Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Origins of human disease: the chrono-epigenetic perspective

Nature Reviews Genetics volume 22, pages 533–546 (2021)Cite this article

Subjects

Abstract

Epigenetics has enriched human disease studies by adding new interpretations to disease features that cannot be explained by genetic and environmental factors. However, identifying causal mechanisms of epigenetic origin has been challenging. New opportunities have risen from recent findings in intra-individual and cyclical epigenetic variation, which includes circadian epigenetic oscillations. Cytosine modifications display deterministic temporal rhythms, which may drive ageing and complex disease. Temporality in the epigenome, or the ‘chrono’ dimension, may help the integration of epigenetic, environmental and genetic disease studies, and reconcile several disparities stemming from the arbitrarily delimited research fields. The ultimate goal of chrono-epigenetics is to predict disease risk, age of onset and disease dynamics from within individual-specific temporal dynamics of epigenomes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Multi-geared network of molecular oscillators.
Fig. 2: Epigenetic oscillations in pseudo-stochastic variability.
Fig. 3: Desynchronosis between internal rhythms and environmental cues.
Fig. 4: Hypothetical features of oscillating modified cytosines during ageing.
Fig. 5: Simulated chrono-epigenetic disease features.

Similar content being viewed by others

Code availability

The source code used in this Perspective is publicly available at https://github.com/saehongoh/chronoepigenetic_simulations

References

  1. Monk, D., Mackay, D. J. G., Eggermann, T., Maher, E. R. & Riccio, A. Genomic imprinting disorders: lessons on how genome, epigenome and environment interact. Nat. Rev. Genet. 20, 235–248 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Castillo-Fernandez, J. E., Spector, T. D. & Bell, J. T. Epigenetics of discordant monozygotic twins: implications for disease. Genome Med. 6, 60 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Smith, G. D. Epidemiology, epigenetics and the “Gloomy Prospect”: embracing randomness in population health research and practice. Int. J. Epidemiol. 40, 537–562 (2011).

    Article  PubMed  Google Scholar 

  6. Stricker, S. H., Köferle, A. & Beck, S. From profiles to function in epigenomics. Nat. Rev. Genet. 18, 51–66 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Miguel-Escalada, I. et al. Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat. Genet. 51, 1137–1148 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Birney, E., Smith, G. D. & Greally, J. M. Epigenome-wide association studies and the interpretation of disease -omics. PLoS Genet. 12, e1006105 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Cedar, H. & Bergman, Y. Programming of DNA methylation patterns. Annu. Rev. Biochem. 81, 97–117 (2012).

    Article  CAS  PubMed  Google Scholar 

  12. Reizel, Y. et al. Postnatal DNA demethylation and its role in tissue maturation. Nat. Commun. 9, 2040 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Greenberg, M. V. C. & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nat. Rev. Mol. Cell Biol. 20, 590–607 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cao, Y., Lopatkin, A. & You, L. Elements of biological oscillations in time and space. Nat. Struct. Mol. Biol. 23, 1030–1034 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Parry, A., Rulands, S. & Reik, W. Active turnover of DNA methylation during cell fate decisions. Nat. Rev. Genet. 22, 59–66 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Kangaspeska, S. et al. Transient cyclical methylation of promoter DNA. Nature 452, 112–115 (2008).

    Article  CAS  PubMed  Google Scholar 

  19. Métivier, R. et al. Cyclical DNA methylation of a transcriptionally active promoter. Nature 452, 45–50 (2008).

    Article  PubMed  CAS  Google Scholar 

  20. Rulands, S. et al. Genome-scale oscillations in DNA methylation during exit from pluripotency. Cell Syst. 7, 63–76.e12 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Harris, K. D., Lloyd, J. P. B., Domb, K., Zilberman, D. & Zemach, A. DNA methylation is maintained with high fidelity in the honey bee germline and exhibits global non-functional fluctuations during somatic development. Epigenetics Chromatin 12, 62 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Xia, L. et al. Daily variation in global and local DNA methylation in mouse livers. PLoS ONE 10, e0118101 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Oh, G. et al. Cytosine modifications exhibit circadian oscillations that are involved in epigenetic diversity and aging. Nat. Commun. 9, 644 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Oh, G. et al. Circadian oscillations of cytosine modification in humans contribute to epigenetic variability, aging, and complex disease. Genome Biol. 20, 2 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Liang, L. et al. Global methylomic and transcriptomic analyses reveal the broad participation of DNA methylation in daily gene expression regulation of Populus trichocarpa. Front. Plant Sci. 10, 243 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Altıntaş, A., Laker, R. C., Garde, C., Barrès, R. & Zierath, J. R. Transcriptomic and epigenomics atlas of myotubes reveals insight into the circadian control of metabolism and development. Epigenomics 12, 701–713 (2020).

