Open Access Research article

Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles

Harish Dharuri1, Peter Henneman2, Ayse Demirkan13, Jan Bert van Klinken1, Dennis Owen Mook-Kanamori124, Rui Wang-Sattler5, Christian Gieger6, Jerzy Adamski78, Kristina Hettne1, Marco Roos1, Karsten Suhre49, Cornelia M Van Duijn3, EUROSPAN consortia, Ko Willems van Dijk110 and Peter AC 't Hoen1*

Author Affiliations

1 Center for Human and Clinical Genetics, Leiden University Medical Center, S4-P, PO Box 9600, 2300, RC Leiden, Netherlands

2 Department of Clinical Genetics, DNA Diagnostics Laboratary, University of Amsterdam, Amsterdam, Netherlands

3 Genetic Epidemiology Unit, Departments of Epidemiology and Clinical Genetics, Erasmus University Medical Center, Rotterdam, Netherlands

4 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, PO Box 24144, Doha, State of Qatar

5 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

6 Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

7 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

8 Chair of Experimental Genetics, Technische Universität München, Munich, Germany

9 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany

10 Department of Endocrinology, Leiden University Medical Center, S4-P, PO Box 9600, 2300, RC Leiden, Netherlands

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BMC Genomics 2013, 14:865  doi:10.1186/1471-2164-14-865

Published: 9 December 2013

Additional files

Additional file 1:

S1. Rules to generate Metabolite-Gene sets. S2. Taverna workflow management system. Figure S1. Snapshot of the Taverna workbench which consists of three panels as pointed to in the figure. Table S3. SNP set generated for ratios of metabolites. S4. Compounds filtered for the Kegg:Reaction Scheme. Table S5. Metabolite specific break-up of the performance of database:interrogation schemes. Table S6. Best case associations of loci with phosphatidylcholines in the Illig et al and Demirkan et al datasets. Table S7. Pleiotropic effect for phosphatidylcholines at select loci.

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