On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies
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
Citation and License
BMC Bioinformatics 2012, 13:120 doi:10.1186/1471-2105-13-120Published: 6 June 2012
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.
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.
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.