1932

Abstract

The interplay of genetically driven biological processes and environmental factors is a key driver of research questions spanning multiple areas of psychology. A nascent area of research focuses on the utility of epigenetic marks in capturing this intersection of genes and environment, as epigenetic mechanisms are both tightly linked to the genome and environmentally responsive. Advances over the past 10 years have allowed large-scale assessment of one epigenetic mark in particular, DNA methylation, in human populations, and the examination of DNA methylation is becoming increasingly common in psychological studies. In this review, we briefly outline some principles of epigenetics, focusing on highlighting important considerations unique to DNA methylation studies to guide psychologists in incorporating DNA methylation into a project. We discuss study design and biological and analytical considerations and conclude by discussing interpretability of epigenetic findings and how these important factors are currently being applied across areas of psychology.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-psych-122414-033653
2018-01-04
2024-12-07
Loading full text...

Full text loading...

/deliver/fulltext/psych/69/1/annurev-psych-122414-033653.html?itemId=/content/journals/10.1146/annurev-psych-122414-033653&mimeType=html&fmt=ahah

Literature Cited

  1. Álvarez-Errico D, Vento-Tormo R, Sieweke M, Ballestar E. 2014. Epigenetic control of myeloid cell differentiation, identity and function. Nat. Rev. Immunol. 15:17–17 [Google Scholar]
  2. Antequera F. 2003. Structure, function and evolution of CpG island promoters. Cell Mol. Life Sci. 60:81647–58 [Google Scholar]
  3. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP. et al. 2014. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:101363–69 [Google Scholar]
  4. Baccarelli A, Tarantini L, Wright RO, Bollati V, Litonjua AA. et al. 2010. Repetitive element DNA methylation and circulating endothelial and inflammation markers in the VA normative aging study. Epigenetics 5:3222–28 [Google Scholar]
  5. Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF. et al. 2014. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLOS Genet 10:9e1004663 [Google Scholar]
  6. Bell JT, Tsai P-C, Yang T-P, Pidsley R, Nisbet J. et al. 2012. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLOS Genet 8:4189–200 [Google Scholar]
  7. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57:1289–300 [Google Scholar]
  8. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A. et al. 2010. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28:101045–48 [Google Scholar]
  9. Bestor TH. 2000. The DNA methyltransferases of mammals. Hum. Mol. Genet. 9:162395–402 [Google Scholar]
  10. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B. et al. 2011. High density DNA methylation array with single CpG site resolution. Genomics 98:4288–95 [Google Scholar]
  11. Bibikova M, Le J Barnes B, Saedinia-Melnyk S, Zhou L. et al. 2009. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics 1:1177–200 [Google Scholar]
  12. Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW. et al. 2006. High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16:3383–93 [Google Scholar]
  13. Bjornsson HT, Fallin MD, Feinberg AP. 2004. An integrated epigenetic and genetic approach to common human disease. Trends Genet 20:8350–58 [Google Scholar]
  14. Bock C. 2012. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13:10705–19 [Google Scholar]
  15. Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E. et al. 2009. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLOS ONE 4:8e6767 [Google Scholar]
  16. Bollati V, Schwartz J, Wright R, Litonjua A, Tarantini L. et al. 2009. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech. Ageing Dev. 130:4234–39 [Google Scholar]
  17. Boyce WT, Kobor MS. 2015. Development and the epigenome: the “synapse” of gene-environment interplay. Dev. Sci. 18:11–23 [Google Scholar]
  18. Breton CV, Marsit CJ, Faustman E, Nadeau K, Goodrich JM. et al. 2017. 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. 125:4511–26 [Google Scholar]
  19. Brody GH, Miller GE, Yu T, Beach SRH, Chen E. 2016a. Supportive family environments amelioratethe link between racial discrimination and epigenetic aging: areplication across two longitudinal cohorts. Psychol. Sci. 27:4530–41 [Google Scholar]
