Zhou, Z., Lunetta, K. L., Smith, A. K., Wolf, E., Stone, A., Schichman, S., McGlinchey, R., Milberg, W., Miller, M. W., & Logue, M. (2019). Correction for multiple testing in candidate-gene methylation studies. Epigenomics, 11, 1089-1105.
NOTES
1750-192xZhou, ZhenweiLunetta, Kathryn LSmith, Alicia KWolf, Erika JStone, AnnjanetteSchichman, Steven AMcGlinchey, Regina EMilberg, William PMiller, Mark WLogue, Mark WI01 BX003477/BX/BLRD VA/United StatesComparative StudyJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tEpigenomics. 2019 Jul;11(9):1089-1105. doi: 10.2217/epi-2018-0204. Epub 2019 Jun 26.
Abstract
Aim: We compared the performance of multiple testing corrections for candidate gene methylation studies, namely Sidak (accurate Bonferroni), false-discovery rate and three adjustments that incorporate the correlation between CpGs: extreme tail theory (ETT), Gao et al. (GEA), and Li and Ji methods. Materials & methods: The experiment-wide type 1 error rate was examined in simulations based on Illumina EPIC and 450K data. Results: For high-correlation genes, Sidak and false-discovery rate corrections were conservative while the Li and Ji method was liberal. The GEA method tended to be conservative unless a threshold parameter was adjusted. The ETT yielded an appropriate type 1 error rate. Conclusion: For genes with substantial correlation across measured CpGs, GEA and ETT can appropriately correct for multiple testing in candidate gene methylation studies.
Last updated on 03/06/2023