Twin Research in the Post-Genomic Era: Dissecting the Pathophysiological Effects of Adversity and the Social Environment
Abstract
:1. Introduction
2. Classical Twin Research
3. From the Genomic to the Post-Genomic Era
4. Epigenetics: Moving from Darwinism towards Lamarckism?
5. From Genomics to Epigenomics
5.1. The Importance of Epigenetics in Human Disease
5.2. Twins as a Resource in Epigenetic/Epigenomic Research
5.3. Discordant Twins as a Resource to Reveal Epigenetic/Epigenomic Contributions to Disease: Case Co-Twin Design
5.4. Discordant Twins (Case Co-Twin Design) as a Tool to for Studying the Social Environment
5.5. Environments and Behaviours Triggering Epigenetic Mechanisms
- -
- Physical and social environment. The physical environment comprises mostly daily stressors for the organism like noise or pollution. The social environment comprises threatening or sheltering and buffering social forces at the levels of individual behaviours, direct social relations, membership in social organizations, and the larger social structure and social institutions of the society.
- -
- Stressors and resources. Stressors may stem from the physical or the social environment, e.g., from burdens and aggression in social relationships. Resources can be material (e.g., money) or immaterial (e.g., social recognition), and they may be linked to the individual or family position in the social inequality structure, or they may be located in the social infrastructure at the levels of neighbourhood, region, or welfare states.
- -
- Daily stressors and singular life course events. In the social sciences especially transient stressful life events found attention, like the loss of significant others, unemployment, or singular experiences of a violent attack, Other studies found, however, a stronger impact of more enduring daily hassles [49], which got less attention. Moreover, both kinds of experiences are not independent from one another: singular events can alter daily life for quite a long time, e.g., if the loss of a close relationship severely limits the availability of social support thereafter.
- -
- The relative role of different life course models of risk and adversity, aiming at identifying mechanisms of cumulative advantage and disadvantage, risk accumulation and risk compensation, the occurrence in sensitive periods, and the duration over lifetime.
6. Position and Hypothesis
7. From Theory to Practice
7.1. Proposed Operational Definition of Adversity
- (i)
- Serious negative health events such as illness or disability;
- (ii)
- Been made redundant, becoming unemployed;
- (iii)
- Being demoted or having an imposed reduction in working hours;
- (iv)
- Serious relationship problems or separation from partner;
- (v)
- Been the victim of a serious crime;
- (vi)
- Being the victim of interpersonal psychological or physical violence, as in bullying or intimate partner violence;
- (vii)
- Experiencing poverty, having a major financial problem;
- (viii)
- Failing a formal education/training program;
- (ix)
- Returning to the parental home/having a child return to your home.
7.2. Measuring Socioeconomically Divergent Twins
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Silber, J.H.; Rosenbaum, P.R.; Ross, R.N.; Reiter, J.G.; Niknam, B.A.; Hill, A.S.; Bongiorno, D.M.; Shah, S.A.; Hochman, L.L.; Even-Shoshan, O.; et al. Disparities in Breast Cancer Survival by Socioeconomic Status Despite Medicare and Medicaid Insurance. Milbank Q. 2018, 96, 706–754. [Google Scholar] [CrossRef] [PubMed]
- Lu, G.; Li, J.; Wang, S.; Pu, J.; Sun, H.; Wei, Z.; Ma, Y.; Wang, J.; Ma, H. The fluctuating incidence, improved survival of patients with breast cancer, and disparities by age, race, and socioeconomic status by decade, 1981-2010. Cancer Manag. Res. 2018, 10, 4899–4914. [Google Scholar] [CrossRef] [Green Version]
- Barker, D.J. The fetal origins of coronary heart disease. Acta Paediatr. Suppl. 1997, 422, 78–82. [Google Scholar] [CrossRef]
