GroupedFDR is designed to test multiple hypotheses and control either the false discovery rate or the family-wise error rate. The method facilitates the input of prior information in the form of groupings of tests, for example, one group might include SNPs in candidate genes, while another includes all other SNPS. For each group a weight is estimated from the observed test statistics within the group. If there is no apparent signal in a group, relative to a group that appears to have several tests with signals, the former group will be down-weighted relative to the latter. In this way, the test has greater power than an unweighted test, provided the groups are well selected. If no groups show apparent signals, then the weights will be approximately equal, and the analysis reverts to an ordinary FDR or Bonferroni analysis. The only restriction on the procedure is that the number of groups be small, relative to the total number of tests performed. For example, one might choose 4 groups of interest and place the remaining tests in a catch-all category.
- Linux / Windows
- R Package
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Roeder K, Devlin B, Wasserman L (2007)
Improving power in genome-wide association studies: weights tip the scale.
Genet Epidemiol. 2007 Nov;31(7):741-7.