    Article  PubMed  CAS  Google Scholar 

  27. Azzi, A. et al. Circadian behavior is light-reprogrammed by plastic DNA methylation. Nat. Neurosci. 17, 377–382 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Li, Y. et al. Epigenetic inheritance of circadian period in clonal cells. eLife 9, e54186 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang, S. When correlation and causation coincide. Bioessays 36, 1–2 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Bestor, T. H., Edwards, J. R. & Boulard, M. Notes on the role of dynamic DNA methylation in mammalian development. Proc. Natl Acad. Sci. USA 112, 6796–6799 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Dopico, X. C. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 6, 7000 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Blewitt, M. & Whitelaw, E. The use of mouse models to study epigenetics. Cold Spring Harb. Perspect. Biol. 5, a017939 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Quadrana, L. & Colot, V. Plant transgenerational epigenetics. Annu. Rev. Genet. 50, 467–491 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Linker, S. M. et al. Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity. Genome Biol. 20, 30 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Laird, C. D. et al. Hairpin-bisulfite PCR: assessing epigenetic methylation patterns on complementary strands of individual DNA molecules. Proc. Natl Acad. Sci. USA 101, 204–209 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Goyal, R., Reinhardt, R. & Jeltsch, A. Accuracy of DNA methylation pattern preservation by the Dnmt1 methyltransferase. Nucleic Acids Res. 34, 1182–1188 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Haerter, J. O., Lövkvist, C., Dodd, I. B. & Sneppen, K. Collaboration between CpG sites is needed for stable somatic inheritance of DNA methylation states. Nucleic Acids Res. 42, 2235–2244 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Busto-Moner, L. et al. Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation. PLoS Comput. Biol. 16, e1007195 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cannon, T. D., Kaprio, J., Lönnqvist, J., Huttunen, M. & Koskenvuo, M. The genetic epidemiology of schizophrenia in a Finnish twin cohort. A population-based modeling study. Arch. Gen. Psychiatry 55, 67–74 (1998).

    Article  CAS  PubMed  Google Scholar 

  40. Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer — analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000).

    Article  CAS  PubMed  Google Scholar 

  41. Pekkanen, J. & Pearce, N. Environmental epidemiology: challenges and opportunities. Environ. Health Perspect. 109, 1–5 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Plomin, R. Commentary: Why are children in the same family so different? Non-shared environment three decades later. Int. J. Epidemiol. 40, 582–592 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wong, C. C. Y. et al. Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits. Mol. Psychiatry 19, 495–503 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Córdova-Palomera, A. et al. Genome-wide methylation study on depression: differential methylation and variable methylation in monozygotic twins. Transl. Psychiatry 5, e557 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Davies, M. N. et al. Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biol. 15, R56 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Dempster, E. L. et al. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol. Psychiatry 76, 977–983 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Byrne, E. M. et al. Monozygotic twins affected with major depressive disorder have greater variance in methylation than their unaffected co-twin. Transl. Psychiatry 3, e269 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Souren, N. Y. et al. DNA methylation signatures of monozygotic twins clinically discordant for multiple sclerosis. Nat. Commun. 10, 2094 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Gervin, K. et al. DNA methylation and gene expression changes in monozygotic twins discordant for psoriasis: identification of epigenetically dysregulated genes. PLoS Genet. 8, e1002454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Webster, A. P. et al. Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins. Genome Med. 10, 64 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Dempster, E. L. et al. Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum. Mol. Genet. 20, 4786–4796 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Paul, D. S. et al. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat. Commun. 7, 13555 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Feinberg, A. P. Epigenetic stochasticity, nuclear structure and cancer: the implications for medicine. J. Intern. Med. 276, 5–11 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Oh, G. et al. DNA modification study of major depressive disorder: beyond locus-by-locus comparisons. Biol. Psychiatry 77, 246–255 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Hannon, E. et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 17, 176 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Masri, S. & Sassone-Corsi, P. The emerging link between cancer, metabolism, and circadian rhythms. Nat. Med. 24, 1795–1803 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Challet, E. The circadian regulation of food intake. Nat. Rev. Endocrinol. 15, 393–405 (2019).

