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Differential DNA methylation in peripheral blood mononuclear cells in adolescents exposed to significant early but not later childhood adversity

Published online by Cambridge University Press:  05 February 2016

Elisa A. Esposito ,
Meaghan J. Jones ,
Jenalee R. Doom ,
Julia L MacIsaac ,
Megan R. Gunnar  and
Michael S. Kobor
Elisa A. Esposito
Affiliation:
University of Minnesota Institute of Child Development Widener University
Meaghan J. Jones
Affiliation:
University of British Columbia Child and Family Research Institute
Jenalee R. Doom
Affiliation:
University of Minnesota Institute of Child Development
Julia L MacIsaac
Affiliation:
University of British Columbia Child and Family Research Institute
Megan R. Gunnar*
Affiliation:
University of Minnesota Institute of Child Development
Michael S. Kobor
Affiliation:
University of British Columbia Child and Family Research Institute University of British Columbia
*
Address correspondence and reprint requests to: Megan R. Gunnar, Institute of Child Development, University of Minnesota, 51 East River Road, Minneapolis, MN 55455; E-mail: gunnar@umn.edu.
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Abstract

Internationally adopted adolescents who are adopted as young children from conditions of poverty and deprivation have poorer physical and mental health outcomes than do adolescents conceived, born, and raised in the United States by families similar to those who adopt internationally. Using a sample of Russian and Eastern European adoptees to control for Caucasian race and US birth, and nonadopted offspring of well-educated and well-resourced parents to control for postadoption conditions, we hypothesized that the important differences in environments, conception to adoption, might be reflected in epigenetic patterns between groups, specifically in DNA methylation. Thus, we conducted an epigenome-wide association study to compare DNA methylation profiles at approximately 416,000 individual CpG loci from peripheral blood mononuclear cells of 50 adopted youth and 33 nonadopted youth. Adopted youth averaged 22 months at adoption, and both groups averaged 15 years at testing; thus, roughly 80% of their lives were lived in similar circumstances. Although concurrent physical health did not differ, cell-type composition predicted using the DNA methylation data revealed a striking difference in the white blood cell-type composition of the adopted and nonadopted youth. After correcting for cell type and removing invariant probes, 30 CpG sites in 19 genes were more methylated in the adopted group. We also used an exploratory functional analysis that revealed that 223 gene ontology terms, clustered in neural and developmental categories, were significantly enriched between groups.

Type
Regular Articles
Information
Development and Psychopathology , Volume 28 , Special Issue 4pt2: Epigenetics: Development, Psychopathology, Resilience, and Preventive Intervention , November 2016 , pp. 1385 - 1399
Copyright
Copyright © Cambridge University Press 2016 

