1,138
Views
0
CrossRef citations to date
0
Altmetric
Research Paper

A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data

, , , , , & show all
Article: 2207959 | Received 18 Sep 2022, Accepted 19 Apr 2023, Published online: 17 May 2023

References

  • Jin B, Li Y, Robertson KD. DNA methylation: superior or subordinate in the epigenetic hierarchy? Genes Cancer. 2011;2(6):607–19.
  • Nishiyama A, Nakanishi M. Navigating the DNA methylation landscape of cancer. Trends Genet. 2021;37(11):1012–1027.
  • Lang AL, Eulalio T, Fox E, et al. Methylation differences in Alzheimer’s disease neuropathologic change in the aged human brain. Acta Neuropathol Commun. 2022;10(1):174.
  • Shireby G, Dempster EL, Policicchio S, et al. DNA methylation signatures of Alzheimer’s disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types. Nat Commun. 2022;13(1):5620.
  • 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;8:389–399.
  • 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.
  • Affinito O, Palumbo D, Fierro A, et al. Nucleotide distance influences co-methylation between nearby CpG sites. Genomics. 2020;112:144–150.
  • 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.
  • Zhang Q, Zhao Y, Zhang R, et al. A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. PLoS ONE. 2016;11:e0156895.
  • Zheng Y, Lunetta KL, Liu C, et al. An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays. Epigenetics. 2022;17(13):1–18.
  • Pedersen BS, Schwartz DA, Yang IV, et al. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28:2986–2988.
  • 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.
  • Peters TJ, Buckley MJ, Statham AL, et al. De Novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8(1):6.
  • Martorell-Marugan J, Gonzalez-Rumayor V, Carmona-Saez P. mCSEA: detecting subtle differentially methylated regions. Bioinformatics. 2019;35:3257–3262.
  • Gomez L, Odom GJ, Young JI, et al. coMethdmr: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes. Nucleic Acids Res. 2019;47(17):e98.
  • Jolliffe IT, eds. Principal component analysis. 2nd ed. New York: Springer; 2002.
  • Mallik S, Odom GJ, Gao Z, et al. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform. 2018;20(6):2224–2235.
  • Farre P, Jones MJ, Meaney MJ, et al. Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenetics Chromatin. 2015;8(1):19.
  • Bair E, Tibshirani R, Golub T. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2004;2(4):E108.
  • Bair E, Hastie T, Paul D, et al. Prediction by Supervised Principal Components. J Am Stat Assoc. 2006;101:119–137.
  • Bell JT, Pai AA, Pickrell JK, et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011;12(1):R10.
  • Du P, Zhang X, Huang CC, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinf. 2010;11(1):587.
  • Team RC. R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2021.
  • 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.
  • Fisher RA. Statistical methods for research workers. Edinburgh, London: Oliver and Boyd; 1925.
  • Stouffer SA. The American soldier. Princeton: Princeton University Press; 1949.
  • Dewey M. Metap: meta-analysis of significance values. R package version 1.8. 2022.
  • ST GL, Wang L, Odom G. coMethdmr: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies. R package version 1.0.0. 2022.
  • Sadeh N, Wolf EJ, Logue MW, et al. Epigenetic Variation at SKA2 Predicts Suicide Phenotypes and Internalizing Psychopathology. Depress Anxiety. 2016;33(4):308–315.
  • 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.
  • Wooten T, Brown E, Sullivan DR, et al. Apolipoprotein E (APOE) epsilon4 moderates the relationship between c-reactive protein, cognitive functioning, and white matter integrity. Brain Behav Immun. 2021;95:84–95.
  • Nievergelt CM, Maihofer AX, Klengel T, et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun. 2019;10(1):4558. DOI:10.1038/s41467-019-12576-w
  • 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;30:1363–1369.
  • Fortin JP, Triche TJ, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33(4):558–560.
  • Li S, Wong EM, Bui M, et al. Causal effect of smoking on DNA methylation in peripheral blood: a twin and family study. Clin Epigenetics. 2018;10(1):18.
  • Logue MW, Miller MW, Wolf EJ, et al. Traumatic Stress Brain Study G (2020) an epigenome-wide association study of posttraumatic stress disorder in US veterans implicates several new DNA methylation loci. Clin Epigenetics. 2020;12(1):46.
  • Gao X, Zhang Y, Breitling LP, et al. Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration. Oncotarget. 2016;7:46878–46889.
  • Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.
  • Liu J, Morgan M, Hutchison K, et al. A study of the influence of sex on genome wide methylation. PLoS ONE. 2010;5:e10028.
  • Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
  • Christiansen C, Castillo-Fernandez JE, Domingo-Relloso A, et al. Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects. Clin Epigenetics. 2021;13(1):36.