- Liew, S.H.; Elsner, H.; Spector, T.D.; Hammond, C.J. The first ‘‘classical’’ twin study? Analysis of refractive error using monozygotic and dizygotic twins published in 1922. Twin Res. Hum. Genet. 2005, 8, 198–200. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, G. Twin Studies: What Can They Tell us About Nature and Nurture? Contexts 2005, 4, 43–47. [Google Scholar] [CrossRef] [Green Version]
- Perbal, L. The case of the gene: Postgenomics between modernity and postmodernity. EMBO Rep. 2015, 16, 777–781. [Google Scholar] [CrossRef] [Green Version]
- Bruder, C.E.; Piotrowski, A.; Gijsbers, A.A.; Andersson, R.; Erickson, S.; Diaz de Stahl, T.; Menzel, U.; Sandgren, J.; von Tell, D.; Poplawski, A.; et al. Phenotypically concordant and discordant monozygotic twins display different DNA copy-number-variation profiles. Am. J. Hum. Genet. 2008, 82, 763–771. [Google Scholar] [CrossRef] [Green Version]
- Redon, R.; Ishikawa, S.; Fitch, K.R.; Feuk, L.; Perry, G.H.; Andrews, T.D.; Fiegler, H.; Shapero, M.H.; Carson, A.R.; Chen, W.; et al. Global variation in copy number in the human genome. Nature 2006, 444, 444–454. [Google Scholar] [CrossRef] [Green Version]
- Bodea, G.O.; McKelvey, E.G.Z.; Faulkner, G.J. Retrotransposon-induced mosaicism in the neural genome. Open Biol. 2018, 8. [Google Scholar] [CrossRef] [Green Version]
- Erwin, J.A.; Marchetto, M.C.; Gage, F.H. Mobile DNA elements in the generation of diversity and complexity in the brain. Nat. Rev. Neurosci. 2014, 15, 497–506. [Google Scholar] [CrossRef]
- Panning, B. X-chromosome inactivation: The molecular basis of silencing. J. Biol. 2008, 7, 30. [Google Scholar] [CrossRef] [PubMed]
- Goodship, J.; Carter, J.; Burn, J. X-inactivation patterns in monozygotic and dizygotic female twins. Am. J. Med. Genet. 1996, 61, 205–208. [Google Scholar] [CrossRef]
- Kristiansen, M.; Knudsen, G.P.; Bathum, L.; Naumova, A.K.; Sorensen, T.I.; Brix, T.H.; Svendsen, A.J.; Christensen, K.; Kyvik, K.O.; Orstavik, K.H. Twin study of genetic and aging effects on X chromosome inactivation. Eur. J. Hum. Genet. 2005, 13, 599–606. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huang, Y.; Zhao, Y.; Ren, Y.; Yi, Y.; Li, X.; Gao, Z.; Zhan, X.; Yu, J.; Wang, D.; Liang, S.; et al. Identifying Genomic Variations in Monozygotic Twins Discordant for Autism Spectrum Disorder Using Whole-Genome Sequencing. Mol. Ther. Nucleic Acids 2019, 14, 204–211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Genomes Project, C.; Auton, A.; Brooks, L.D.; Durbin, R.M.; Garrison, E.P.; Kang, H.M.; Korbel, J.O.; Marchini, J.L.; McCarthy, S.; McVean, G.A.; et al. A global reference for human genetic variation. Nature 2015, 526, 68–74. [Google Scholar] [CrossRef] [Green Version]
- Barker, D.J.P. Editorial: The Developmental Origins of Adult Disease. Eur. J. Epidemiol. 2003, 18, 733–736. [Google Scholar] [CrossRef]
- Euser, A.M.; Finken, M.J.; Keijzer-Veen, M.G.; Hille, E.T.; Wit, J.M.; Dekker, F.W.; Dutch, P.-C.S.G. Associations between prenatal and infancy weight gain and BMI, fat mass, and fat distribution in young adulthood: A prospective cohort study in males and females born very preterm. Am. J. Clin. Nutr. 2005, 81, 480–487. [Google Scholar] [CrossRef] [Green Version]
- Monteiro, P.O.; Victora, C.G. Rapid growth in infancy and childhood and obesity in later life--a systematic review. Obes. Rev. 2005, 6, 143–154. [Google Scholar] [CrossRef]
- Oken, E.; Rifas-Shiman, S.L.; Field, A.E.; Frazier, A.L.; Gillman, M.W. Maternal gestational weight gain and offspring weight in adolescence. Obstet. Gynecol. 2008, 112, 999–1006. [Google Scholar] [CrossRef]
- Armitage, J.A.; Poston, L.; Taylor, P.D. Developmental origins of obesity and the metabolic syndrome: The role of maternal obesity. Front. Horm. Res. 2008, 36, 73–84. [Google Scholar] [CrossRef]
- Ong, K.K. Size at birth, postnatal growth and risk of obesity. Horm. Res. 2006, 65 (Suppl. 3), 65–69. [Google Scholar] [CrossRef]
