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

Harish Dharuri, Peter Henneman, Ayse Demirkan, Jan B. van Klinken, Dennis O. Mook-Kanamori, Rui Wang-Sattler, Christian Gieger, Jerzy Adamski, Kristina Hettne, Marco Roos, Karsten Suhre, Cornelia M. Van Duijn, Ko W. van Dijk, Peter A.C. 't Hoen, Peter Ugocsai, Aaron Isaacs, Peter P. Pramstaller, Gerhard Liebisch, James F. Wilson, Åsa JohanssonIgor Rudan, Yurii S. Aulchenko, Anatoly V. Kirichenko, A. Cecile J.W. Janssens, Ritsert C. Jansen, Carsten Gnewuch, Francisco S. Domingues, Cristian Pattaro, Sarah H. Wild, Inger Jonasson, Ozren Polasek, Irina V. Zorkoltseva, Albert Hofman, Lennart Karssen, Maksim Struchalin, James Floyd, Wilmar Igl, Zrinka Biloglav, Linda Broer, Arne Pfeufer, Irene Pichler, Susan Campbell, Ghazal Zaboli, Ivana Kolcic, Fernando Rivadeneira, Jennifer Huffman, Nicholas D. Hastie, Andre Uitterlinden, Lude Franke, Christopher S. Franklin, Veronique Vitart, Jacqueline C.M. Witteman, Tatiana Axenovich, Ben A. Oostra, Thomas Meitinger, Andrew A. Hicks, Caroline Hayward, Alan F. Wright, Ulf Gyllensten, Harry Campbell, Gerd Schmitz

Research output: Contribution to journalArticle

14 Citations (Scopus)


Background: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets. Results: Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (, confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression. Conclusions: We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression.

Original languageEnglish
Article number865
JournalBMC genomics
Issue number1
Publication statusPublished - 9 Dec 2013



  • Bioinformatics
  • Genome-wide association
  • Genotype-phenotype prioritization
  • Metabolite
  • Pathway databases

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Dharuri, H., Henneman, P., Demirkan, A., van Klinken, J. B., Mook-Kanamori, D. O., Wang-Sattler, R., Gieger, C., Adamski, J., Hettne, K., Roos, M., Suhre, K., Van Duijn, C. M., van Dijk, K. W., 't Hoen, P. A. C., Ugocsai, P., Isaacs, A., Pramstaller, P. P., Liebisch, G., Wilson, J. F., ... Schmitz, G. (2013). Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles. BMC genomics, 14(1), [865].