Brigitte Wägele

TL;DR: A comprehensive analysis of genotype-dependent metabolic phenotypes using a genome-wide association study with non-targeted metabolomics to identify genetic loci associated with blood metabolite concentrations and generates many new hypotheses for biomedical and pharmaceutical research.

TL;DR: A systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites is presented and testable hypotheses on the biochemical identities of 106 unknown metabolites are derived.

TL;DR: It is shown that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and a conservative significance cut-off is provided for it for use in future association studies with metabolic traits.

TL;DR: Both transcriptomic and metabolomic data types produce information on biological entities, either transcripts or metabolites, but both can be overlaid on metabolic pathways to obtain biological information on the studied system.

TL;DR: MetaP-server as mentioned in this paper provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotype, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps.