Zheleznyakova GY, Piket E, Marabita F, et al. Epigenetic research in multiple sclerosis: progress, challenges, and opportunities. Physiol Genomics. 2017;49(9):447–61.
Li X, Xiao B, Chen X-S. DNA methylation: a new player in multiple sclerosis. Mol Neurobiol. 2017;54(6):4049–59.
Hedrich CM, Mäbert K, Rauen T, Tsokos GC. DNA methylation in systemic lupus erythematosus. Epigenomics. 2017;9(4):505–25.
Guo S, Xu L, Chang C, et al. Epigenetic regulation mediated by methylation in the pathogenesis and precision medicine of rheumatoid arthritis. Front Genet. 2020;11:811.
Bibikova M, Le J, Barnes B, et al. Genome-wide DNA methylation profiling using Infinium assay. Epigenomics. 2009;1(1):177–200.
Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98(4):288–95.
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):389–99.
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):1363–9.
Morris TJ, Butcher LM, Feber A, et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics. 2014;30(3):428–30.
Fortin J-P, Triche TJ, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33(4):558–60.
Tian Y, Morris TJ, Webster AP, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33(24):3982–4.
Tan Q, Christiansen L, von Bornemann Hjelmborg J, Christensen K. Twin methodology in epigenetic studies. J Exp Biol. 2015;218(1):134–9.
Maltby VE, Graves MC, Lea RA, et al. Genome-wide DNA methylation profiling of CD8+ T cells shows a distinct epigenetic signature to CD4+ T cells in multiple sclerosis patients. Clin Epigenet. 2015;7(1):118.
Maltby VE, Lea RA, Sanders KA, et al. Differential methylation at MHC in CD4+ T cells is associated with multiple sclerosis independently of HLA-DRB1. Clin Epigenet. 2017;9(1):71.
Maltby VE, Lea RA, Graves MC, et al. Genome-wide DNA methylation changes in CD19+ B cells from relapsing-remitting multiple sclerosis patients. Sci Rep. 2018;8(1):17418.
Kulakova OG, Kabilov MR, et al. Whole-genome DNA methylation analysis of peripheral blood mononuclear cells in multiple sclerosis patients with different disease courses. Acta Nat. 2016;8(3):103–10.
Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–41.
Jamieson E, Korologou-Linden R, Wootton RE, et al. Smoking, DNA methylation, and lung function: a mendelian randomization analysis to investigate causal pathways. Am J Hum Genet. 2020;106(3):315–26.
Martino DJ, Tulic MK, Gordon L, et al. Evidence for age-related and individual-specific changes in DNA methylation profile of mononuclear cells during early immune development in humans. Epigenetics. 2011;6(9):1085–94.
Wang D, Liu X, Zhou Y, et al. Individual variation and longitudinal pattern of genome-wide DNA methylation from birth to the first two years of life. Epigenetics. 2012;7(6):594–605.
Acevedo N, Reinius LE, Vitezic M, et al. Age-associated DNA methylation changes in immune genes, histone modifiers and chromatin remodeling factors within 5 years after birth in human blood leukocytes. Clin Epigenet. 2015;7(1):34.
Urdinguio RG, Torró MI, Bayón GF, et al. Longitudinal study of DNA methylation during the first 5 years of life. J Transl Med. 2016;14(1):160.
Pérez RF, Santamarina P, Tejedor JR, et al. Longitudinal genome-wide DNA methylation analysis uncovers persistent early-life DNA methylation changes. J Transl Med. 2019;17(1):15.
Herbstman JB, Wang S, Perera FP, et al. Predictors and consequences of global DNA methylation in cord blood and at three years. PLoS ONE. 2013;8(9):e72824.
Torow N, Hornef MW. The neonatal window of opportunity: setting the stage for life-long host-microbial interaction and immune homeostasis. J Immunol Baltim Md. 2017;198(2):557–63.
Bjornsson HT, Sigurdsson MI, Fallin MD, et al. Intra-individual change in DNA methylation over time with familial clustering. JAMA J Am Med Assoc. 2008;299(24):2877–83.
Bollati V, Schwartz J, Wright R, et al. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech Ageing Dev. 2009;130(4):234–9.
Talens RP, Christensen K, Putter H, et al. Epigenetic variation during the adult lifespan: cross-sectional and longitudinal data on monozygotic twin pairs. Aging Cell. 2012;11(4):694–703.
