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Automated wokflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.
BMC Genomics 14:865 (2013)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Genome-wide Association ; Metabolite ; Genotype-phenotype Prioritization ; Bioinformatics ; Pathway Databases; Genome-wide Association; 10-formyltetrahydrofolate Dehydrogenase; Folate; Hyperhomocysteinemia; Metabolomics; Integration; Deficiency; Mice
ISSN (print) / ISBN
1471-2164
e-ISSN
1471-2164
Journal
BMC Genomics
Quellenangaben
Volume: 14,
Issue: 1,
Article Number: 865
Publisher
BioMed Central
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Molekulare Endokrinologie und Metabolismus (MEM)
Research Unit Molecular Epidemiology (AME)
Institute of Genetic Epidemiology (IGE)
Institute of Bioinformatics and Systems Biology (IBIS)
Research Unit Molecular Epidemiology (AME)
Institute of Genetic Epidemiology (IGE)
Institute of Bioinformatics and Systems Biology (IBIS)