electronic):
      Journal: Genome Medicine
      Publisher: BioMed Central
      Keywords: complex disease, genetics, metabolomics, metabolic traits, pathway inference

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          Abstract

          Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.

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          Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.

          Arun Sreekumar, Laila Poisson, Thekkelnaycke M Rajendiran … (2009)
          Multiple, complex molecular events characterize cancer development and progression. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
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            HMDB: a knowledgebase for the human metabolome

            David Wishart, Craig Knox, An Guo … (2009)
            The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.
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              Epigenome-wide association studies for common human diseases.

              Vardhman Rakyan, Thomas A. Down, David Balding … (2011)
              Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.
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                Author and article information

                Contributors
                Jerzy Adamski
                Journal
                Journal ID (nlm-ta): Genome Med
                Journal ID (iso-abbrev): Genome Med
                Title: Genome Medicine
                Publisher: BioMed Central
                ISSN (Electronic): 1756-994X
                Publication date Collection: 2012
                Publication date (Electronic): 30 April 2012
                Publication date PMC-release: 30 April 2013
                Volume: 4
                Issue: 4
                Page: 34
                Affiliations
                [1 ]Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Genome Analysis Center, 85764 Neuherberg, Germany
                [2 ]Lehrstuhl für Experimentelle Genetik, Technische Universität München, 85350 Freising-Weihenstephan, Germany
                Article
                Publisher ID: gm333
                DOI: 10.1186/gm333
                PMC ID: 3446262
                PubMed ID: 22546499
                SO-VID: a36b657e-c92b-4261-a88b-8052ad8d4247
                Copyright © Copyright ©2012 BioMed Central Ltd.
                History
                Categories
                Subject: Review

                ScienceOpen disciplines: Molecular medicine
                Keywords: complex disease,genetics,metabolomics,metabolic traits,pathway inference
                Data availability:
                ScienceOpen disciplines: Molecular medicine
                Keywords: complex disease, genetics, metabolomics, metabolic traits, pathway inference

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