Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-14T13:25:27.119Z Has data issue: false hasContentIssue false
  • English
  • Français

Genome organization: connecting the developmental origins of disease and genetic variation

Part of: Liggins Institute, New Zealand and the Japanese DOHaD Society

Published online by Cambridge University Press:  29 August 2017

E. Jacobson ,
M. H. Vickers ,
J. K. Perry  and
J. M. O’Sullivan Open the ORCID record for J. M. O’Sullivan [Opens in a new window]
E. Jacobson
Affiliation:
Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand
M. H. Vickers
Affiliation:
Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand
J. K. Perry
Affiliation:
Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand
J. M. O’Sullivan*
Affiliation:
Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand
*
*Address for correspondence: J. M. O’Sullivan, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand. (Email [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

An adverse early life environment can increase the risk of metabolic and other disorders later in life. Genetic variation can modify an individual’s susceptibility to these environmental challenges. These gene by environment interactions are important, but difficult, to dissect. The nucleus is the primary organelle where environmental responses impact directly on the genetic variants within the genome, resulting in changes to the biology of the genome and ultimately the phenotype. Understanding genome biology requires the integration of the linear DNA sequence, epigenetic modifications and nuclear proteins that are present within the nucleus. The interactions between these layers of information may be captured in the emergent spatial genome organization. As such genome organization represents a key research area for decoding the role of genetic variation in the Developmental Origins of Health and Disease.

