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MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results.

Following QC analysis, MWASTools tests for association between the phenotype under investigation [e.g. type II diabetes (T2D)] and each metabolic feature (or metabolite). Depending on the nature of data to be modeled, the user can choose among the following association methods: linear/logistic regression or Pearson/Spearman/ Kendall correlation. The models can be adjusted for confounder factors, including age, gender or other clinical covariates (e.g. medication). The P-values are corrected for multiple-testing with several possible methods, such as Benjamini–Hochberg (BH) procedure (Benjamini and Hochberg, 1995). MWASTools allows performing model validation through non-parametric bootstrapping. Finally, MWAS analysis results can be filtered according to a given significance threshold.

MWASTools can be downloaded from the following link (Bioconductor):

 

 

More information can be found in: Rodriguez-Martinez et al. (2017) MWASTools: an R/bioconductor package for metabolome-wide association studies, Bioinformatics btx477

Note: Figure above features in this article

 

Internal case number 8156

MWASTools

An integrated pipeline to perform metabolome-wide association studies

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