- Heijmans, B.T.; Tobi, E.W.; Stein, A.D.; Putter, H.; Blauw, G.J.; Susser, E.S.; Slagboom, P.E.; Lumey, L.H. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc. Natl. Acad. Sci. USA 2008, 105, 17046–17049. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Soubry, A.; Schildkraut, J.M.; Murtha, A.; Wang, F.; Huang, Z.; Bernal, A.; Kurtzberg, J.; Jirtle, R.L.; Murphy, S.K.; Hoyo, C. Paternal obesity is associated with IGF2 hypomethylation in newborns: Results from a Newborn Epigenetics Study (NEST) cohort. BMC Med. 2013, 11, 29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Morales, E.; Groom, A.; Lawlor, D.A.; Relton, C.L. DNA methylation signatures in cord blood associated with maternal gestational weight gain: Results from the ALSPAC cohort. BMC Res. Notes 2014, 7, 278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Godfrey, K.M.; Sheppard, A.; Gluckman, P.D.; Lillycrop, K.A.; Burdge, G.C.; McLean, C.; Rodford, J.; Slater-Jefferies, J.L.; Garratt, E.; Crozier, S.R.; et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes 2011, 60, 1528–1534. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Waddington, C.H. The epigenotype. 1942. Int. J. Epidemiol. 2012, 41, 10–13. [Google Scholar] [CrossRef] [Green Version]
- Bird, A. Perceptions of epigenetics. Nature 2007, 447, 396–398. [Google Scholar] [CrossRef]
- Van der Maarel, S.M. Epigenetic mechanisms in health and disease. Ann. Rheum. Dis. 2008, 67 (Suppl. 3), iii97–iii100. [Google Scholar] [CrossRef]
- Czyz, W.; Morahan, J.M.; Ebers, G.C.; Ramagopalan, S.V. Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences. BMC Med. 2012, 10, 93. [Google Scholar] [CrossRef] [Green Version]
- Wadhwa, P.D.; Buss, C.; Entringer, S.; Swanson, J.M. Developmental origins of health and disease: Brief history of the approach and current focus on epigenetic mechanisms. Semin. Reprod. Med. 2009, 27, 358–368. [Google Scholar] [CrossRef] [Green Version]
- Rijsdijk, F.V.; Sham, P.C. Analytic approaches to twin data using structural equation models. Brief. Bioinform. 2002, 3, 119–133. [Google Scholar] [CrossRef]
- Tan, Q.; Christiansen, L.; Thomassen, M.; Kruse, T.A.; Christensen, K. Twins for epigenetic studies of human aging and development. Ageing Res. Rev. 2013, 12, 182–187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, C.; Molenaar, P.C.M.; Neiderhiser, J.M. The Impact of Variation in Twin Relatedness on Estimates of Heritability and Environmental Influences. Behav. Genet. 2018, 48, 44–54. [Google Scholar] [CrossRef] [PubMed]
- Tan, Q.; Christiansen, L.; von Bornemann Hjelmborg, J.; Christensen, K. Twin methodology in epigenetic studies. J. Exp. Biol. 2015, 218, 134–139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Castillo-Fernandez, J.E.; Spector, T.D.; Bell, J.T. Epigenetics of discordant monozygotic twins: Implications for disease. Genome Med. 2014, 6, 60. [Google Scholar] [CrossRef]
- Bell, J.T.; Spector, T.D. A twin approach to unraveling epigenetics. Trends Genet. 2011, 27, 116–125. [Google Scholar] [CrossRef] [Green Version]
- Craig, J.M. Epigenetics in Twin Studies. Med Epigenetics 2013, 1, 78–87. [Google Scholar] [CrossRef]
- Turkheimer, E.; Waldron, M. Nonshared environment: A theoretical, methodological, and quantitative review. Psychol. Bull. 2000, 126, 78–108. [Google Scholar] [CrossRef]
- Lam, J.R.; Tyler, J.; Scurrah, K.J.; Reavley, N.J.; Dite, G.S. The Association between Socioeconomic Status and Psychological Distress: A Within and Between Twin Study. Twin Res. Hum. Genet. 2019, 22, 312–320. [Google Scholar] [CrossRef]
- Bowyer, R.C.E.; Jackson, M.A.; Le Roy, C.I.; Ni Lochlainn, M.; Spector, T.D.; Dowd, J.B.; Steves, C.J. Socioeconomic Status and the Gut Microbiome: A TwinsUK Cohort Study. Microorganisms 2019, 7, 17. [Google Scholar] [CrossRef] [Green Version]
