Unknown

Dataset Information

0

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


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*?) is a conservative critical value for the p-gain, where ? 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.

SUBMITTER: Petersen AK 

PROVIDER: S-EPMC3537592 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Petersen Ann-Kristin AK   Krumsiek Jan J   Wägele Brigitte B   Theis Fabian J FJ   Wichmann H-Erich HE   Gieger Christian C   Suhre Karsten K  

BMC bioinformatics 20120606


<h4>Background</h4>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 st  ...[more]

Similar Datasets

| S-EPMC7881646 | biostudies-literature
| S-EPMC5125008 | biostudies-literature
| S-EPMC3892684 | biostudies-literature
| S-EPMC4487553 | biostudies-literature
| S-EPMC4406854 | biostudies-literature
| S-EPMC10276986 | biostudies-literature
| S-EPMC10709128 | biostudies-literature
| S-EPMC3322627 | biostudies-literature
| S-EPMC10507155 | biostudies-literature
| S-EPMC4201153 | biostudies-literature