Lin Y.-T.WEN-CHUNG LEE2020-11-192020-11-1920151471-2156https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938598181&doi=10.1186%2fs12863-015-0259-z&partnerID=40&md5=304364d3043012874b6744a7f034cf59https://scholars.lib.ntu.edu.tw/handle/123456789/521814Background: Multiple hypothesis testing is a pervasive problem in genomic data analysis. The conventional Bonferroni method which controls the family-wise error rate is conservative and with low power. The current paradigm is to control the false discovery rate. Results: We characterize the variability of the false discovery rate indices (local false discovery rates, q-value and false discovery proportion) using the bootstrapped method. A colon cancer gene-expression data and a visual refractive errors genome-wide association study data are analyzed as demonstration. We found a high variability in false discovery rate controls for typical genomic studies. Conclusions: We advise researchers to present the bootstrapped standard errors alongside with the false discovery rate indices. ? 2015 Lin and Lee.English[SDGs]SDG3Article; bootstrapping; colon cancer; data analysis; false discovery rate control; gene expression; genetic association; genetic procedures; genome analysis; genomics; refraction error; algorithm; colon tumor; genetics; genome-wide association study; human; procedures; statistical model; Algorithms; Colonic Neoplasms; Genome-Wide Association Study; Humans; Models, Statisticaljournal article10.1186/s12863-015-0259-z262396422-s2.0-84938598181