    Article  PubMed  Google Scholar 

  59. Gulick, D. & Gamsby, J. J. Racing the clock: the role of circadian rhythmicity in addiction across the lifespan. Pharmacol. Ther. 188, 124–139 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Zhu, Y. et al. Epigenetic impact of long-term shiftwork: pilot evidence from circadian genes and whole-genome methylation analysis. Chronobiol. Int. 28, 852–861 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Park, C. et al. Stress, epigenetics and depression: a systematic review. Neurosci. Biobehav. Rev. 102, 139–152 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Mahna, D., Puri, S. & Sharma, S. DNA methylation signatures: biomarkers of drug and alcohol abuse. Mutat. Res. 777, 19–28 (2018).

    Article  CAS  Google Scholar 

  63. Zhang, W., Qu, J., Liu, G.-H. & Belmonte, J. C. I. The ageing epigenome and its rejuvenation. Nat. Rev. Mol. Cell Biol. 21, 137–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Fraga, M. F. et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl Acad. Sci. USA 102, 10604–10609 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Poulsen, P., Esteller, M., Vaag, A. & Fraga, M. F. The epigenetic basis of twin discordance in age-related diseases. Pediatr. Res. 61, 38R–42R (2007).

    Article  PubMed  Google Scholar 

  66. Unnikrishnan, A. et al. The role of DNA methylation in epigenetics of aging. Pharmacol. Ther. 195, 172–185 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Bocklandt, S. et al. Epigenetic predictor of age. PLoS ONE 6, e14821 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Fransquet, P. D., Wrigglesworth, J., Woods, R. L., Ernst, M. E. & Ryan, J. The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clin. Epigenetics 11, 1–17 (2019).

    Article  Google Scholar 

  70. Frobel, J., Rahmig, S., Franzen, J., Waskow, C. & Wagner, W. Epigenetic aging of human hematopoietic cells is not accelerated upon transplantation into mice. Clin. Epigenetics 10, 67 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Stölzel, F. et al. Dynamics of epigenetic age following hematopoietic stem cell transplantation. Haematologica 102, e321–e323 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Søraas, A. et al. Epigenetic age is a cell-intrinsic property in transplanted human hematopoietic cells. Aging Cell 18, e12897 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Fedak, K. M., Bernal, A., Capshaw, Z. A. & Gross, S. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerg. Themes Epidemiol. 12, 14 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bergman, Y. & Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).

    Article  CAS  PubMed  Google Scholar 

  76. Timp, W. et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Oh, G. et al. Epigenetic assimilation in the aging human brain. Genome Biol. 17, 76 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Gasparoni, G. et al. DNA methylation analysis on purified neurons and glia dissects age and Alzheimer’s disease-specific changes in the human cortex. Epigenetics Chromatin 11, 41 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Coulson, R. L. et al. Snord116-dependent diurnal rhythm of DNA methylation in mouse cortex. Nat. Commun. 9, 1616 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: in the right place at the right time. Science 361, 1336–1340 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Wu, H. & Zhang, Y. Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156, 45–68 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Avgustinova, A. & Benitah, S. A. Epigenetic control of adult stem cell function. Nat. Rev. Mol. Cell Biol. 17, 643–658 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Hernando-Herraez, I. et al. Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells. Nat. Commun. 10, 4361 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Solanas, G. et al. Aged stem cells reprogram their daily rhythmic functions to adapt to stress. Cell 170, 678–692.e20 (2017).

    Article  CAS  PubMed  Google Scholar 

  85. Levine, D. C. et al. NAD+ controls circadian reprogramming through PER2 nuclear translocation to counter aging. Mol. Cell 78, 835–849.e7 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Sato, S. et al. Circadian reprogramming in the liver identifies metabolic pathways of aging. Cell 170, 664–677.e11 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Zane, L., Sharma, V. & Misteli, T. Common features of chromatin in aging and cancer: cause or coincidence? Trends Cell Biol. 24, 686–694 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Doi, A. et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350–1353 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Musiek, E. S. & Holtzman, D. M. Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science 354, 1004–1008 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Labrie, V. et al. Lactase nonpersistence is directed by DNA-variation-dependent epigenetic aging. Nat. Struct. Mol. Biol. 23, 566–573 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Oh, E. et al. Transcriptional heterogeneity in the lactase gene within cell-type is linked to the epigenome. Sci. Rep. 7, 41843 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Storhaug, C. L., Fosse, S. K. & Fadnes, L. T. Country, regional, and global estimates for lactose malabsorption in adults: a systematic review and meta-analysis. Lancet Gastroenterol. Hepatol. 2, 738–746 (2017).