- Van Baak, T.E.; Coarfa, C.; Dugue, P.A.; Fiorito, G.; Laritsky, E.; Baker, M.S.; Kessler, N.J.; Dong, J.; Duryea, J.D.; Silver, M.J.; et al. Epigenetic supersimilarity of monozygotic twin pairs. Genome Biol. 2018, 19, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ollikainen, M.; Smith, K.R.; Joo, E.J.; Ng, H.K.; Andronikos, R.; Novakovic, B.; Abdul Aziz, N.K.; Carlin, J.B.; Morley, R.; Saffery, R.; et al. DNA methylation analysis of multiple tissues from newborn twins reveals both genetic and intrauterine components to variation in the human neonatal epigenome. Hum. Mol. Genet. 2010, 19, 4176–4188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gordon, L.; Joo, J.E.; Powell, J.E.; Ollikainen, M.; Novakovic, B.; Li, X.; Andronikos, R.; Cruickshank, M.N.; Conneely, K.N.; Smith, A.K.; et al. Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence. Genome Res. 2012, 22, 1395–1406. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Talens, R.P.; Christensen, K.; Putter, H.; Willemsen, G.; Christiansen, L.; Kremer, D.; Suchiman, H.E.; Slagboom, P.E.; Boomsma, D.I.; Heijmans, B.T. Epigenetic variation during the adult lifespan: Cross-sectional and longitudinal data on monozygotic twin pairs. Aging Cell 2012, 11, 694–703. [Google Scholar] [CrossRef]
- Fraga, M.F.; Ballestar, E.; Paz, M.F.; Ropero, S.; Setien, F.; Ballestar, M.L.; Heine-Suner, D.; Cigudosa, J.C.; Urioste, M.; Benitez, J.; et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl. Acad. Sci. USA 2005, 102, 10604–10609. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fraga, M.F.; Esteller, M. Epigenetics and aging: The targets and the marks. Trends Genet. 2007, 23, 413–418. [Google Scholar] [CrossRef]
- Feinberg, A.P. The Key Role of Epigenetics in Human Disease Prevention and Mitigation. N. Engl. J. Med. 2018, 378, 1323–1334. [Google Scholar] [CrossRef]
- Sierra, M.I.; Fernandez, A.F.; Fraga, M.F. Epigenetics of Aging. Curr. Genom. 2015, 16, 435–440. [Google Scholar] [CrossRef] [Green Version]
- Zannas, A.S.; Arloth, J.; Carrillo-Roa, T.; Iurato, S.; Röh, S.; Ressler, K.J.; Nemeroff, C.B.; Smith, A.K.; Bradley, B.; Heim, C.J.G.b. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: Relevance of glucocorticoid signaling. Genome Biol. 2015, 16, 266. [Google Scholar] [CrossRef] [Green Version]
- Grova, N.; Schroeder, H.; Olivier, J.L.; Turner, J.D. Epigenetic and Neurological Impairments Associated with Early Life Exposure to Persistent Organic Pollutants. Int. J. Genom. 2019, 2019, 2085496. [Google Scholar] [CrossRef] [Green Version]
- Alfano, R.; Herceg, Z.; Nawrot, T.S.; Chadeau-Hyam, M.; Ghantous, A.; Plusquin, M. The Impact of Air Pollution on Our Epigenome: How Far Is the Evidence? (A Systematic Review). Curr. Environ. Health Rep. 2018, 5, 544–578. [Google Scholar] [CrossRef] [PubMed]
- Ward-Caviness, C.K.; Nwanaji-Enwerem, J.C.; Wolf, K.; Wahl, S.; Colicino, E.; Trevisi, L.; Kloog, I.; Just, A.C.; Vokonas, P.; Cyrys, J.; et al. Long-term exposure to air pollution is associated with biological aging. Oncotarget 2016, 7, 74510–74525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, H.; Chen, R.; Cai, J.; Cui, X.; Huang, N.; Kan, H. Short-term exposure to fine particulate air pollution and genome-wide DNA methylation: A randomized, double-blind, crossover trial. Environ. Int. 2018, 120, 130–136. [Google Scholar] [CrossRef] [PubMed]
- Zakarya, R.; Adcock, I.; Oliver, B.G. Epigenetic impacts of maternal tobacco and e-vapour exposure on the offspring lung. Clin. Epigenetics 2019, 11, 32. [Google Scholar] [CrossRef]
- Alghanim, H.; Wu, W.; McCord, B. DNA methylation assay based on pyrosequencing for determination of smoking status. Electrophoresis 2018, 39, 2806–2814. [Google Scholar] [CrossRef]
- Philibert, R.; Dogan, M.; Noel, A.; Miller, S.; Krukow, B.; Papworth, E.; Cowley, J.; Long, J.D.; Beach, S.R.H.; Black, D.W. Dose Response and Prediction Characteristics of a Methylation Sensitive Digital PCR Assay for Cigarette Consumption in Adults. Front. Genet. 2018, 9, 137. [Google Scholar] [CrossRef] [Green Version]
- Liu, F.; Killian, J.K.; Yang, M.; Walker, R.L.; Hong, J.A.; Zhang, M.; Davis, S.; Zhang, Y.; Hussain, M.; Xi, S.; et al. Epigenomic alterations and gene expression profiles in respiratory epithelia exposed to cigarette smoke condensate. Oncogene 2010, 29, 3650–3664. [Google Scholar] [CrossRef]
