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.
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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).
Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).
Castillo-Fernandez, J. E., Spector, T. D. & Bell, J. T. Epigenetics of discordant monozygotic twins: implications for disease. Genome Med. 6, 60 (2014).
Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2012).
Smith, G. D. Epidemiology, epigenetics and the “Gloomy Prospect”: embracing randomness in population health research and practice. Int. J. Epidemiol. 40, 537–562 (2011).
Stricker, S. H., Köferle, A. & Beck, S. From profiles to function in epigenomics. Nat. Rev. Genet. 18, 51–66 (2017).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
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).
Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Birney, E., Smith, G. D. & Greally, J. M. Epigenome-wide association studies and the interpretation of disease -omics. PLoS Genet. 12, e1006105 (2016).
Cedar, H. & Bergman, Y. Programming of DNA methylation patterns. Annu. Rev. Biochem. 81, 97–117 (2012).
Reizel, Y. et al. Postnatal DNA demethylation and its role in tissue maturation. Nat. Commun. 9, 2040 (2018).
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).
Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).
Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).
Cao, Y., Lopatkin, A. & You, L. Elements of biological oscillations in time and space. Nat. Struct. Mol. Biol. 23, 1030–1034 (2016).
Parry, A., Rulands, S. & Reik, W. Active turnover of DNA methylation during cell fate decisions. Nat. Rev. Genet. 22, 59–66 (2021).
Kangaspeska, S. et al. Transient cyclical methylation of promoter DNA. Nature 452, 112–115 (2008).
Métivier, R. et al. Cyclical DNA methylation of a transcriptionally active promoter. Nature 452, 45–50 (2008).
Rulands, S. et al. Genome-scale oscillations in DNA methylation during exit from pluripotency. Cell Syst. 7, 63–76.e12 (2018).
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).
Xia, L. et al. Daily variation in global and local DNA methylation in mouse livers. PLoS ONE 10, e0118101 (2015).
Oh, G. et al. Cytosine modifications exhibit circadian oscillations that are involved in epigenetic diversity and aging. Nat. Commun. 9, 644 (2018).
Oh, G. et al. Circadian oscillations of cytosine modification in humans contribute to epigenetic variability, aging, and complex disease. Genome Biol. 20, 2 (2019).
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).
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).
Azzi, A. et al. Circadian behavior is light-reprogrammed by plastic DNA methylation. Nat. Neurosci. 17, 377–382 (2014).
Li, Y. et al. Epigenetic inheritance of circadian period in clonal cells. eLife 9, e54186 (2020).
Huang, S. When correlation and causation coincide. Bioessays 36, 1–2 (2014).
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).
Dopico, X. C. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 6, 7000 (2015).
Blewitt, M. & Whitelaw, E. The use of mouse models to study epigenetics. Cold Spring Harb. Perspect. Biol. 5, a017939 (2013).
Quadrana, L. & Colot, V. Plant transgenerational epigenetics. Annu. Rev. Genet. 50, 467–491 (2016).
Linker, S. M. et al. Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity. Genome Biol. 20, 30 (2019).
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).
Goyal, R., Reinhardt, R. & Jeltsch, A. Accuracy of DNA methylation pattern preservation by the Dnmt1 methyltransferase. Nucleic Acids Res. 34, 1182–1188 (2006).
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).
Busto-Moner, L. et al. Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation. PLoS Comput. Biol. 16, e1007195 (2020).
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).
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).
Pekkanen, J. & Pearce, N. Environmental epidemiology: challenges and opportunities. Environ. Health Perspect. 109, 1–5 (2001).
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).
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).
Córdova-Palomera, A. et al. Genome-wide methylation study on depression: differential methylation and variable methylation in monozygotic twins. Transl. Psychiatry 5, e557 (2015).
Davies, M. N. et al. Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biol. 15, R56 (2014).
Dempster, E. L. et al. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol. Psychiatry 76, 977–983 (2014).
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).
Souren, N. Y. et al. DNA methylation signatures of monozygotic twins clinically discordant for multiple sclerosis. Nat. Commun. 10, 2094 (2019).
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).
Webster, A. P. et al. Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins. Genome Med. 10, 64 (2018).
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).
Paul, D. S. et al. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat. Commun. 7, 13555 (2016).
