On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies.
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.
Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
Ann-Kristin
AK
Petersen
Jan
J
Krumsiek
Brigitte
B
Wägele
Fabian J
FJ
Theis
H-Erich
HE
Wichmann
Christian
C
Gieger
Karsten
K
Suhre
D003198:Computer Simulation; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D055442:Metabolome / Q000235:genetics*