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

Harish Dharuri, Peter Henneman, Ayse Demirkan, Jan Bert van Klinken, Dennis Owen Mook-Kanamori, Rui Wang-Sattler, Christian Gieger, Jerzy Adamski, Kristina Hettne, Marco Roos, Karsten Suhre, Cornelia M Van Duijn, EUROSPAN consortia, Ko Willems van Dijk, Peter AC 't Hoen

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.