    Article  PubMed  Google Scholar 

  93. Rasinperä, H. et al. A genetic test which can be used to diagnose adult-type hypolactasia in children. Gut 53, 1571–1576 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Geoffroy, P. A. et al. Bipolar disorder with seasonal pattern: clinical characteristics and gender influences. Chronobiol. Int. 30, 1101–1107 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Ferguson, F. J. et al. Diurnal and seasonal variation in psoriasis symptoms. J. Eur. Acad. Dermatol. Venereol. 35, e45–e47 (2021).

    Article  CAS  PubMed  Google Scholar 

  96. Mori, H. et al. Influence of seasonal changes on disease activity and distribution of affected joints in rheumatoid arthritis. BMC Musculoskelet. Disord. 20, 30 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Ryu, O.-H., Lee, S., Yoo, H. J. & Choi, M.-G. Seasonal variations in glycemic control of type 2 diabetes in Korean women. J. Endocrinol. Invest. 37, 575–581 (2014).

    Article  CAS  PubMed  Google Scholar 

  98. Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).

    Article  CAS  PubMed  Google Scholar 

  99. Fisher, A. J., Medaglia, J. D. & Jeronimus, B. F. Lack of group-to-individual generalizability is a threat to human subjects research. Proc. Natl Acad. Sci. USA 115, E6106–E6115 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Blum, I. D. et al. A highly tunable dopaminergic oscillator generates ultradian rhythms of behavioral arousal. eLife 3, e05105 (2014).

    Article  PubMed Central  Google Scholar 

  101. Bechtel, W. Circadian rhythms and mood disorders: are the phenomena and mechanisms causally related? Front. Psychiatry 6, 118 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Hughes, M. E. et al. Guidelines for genome-scale analysis of biological rhythms. J. Biol. Rhythm. 32, 380–393 (2017).

    Article  CAS  Google Scholar 

  103. Ginno, P. A. et al. A genome-scale map of DNA methylation turnover identifies site-specific dependencies of DNMT and TET activity. Nat. Commun. 11, 2680 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Charlton, J. et al. TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers. Nat. Genet. 52, 819–827 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Scheiermann, C., Kunisaki, Y. & Frenette, P. S. Circadian control of the immune system. Nat. Rev. Immunol. 13, 190–198 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Cornelissen, G. Cosinor-based rhythmometry. Theor. Biol. Med. Model. 11, 16 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Rakyan, V. K., Down, T. A., Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Mill, J. & Heijmans, B. T. From promises to practical strategies in epigenetic epidemiology. Nat. Rev. Genet. 14, 585–594 (2013).

    Article  CAS  PubMed  Google Scholar 

  109. Heijmans, B. T. & Mill, J. Commentary: The seven plagues of epigenetic epidemiology. Int. J. Epidemiol. 41, 74–78 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Michels, K. B. et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10, 949–955 (2013).

    Article  CAS  PubMed  Google Scholar 

  111. Richmond, R. C., Suderman, M., Langdon, R., Relton, C. L. & Davey Smith, G. DNA methylation as a marker for prenatal smoke exposure in adults. Int. J. Epidemiol. 47, 1120–1130 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Dick, K. J. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).

    Article  CAS  PubMed  Google Scholar 

  113. Story Jovanova, O. et al. DNA methylation signatures of depressive symptoms in middle-aged and elderly persons: meta-analysis of multiethnic epigenome-wide studies. JAMA Psychiatry 75, 949–959 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Teschendorff, A. E. & Relton, C. L. Statistical and integrative system-level analysis of DNA methylation data. Nat. Rev. Genet. 19, 129–147 (2018).

    Article  CAS  PubMed  Google Scholar 

  115. Lohoff, F. W. et al. Epigenome-wide association study and multi-tissue replication of individuals with alcohol use disorder: evidence for abnormal glucocorticoid signaling pathway gene regulation. Mol. Psychiatry https://doi.org/10.1038/s41380-020-0734-4 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Hannon, E., Lunnon, K., Schalkwyk, L. & Mill, J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 10, 1024–1032 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Jaffe, A. E. & Kleinman, J. E. Genetic and epigenetic analysis of schizophrenia in blood — a no-brainer? Genome Med. 8, 96 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Teschendorff, A. E. et al. The dynamics of DNA methylation covariation patterns in carcinogenesis. PLoS Comput. Biol. 10, e1003709 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Hannon, E. et al. Elevated polygenic burden for autism is associated with differential DNA methylation at birth. Genome Med. 10, 19 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Teschendorff, A. E., Breeze, C. E., Zheng, S. C. & Beck, S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinforma. 18, 105 (2017).