Reynolds CA, Tan Q, Munoz E, et al. A decade of epigenetic change in aging twins: genetic and environmental contributions to longitudinal DNA methylation. Aging Cell. 2020;19(8):e13197.
Wang Y, Karlsson R, Lampa E, et al. Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins. Epigenetics. 2018;13(9):975–87.
Tan Q, Heijmans BT, von Bornemann Hjelmborg J, et al. Epigenetic drift in the aging genome: a ten-year follow-up in an elderly twin cohort. Int J Epidemiol. 2016;45:1146–58.
Gutierrez-Arcelus M, Ongen H, Lappalainen T, et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 2015;11(1):e1004958.
Mansell G, Gorrie-Stone TJ, Bao Y, et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics. 2019;20(1):366.
Tsai P-C, Bell JT. Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int J Epidemiol. 2015;44(4):1429–41.
Saffari A, Silver MJ, Zavattari P, et al. Estimation of a significance threshold for epigenome-wide association studies. Genet Epidemiol. 2018;42(1):20–33.
Henderson-Smith A, Fisch KM, Hua J, et al. DNA methylation changes associated with Parkinson’s disease progression: outcomes from the first longitudinal genome-wide methylation analysis in blood. Epigenetics. 2019;14(4):365–82.
Johnson RK, Vanderlinden LA, Dong F, et al. Longitudinal DNA methylation differences precede type 1 diabetes. Sci Rep. 2020;10(1):3721.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2020.
Chen Y, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9.
Nordlund J, Bäcklin CL, Wahlberg P, et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 2013;14(9):r105.
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.
Du P, Zhang X, Huang C-C, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11:587.
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):189–96.
Maksimovic J, Gordon L, Oshlack A. SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 2012;13(6):R44.
Dedeurwaerder S, Defrance M, Calonne E, et al. Evaluation of the infinium methylation 450K technology. Epigenomics. 2011;3(6):771–84.
Fortin J-P, Labbe A, Lemire M, et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15(11):1–17.
Wang T, Guan W, Lin J, et al. A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data. Epigenetics. 2015;10(7):662–9.
Dedeurwaerder S, Defrance M, Bizet M, et al. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2014;15(6):929–41.
Marabita F, Almgren M, Lindholm ME, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics. 2013;8(3):333–46.
Wu MC, Joubert BR, Kuan P, et al. A systematic assessment of normalization approaches for the Infinium 450K methylation platform. Epigenetics. 2014;9(2):318–29.
van Rooij J, Mandaviya PR, Claringbould A, et al. Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies. Genome Biol. 2019;20:1–14.
Price EM, Robinson WP. Adjusting for batch effects in DNA methylation microarray data, a lesson learned. Front Genet. 2018;9:83.
Buhule OD, Minster RL, Hawley NL, et al. Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale. Front Genet. 2014. https://doi.org/10.3389/fgene.2014.00354/full.
Nygaard V, Rødland EA, Hovig E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostat Oxf Engl. 2016;17(1):29–39.
Zindler T, Frieling H, Neyazi A, et al. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies. BMC Bioinform. 2020;21:1–15.
Müller C, Schillert A, Röthemeier C, et al. Removing batch effects from longitudinal gene expression-quantile normalization plus ComBat as best approach for microarray transcriptome data. PLoS ONE. 2016;11(6):e0156594.
Fortin J-P, Parker D, Tunç B, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage. 2017;161:149–70.
Yu M, Linn KA, Cook PA, et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp. 2018;39(11):4213–27.
Jones SA, Morales AM, Holley AL, et al. Default mode network connectivity is related to pain frequency and intensity in adolescents. NeuroImage Clin. 2020;27:102326.
Gagnon-Bartsch JA, Speed TP. Using control genes to correct for unwanted variation in microarray data. Biostat Oxf Engl. 2012;13(3):539–52.
Removing Unwanted Variation from High Dimensional Data with Negative Controls | Department of Statistics. [cited 2021 Jan 20]. https://statistics.berkeley.edu/tech-reports/820.
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–e47.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
Gerring ZF, McRae AF, Montgomery GW, Nyholt DR. Genome-wide DNA methylation profiling in whole blood reveals epigenetic signatures associated with migraine. BMC Genomics. 2018;19:69.
