Hierarchical False Discovery Rate–Controlling Methodology

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Discovering the false discovery rate (2010, background): “Reiner-Benaim et al. (2007) have presented a framework, and Yekutieli (2008) has provided theoretical analysis for simple but important cases.”
A unified treatment of multiple testing with prior knowledge using the p-filter (2017, methodology): “ the weighted Simes p-value proposed by Benjamini and Hochberg [4]. Hu et al. [21] and Benjamini and Bogomolov [2] also propose ways to take a single partition of groups into account, while Yekutieli [42] discusses hierarchical testing. The super-uniformity lemmas (Lemma1and, in the grouped setting,”
Multiple hypothesis testing in genomics (2014, methodology): “Hierarchical methods are available for both FDR and FWER control [81, 82, 83, 84].”
Leveraging polygenic functional enrichment to improve GWAS power (2017, methodology): “Furthermore, while GBH and IHW were consistently well-calibrated under random stratification (see Figure 1, purple bars), S-FDR was not, perhaps because S-FDR requires additional adjustments for the number of strata used.(42) In contrast, GenoWAP remained well calibrated under baseLD stratification ”