On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies

Ann Kristin Petersen, Jan Krumsiek, Brigitte Wägele, Fabian J. Theis, H. Erich Wichmann, Christian Gieger, Karsten Suhre

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Background: Genome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain.Results: Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.Conclusions: We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.

Original languageEnglish
Article number120
JournalBMC bioinformatics
Volume13
Issue number1
DOIs
Publication statusPublished - 6 Jun 2012

    Fingerprint

Keywords

  • GWAS
  • Genome-wide association studies
  • MWAS
  • Metabolome-wide association studies
  • Metabolomics
  • P-gain

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this