Feinberg, A. P. Epigenetic stochasticity, nuclear structure and cancer: the implications for medicine. J. Intern. Med. 276, 5–11 (2014).
Oh, G. et al. DNA modification study of major depressive disorder: beyond locus-by-locus comparisons. Biol. Psychiatry 77, 246–255 (2015).
Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).
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).
Masri, S. & Sassone-Corsi, P. The emerging link between cancer, metabolism, and circadian rhythms. Nat. Med. 24, 1795–1803 (2018).
Challet, E. The circadian regulation of food intake. Nat. Rev. Endocrinol. 15, 393–405 (2019).
Gulick, D. & Gamsby, J. J. Racing the clock: the role of circadian rhythmicity in addiction across the lifespan. Pharmacol. Ther. 188, 124–139 (2018).
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).
Park, C. et al. Stress, epigenetics and depression: a systematic review. Neurosci. Biobehav. Rev. 102, 139–152 (2019).
Mahna, D., Puri, S. & Sharma, S. DNA methylation signatures: biomarkers of drug and alcohol abuse. Mutat. Res. 777, 19–28 (2018).
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).
Fraga, M. F. et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl Acad. Sci. USA 102, 10604–10609 (2005).
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).
Unnikrishnan, A. et al. The role of DNA methylation in epigenetics of aging. Pharmacol. Ther. 195, 172–185 (2019).
Bocklandt, S. et al. Epigenetic predictor of age. PLoS ONE 6, e14821 (2011).
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
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).
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).
Stölzel, F. et al. Dynamics of epigenetic age following hematopoietic stem cell transplantation. Haematologica 102, e321–e323 (2017).
Søraas, A. et al. Epigenetic age is a cell-intrinsic property in transplanted human hematopoietic cells. Aging Cell 18, e12897 (2019).
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).
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).
Bergman, Y. & Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).
Timp, W. et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).
Oh, G. et al. Epigenetic assimilation in the aging human brain. Genome Biol. 17, 76 (2016).
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).
Coulson, R. L. et al. Snord116-dependent diurnal rhythm of DNA methylation in mouse cortex. Nat. Commun. 9, 1616 (2018).
Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: in the right place at the right time. Science 361, 1336–1340 (2018).
Wu, H. & Zhang, Y. Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156, 45–68 (2014).
Avgustinova, A. & Benitah, S. A. Epigenetic control of adult stem cell function. Nat. Rev. Mol. Cell Biol. 17, 643–658 (2016).
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).
Solanas, G. et al. Aged stem cells reprogram their daily rhythmic functions to adapt to stress. Cell 170, 678–692.e20 (2017).
Levine, D. C. et al. NAD+ controls circadian reprogramming through PER2 nuclear translocation to counter aging. Mol. Cell 78, 835–849.e7 (2020).
Sato, S. et al. Circadian reprogramming in the liver identifies metabolic pathways of aging. Cell 170, 664–677.e11 (2017).
Zane, L., Sharma, V. & Misteli, T. Common features of chromatin in aging and cancer: cause or coincidence? Trends Cell Biol. 24, 686–694 (2014).
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).
Musiek, E. S. & Holtzman, D. M. Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science 354, 1004–1008 (2016).
Labrie, V. et al. Lactase nonpersistence is directed by DNA-variation-dependent epigenetic aging. Nat. Struct. Mol. Biol. 23, 566–573 (2016).
Oh, E. et al. Transcriptional heterogeneity in the lactase gene within cell-type is linked to the epigenome. Sci. Rep. 7, 41843 (2017).
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).
Rasinperä, H. et al. A genetic test which can be used to diagnose adult-type hypolactasia in children. Gut 53, 1571–1576 (2004).
Geoffroy, P. A. et al. Bipolar disorder with seasonal pattern: clinical characteristics and gender influences. Chronobiol. Int. 30, 1101–1107 (2013).
Ferguson, F. J. et al. Diurnal and seasonal variation in psoriasis symptoms. J. Eur. Acad. Dermatol. Venereol. 35, e45–e47 (2021).
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).
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).
Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).
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).
Blum, I. D. et al. A highly tunable dopaminergic oscillator generates ultradian rhythms of behavioral arousal. eLife 3, e05105 (2014).
Bechtel, W. Circadian rhythms and mood disorders: are the phenomena and mechanisms causally related? Front. Psychiatry 6, 118 (2015).