  20. Brody GH, Yu T, Chen E, Beach SRH, Miller GE. 2016b. Family-centered prevention ameliorates the longitudinal association between risky family processes and epigenetic aging. J. Child Psychol. Psychiatry 57:5566–74 [Google Scholar]
  21. Chen L, Kostadima M, Martens JHA, Canu G, Garcia SP. et al. 2014. Transcriptional diversity during lineage commitment of human blood progenitors. Science 345:62041251033 [Google Scholar]
  22. Chen L, Pan H, Tuan TA, Teh AL, MacIsaac JL. et al. 2015. Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism influences the association of the methylome with maternal anxiety and neonatal brain volumes. Dev. Psychopathol. 27:1137–50 [Google Scholar]
  23. Chen Y-A, Lemire M, Choufani S, Butcher DT, Grafodatskaya D. et al. 2014. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8:2203–9 [Google Scholar]
  24. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR. et al. 2009. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLOS Genet 5:8e1000602 [Google Scholar]
  25. Cotton AM, Price EM, Jones MJ, Balaton BP, Kobor MS, Brown CJ. 2015. Landscape of DNA methylation on the X chromosome reflects CpG density, functional chromatin state and X-chromosome inactivation. Hum. Mol. Genet. 24:61528–39 [Google Scholar]
  26. Davies M. 2009. To what extent is blood a reasonable surrogate for brain in gene expression studies: estimation from mouse hippocampus and spleen. Front. Neurosci. 3:54 [Google Scholar]
  27. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A. et al. 2012. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol 13:6R43 [Google Scholar]
  28. Dempster EL, Wong CCY, Lester KJ, Burrage J, Gregory AM. et al. 2014. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol. Psychiatry 76:12977–83 [Google Scholar]
  29. Diehl AG, Boyle AP. 2016. Deciphering ENCODE. Trends Genet 32:4238–49 [Google Scholar]
  30. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA. et al. 2010. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform 11:1587 [Google Scholar]
  31. Edgar RD, Jones MJ, Meaney MJ, Turecki G, Kobor MS. 2017a. BECon: a tool for interpreting DNA methylation findings from blood in the context of brain. Transl. Psychiatry 7:e1187 [Google Scholar]
  32. Edgar RD, Jones MJ, Robinson WP, Kobor MS. 2017b. An empirically driven data reduction method on the human 450K methylation array to remove tissue specific non-variable CpGs. Clin. Epigenet. 9:11 [Google Scholar]
  33. Esposito EA, Jones MJ, Doom JR, MacIsaac JL, Gunnar MR, Kobor MS. 2016. Differential DNA methylation in peripheral blood mononuclear cells in adolescents exposed to significant early but not later childhood adversity. Dev. Psychopathol. 28:41385–99 [Google Scholar]
  34. Essex MJ, Boyce WT, Hertzman C, Lam LL, Armstrong JM. et al. 2013. Epigenetic vestiges of early developmental adversity: childhood stress exposure and DNA methylation in adolescence. Child Dev 84:158–75 [Google Scholar]
  35. Fagny M, Patin E, MacIsaac JL, Rotival M, Flutre T. et al. 2015. The epigenomic landscape of African rainforest hunter-gatherers and farmers. Nat. Commun. 6:10047 [Google Scholar]
  36. Farré P, Jones MJ, Meaney MJ, Emberly E, Turecki G, Kobor MS. 2015. Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenet. Chromatin 8:19 [Google Scholar]
  37. Ficz G, Branco MR, Seisenberger S, Santos F, Krueger F. et al. 2011. Dynamic regulation of 5-hydroxymethylcytosine in mouse ES cells and during differentiation. Nature 473:7347398–402 [Google Scholar]
  38. Florath I, Butterbach K, Müller H, Bewerunge-Hudler M, Brenner H. 2014. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum. Mol. Genet. 23:51186–201 [Google Scholar]
  39. Fortin J-P, Triche TJ, Hansen KD. 2016. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics 33:4558–60 [Google Scholar]
  40. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F. et al. 2005. Epigenetic differences arise during the lifetime of monozygotic twins. PNAS 102:3010604–9 [Google Scholar]
  41. Fraser HB, Lam LL, Neumann SM, Kobor MS. 2012. Population-specificity of human DNA methylation. Genome Biol 13:2R8 [Google Scholar]
  42. Galanter JM, Gignoux CR, Oh SS, Torgerson D, Pino-Yanes M. et al. 2017. Differential methylation between ethnic sub-groups reflects the effect of genetic ancestry and environmental exposures. eLife 6:e20532 [Google Scholar]
