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Open Access Highly Accessed Research article

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

Ann-Kristin Petersen1, Jan Krumsiek2, Brigitte Wägele23, Fabian J Theis2, H-Erich Wichmann456, Christian Gieger1 and Karsten Suhre278*

Author Affiliations

1 Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany

2 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany

3 Department of Genome-oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising, Germany

4 Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg, Germany

5 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, München, Germany

6 Klinikum Grosshadern, Munich, Germany

7 Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany

8 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City - Qatar Foundation, Doha, Qatar

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BMC Bioinformatics 2012, 13:120  doi:10.1186/1471-2105-13-120

Published: 6 June 2012

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.

Keywords:
p-gain; Metabolomics; MWAS; GWAS; Genome-wide association studies; Metabolome-wide association studies