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

42 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
Externally publishedYes

Fingerprint

Metabolome
Genome-Wide Association Study
Metabolites
Genome
Genes
Testing
Life Style
Critical value
Statistical property
Statistic
Well-defined
Statistics
Sampling
Experimental Data

Keywords

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

ASJC Scopus subject areas

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

Cite this

On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. / Petersen, Ann Kristin; Krumsiek, Jan; Wägele, Brigitte; Theis, Fabian J.; Wichmann, H. Erich; Gieger, Christian; Suhre, Karsten.

In: BMC Bioinformatics, Vol. 13, No. 1, 120, 06.06.2012.

Research output: Contribution to journalArticle

Petersen, Ann Kristin ; Krumsiek, Jan ; Wägele, Brigitte ; Theis, Fabian J. ; Wichmann, H. Erich ; Gieger, Christian ; Suhre, Karsten. / On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. In: BMC Bioinformatics. 2012 ; Vol. 13, No. 1.
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