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):200–9.
Maltby VE, Lea RA, Ribbons KA, et al. DNA methylation changes in CD4+ T cells isolated from multiple sclerosis patients on dimethyl fumarate. Mult Scler J Exp Transl Clin. 2018;4(3):2055217318787826.
Spindola LM, Santoro ML, Pan PM, et al. Detecting multiple differentially methylated CpG sites and regions related to dimensional psychopathology in youths. Clin Epigenet. 2019;11(1):146.
Eze IC, Jeong A, Schaffner E, et al. Genome-wide DNA methylation in peripheral blood and long-term exposure to source-specific transportation noise and air pollution: the SAPALDIA study. Environ Health Perspect. 2020;128(6):67003.
Nilsson EE, Thorson JLM, Ben Maamar M, et al. Epigenome-wide association study (EWAS) for potential transgenerational disease epigenetic biomarkers in sperm following ancestral exposure to the pesticide methoxychlor. Environ Epigenet. 2020;6(1):dvaa020.
Sherwood WB, Kothalawala DM, Kadalayil L, et al. Epigenome-wide association study reveals duration of breastfeeding is associated with epigenetic differences in children. Int J Environ Res Public Health. 2020;17(10):E3569.
Li QS, Sun Y, Wang T. Epigenome-wide association study of Alzheimer’s disease replicates 22 differentially methylated positions and 30 differentially methylated regions. Clin Epigenet. 2020;12(1):149.
Abeni E, Salvi A, Marchina E, et al. Sorafenib induces variations of the DNA methylome in HA22T/VGH human hepatocellular carcinoma-derived cells. Int J Oncol. 2017;51(1):128–44.
Mallik S, Odom GJ, Gao Z, et al. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform. 2019;20(6):2224–35.
Peters TJ, Buckley MJ, Statham AL, et al. De novo identification of differentially methylated regions in the human genome. Epigenet Chromatin. 2015;8:6.
Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated p values. Bioinform Oxf Engl. 2012;28(22):2986–8.
Butcher LM, Beck S. Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods San Diego Calif. 2015;72:21–8.
Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305–11.
Maksimovic J, Oshlack A, Phipson B. Gene set enrichment analysis for genome-wide DNA methylation data. Bioinformatics. 2020. https://doi.org/10.1101/2020.08.24.265702.
Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009;4:14.
Dong D, Tian Y, Zheng SC, Teschendorff AE. ebGSEA: an improved gene set enrichment analysis method for epigenome-wide-association studies. Bioinformatics. 2019;35(18):3514–6.
Parks MM. An exact test for comparing a fixed quantitative property between gene sets. Bioinform Oxf Engl. 2018;34(6):971–7.
Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, et al. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014;7(1):17–31.
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(Database issue):D607–13.
Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinform Oxf Engl. 2014;30(16):2360–6.
Odintsova VV, Rebattu V, Hagenbeek FA, et al. Predicting complex traits and exposures from polygenic scores and blood and buccal DNA methylation profiles. Front Psychiatry. 2021;12:688464.
Onwuka JU, Li D, Liu Y, et al. A panel of DNA methylation signature from peripheral blood may predict colorectal cancer susceptibility. BMC Cancer. 2020;20(1):692.
Westerman K, Fernández-Sanlés A, Patil P, et al. Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics. J Am Heart Assoc. 2020;9(8):e015299.
Hüls A, Czamara D. Methodological challenges in constructing DNA methylation risk scores. Epigenetics. 2020;15(1–2):1–11.
Abiola O, Angel JM, Avner P, et al. The nature and identification of quantitative trait loci: a community’s view. Nat Rev Genet. 2003;4(11):911–6.
Smith AK, Kilaru V, Kocak M, et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics. 2014;15:145.
Zhou F, Shen C, Xu J, et al. Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis. Clin Epigenet. 2016;8:131.
Han H, Liu Q, Yang Z, et al. Association and cis-mQTL analysis of variants in serotonergic genes associated with nicotine dependence in Chinese Han smokers. Transl Psychiatry. 2018;8(1):243.
van Dongen J, Ehli EA, Jansen R, et al. Genome-wide analysis of DNA methylation in buccal cells: a study of monozygotic twins and mQTLs. Epigenet Chromatin. 2018;11(1):54.
Fu X, Wang J, Du J, et al. BDNF gene’s role in schizophrenia: from risk allele to methylation implications. Front Psychiatry. 2020;11:564277.
Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.
Pan H, Holbrook JD, Karnani N, Kwoh CK. Gene, environment and methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment. BMC Bioinform. 2016;17(1):299.
Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinform Oxf Engl. 2012;28(10):1353–8.
Gresle MM, Jordan MA, Stankovich J, et al. Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells. Life Sci Alliance. 2020;3(7):e202000650.
Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009;10:23.
Liu Y, Aryee MJ, Padyukov L, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis. Nat Biotechnol. 2013;31(2):142–7.
Min JL, Hemani G, Hannon E, et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet. 2021;53(9):1311–21.
Võsa U, Claringbould A, Westra H-J, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300–10.
Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 2012;13:86.
Zou J, Lippert C, Heckerman D, et al. Epigenome-wide association studies without the need for cell-type composition. Nat Methods. 2014;11(3):309–11.
Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinform Oxf Engl. 2014;30(10):1431–9.
Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinform. 2017;18(1):105.
Teschendorff A. Epigenetic Dissection of intra-sample-heterogeneity. 2017. https://www.bioconductor.org/packages/release/bioc/html/EpiDISH.html.
Zheng SC, Breeze CE, Beck S, Teschendorff AE. Identification of differentially methylated cell-types in epigenome-wide association studies. Nat Methods. 2018;15(12):1059–66.
Zheng S. CellDMC—a function which allows the identification of differentially methylated cell-types in Epigenome-Wide Association Studies (EWAS). 2018. https://rdrr.io/github/sjczheng/EpiDISH/man/CellDMC.html.
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.
Hannum G, Guinney J, Zhao L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.
Zhang Q, Vallerga CL, Walker RM, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019;11(1):54.
Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91.
Yang Z, Wong A, Kuh D, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016;17(1):205.
Lu AT, Quach A, Wilson JG, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11(2):303–27.
Yang R, Wu GWY, Verhoeven JE, et al. A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD. Mol Psychiatry 2020;1–11.
Nejman D, Straussman R, Steinfeld I, et al. Molecular rules governing de novo methylation in cancer. Cancer Res. 2014;74(5):1475–83.
Tomasetti C, Vogelstein B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015;347(6217):78–81.
Teschendorff AE. A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Med. 2020;12:1–17.
Horvath S. DNA methylation age calculator. http://dnamage.genetics.ucla.edu/.
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.
EWAS Datahub—National Genomics Data Center. [cited 2021 Apr 11 ]. https://bigd.big.ac.cn/ewas/datahub.
Xiong Z, Li M, Yang F, et al. EWAS Data Hub: a resource of DNA methylation array data and metadata. Nucleic Acids Res. 2020;48(D1):D890–5.
EWAS Atlas. [cited 2021 Apr 11]. http://bigd.big.ac.cn/ewas.
Li M, Zou D, Li Z, et al. EWAS Atlas: a curated knowledgebase of epigenome-wide association studies. Nucleic Acids Res. 2019;47(Database issue):D983–8.
Domingo-Relloso A, Huan T, Haack K, et al. DNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study. Clin Epigenet. 2021;13(1):43.
Husquin LT, Rotival M, Fagny M, et al. Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. Genome Biol. 2018;19(1):222.
Galanter JM, Gignoux CR, Oh SS, et al. Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures. Elife. 2017;6:e20532.
Bayega A, Fahiminiya S, Oikonomopoulos S, Ragoussis J. Current and future methods for mRNA analysis: a drive toward single molecule sequencing. Methods Mol Biol Clifton NJ. 2018;1783:209–41.
Nakato R, Sakata T. Methods for ChIP-seq analysis: a practical workflow and advanced applications. Methods San Diego Calif. 2021;187:44–53.
Zhou L, Ng HK, Drautz-Moses DI, et al. Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing. Sci Rep. 2019;9(1):10383.
Carmona JJ, Accomando WP, Binder AM, et al. Empirical comparison of reduced representation bisulfite sequencing and Infinium BeadChip reproducibility and coverage of DNA methylation in humans. NPJ Genomic Med. 2017;2:13.
Simpson JT, Workman RE, Zuzarte PC, et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat Methods. 2017;14(4):407–10.