    Article  CAS  Google Scholar 

  123. Holbrook, J. D., Huang, R.-C., Barton, S. J., Saffery, R. & Lillycrop, K. A. Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies? Epigenomics 9, 1143–1150 (2017).

    Article  CAS  PubMed  Google Scholar 

  124. Clevers, H. et al. What is your conceptual definition of “cell type” in the context of a mature organism? Cell Syst. 4, 255–259 (2017).

    Article  CAS  Google Scholar 

  125. Kriaucionis, S. & Tahiliani, M. Expanding the epigenetic landscape: novel modifications of cytosine in genomic DNA. Cold Spring Harb. Perspect. Biol. 6, a018630 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Bachman, M. et al. 5-Hydroxymethylcytosine is a predominantly stable DNA modification. Nat. Chem. 6, 1049–1055 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Marzi, S. J. et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat. Neurosci. 21, 1618–1627 (2018).

    Article  CAS  PubMed  Google Scholar 

  128. Bell-Pedersen, D. et al. Circadian rhythms from multiple oscillators: lessons from diverse organisms. Nat. Rev. Genet. 6, 544–556 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Reppert, S. M. & Weaver, D. R. Coordination of circadian timing in mammals. Nature 418, 935–941 (2002).

    Article  CAS  PubMed  Google Scholar 

  130. Le Martelot, G. et al. Genome-wide RNA polymerase II profiles and RNA accumulation reveal kinetics of transcription and associated epigenetic changes during diurnal cycles. PLoS Biol. 10, e1001442 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Koike, N. et al. Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science 338, 349–354 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Rey, G. et al. Genome-wide and phase-specific DNA-binding rhythms of BMAL1 control circadian output functions in mouse liver. PLoS Biol. 9, e1000595 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Mure, L. S. et al. Diurnal transcriptome atlas of a primate across major neural and peripheral tissues. Science 359, eaao0318 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Ruben, M. D. et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine. Sci. Transl. Med. 10, eaat8806 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  135. Takahashi, J. S. Transcriptional architecture of the mammalian circadian clock. Nat. Rev. Genet. 18, 164–179 (2017).

    Article  CAS  PubMed  Google Scholar 

  136. Pacheco-Bernal, I., Becerril-Pérez, F. & Aguilar-Arnal, L. Circadian rhythms in the three-dimensional genome: implications of chromatin interactions for cyclic transcription. Clin. Epigenetics 11, 79 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Yeung, J. & Naef, F. Rhythms of the genome: circadian dynamics from chromatin topology, tissue-specific gene expression, to behavior. Trends Genet. 34, 915–926 (2018).

    Article  CAS  PubMed  Google Scholar 

  138. Singh, K., Jha, N. K. & Thakur, A. Spatiotemporal chromatin dynamics — a telltale of circadian epigenetic gene regulation. Life Sci. 221, 377–391 (2019).

    Article  CAS  PubMed  Google Scholar 

  139. Etchegaray, J.-P., Lee, C., Wade, P. A. & Reppert, S. M. Rhythmic histone acetylation underlies transcription in the mammalian circadian clock. Nature 421, 177–182 (2003).

    Article  CAS  PubMed  Google Scholar 

  140. Aguilar-Arnal, L. & Sassone-Corsi, P. Chromatin landscape and circadian dynamics: spatial and temporal organization of clock transcription. Proc. Natl Acad. Sci. USA 112, 6863–6870 (2015).

    Article  CAS  PubMed  Google Scholar 

  141. Beytebiere, J. R. et al. Tissue-specific BMAL1 cistromes reveal that rhythmic transcription is associated with rhythmic enhancer–enhancer interactions. Genes Dev. 33, 294–309 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Aguilar-Arnal, L. et al. Cycles in spatial and temporal chromosomal organization driven by the circadian clock. Nat. Struct. Mol. Biol. 20, 1206–1213 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Mermet, J. et al. Clock-dependent chromatin topology modulates circadian transcription and behavior. Genes Dev. 32, 347–358 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Kim, Y. H. et al. Rev-erbα dynamically modulates chromatin looping to control circadian gene transcription. Science 359, 1274–1277 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Chen, H. et al. Functional organization of the human 4D nucleome. Proc. Natl Acad. Sci. USA 112, 8002–8007 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Fustin, J.-M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013).