  43. Gao X, Jia M, Zhang Y, Breitling LP, Brenner H. 2015. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin. Epigenet. 7:113 [Google Scholar]
  44. Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A. 2011. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6:4468–81 [Google Scholar]
  45. Guintivano J, Aryee MJ, Kaminsky ZA. 2013. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 8:3290–302 [Google Scholar]
  46. Guo JU, Su Y, Shin JH, Shin J, Li H. et al. 2013. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17:2215–22 [Google Scholar]
  47. Gutierrez Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H. et al. 2013. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife 2:e00523 [Google Scholar]
  48. Hannon E, Lunnon K, Schalkwyk L, Mill J. 2015. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 10:111024–32 [Google Scholar]
  49. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J. et al. 2016. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat. Neurosci. 19:148–54 [Google Scholar]
  50. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G. et al. 2013. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49:2359–67 [Google Scholar]
  51. Hattab MW, Shabalin AA, Clark SL, Zhao M, Kumar G. et al. 2017. Correcting for cell-type effects in DNA methylation studies: Reference-based method outperforms latent variable approaches in empirical studies. Genome Biol 18:24 [Google Scholar]
  52. Heard E, Martienssen RA. 2014. Transgenerational epigenetic inheritance: myths and mechanisms. Cell 157:195–109 [Google Scholar]
  53. Henikoff S, Greally JM. 2016. Epigenetics, cellular memory and gene regulation. Curr. Biol. 26:14R644–48 [Google Scholar]
  54. Hertzman C. 1999. The biological embedding of early experience and its effects on health in adulthood. Ann. N. Y. Acad. Sci. 896:85–95 [Google Scholar]
  55. Horvath S. 2013. DNA methylation age of human tissues and cell types. Genome Biol 14:10R115 [Google Scholar]
  56. Horvath S, Garagnani P, Bacalini MG, Pirazzini C, Salvioli S. et al. 2015. Accelerated epigenetic aging in Down syndrome. Aging Cell 14:3491–95 [Google Scholar]
  57. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H. et al. 2016. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol 17:1171 [Google Scholar]
  58. Horvath S, Levine AJ. 2015. HIV-1 infection accelerates age according to the epigenetic clock. J. Infect. Dis. 212:101563–73 [Google Scholar]
  59. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP. et al. 2012. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13:10R97 [Google Scholar]
  60. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ. et al. 2012. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform 13:86 [Google Scholar]
  61. Houseman EA, Molitor J, Marsit CJ. 2014. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30:101431–39 [Google Scholar]
  62. Illingworth RS, Bird AP. 2009. CpG islands—“a rough guide”. FEBS Lett 583:111713–20 [Google Scholar]
  63. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C. et al. 2009. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41:2178–86 [Google Scholar]
  64. Issa J-P. 2014. Aging and epigenetic drift: a vicious cycle. J. Clin. Invest. 124:124–29 [Google Scholar]
  65. Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD. et al. 2012. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol. 41:1200–9 [Google Scholar]
  66. Jiao Y, Widschwendter M, Teschendorff AE. 2014. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics 30:162360–66 [Google Scholar]
  67. Jin SG, Wu X, Li AX, Pfeifer GP. 2011. Genomic mapping of 5-hydroxymethylcytosine in the human brain. Nucleic Acids Res 39:125015–24 [Google Scholar]
  68. Johansson A, Enroth S, Gyllensten U. 2013. Continuous aging of the human DNA methylome throughout the human lifespan. PLOS ONE 8:6e67378 [Google Scholar]
  69. Jones MJ, Farré P, McEwen LM, MacIsaac JL, Watt K. et al. 2013. Distinct DNA methylation patterns of cognitive impairment and trisomy 21 in Down syndrome. BMC Med. Genom. 6:58 [Google Scholar]
  70. Jones MJ, Goodman SJ, Kobor MS. 2015a. DNA methylation and healthy human aging. Aging Cell 14:6924–32 [Google Scholar]
  71. Jones MJ, Islam SA, Edgar RD, Kobor MS. 2015b. Adjusting for cell type composition in DNA methylation data using a regression-based approach. Methods Mol. Biol. 1589:99–106 [Google Scholar]
