An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays

References

• Phillips T. The role of methylation in gene expression. Nat Educ. 2008;1(1):116.
   Google Scholar
• Kulis M, Esteller M. DNA methylation and cancer. Adv Genet. 2010;70:27–56.
   Google Scholar
• Ehrlich M. DNA methylation in cancer: too much, but also too little. Oncogene. 2002;21(35):5400–5413.
   Google Scholar
• Grayson DR, Guidotti A. The dynamics of DNA methylation in schizophrenia and related psychiatric disorders. Neuropsychopharmacology. 2013;38(1):138–166.
   Google Scholar
• Logue MW, Miller MW, Wolf EJ, et al. An epigenome-wide association study of posttraumatic stress disorder in US veterans implicates several new DNA methylation loci. Clin Epigenetics. 2020;12(1):46.
   Google Scholar
• Katrinli S, Zheng Y, Gautam A, et al. PTSD is associated with increased DNA methylation across regions of HLA-DPB1 and SPATC1L. Brain Behav Immun. 2021;91:429–436.
   Google Scholar
• Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8(3):389–399.
   Google Scholar
• Pidsley R, Zotenko E, Peters TJ, et al. Critical evaluation of the illumina methylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208.
   Google Scholar
• Affinito O, Palumbo D, Fierro A, et al. Nucleotide distance influences co-methylation between nearby CpG sites. Genomics. 2020;112(1):144–150.
   Google Scholar
• Gu H, Bock C, Mikkelsen TS, et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods. 2010;7(2):133–136.
   Google Scholar
• Mallik S, Odom, Gj, Gao, Z et al, et al. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform; 2018 https://doi.org/10.1093/bib/bby085.
   Google Scholar
• Jaffe AE, Murakami P, Lee H, et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. 2012;41(1):200–209.
   Google Scholar
• Pedersen BS, Schwartz DA, Yang IV, et al. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28(22):2986–2988.
   Google Scholar
• Peters TJ, Buckley, MJ, Statham, AL et al, et al. De novo identification of differentially methylated regions in the human genome. Vol. 8. Epigenetics Chromatin; 2015. p. 6 https://doi.org/10.1186/1756-8935-8-6.
   Google Scholar
• Butcher LM, Beck S. Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data. Vol. 72. Methods; 2015. p. 21–28 https://doi.org/10.1016/j.ymeth.2014.10.036.
   Google Scholar
• Martorell-Marugan J, Gonzalez-Rumayor V, Carmona-Saez P. mCSEA: detecting subtle differentially methylated regions. Bioinformatics. 2019;35(18):3257–3262.
   Google Scholar
• Gomez L, Odom, GJ, Young, JI et al, et al. coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes 47 . Nucleic Acids Res; 2019 e98 https://doi.org/10.1093/nar/gkz590 .
   Google Scholar
• Lent S, Xu H, Wang L, et al. Comparison of novel and existing methods for detecting differentially methylated regions. BMC Genet. 2018;19(Suppl 1):84.
   Google Scholar
• Chen DP, Lin YC, Fann CS. Methods for identifying differentially methylated regions for sequence- and array-based data. Brief Funct Genomics. 2016;15(6):485–490.
   Google Scholar
• Hsiao CL, Hsieh, AR, Lian, IB et al, et al. A novel method for identification and quantification of consistently differentially methylated regions. PLoS One. 2014;9(5):e97513.
   Google Scholar
• Zhang Y, Liu H, Lv J, et al. QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res. 2011;39(9):e58.
   Google Scholar
• Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724–1735.
   Google Scholar
• Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74(368):829–836.
   Google Scholar
• Shaffer JP. Multiple Hypothesis-Testing. Annu Rev Psychol. 1995;46:561–584.
   Google Scholar
• Efron B, Tibshirani R. An introduction to the bootstrap. Monographs on statistics and applied probability. Vol. xvi. New York: Chapman & Hall; 1993. p. 436.
   Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369.
   Google Scholar
• Fortin JP, Triche TJ, Hansen KD. Preprocessing, normalization and integration of the illumina humanMethylationEPIC array with minfi. Bioinformatics. 2017;33(4):558–560.
   Google Scholar
• Kechris KJ, Biehs B, Kornberg TB. Generalizing moving averages for tiling arrays using combined p-value statistics. Stat Appl Genet Mol Biol. 2010;9:Article29.
   Google Scholar
• Stouffer SA, Suchman, EA, DeVinney, LC et al. The American soldier Osborn, Frederick. In: Studies in social psychology in World War II 1 . Princeton: Princeton University Press; 1949 45 .
   Google Scholar
• Liptak T. On the combination of independent tests. Magyar Tudomanyos. Akademia Matematikai Kutato Intezetenek Kozlemenyei. 1958;3:171–197.
   Google Scholar
• Šidák Z. Rectangular confidence region for the means of multivariate normal distributions. J Am Stat Assoc. 1967;62(318):626–633.
   Google Scholar
• Satterthwaite FE. An approximate distribution of estimates of variance components. Biometrics. 1946;2(6):110–114.
   Google Scholar
• Benjamini Y, Drai D, Elmer G, et al. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125(1–2):279–284.
   Google Scholar
• Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–15550.
   Google Scholar
• Hollander M, Wolfe DA. Nonparametric statistical methods. In: Wiley series in probability and statistics Texts and references section. 2nd ed. Vol. xiv. New York: Wiley;1999. p. 787.
   Google Scholar
• Reynolds LM, Taylor, JR, Ding, J et al, et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Vol. 5. Nat Commun; 2014. p. 5366 https://doi.org/10.1038/ncomms6366.
   Google Scholar
• Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–739.
   Google Scholar
• Sadeh N, Wolf EJ, Logue MW, et al. Epigenetic variation at SKA2 predicts suicide phenotypes and internalizing psychopathology. Depression and Anxiety. 2016;33(4):308–315.
   Google Scholar
• Ratanatharathorn A, Boks MP, Maihofer AX, et al. Epigenome-wide association of PTSD from heterogeneous cohorts with a common multi-site analysis pipeline. Am J Med Genet B Neuropsychiatr Genet. 2017;174(6):619–630.
   Google Scholar
• Logue MW, Smith AK, Wolf EJ, et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics. 2017;9(11):1363–1371.
   Google Scholar
• Sadeh N, Spielberg JM, Logue MW, et al. SKA2 methylation is associated with decreased prefrontal cortical thickness and greater PTSD severity among trauma-exposed veterans. Mol Psychiatry. 2016;21(3):357–363.
   Google Scholar
• Chen YA, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the illumina infinium humanMethylation450 microarray. Epigenetics. 2013;8(2):203–209.
   Google Scholar
• Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189–196.
   Google Scholar
• Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–127.
   Google Scholar
• Du P, Zhang, X, Huang, CC et al, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. Vol. 11. BMC Bioinformatics; 2010. p. 587 https://doi.org/10.1186/1471-2105-11-587.
   Google Scholar
• Johnson NL. Systems of frequency curves generated by methods of translation. Biometrika. 1949;36(Pt. 1–2):149–176.
   Google Scholar
• Lun AT, Smyth GK. De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly. Nucleic Acids Res. 2014;42(11):e95.
   Google Scholar
• Zhou Z, Lunetta KL, Smith AK, et al. Correction for multiple testing in candidate-gene methylation studies. Epigenomics. 2019;11(9):1089–1105.
   Google Scholar