- Sundar, I.K.; Rahman, I. Gene expression profiling of epigenetic chromatin modification enzymes and histone marks by cigarette smoke: Implications for COPD and lung cancer. Am. J. Physiol. Lung Cell. Mol. Physiol. 2016, 311, L1245–L1258. [Google Scholar] [CrossRef] [Green Version]
- Glass, K.; Thibault, D.; Guo, F.; Mitchel, J.A.; Pham, B.; Qiu, W.; Li, Y.; Jiang, Z.; Castaldi, P.J.; Silverman, E.K.; et al. Integrative epigenomic analysis in differentiated human primary bronchial epithelial cells exposed to cigarette smoke. Sci. Rep. 2018, 8, 12750. [Google Scholar] [CrossRef]
- Simpkin, A.J.; Hemani, G.; Suderman, M.; Gaunt, T.R.; Lyttleton, O.; McArdle, W.L.; Ring, S.M.; Sharp, G.C.; Tilling, K.; Horvath, S.; et al. Prenatal and early life influences on epigenetic age in children: A study of mother-offspring pairs from two cohort studies. Hum. Mol. Genet. 2016, 25, 191–201. [Google Scholar] [CrossRef] [Green Version]
- Crider, K.S.; Yang, T.P.; Berry, R.J.; Bailey, L.B. Folate and DNA Methylation: A Review of Molecular Mechanisms and the Evidence for Folate’s Role. Adv. Nutr. 2012, 3, 21–38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Choi, S.W.; Friso, S. Epigenetics: A New Bridge between Nutrition and Health. Adv. Nutr. 2010, 1, 8–16. [Google Scholar] [CrossRef] [PubMed]
- Kok, D.E.; Dhonukshe-Rutten, R.A.; Lute, C.; Heil, S.G.; Uitterlinden, A.G.; van der Velde, N.; van Meurs, J.B.; van Schoor, N.M.; Hooiveld, G.J.; de Groot, L.C.; et al. The effects of long-term daily folic acid and vitamin B12 supplementation on genome-wide DNA methylation in elderly subjects. Clin. Epigenetics 2015, 7, 121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tiffon, C. The Impact of Nutrition and Environmental Epigenetics on Human Health and Disease. Int. J. Mol. Sci. 2018, 19, 3425. [Google Scholar] [CrossRef] [Green Version]
- Pandey, S.C.; Kyzar, E.J.; Zhang, H. Epigenetic basis of the dark side of alcohol addiction. Neuropharmacology 2017, 122, 74–84. [Google Scholar] [CrossRef]
- Pandey, S.C.; Ugale, R.; Zhang, H.; Tang, L.; Prakash, A. Brain chromatin remodeling: A novel mechanism of alcoholism. J. Neurosci. 2008, 28, 3729–3737. [Google Scholar] [CrossRef]
- Mews, P.; Egervari, G.; Nativio, R.; Sidoli, S.; Donahue, G.; Lombroso, S.I.; Alexander, D.C.; Riesche, S.L.; Heller, E.A.; Nestler, E.J.; et al. Alcohol metabolism contributes to brain histone acetylation. Nature 2019, 574, 717–721. [Google Scholar] [CrossRef]
- Hagerty, S.L.; Bidwell, L.C.; Harlaar, N.; Hutchison, K.E. An Exploratory Association Study of Alcohol Use Disorder and DNA Methylation. Alcohol. Clin. Exp. Res. 2016, 40, 1633–1640. [Google Scholar] [CrossRef] [Green Version]
- Liu, C.; Marioni, R.E.; Hedman, A.K.; Pfeiffer, L.; Tsai, P.C.; Reynolds, L.M.; Just, A.C.; Duan, Q.; Boer, C.G.; Tanaka, T.; et al. A DNA methylation biomarker of alcohol consumption. Mol. Psychiatry 2018, 23, 422–433. [Google Scholar] [CrossRef]
- Wilson, L.E.; Xu, Z.; Harlid, S.; White, A.J.; Troester, M.A.; Sandler, D.P.; Taylor, J.A. Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue. Am. J. Epidemiol. 2019, 188, 1055–1065. [Google Scholar] [CrossRef] [Green Version]
- Kim, D.S.; Kim, Y.H.; Lee, W.K.; Na, Y.K.; Hong, H.S. Effect of alcohol consumption on peripheral blood Alu methylation in Korean men. Biomarkers 2016, 21, 243–248. [Google Scholar] [CrossRef] [PubMed]
- Vaiserman, A. Epidemiologic evidence for association between adverse environmental exposures in early life and epigenetic variation: A potential link to disease susceptibility? Clin. Epigenetics 2015, 7, 96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vick, A.D.; Burris, H.H. Epigenetics and Health Disparities. Curr. Epidemiol. Rep. 2017, 4, 31–37. [Google Scholar] [CrossRef] [Green Version]
- Vinkers, C.H.; Kalafateli, A.L.; Rutten, B.P.; Kas, M.J.; Kaminsky, Z.; Turner, J.D.; Boks, M.P. Traumatic stress and human DNA methylation: A critical review. Epigenomics 2015, 7, 593–608. [Google Scholar] [CrossRef] [PubMed]
