The test was originally developed for genetic analyses, but has since found use in the field of targeted metabolomics as well . The test is based on a generalized linear model which aims to assess whether any of the feature-specific effects in a pre-specified group of features have predictive utility in differentiating treatment groups, a task that is closely related to testing whether treatment group-specific effects are not null (for further details see supporting information section 4). This test does not require concordance of effects but is considered powerful at detecting even small concordant effects . Within msFeaST, each feature-set and contrast combination, that is control vs [treatment group 1, treatment group 2, etc.], is tested using this model, and p-values for the group as a whole, as well as feature-specific p-value contributions, are extracted. Multiple testing correction is done using the Bonferroni method at the family size given by the number of groups times the number of contrasts. This correction is usually substantially smaller than the correction needed if feature-specific testing was performed, resulting in less stringent cutoff adjustments. However, special attention is needed when using globaltest with small sample sizes as results may be unreliable when using very small numbers of statistical units per group .