  72. Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC. et al. 2016. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am. J. Hum. Genet. 98:4680–96 [Google Scholar]
  73. Khulan B, Manning JR, Dunbar DR, Seckl JR, Raikkonen K. et al. 2014. Epigenomic profiling of men exposed to early-life stress reveals DNA methylation differences in association with current mental state. Transl. Psychiatry 4:e448 [Google Scholar]
  74. Kim D, Kubzansky LD, Baccarelli A, Sparrow D, Spiro A. et al. 2016. Psychological factors and DNA methylation of genes related to immune/inflammatory system markers: the VA Normative Aging Study. BMJ Open 6:1e009790 [Google Scholar]
  75. Klengel T, Mehta D, Anacker C, Rex-Haffner M, Pruessner JC. et al. 2013. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat. Neurosci. 16:133–41 [Google Scholar]
  76. Kozlenkov A, Roussos P, Timashpolsky A, Barbu M, Rudchenko S. et al. 2014. Differences in DNA methylation between human neuronal and glial cells are concentrated in enhancers and non-CpG sites. Nucleic Acids Res 42:1109–27 [Google Scholar]
  77. Ladd-Acosta C. 2015. Epigenetic signatures as biomarkers of exposure. Curr. Environ. Health Rep. 2:2117–25 [Google Scholar]
  78. Ladd-Acosta C, Hansen KD, Briem E, Fallin MD, Kaufmann WE, Feinberg AP. 2014. Common DNA methylation alterations in multiple brain regions in autism. Mol. Psychiatry 19:8862–71 [Google Scholar]
  79. Lam LL, Emberly E, Fraser HB, Neumann SM, Chen E. et al. 2012. Factors underlying variable DNA methylation in a human community cohort. PNAS 109:Suppl. 217253–60 [Google Scholar]
  80. Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559 [Google Scholar]
  81. Lappalainen T, Greally JM. 2017. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18:441–51 [Google Scholar]
  82. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. 2012. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:6882–83 [Google Scholar]
  83. Levine ME, Lu AT, Bennett DA, Horvath S. 2015. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning. Aging 7:121198–211 [Google Scholar]
  84. Libertini E, Heath SC, Hamoudi RA, Gut M, Ziller MJ. et al. 2016a. Information recovery from low coverage whole-genome bisulfite sequencing. Nat. Commun. 7:11306 [Google Scholar]
  85. Libertini E, Heath SC, Hamoudi RA, Gut M, Ziller MJ. et al. 2016b. Saturation analysis for whole-genome bisulfite sequencing data. Nat. Biotechnol. 34:691–93 [Google Scholar]
  86. Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN. et al. 2016. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol 17:1255 [Google Scholar]
  87. Lister R, Mukamel EA, Nery JR, Urich M, Puddifoot CA. et al. 2013. Global epigenomic reconfiguration during mammalian brain development. Science 341:61461237905 [Google Scholar]
  88. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E. et al. 2013. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31:2142–47 [Google Scholar]
  89. Lowe R, Gemma C, Beyan H, Hawa MI, Bazeos A. et al. 2013. Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies. Epigenetics 8:4445–54 [Google Scholar]
  90. MacKinnon DP, Krull JL, Lockwood CM. 2000. Equivalence of the mediation, confounding and suppression effect. Prev. Sci. 1:4173–81 [Google Scholar]
  91. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E. et al. 2015. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol 16:125 [Google Scholar]
  92. McGowan PO, Sasaki A, D'Alessio AC, Dymov S, Labonté B. et al. 2009. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat. Neurosci. 12:3342–48 [Google Scholar]
  93. McGregor K, Bernatsky S, Colmegna I, Hudson M, Pastinen T. et al. 2016. An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies. Genome Biol 17:84 [Google Scholar]
  94. McGregor K, Labbe A, Greenwood CMT. 2017. Response to: correcting for cell-type effects in DNA methylation studies: Reference-based method outperforms latent variable approaches in empirical studies. Genome Biol 18:25 [Google Scholar]
  95. Meaney MJ, Ferguson-Smith AC. 2010. Epigenetic regulation of the neural transcriptome: the meaning of the marks. Nat. Neurosci. 13:111313–18 [Google Scholar]
  96. Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM. et al. 2013. The UCSC Genome Browser database: extensions and updates 2013. Nucleic Acids Res 41:D64–69 [Google Scholar]