- Yehuda, R.; Daskalakis, N.P.; Bierer, L.M.; Bader, H.N.; Klengel, T.; Holsboer, F.; Binder, E.B. Holocaust Exposure Induced Intergenerational Effects on FKBP5 Methylation. Biol. Psychiatry 2016, 80, 372–380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yehuda, R.; Flory, J.D.; Bierer, L.M.; Henn-Haase, C.; Lehrner, A.; Desarnaud, F.; Makotkine, I.; Daskalakis, N.P.; Marmar, C.R.; Meaney, M.J. Lower methylation of glucocorticoid receptor gene promoter 1F in peripheral blood of veterans with posttraumatic stress disorder. Biol. Psychiatry 2015, 77, 356–364. [Google Scholar] [CrossRef]
- Yehuda, R.; Daskalakis, N.P.; Lehrner, A.; Desarnaud, F.; Bader, H.N.; Makotkine, I.; Flory, J.D.; Bierer, L.M.; Meaney, M.J. Influences of maternal and paternal PTSD on epigenetic regulation of the glucocorticoid receptor gene in Holocaust survivor offspring. Am. J. Psychiatry 2014, 171, 872–880. [Google Scholar] [CrossRef]
- Kim, D.; Kubzansky, L.D.; Baccarelli, A.; Sparrow, D.; Spiro, A., 3rd; Tarantini, L.; Cantone, L.; Vokonas, P.; Schwartz, J. Psychological factors and DNA methylation of genes related to immune/inflammatory system markers: The VA Normative Aging Study. BMJ Open 2016, 6, e009790. [Google Scholar] [CrossRef] [Green Version]
- Bam, M.; Yang, X.; Zumbrun, E.E.; Zhong, Y.; Zhou, J.; Ginsberg, J.P.; Leyden, Q.; Zhang, J.; Nagarkatti, P.S.; Nagarkatti, M. Dysregulated immune system networks in war veterans with PTSD is an outcome of altered miRNA expression and DNA methylation. Sci. Rep. 2016, 6, 31209. [Google Scholar] [CrossRef]
- Elwenspoek, M.M.C.; Hengesch, X.; Leenen, F.A.D.; Schritz, A.; Sias, K.; Schaan, V.K.; Meriaux, S.B.; Schmitz, S.; Bonnemberger, F.; Schachinger, H.; et al. Proinflammatory T Cell Status Associated with Early Life Adversity. J. Immunol. 2017, 199, 4046–4055. [Google Scholar] [CrossRef] [Green Version]
- Elwenspoek, M.M.C.; Sias, K.; Hengesch, X.; Schaan, V.K.; Leenen, F.A.D.; Adams, P.; Meriaux, S.B.; Schmitz, S.; Bonnemberger, F.; Ewen, A.; et al. T Cell Immunosenescence after Early Life Adversity: Association with Cytomegalovirus Infection. Front. Immunol. 2017, 8, 1263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Reid, B.M.; Coe, C.L.; Doyle, C.M.; Sheerar, D.; Slukvina, A.; Donzella, B.; Gunnar, M.R. Persistent Skewing of the T-Cell Profile in Adolescents Adopted Internationally from Institutional Care. Brain Behav. Immun. 2019. [Google Scholar] [CrossRef] [PubMed]
- Turner, J.D. Holistic, personalized, immunology? The effects of socioeconomic status on the transcriptional milieu of immune cells. Pediatr. Pulmonol. 2018. [Google Scholar] [CrossRef] [PubMed]
- McGuinness, D.; McGlynn, L.M.; Johnson, P.C.; MacIntyre, A.; Batty, G.D.; Burns, H.; Cavanagh, J.; Deans, K.A.; Ford, I.; McConnachie, A.; et al. Socio-economic status is associated with epigenetic differences in the pSoBid cohort. Int. J. Epidemiol. 2012, 41, 151–160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- van Dongen, J.; Nivard, M.G.; Willemsen, G.; Hottenga, J.J.; Helmer, Q.; Dolan, C.V.; Ehli, E.A.; Davies, G.E.; van Iterson, M.; Breeze, C.E.; et al. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat. Commun. 2016, 7, 11115. [Google Scholar] [CrossRef] [Green Version]
- Talens, R.P.; Boomsma, D.I.; Tobi, E.W.; Kremer, D.; Jukema, J.W.; Willemsen, G.; Putter, H.; Slagboom, P.E.; Heijmans, B.T. Variation, patterns, and temporal stability of DNA methylation: Considerations for epigenetic epidemiology. FASEB J. 2010, 24, 3135–3144. [Google Scholar] [CrossRef]
- Miller, G.E.; Chen, E.; Shalowitz, M.U.; Story, R.E.; Leigh, A.K.K.; Ham, P.; Arevalo, J.M.G.; Cole, S.W. Divergent transcriptional profiles in pediatric asthma patients of low and high socioeconomic status. Pediatr. Pulmonol. 2018, 53, 710–719. [Google Scholar] [CrossRef]