    Article  CAS  PubMed  Google Scholar 

  147. Gaucher, J., Montellier, E. & Sassone-Corsi, P. Molecular cogs: interplay between circadian clock and cell cycle. Trends Cell Biol. 28, 368–379 (2018).

    Article  CAS  PubMed  Google Scholar 

  148. Dyar, K. A. et al. Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell 174, 1571–1585.e11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Walker, W. H., Walton, J. C., DeVries, A. C. & Nelson, R. J. Circadian rhythm disruption and mental health. Transl. Psychiatry 10, 28 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Leng, Y., Musiek, E. S., Hu, K., Cappuccio, F. P. & Yaffe, K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol. 18, 307–318 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Stenvers, D. J., Scheer, F. A. J. L., Schrauwen, P., la Fleur, S. E. & Kalsbeek, A. Circadian clocks and insulin resistance. Nat. Rev. Endocrinol. 15, 75–89 (2019).

    Article  PubMed  CAS  Google Scholar 

  152. Guertin, K. A. et al. Time to first morning cigarette and risk of chronic obstructive pulmonary disease: smokers in the PLCO cancer screening trial. PLoS ONE 10, e0125973 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Kurhaluk, N. & Tkachenko, H. Melatonin and alcohol-related disorders. Chronobiol. Int. 37, 781–803 (2020).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This article is dedicated to the memory of V. Labrie-Walters, a friend, colleague and mentor. The authors thank G. Oh, R. Jeremian, K. Koncevičius, A. Kriščiūnas and M. Carlucci for general assistance and comments on the manuscript. This work was supported by grants to A.P. from the Krembil Foundation, the Canadian Institutes of Health and Research (TGH-158223; PJT 148719; IGH-155180; NTC-154084; MOP-133496), Lithuanian Science Foundation (S-MIP-19-66; S-SEN-20-19; 09.3.3-LMT-K-712-17-0008) and Brain Canada.

Author information

Authors and Affiliations

  1. The Krembil Family Epigenetics Laboratory, The Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

    Edward Saehong Oh & Art Petronis

  2. Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania

    Art Petronis

Authors
  1. Edward Saehong Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Art Petronis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Art Petronis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Genetics thanks L. Aguilar Arnal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Acrophase

The time in a cycle when it peaks.

Bradford Hill’s criteria for causal inference

Guidelines to help assess the strength and causal inference of an association. The criteria were originally developed in epidemiological studies.

Chrono-epigenetics

An umbrella term for temporal dynamics of epigenetic processes. When expanded to epigenetic dynamics at the genome scale, ‘chrono-epigenomics’ can be used.

Circannual

Oscillations with periods of approximately 1 year.

CpG islands

Regions in the genome that contain a large frequency of cytosine–guanine dinucleotide (CpG) dinucleotides.

CpG island seas

Regions located 4 kb outside cytosine–guanine dinucleotide (CpG) islands.

CpG island shelves

Regions located 2–4 kb from cytosine–guanine dinucleotide (CpG) islands.

CpG island shores

Regions located 0–2 kb from cytosine–guanine dinucleotide (CpG) islands.

Cytosine modifications

An encompassing term for 5-methylcytosine (5-mC), 5-hydroxymethylcytosine (5-hmC), 5-carboxylcytosine (5-caC) and 5-formylcytosine (5-fC).

Differentially modified positions

(DMPs). Cytosines with different mean modification status in group-wise comparisons.

Differentially variable positions

(DVPs). Cytosines with higher variance in modification status in group-wise comparisons.

Epigenetic clock

A mathematical estimator of epigenetic age using epigenetic marks. Epigenetic age may be similar or different to chronological or biological age.

Epigenetic drift

Random divergence of cytosine modifications within ageing individuals.

Epigenetic oscillations

Oscillating patterns in cytosine modification density due to periodic reprogramming.

Epigenome-wide association studies

(EWAS). Studies of design to derive associations between epigenetic modifications (predominantly cytosine modification) and identifiable phenotypes or traits.

Mesor

A mean value based on the distribution of values across the cycles of the rhythm.

Non-shared environment

Environmental factors that drive phenotypic differences among genetically related individuals.

Ultradian

Oscillations with periods shorter than 24 h.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oh, E.S., Petronis, A. Origins of human disease: the chrono-epigenetic perspective. Nat Rev Genet 22, 533–546 (2021). https://doi.org/10.1038/s41576-021-00348-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-021-00348-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing