References
- Handy DE, Castro R, Loscalzo J. Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation. 2011;123:2145–2156.
- Smith A, Kaufman F, Sandy MS, et al. Cannabis exposure during critical windows of development: epigenetic and molecular pathways implicated in neuropsychiatric disease. Curr Envir Health Rpt. 2020;7:325–342.
- Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987;196:261–282.
- Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13:484–492.
- Armstrong DA, Lesseur C, Conradt E, et al. Global and gene-specific DNA methylation across multiple tissues in early infancy: implications for children’s health research. FASEB J. 2014;28:2088–2097.
- Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6:597–610.
- Kwok JB. Role of epigenetics in Alzheimer’s and Parkinson’s disease. Epigenomics. 2010;2:671–682.
- Low FM, Gluckman PD, Hanson MA. Developmental plasticity and epigenetic mechanisms underpinning metabolic and cardiovascular diseases. Epigenomics. 2011;3:279–294.
- Salameh Y, Bejaoui Y, El Hajj N. DNA methylation biomarkers in aging and age-related diseases. Front Genet. 2020;11:171.
- Wilkinson GS, Adams DM, Haghani A, et al. DNA methylation predicts age and provides insight into exceptional longevity of bats. Nat Commun. 2021;12:1615.
- Tammen SA, Friso S, Choi S-W. Epigenetics: the link between nature and nurture. Mol Aspects Med. 2013;34:753–764.
- Solomon O, MacIsaac J, Quach H, et al. Comparison of DNA methylation measured by Illumina 450K and EPIC BeadChips in blood of newborns and 14-year-old children. Epigenetics. 2018;13:655–664.
- Teh AL, Pan H, Lin X, et al. Comparison of methyl-capture sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples. Epigenetics. 2016;11:36–48.
- Sequencing R. SeqCapEpi CpGiant Probes. Next Generation Sequencing (NGS) solutions 2021. Accessed 2 March 2022; https://sequencing.roche.com/content/rochesequence/en/support-resources/discontinued-products/seqcap-epi-cpgiant-enrichment-kit/resources.html.
- Sun Z, Cunningham J, Slager S, et al. Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis. Epigenomics. 2015;7:813–828.
- Masser DR, Stanford DR, Freeman WM. Targeted DNA methylation analysis by next-generation sequencing. J Vis Exp. 2015;52488. DOI:10.3791/52488.
- Noble AJ, Pearson JF, Boden JM, et al. A validation of Illumina EPIC array system with bisulfite-based amplicon sequencing. PeerJ. 2021;9:e10762.
- Heiss JA, Brennan KJ, Baccarelli AA, et al. Battle of epigenetic proportions: comparing Illumina’s EPIC methylation microarrays and TruSeq targeted bisulfite sequencing. Epigenetics. 2019;15:174–182.
- Huen K, Yousefi P, Street K, et al. PON1 as a model for integration of genetic, epigenetic, and expression data on candidate susceptibility genes. Environ Epigenet. 2015;1. DOI:10.1093/eep/dvv003.
- Eskenazi B, Bradman A, Gladstone EA, et al. CHAMACOS, A longitudinal birth cohort study: lessons from the fields. J Children’s Health. 2003; 1: 3–27.
- Yousefi P, Huen K, Schall RA, et al. Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies. Epigenetics. 2013;8:1141–1152.
- Draganov DI, Teiber JF, Speelman A, et al. Human Paraoxonases (PON1, PON2, and PON3) are lactonases with overlapping and distinct substrate specificities. J Lipid Res. 2005;46:1239–1247.
- Bryk B, BenMoyal-Segal L, Podoly E, et al. Inherited and acquired interactions between ACHE and PON1 polymorphisms modulate plasma acetylcholinesterase and Paraoxonase activities. J Neurochem. 2005;92:1216–1227.
- Gouédard C, Barouki R, Morel Y. Induction of the paraoxonase-1 gene expression by resveratrol. Arterioscler Thromb Vasc Biol. 2004;24:2378–2383.
- Rhee I, Bachman KE, Park BH, et al. DNMT1 and DNMT3b cooperate to silence genes in human cancer cells. Nature. 2002;416:552–556.
- Osaki F, Ikeda Y, Suehiro T, et al. Roles of Sp1 and protein kinase C in regulation of human serum paraoxonase 1 (PON1) gene transcription in HepG2 cells. Atherosclerosis. 2004;176:279–287.
- Solomon O, Yousefi P, Huen K, et al. Prenatal phthalate exposure and altered patterns of DNA methylation in cord blood. Environ Mol Mutagen. 2017;58:398–410.
- Holland N, Furlong C, Bastaki M, et al. Paraoxonase polymorphisms, haplotypes, and enzyme activity in latino mothers and newborns. Environ Health Perspect. 2006;114:985–991.
- Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288–295.
- 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;17:208.
- Sandoval J, Heyn H, Moran S, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6:692–702.
- 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.
- Niu L, Xu Z, Taylor JA. RCP: a novel probe design bias correction method for Illumina methylation BeadChip. Bioinformatics. 2016;32:2659–2663.
- Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–127.
- Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–883.
- UCSC Genome Browser. UCSC genome browser gateway. Accessed 2 March 2022. University of California Santa Cruz Genomics Institute. https://genome.ucsc.edu/cgi-bin/hgGateway.
- UCSC Genome Browser. Lift genome annotations. Accessed 2 March 2022. University of California Santa Cruz Genomics Institute. https://genome.ucsc.edu/cgi-bin/hgLiftOver.
- Guo W, Fiziev P, Yan W, et al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics. 2013;14:774.
- Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–359.
- Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–842.
- 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 Bioinformatics. 2010;11:587.
- Breton CV, Marsit CJ, Faustman E, et al. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environ Health Perspect. 2017;125:511–526.
- Jjingo D, Conley AB, Yi SV, et al. On the presence and role of human gene-body DNA methylation. Oncotarget. 2012;3:462–474.
- Wen K. X, Milic, J., El-Khodor, B, et al. The role of DNA methylation and histone modifications in neurodegenerative diseases: a systematic review. PLoS One. 2016;11:e0167201.
- Osborne AJ, Pearson JF, Noble AJ, et al. Genome-wide DNA methylation analysis of heavy cannabis exposure in a New Zealand longitudinal cohort. Transl Psychiatry. 2020;10:1–10.
- Foox J, Nordlund J, Lalancette C, et al. The SEQC2 epigenomics quality control (EpiQC) study. Genome Biol. 2021;22:332.
- Tanić M, Moghul, I., Rodney, S., et al. Comparison and imputation-aided integration of five commercial platforms for targeted DNA methylome analysis. Nat Biotechnol. 2022;1–10. DOI:10.1038/s41587-022-01336-9.