Keywords

epigeneticsfunctional annotationgenome organizationGWASSNPs
Type
Review
Information
Journal of Developmental Origins of Health and Disease , Volume 9 , Issue 3: Themed Issue: NZ-Japan , June 2018 , pp. 260 - 265
Check for updates
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Godfrey, KM, Reynolds, RM, Prescott, SL, et al. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 2017; 5, 53–64.Google Scholar
2. O’Reilly, JR, Reynolds, RM. The risk of maternal obesity to the long-term health of the offspring. Clin Endocrinol (Oxf). 2013; 78, 9–16.Google Scholar
3. Ong, M-L, Lin, X, Holbrook, JD. Measuring epigenetics as the mediator of gene/environment interactions in DOHaD. J Dev Orig Health Dis. 2015; 6, 10–16.Google Scholar
4. Carlson, EA. The Gene; A Critical History. 1966. Saunders: Philadelphia.Google Scholar
5. Everson, T. The Gene: A Historical Perspective. 2007. Greenwood Press: Westport.Google Scholar
6. Fox Keller, E. The Century of the Gene. 2000. Harvard University Press: Cambridge.Google Scholar
7. Gerstein, MB, Bruce, C, Rozowsky, JS, et al. What is a gene, post-ENCODE? History and updated definition. Genome Res. 2007; 17, 669–681.CrossRefGoogle ScholarPubMed
8. Lamm, E. The metastable genome: a Lamarckian organ in a Darwinian world? In Transformations of Lamarckism: From Subtle Fluids to Molecular Biology (eds. Jablonka E, Gissis S), 2011; 480pp. MIT Press: Cambridge, Massachusetts.Google Scholar
9. Griffiths, PE, Neumann-Held, EM. The many faces of the gene. Bioscience. 1999; 49, 656–662.Google Scholar
10. Akiva, P, Toporik, A, Edelheit, S, et al. Transcription-mediated gene fusion in the human genome. Genome Res. 2006; 16, 30–36.Google Scholar
11. Spilianakis, CG, Lalioti, MD, Town, T, et al. Interchromosomal associations between alternatively expressed loci. Nature. 2005; 435, 637–645.Google Scholar
12. Dixon, JR, Jung, I, Selvaraj, S, et al. Chromatin architecture reorganization during stem cell differentiation. Nature. 2015; 518, 331–336.Google Scholar
13. Bouwman, BAM, de Laat, W. Getting the genome in shape: the formation of loops, domains and compartments. Genome Biol. 2015; 16, 154.Google Scholar
14. Fraser, J, Ferrai, C, Chiariello, AM, et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol Syst Biol. 2015; 11, 852–852.Google Scholar
15. Rao, SSP, Huntley, MH, Durand, NC, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014; 159, 1665–1680.Google Scholar
16. Bischof, M. Introduction to integrative biophysics. In Integrative Biophysics (eds. Popp F-A, Beloussov L), 2010; pp. 1–115. Springer-Science+Business Media: Dordrecht.Google Scholar
17. O’Sullivan, J, Hendy, M, Pichugina, T, et al. The statistical-mechanics of chromosome conformation capture. Nucleus. 2013; 4, 1–9.Google Scholar
18. Grand, RS, Gehlen, LR, O’Sullivan, JM. Methods for the investigation of chromosome organization. In Advances in Genetics Research (ed. Urbano KV), 2011; 5, 111–129. NOVA: Science publishers; ebook.Google Scholar
19. Kauffman, SA. The Origins of Order: Self Organization and Selection in Evolution. 1993. Oxford University Press: New York.Google Scholar
20. Kapranov, P, Willingham, AT, Gingeras, TR. Genome-wide transcription and the implications for genomic organization. Nat Rev Genet. 2007; 8, 413–423.Google Scholar
21. Dixon, JR, Selvaraj, S, Yue, F, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012; 485, 376–380.Google Scholar
22. de Wit, E, Bouwman, BAM, Zhu, Y, et al. The pluripotent genome in three dimensions is shaped around pluripotency factors. Nature. 2013; 501, 227–231.Google Scholar
23. Krijger, PHL, Di Stefano, B, de Wit, E, et al. Cell-of-origin-specific 3D genome structure acquired during somatic cell reprogramming. Cell Stem Cell. 2016; 18, 597–610.CrossRefGoogle ScholarPubMed
24. Holwerda, SJB, de Laat, W. CTCF: the protein, the binding partners, the binding sites and their chromatin loops. Philos Trans R Soc Lond B Biol Sci. 2013; 368, 20120369.Google Scholar
25. Merkenschlager, M, Nora, EP. CTCF and cohesin in genome folding and transcriptional gene regulation. Annu Rev Genomics Hum Genet. 2016; 17, 17–43.Google Scholar
26. Mizuguchi, T, Fudenberg, G, Mehta, S, et al. Cohesin-dependent globules and heterochromatin shape 3D genome architecture in S. pombe . Nature. 2014; 516, 432–435.Google Scholar
27. Brangwynne, CP, Tompa, P, Pappu, RV. Polymer physics of intracellular phase transitions. Nat Phys. 2015; 11, 899–904.Google Scholar
28. Kampmann, M. Facilitated diffusion in chromatin lattices: mechanistic diversity and regulatory potential. Mol Microbiol. 2005; 57, 889–899.Google Scholar
29. Bénichou, O, Chevalier, C, Meyer, B, Voituriez, R. Facilitated diffusion of proteins on chromatin. Phys Rev Lett. 2011; 106, 38102.CrossRefGoogle ScholarPubMed
