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

BMC Bioinformatics. 2012 Jun 6;13:120. doi: 10.1186/1471-2105-13-120.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation*
  • Genome, Human
  • Genome-Wide Association Study / statistics & numerical data*
  • Humans
  • Male
  • Metabolome / genetics*