  97. Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM. et al. 2013. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10:10949–55 [Google Scholar]
  98. Mill J, Tang T, Kaminsky Z, Khare T, Yazdanpanah S. et al. 2008. Epigenomic profiling reveals DNA-methylation changes associated with major psychosis. Am. J. Hum. Genet. 82:3696–711 [Google Scholar]
  99. Miller GE, Chen E, Fok AK, Walker H, Lim A. et al. 2009. Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. PNAS 106:3414716–21 [Google Scholar]
  100. Miller MW, Sadeh N. 2014. Traumatic stress, oxidative stress and post-traumatic stress disorder: neurodegeneration and the accelerated-aging hypothesis. Mol. Psychiatry 19:111156–62 [Google Scholar]
  101. Miska EA, Ferguson-Smith AC. 2016. Transgenerational inheritance: nodels and mechanisms of non-DNA sequence-based inheritance. Science 354:630859–63 [Google Scholar]
  102. Moran S, Arribas C, Esteller M. 2016. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8:3389–99 [Google Scholar]
  103. Nikolova YS, Hariri AR. 2015. Can we observe epigenetic effects on human brain function. Trends Cogn. Sci. 19:7366–73 [Google Scholar]
  104. Open Sci. Collab. 2015. Estimating the reproducibility of psychological science. Science 349:6251aac4716 [Google Scholar]
  105. Pan P, Fleming AS, Lawson D, Jenkins JM, McGowan PO. 2014. Within- and between-litter maternal care alter behavior and gene regulation in female offspring. Behav. Neurosci. 128:6736–48 [Google Scholar]
  106. Paul DS, Teschendorff AE, Dang MAN, Lowe R, Hawa MI. et al. 2016. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat. Commun. 7:13555 [Google Scholar]
  107. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K. et al. 2015. De novo identification of differentially methylated regions in the human genome. Epigenet. Chromatin 8:6 [Google Scholar]
  108. Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. 2013. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genom 14:293 [Google Scholar]
  109. Pidsley R, Zotenko E, Peters TJ. 2016. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol 17:1208 [Google Scholar]
  110. Price EM, Penaherrera MS, Portales-Casamar E, Pavlidis P, Van Allen MI. et al. 2016. Profiling placental and fetal DNA methylation in human neural tube defects. Epigenet. Chromatin 9:6 [Google Scholar]
  111. Price ME, Cotton AM, Lam LL, Farré P, Emberly E. et al. 2013. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenet. Chromatin 6:14 [Google Scholar]
  112. Radtke KM, Ruf M, Gunter HM, Dohrmann K, Schauer M. et al. 2011. Transgenerational impact of intimate partner violence on methylation in the promoter of the glucocorticoid receptor. Transl. Psychiatry 1:7e21 [Google Scholar]
  113. Rahmani E, Shenhav L, Schweiger R, Yousefi P, Huen K. et al. 2016. Genome-wide methylation data mirror ancestry information. Epigenet. Chromatin 10:1 [Google Scholar]
  114. Rakyan VK, Down TA, Balding DJ, Beck S. 2011. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12:8529–41 [Google Scholar]
  115. Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen S-E. et al. 2012. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLOS ONE 7:7e41361 [Google Scholar]
  116. Relton CL, Davey Smith G. 2012. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41:1161–76 [Google Scholar]
  117. Rivera CM, Ren B. 2013. Mapping human epigenomes. Cell 155:139–55 [Google Scholar]
  118. Schübeler D. 2015. Function and information content of DNA methylation. Nature 517:7534321–26 [Google Scholar]
  119. Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS. et al. 2016. ENCODE data at the ENCODE portal. Nucleic Acids Res 44:D1D726–32 [Google Scholar]
  120. Smith AK, Kilaru V, Klengel T, Mercer KB, Bradley B. et al. 2015. DNA extracted from saliva for methylation studies of psychiatric traits: evidence tissue specificity and relatedness to brain. Am. J. Med. Genet. B 168B:136–44 [Google Scholar]
  121. Smith ZD, Meissner A. 2013. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14:3204–20 [Google Scholar]
  122. Song C-X, Szulwach KE, Dai Q, Fu Y, Mao S-Q. et al. 2013. Genome-wide profiling of 5-formylcytosine reveals its roles in epigenetic priming. Cell 153:3678–91 [Google Scholar]