30. Erdel, F, Müller-Ott, K, Rippe, K. Establishing epigenetic domains via chromatin-bound histone modifiers. Ann N Y Acad Sci. 2013; 1305, 29–43.Google Scholar
31. Buckley, SM, Aranda-Orgilles, B, Strikoudis, A, et al. Regulation of pluripotency and cellular reprogramming by the ubiquitin-proteasome system. Cell Stem Cell. 2012; 11, 783–798.Google Scholar
32. Kim, DH, Marinov, GK, Pepke, S, et al. Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming. Cell Stem Cell. 2015; 16, 88–101.Google Scholar
33. Grand, RS, Pichugina, T, Gehlen, LR, et al. Chromosome conformation maps in fission yeast reveal cell cycle dependent sub nuclear structure. Nucleic Acids Res. 2014; 42, 12585–12599.Google Scholar
34. Pichugina, T, Sugawara, T, Kaykov, A, et al. A diffusion model for the coordination of DNA replication in Schizosaccharomyces pombe . Sci Rep. 2016; 6, 18757.CrossRefGoogle ScholarPubMed
35. Dryden, NH, Broome, LR, Dudbridge, F, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by capture Hi-C. Genome Res. 2014; 24, 1854–1868.Google Scholar
36. Jäger, R, Migliorini, G, Henrion, M, et al. Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci. Nat Commun. 2015; 6, 6178.Google Scholar
37. Mifsud, B, Tavares-Cadete, F, Young, AN, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet. 2015; 47, 598–606.Google Scholar
38. Williams, A, Spilianakis, CG, Flavell, RA. Interchromosomal association and gene regulation in trans. Trends Genet. 2010; 26, 188–197.CrossRefGoogle ScholarPubMed
39. Felipe Barella, L, ulio Cezar de Oliveira, J, Cezar de Freitas Mathias, P. Pancreatic islets and their roles in metabolic programming. Nutrition. 2014; 30, 373–379.CrossRefGoogle Scholar
40. Vickers, MH. Early life nutrition, epigenetics and programming of later life disease. Nutrients. 2014; 6, 2165–2178.Google Scholar
41. Jarick, I, Vogel, CIG, Scherag, S, et al. Novel common copy number variation for early onset extreme obesity on chromosome 11q11 identified by a genome-wide analysis. Hum Mol Genet. 2011; 20, 840–852.Google Scholar
42. Comuzzie, AG, Cole, SA, Laston, SL, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012; 7, e51954.Google Scholar
43. Fall, T, Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol. 2014; 382, 740–757.Google Scholar
44. Sjögren, M, Lyssenko, V, Jonsson, A, et al. The search for putative unifying genetic factors for components of the metabolic syndrome. Diabetologia. 2008; 51, 2242–2251.Google Scholar
45. Hara, K, Fujita, H, Johnson, TA, et al. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum Mol Genet. 2014; 23, 239–246.Google Scholar
46. Zeggini, E, Scott, LJ, Saxena, R, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008; 40, 638–645.CrossRefGoogle ScholarPubMed
47. Morris, AP, Voight, BF, Teslovich, TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012; 44, 981–990.Google Scholar
48. Sladek, R, Prokopenko, I. Genome-wide association studies of type 2 diabetes. In The Genetics of Type 2 Diabetes and Related Traits: Biology, Physiology and Translation (ed. Florez CJ), 2016; pp. 13–61. Springer International Publishing: Cham.Google Scholar
49. Manolio, TA, Collins, FS, Cox, NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009; 461, 747–753.Google Scholar
50. Vattikuti, S, Guo, J, Chow, CC. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 2012; 8, e1002637.CrossRefGoogle ScholarPubMed
51. Farh, KK, Marson, A, Zhu, J, et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015; 518, 337–343.Google Scholar
52. Schierding, W, Cutfield, WS, O’Sullivan, JM. The missing story behind genome wide association studies: single nucleotide polymorphisms in gene deserts have a story to tell. Front Genet. 2014; 5, 39.Google Scholar
53. Marsman, J, Horsfield, JA. Long distance relationships: enhancer–promoter communication and dynamic gene transcription. Biochim Biophys Acta Gene Regul Mech. 2012; 1819, 1217–1227.Google Scholar
54. Sanyal, A, Lajoie, BR, Jain, G, Dekker, J. The long-range interaction landscape of gene promoters. Nature. 2012; 489, 109–113.Google Scholar
55. Chen, J, Tian, W. Explaining the disease phenotype of intergenic SNP through predicted long range regulation. Nucleic Acids Res. 2016; 44, 8641–8654.Google Scholar
56. Schierding, W, Antony, J, Cutfield, WS, et al. Intergenic GWAS SNPs are key components of the spatial and regulatory network for human growth. Hum Mol Genet. 2016; 25, 3372–3382.Google Scholar
57. Smemo, S, Tena, JJ, Kim, K-H, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 2014; 507, 371–375.Google Scholar
58. Claussnitzer, M, Dankel, SN, Kim, K-H, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015; 373, 895–907.Google Scholar
