Metabotype and genotype mapping of KORA and TwinsUK cohorts

A network view of data published in this study. 

Human metabolic individuality in biomedical and pharmaceutical research

Abstract

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Network Graph

It is an output of KEGG reaction pair (RED), Tanimoto Similarity Distances (Blue) and SNP-metabolite associations (yellow). KRP and Tanimoto provide biochemical/biochemical backbone to the network, whereas SNP link are indicating probable genetic link for the variability. Bigger node size reflect that the metabolite is associated with many SNPs. Many SNPs reported in this table  can be easily pointed out here. But, the table contain only 37 links and we see here more than that, suggesting that many links cannot be mapped to a fully annotated loci. Also, many links does not look realistic due to chemistry constraints, therefore these graphs are very useful to visualize/find gene-metabolite associations that could be supported by chemical/biochemical relationships. 261 compounds out of 275 metabolites are included in this network. A p-value threshold of less than P-08 was used to find significant associations.