  123. Stewart SK, Morris TJ, Guilhamon P, Bulstrode H, Bachman M. et al. 2015. oxBS-450K: a method for analysing hydroxymethylation using 450K BeadChips. Methods 72:9–15 [Google Scholar]
  124. Taylor KH, Kramer RS, Davis JW, Guo J, Duff DJ. et al. 2007. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res 67:188511–18 [Google Scholar]
  125. Teh AL, Pan H, Chen L, Ong M-L, Dogra S. et al. 2014. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res 24:71064–74 [Google Scholar]
  126. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J. et al. 2013a. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29:2189–96 [Google Scholar]
  127. Teschendorff AE, West J, Beck S. 2013b. Age-associated epigenetic drift: implications, and a case of epigenetic thrift?. Hum. Mol. Genet. 22:R1R7–15 [Google Scholar]
  128. Teschendorff AE, Zhuang J, Widschwendter M. 2011. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27:111496–505 [Google Scholar]
  129. Turecki G, Meaney MJ. 2016. Effects of the social environment and stress on glucocorticoid receptor gene methylation: a systematic review. Biol. Psychiatry 79:287–96 [Google Scholar]
  130. Unternaehrer E, Luers P, Mill J, Dempster E, Meyer AH. et al. 2012. Dynamic changes in DNA methylation of stress-associated genes (OXTR, BDNF) after acute psychosocial stress. Transl. Psychiatry 2:e150 [Google Scholar]
  131. van Dongen J, Nivard MG, Willemsen G, Hottenga J-J, Helmer Q. et al. 2016. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat. Commun. 7:11115 [Google Scholar]
  132. van Otterdijk SD, Michels KB. 2016. Transgenerational epigenetic inheritance in mammals: How good is the evidence?. FASEB J 30:72457–65 [Google Scholar]
  133. Waddington CH. 1959. Canalization of development and genetic assimilation of acquired characters. Nature 183:46761654–55 [Google Scholar]
  134. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. 2014. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol 15:2R37 [Google Scholar]
  135. Weaver ICG, Cervoni N, Champagne FA, D'Alessio AC, Sharma S. et al. 2004. Epigenetic programming by maternal behavior. Nat. Neurosci. 7:8847–54 [Google Scholar]
  136. Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S. et al. 2007. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39:4457–66 [Google Scholar]
  137. Weidner CI, Lin Q, Koch CM, Eisele L, Beier F. et al. 2014. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol 15:2R24 [Google Scholar]
  138. Wen L, Li X, Yan L, Tan Y, Li R. et al. 2014. Whole-genome analysis of 5-hydroxymethylcytosine and 5-methylcytosine at base resolution in the human brain. Genome Biol 15:3R49 [Google Scholar]
  139. Wu H, Zhang Y. 2014. Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156:1–245–68 [Google Scholar]
  140. Yousefi P, Huen K, Quach H, Motwani G, Hubbard A. et al. 2015. Estimation of blood cellular heterogeneity in newborns and children for epigenome-wide association studies. Environ. Mol. Mutagen. 56:9751–58 [Google Scholar]
  141. Yu M, Hon GC, Szulwach KE, Song C-X, Jin P. et al. 2012. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat. Protoc. 7:122159–70 [Google Scholar]
  142. Zampieri M, Ciccarone F, Calabrese R, Franceschi C, Bürkle A, Caiafa P. 2015. Reconfiguration of DNA methylation in aging. Mech. Ageing Dev. 151:60–70 [Google Scholar]
  143. Zannas AS, Provençal N, Binder EB. 2015. Epigenetics of posttraumatic stress disorder: current evidence, challenges, and future directions. Biol. Psychiatry 78:5327–35 [Google Scholar]
  144. Ziller MJ, Gu H, Müller F, Donaghey J, Tsai LTY. et al. 2013. Charting a dynamic DNA methylation landscape of the human genome. Nature 500:7463477–81 [Google Scholar]
  145. Ziller MJ, Müller F, Liao J, Zhang Y, Gu H. et al. 2011. Genomic distribution and inter-sample variation of non-CpG methylation across human cell types. PLoS Genet. 7:e1002389 [Google Scholar]
  146. Ziller MJ, Stamenova EK, Gu H, Gnirke A, Meissner A. 2016. Targeted bisulfite sequencing of the dynamic DNA methylome. Epigenet. Chromatin 9:55 [Google Scholar]
  147. Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J. 2014. Epigenome-wide association studies without the need for cell-type composition. Nat. Methods 11:3309–11 [Google Scholar]
/content/journals/10.1146/annurev-psych-122414-033653
Loading
/content/journals/10.1146/annurev-psych-122414-033653
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error