- Stringhini, S.; Polidoro, S.; Sacerdote, C.; Kelly, R.S.; van Veldhoven, K.; Agnoli, C.; Grioni, S.; Tumino, R.; Giurdanella, M.C.; Panico, S.; et al. Life-course socioeconomic status and DNA methylation of genes regulating inflammation. Int. J. Epidemiol. 2015, 44, 1320–1330. [Google Scholar] [CrossRef] [Green Version]
- Needham, B.L.; Smith, J.A.; Zhao, W.; Wang, X.; Mukherjee, B.; Kardia, S.L.; Shively, C.A.; Seeman, T.E.; Liu, Y.; Diez Roux, A.V. Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis. Epigenetics 2015, 10, 958–969. [Google Scholar] [CrossRef] [Green Version]
- Gallo, W.T.; Teng, H.M.; Falba, T.A.; Kasl, S.V.; Krumholz, H.M.; Bradley, E.H. The impact of late career job loss on myocardial infarction and stroke: A 10 year follow up using the health and retirement survey. Occup. Environ. Med. 2006, 63, 683–687. [Google Scholar] [CrossRef]
- Cairney, J.; Krause, N. Negative life events and age-related decline in mastery: Are older adults more vulnerable to the control-eroding effect of stress? J. Gerontol. B Psychol. Sci. Soc. Sci. 2008, 63, S162–S170. [Google Scholar] [CrossRef] [PubMed]
- Bosch, J.A.; Fischer, J.E.; Fischer, J.C. Psychologically adverse work conditions are associated with CD8+ T cell differentiation indicative of immunesenescence. Brain Behav. Immun. 2009, 23, 527–534. [Google Scholar] [CrossRef] [PubMed]
- Tamers, S.L.; Okechukwu, C.; Bohl, A.A.; Gueguen, A.; Goldberg, M.; Zins, M. The impact of stressful life events on excessive alcohol consumption in the French population: Findings from the GAZEL cohort study. PLoS ONE 2014, 9, e87653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cleland, C.; Kearns, A.; Tannahill, C.; Ellaway, A. The impact of life events on adult physical and mental health and well-being: Longitudinal analysis using the GoWell health and well-being survey. BMC Res. Notes 2016, 9, 470. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baier, T.; Lang, V.J.S.S. The Social Stratification of Environmental and Genetic Influences on Education: New Evidence Using a Register-Based Twin Sample. Sociol. Sci. 2019, 6, 143–171. [Google Scholar] [CrossRef] [Green Version]
- Diewald, M.; Riemann, R.; Spinath, F.; Gottschling, J.; Hahn, E.; Kornadt, A.; Peters, A.J.Z. TwinLife: GESIS Data Archive; Accession Number ZA6701, Data file Version 3.0.0; Leibnitz Institute for the Social Sciences: Cologne, Germany, 2019. [Google Scholar] [CrossRef]
- Gottschling, J.; Hahn, E.; Beam, C.; Spinath, F.M.; Carroll, S.; Turkheimer, E. Socioeconomic status amplifies genetic effects in middle childhood in a large German twin sample. Intelligence 2019, 72, 20–27. [Google Scholar] [CrossRef]
- Hahn, E.; Gottschling, J.; Bleidorn, W.; Kandler, C.; Spengler, M.; Kornadt, A.E.; Schulz, W.; Schunck, R.; Baier, T.; Krell, K.J.T.R.; et al. What drives the development of social inequality over the life course? The German TwinLife study. Twin Res. Hum. Genet. 2016, 19, 659–672. [Google Scholar] [CrossRef] [Green Version]
- Lang, V.; Kottwitz, A. The sampling design and socio-demographic structure of the first wave of the TwinLife panel study: A comparison with the Microcensus TwinLife Technical Report Series. In Bielefeld: Project TwinLife “Genetic and Social Causes of Life Chances”; Universität Bielefeld/Universität des Saarlandes: Bielefeld, Germany, 2017; Volume 3, updated version. [Google Scholar]
- Schur, E.A.; Kleinhans, N.M.; Goldberg, J.; Buchwald, D.S.; Polivy, J.; Del Parigi, A.; Maravilla, K.R. Acquired differences in brain responses among monozygotic twins discordant for restrained eating. Physiol. Behav. 2012, 105, 560–567. [Google Scholar] [CrossRef] [Green Version]
Age 11 | Age 17 | Age 23–24 | ||||
---|---|---|---|---|---|---|
Life Events 1 | Discordant | Concordant | Discordant | Concordant | Discordant | Concordant |
Experience with discrimination | 122 | 844 | 130 | 906 | ||
Own separation | NE | NE | 200 | 368 | 162 | 244 |
Separation parents | 10 | 514 | 34 | 534 | 16 | 390 |
New relationship parents | 42 | 482 | 34 | 534 | 32 | 374 |
Own money worries | NE | NE | 38 | 530 | 80 | 326 |
Money worries family members | 28 | 466 | 88 | 470 | 96 | 308 |