Hughes, M. E. et al. Guidelines for genome-scale analysis of biological rhythms. J. Biol. Rhythm. 32, 380–393 (2017).
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).
Charlton, J. et al. TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers. Nat. Genet. 52, 819–827 (2020).
Scheiermann, C., Kunisaki, Y. & Frenette, P. S. Circadian control of the immune system. Nat. Rev. Immunol. 13, 190–198 (2013).
Cornelissen, G. Cosinor-based rhythmometry. Theor. Biol. Med. Model. 11, 16 (2014).
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).
Mill, J. & Heijmans, B. T. From promises to practical strategies in epigenetic epidemiology. Nat. Rev. Genet. 14, 585–594 (2013).
Heijmans, B. T. & Mill, J. Commentary: The seven plagues of epigenetic epidemiology. Int. J. Epidemiol. 41, 74–78 (2012).
Michels, K. B. et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10, 949–955 (2013).
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).
Dick, K. J. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).
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).
Teschendorff, A. E. & Relton, C. L. Statistical and integrative system-level analysis of DNA methylation data. Nat. Rev. Genet. 19, 129–147 (2018).
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).
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).
Jaffe, A. E. & Kleinman, J. E. Genetic and epigenetic analysis of schizophrenia in blood — a no-brainer? Genome Med. 8, 96 (2016).
Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).
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).
Teschendorff, A. E. et al. The dynamics of DNA methylation covariation patterns in carcinogenesis. PLoS Comput. Biol. 10, e1003709 (2014).
Hannon, E. et al. Elevated polygenic burden for autism is associated with differential DNA methylation at birth. Genome Med. 10, 19 (2018).
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).
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).
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).
Kriaucionis, S. & Tahiliani, M. Expanding the epigenetic landscape: novel modifications of cytosine in genomic DNA. Cold Spring Harb. Perspect. Biol. 6, a018630 (2014).
Bachman, M. et al. 5-Hydroxymethylcytosine is a predominantly stable DNA modification. Nat. Chem. 6, 1049–1055 (2014).
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).
Bell-Pedersen, D. et al. Circadian rhythms from multiple oscillators: lessons from diverse organisms. Nat. Rev. Genet. 6, 544–556 (2005).
Reppert, S. M. & Weaver, D. R. Coordination of circadian timing in mammals. Nature 418, 935–941 (2002).
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).
Koike, N. et al. Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science 338, 349–354 (2012).
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).
Mure, L. S. et al. Diurnal transcriptome atlas of a primate across major neural and peripheral tissues. Science 359, eaao0318 (2018).
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).
Takahashi, J. S. Transcriptional architecture of the mammalian circadian clock. Nat. Rev. Genet. 18, 164–179 (2017).
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).
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).
Singh, K., Jha, N. K. & Thakur, A. Spatiotemporal chromatin dynamics — a telltale of circadian epigenetic gene regulation. Life Sci. 221, 377–391 (2019).
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).
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).
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).
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).
Mermet, J. et al. Clock-dependent chromatin topology modulates circadian transcription and behavior. Genes Dev. 32, 347–358 (2018).
Kim, Y. H. et al. Rev-erbα dynamically modulates chromatin looping to control circadian gene transcription. Science 359, 1274–1277 (2018).
Chen, H. et al. Functional organization of the human 4D nucleome. Proc. Natl Acad. Sci. USA 112, 8002–8007 (2015).
Fustin, J.-M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013).
Gaucher, J., Montellier, E. & Sassone-Corsi, P. Molecular cogs: interplay between circadian clock and cell cycle. Trends Cell Biol. 28, 368–379 (2018).
Dyar, K. A. et al. Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell 174, 1571–1585.e11 (2018).
Walker, W. H., Walton, J. C., DeVries, A. C. & Nelson, R. J. Circadian rhythm disruption and mental health. Transl. Psychiatry 10, 28 (2020).
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).
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).
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).
Kurhaluk, N. & Tkachenko, H. Melatonin and alcohol-related disorders. Chronobiol. Int. 37, 781–803 (2020).
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.
The authors declare no competing interests.
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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.
An umbrella term for temporal dynamics of epigenetic processes. When expanded to epigenetic dynamics at the genome scale, ‘chrono-epigenomics’ can be used.
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.
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.
Oscillations with periods shorter than 24 h.
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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
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