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Petersen, A.-K. ; Krumsiek, J. ; Wägele, B. ; Theis, F.J. ; Wichmann, H.-E. ; Gieger, C. ; Suhre, K.

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

BMC Bioinformatics 13:120 (2012)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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*alpha) is a conservative critical value for the p-gain, where alpha 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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords p-gain, Metabolomics, MWAS, GWAS, Genome-wide association studies, Metabolome-wide association studies; Targeted Metabolomics ; Identification ; Phenotypes ; Spectrum ; Kora
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Journal BMC Bioinformatics
Quellenangaben Volume: 13, Issue: 1, Pages: , Article Number: 120 Supplement: ,
Publisher BioMed Central
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Genetic Epidemiology (IGE)
Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Epidemiology (EPI)