title: Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.
abstract: 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 (http://www.myexperiment.org/packs/319.html), 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.
authors: Harish
authors: H
authors: Dharuri
authors: Peter
authors: P
authors: Henneman
authors: Ayse
authors: A
authors: Demirkan
authors: Jan Bert
authors: JB
authors: van Klinken
authors: Dennis Owen
authors: DO
authors: Mook-Kanamori
authors: Rui
authors: R
authors: Wang-Sattler
authors: Christian
authors: C
authors: Gieger
authors: Jerzy
authors: J
authors: Adamski
authors: Kristina
authors: K
authors: Hettne
authors: Marco
authors: M
authors: Roos
authors: Karsten
authors: K
authors: Suhre
authors: Cornelia M
authors: CM
authors: Van Duijn
authors: Ko Willems
authors: KW
authors: van Dijk
authors: Center for Human and Clinical Genetics, Leiden University Medical Center, S4-P, PO Box 9600, 2300, RC Leiden, Netherlands. P.A.C._t_Hoen@lumc.nl.
authors: Peter A C
authors: PA
authors: 't Hoen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D001330:Electronic Data Processing; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D055442:Metabolome; D020641:Polymorphism, Single Nucleotide; D012984:Software; D057188:Workflow