59. Tolhuis, B, Palstra, RJ, Splinter, E, et al. Looping and interaction between hypersensitive sites in the active β-globin locus. Mol Cell. 2002; 10, 1453–1465.Google Scholar
60. Drissen, R, Palstra, R-J, Gillemans, N, et al. The active spatial organization of the beta-globin locus requires the transcription factor EKLF. Genes Dev. 2004; 18, 2485–2490.Google Scholar
61. Albert, FW, Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat Rev Genet. 2015; 16, 197–212.CrossRefGoogle ScholarPubMed
62. Naumova, N, Smith, EM, Zhan, Y, Dekker, J. Analysis of long-range chromatin interactions using chromosome conformation capture. Methods. 2012; 58, 192–203.Google Scholar
63. Zhao, Z, Tavoosidana, G, Sjölinder, M, et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat Genet. 2006; 38, 1341–1347.Google Scholar
64. Rodley, CDM, Bertels, F, Jones, B, O’Sullivan, JM. Global identification of yeast chromosome interactions using genome conformation capture. Fungal Genet Biol. 2009; 46, 879–886.Google Scholar
65. Schierding, W, O’Sullivan, JM. Connecting SNPs in diabetes: a spatial analysis of meta-GWAS loci. Front Endocrinol (Lausanne). 2015; 6, doi: 10.3389/fendo.2015.00102.Google Scholar
66. Dean, A. In the loop: long range chromatin interactions and gene regulation. Brief Funct Genomics. 2011; 10, 3–10.Google Scholar
67. Harmston, N, Lenhard, B. Chromatin and epigenetic features of long-range gene regulation. Nucleic Acids Res. 2013; 41, 7185–7199.Google Scholar
68. Doss, S. Cis-acting expression quantitative trait loci in mice. Genome Res. 2005; 15, 681–691.Google Scholar
69. Davis, JR, Fresard, L, Knowles, DA, et al. An efficient multiple-testing adjustment for eQTL studies that accounts for linkage disequilibrium between variants. Am J Hum Genet. 2016; 98, 216–224.Google Scholar
70. Corradin, O, Cohen, AJ, Luppino, JM, et al. Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry. Nat Genet. 2016; 48, 1313–1320.Google Scholar
71. Ong, C-T, Corces, VG. CTCF: an architectural protein bridging genome topology and function. Nat Rev Genet. 2014; 15, 239–246.Google Scholar
72. Nora, EP, Goloborodko, A, Valton, A-L, et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell. 2017; 169, 930–944.e22.Google Scholar
73. Wang, H, Maurano, MT, Qu, H, et al. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res. 2012; 22, 1680–1688.CrossRefGoogle ScholarPubMed
74. Maurano, M, Wang, H, John, S, et al. Role of DNA methylation in modulating transcription factor occupancy. Cell Rep. 2015; 12, 1184–1195.Google Scholar
75. Banovich, NE, Lan, X, McVicker, G, et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 2014; 10, e1004663.Google Scholar
76. Flavahan, WA, Drier, Y, Liau, BB, et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature. 2015; 529, 110–114.Google Scholar
77. Martin, P, McGovern, A, Orozco, G, et al. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci. Nat Commun. 2015; 6, 10069.Google Scholar
78. Dekker, J. The three “C” s of chromosome conformation capture: controls, controls, controls. Nat Methods. 2006; 3, 17–21.Google Scholar
79. de Wit, E, de Laat, W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 2012; 26, 11–24.Google Scholar
80. Tak, YG, Farnham, PJ. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenet Chromat. 2015; 8, 57.Google Scholar
81. Kichaev, G, Yang, W-Y, Lindstrom, S, et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 2014; 10, e1004722.Google Scholar
82. Pasaniuc, B, Price, AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet. 2016; 18, 117–127.Google Scholar
83. Huang, Y, Cate, SP, Battistuzzi, C, et al. An association between a functional polymorphism in the monoamine oxidase a gene promoter, impulsive traits and early abuse experiences. Neuropsychopharmacology. 2004; 29, 1498–1505.CrossRefGoogle ScholarPubMed
84. Yilmaz, Z, Davis, C, Loxton, NJ, et al. Association between MC4R rs17782313 polymorphism and overeating behaviors. Int J Obes. 2015; 39, 114–120.Google Scholar
85. Rodley, CDM, Grand, RS, Gehlen, LR, et al. Mitochondrial-nuclear DNA interactions contribute to the regulation of nuclear transcript levels as part of the inter-organelle communication system. PLoS One. 2012; 7, e30943.Google Scholar
86. Doynova, MD, Berretta, A, Jones, MB, et al. Interactions between mitochondrial and nuclear DNA in mammalian cells are non-random. Mitochondrion. 2016; 30, 187–196.Google Scholar
87. Jacobson, E, Perry, JK, Long, DS, et al. A potential role for genome structure in the translation of mechanical force during immune cell development. Nucleus. 2016; 7, 462–475.Google Scholar
88. Lamm, E. The genome as a developmental organ. J Physiol. 2014; 592, 2283–2293.Google Scholar
89. Bard, JBL. Waddington’s legacy to developmental and theoretical biology. Biol Theory. 2008; 3, 188–197.Google Scholar