Own accident/illness | 184 | 340 | 164 | 404 | 116 | 290 |
Accident/illness family members | 146 | 370 | 190 | 380 | 124 | 282 |
Own job loss | NE | NE | 28 | 540 | 48 | 356 |
Job loss parents | 50 | 470 | 90 | 478 | 72 | 334 |
Dropping out from voc. training/university | 12 | 108 | 76 | 230 | ||
Victim of violence | 34 | 488 | 66 | 504 | 72 | 334 |
Victim violence family member | 46 | 464 | 100 | 468 | 98 | 306 |
Death family member | 82 | 438 | 104 | 464 | 72 | 334 |
Discordance in number of events experienced 2 | 88 | 198 | 184 | 254 | 162 | 192 |
Age 11 | Age 17 | Age 23–24 | ||||
---|---|---|---|---|---|---|
Discordant | Concordant | Discordant | Concordant | Discordant | Concordant | |
Evaluation of life events 1: | ||||||
Own separation | NE | NE | 54 | 54 | 68 | 74 |
Separation parents | 10 | 26 | 34 | 86 | 24 | 74 |
New relationship parents | 6 | 24 | 30 | 88 | 36 | 60 |
Own money worries | NE | NE | 6 | 4 | 8 | 22 |
Money worries family members | 8 | 14 | 10 | 28 | 12 | 40 |
Own accident/illness | 18 | 44 | 16 | 28 | 8 | 24 |
Accident/illness family members | 24 | 118 | 38 | 174 | 24 | 170 |
Own job loss | NE | NE | 2 | 0 | 6 | 6 |
Job loss parents | 14 | 22 | 18 | 30 | 24 | 40 |
Victim of violence | 0 | 4 | 6 | 14 | 8 | 14 |
Victim violence family member | 4 | 12 | 4 | 20 | 8 | 14 |
Death family member | 40 | 214 | 36 | 312 | 46 | 222 |
Chaotic home (CHAOS scale)2 | ||||||
Regular bedtime routine | 232 | 556 | NE | NE | NE | NE |
Cannot clearly think at home | 180 | 580 | 176 | 788 | 372 | 770 |
Chaotic home | 138 | 648 | 172 | 806 | 398 | 630 |
Everything under control | 128 | 652 | 96 | 870 | 402 | 622 |
TV almost always on | 174 | 630 | 182 | 800 | 422 | 616 |
Quiet atmosphere | 228 | 564 | 164 | 810 | 474 | 556 |
CHAOS discordance scale3 | 110 | 582 | 150 | 794 | 162 | 842 |
Age 11 | Age 17 | Age 23–24 | ||||
---|---|---|---|---|---|---|
Self-Report Illness (Reported/Not Reported) | Discordant | Concordant | Discordant | Concordant | Discordant | Concordant |
Sleeping disorder | 42 | 952 | 48 | 996 | 42 | 952 |
Diabetes | 4 | 990 | 4 | 1040 | 4 | 990 |
Asthma | 100 | 894 | 100 | 944 | 100 | 894 |
Heart disease | 36 | 958 | 26 | 1018 | 36 | 958 |
Cancer | 8 | 986 | 14 | 1030 | 8 | 986 |
Stroke | 6 | 988 | 2 | 1042 | 6 | 988 |
Migraine | 54 | 940 | 92 | 952 | 54 | 940 |
High blood pressure | 22 | 972 | 34 | 1010 | 22 | 972 |
Anxiety disorder | 28 | 966 | 32 | 1012 | 28 | 966 |
Alcohol addiction | 6 | 988 | 0 | 1044 | 6 | 988 |
Depression | 36 | 958 | 76 | 968 | 36 | 958 |
Degenerative joint disease | 26 | 968 | 38 | 1006 | 26 | 968 |
Chronic back problems | 58 | 936 | 56 | 988 | 58 | 936 |
Physical disability | 22 | 972 | 24 | 1020 | 22 | 972 |
Other physical/mental illness | 114 | 880 | 170 | 874 | 114 | 880 |
No illness/disease diagnosed | 326 | 668 | 358 | 686 | 326 | 668 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Turner, J.D.; D’Ambrosio, C.; Vögele, C.; Diewald, M. Twin Research in the Post-Genomic Era: Dissecting the Pathophysiological Effects of Adversity and the Social Environment. Int. J. Mol. Sci. 2020, 21, 3142. https://doi.org/10.3390/ijms21093142
Turner JD, D’Ambrosio C, Vögele C, Diewald M. Twin Research in the Post-Genomic Era: Dissecting the Pathophysiological Effects of Adversity and the Social Environment. International Journal of Molecular Sciences. 2020; 21(9):3142. https://doi.org/10.3390/ijms21093142
Chicago/Turabian StyleTurner, Jonathan D., Conchita D’Ambrosio, Claus Vögele, and Martin Diewald. 2020. "Twin Research in the Post-Genomic Era: Dissecting the Pathophysiological Effects of Adversity and the Social Environment" International Journal of Molecular Sciences 21, no. 9: 3142. https://doi.org/10.3390/ijms21093142
APA StyleTurner, J. D., D’Ambrosio, C., Vögele, C., & Diewald, M. (2020). Twin Research in the Post-Genomic Era: Dissecting the Pathophysiological Effects of Adversity and the Social Environment. International Journal of Molecular Sciences, 21(9), 3142. https://doi.org/10.3390/ijms21093142