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References

Balakathiresan, N. S., Chandran, R., Bhomia, M., Jia, M., Li, H., & Maheshwari, R. K. (2014). Serum and amygdala microRNA signatures of posttraumatic stress: Fear correlation and biomarker potential. Journal of Psychiatry Research, 57, 65–73.CrossRefGoogle ScholarPubMed
Barker, D. J. (1997). Maternal nutrition, fetal nutrition, and disease in later life. Nutrition, 13, 807–813.CrossRefGoogle ScholarPubMed
Bird, A. (2007). Perceptions of epigenetics. Nature, 447, 396–398.Google Scholar
Borghol, N., Suderman, M., McArdle, W., Racine, A., Hallett, M., Pembrey, M., et al. (2012). Associations with early-life socio-economic position in adult DNA methylation. International Journal of Epidemiology, 41, 62–74.Google Scholar
Bornancin, F. (2011). Ceramide kinase: The first decade. Cell Signal, 23, 999–1008.CrossRefGoogle ScholarPubMed
Bourgon, R., Gentleman, R., & Huber, W. (2010). Independent filtering increases detection power for high-throughput experiments. Proceedings of the National Academy of Sciences, 107, 9546–9551.Google Scholar
Boyce, W. T., & Kobor, M. S. (2015). Development and the epigenome: The “synapse” of gene–environment interplay. Developmental Science, 18, 1–23.CrossRefGoogle ScholarPubMed
Brady, K. T., & Back, S. E. (2012). Childhood trauma, posttraumatic stress disorder, and alcohol dependence. Alcohol Research, 34, 408–413.Google ScholarPubMed
Carlson, E. A., Hostinar, C. E., Mliner, S. B., & Gunnar, M. R. (2014). The emergence of attachment following early social deprivation. Development and Psychopathology, 26, 479–489.Google Scholar
Chami, N., & Lettre, G. (2014). Lessons and implications from genome-wide association studies (GWAS): Findings of blood cell phenotypes. Genes (Basel), 5, 51–64.CrossRefGoogle ScholarPubMed
Chang, C.-C., Lin, C.-C., Hsieh, W.-L., Lai, H.-W., Tsai, C.-H., & Cheng, Y.-W. M. (2014). MicroRNA expression profiling in PBMCs: A potential diagnostic biomarker of chronic hepatitis C. Disease Markers, 2014, 367157.Google Scholar
Chen, C., Grennan, K., Badner, J., Zhang, D., Gershon, E., Jin, L., et al. (2011). Removing batch effects in analysis of expression microarray data: An evaluation of six batch adjustment methods. PLos One, 6, e17238.Google Scholar
Chugani, H. T., Behen, M. E., Muzik, O., Juhasz, C., Nagy, F., & Chugani, D. C. (2001). Local brain functional activity following early deprivation: A study of postinstitutionalized Romanian orphans. NeuroImage, 14, 1290–1301.CrossRefGoogle ScholarPubMed
Cicchetti, D. (2013). Annual Research Review: Resilient functioning in maltreated children—Past, present, and future perspectives. Journal of Child Psychology and Psychiatry, 54, 402–422.Google Scholar
Coelho, R., Viola, T. W., Walss-Bass, C., Brietzke, E., & Grassi-Oliveira, R. (2014). Childhood maltreatment and inflammatory markers: A systematic review. Acta Psychiatrica Scandianvia, 129, 180–192.Google Scholar
Coleman, J. A., Zhu, X., Djajadi, H. R., Molday, L. L., Smith, R. S., Libby, R. T., et al. (2014). Phospholipid flippase ATP8A2 is required for normal visual and auditory function and photoreceptor and spiral ganglion cell survival. Journal of Cell Science, 127, 1138–1149.Google Scholar
Colige, A., Nuytinck, L., Hausser, I., van Essen, A. J., Thiry, M., Herens, C., et al. (2004). Novel types of mutation responsible for the dermatosparactic type of Ehlers-Danlos syndrome (type VIIC) and common polymorphisms in the ADAMTS2 gene. Journal of Investigative Dermatology, 123, 656–663.Google Scholar
Colige, A., Ruggiero, F., Vandenberghe, I., Dubail, J., Kesteloot, F., Van Beeumen, J., et al. (2005). Domain and maturation processes that regulate the activity of ADAMTS-2, a metalloproteinase cleaving the aminopropeptide of fibrillar procollagens types I–III and V. Journal of Biology and Chemistry, 280, 34397–34408.Google Scholar
Croft, C., O'Connor, T. G., Keavene, L., Groothues, C., & Rutter, M. (2001). Longitudinal change in parenting associated with developmental delay and catch-up. Journal of Child Psychology and Psychiatry, 42, 649–659.Google Scholar
Davies, M. N., Volta, M., Pidsley, R., Lunnon, K., Dixit, A., Lovestone, S., et al. (2012). Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biology, 13, R43.CrossRefGoogle ScholarPubMed
Dogan, M. V., Shields, B., Cutrona, C., Gao, L., Gibbons, F. X., Simons, R., et al. (2014). The effect of smoking on DNA methylation of peripheral blood mononuclear cells from African American women. BMC Genomics, 15, 151.Google Scholar
Drury, S. S., Theall, K., Gleason, M. M., Smyke, A. T., De Vivo, I., Wong, J. Y., et al. (2012). Telomere length and early severe social deprivation: Linking early adversity and cellular aging. Molecular Psychiatry, 17, 719–727.Google Scholar
Du, P., Kibbe, W. A., & Lin, S. M. (2008). Lumi: A pipeline for processing Illumina microarray. Bioinformatics, 24, 1547–1548.Google Scholar
Eckerle, J. K., Hill, L. K., Iverson, S., Hellerstedt, W., Gunnar, M. R., & Johnson, D. E. (2014). Vision and hearing deficits and associations with parent-reported behavioral and developmental problems in international adoptees. Maternal and Child Health, 18, 575–583.Google Scholar
Elliott, H. R., Tillin, T., McArdle, W. L., Ho, K., Duggirala, A., Frayling, T. M., et al. (2014). Differences in smoking associated DNA methylation patterns in South Asians and Europeans. Clinical Epigenetics, 6, 4.Google Scholar
Eriksen, H. B., Biering-Sørensen, S., Lund, N., Correia, C., Rodrigues, A., Andersen, A., et al. (2014). Factors associated with thymic size at birth among low and normal birthweight infants. Journal of Pediatrics, 165, 713–721.Google Scholar
Essex, M. J., Boyce, T., Goldstein, L. H., Armstrong, J. M., Kraemer, H. C., & Kupfer, D. (2002). The confluence of mental, physical, social and academic difficulties in middle childhood: II. Developing the MacArthur Health and Behavior Questionnaire. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 588–603.Google Scholar
Essex, M. J., Boyce, W. T., Hertzman, C., Lam, L. L., Armstrong, J. M., Neumann, S. M., et al. (2013). Epigenetic vestiges of early developmental adversity: Childhood stress exposure and DNA methylation in adolescence. Child Development, 84, 58–75.CrossRefGoogle ScholarPubMed
Farré, P., Jones, M. J., Meaney, M. J., Emberly, E., Turecki, G., & Kobor, M. S. (2015). Concordant and discordant DNA methylation signatures of aging in human blood and brain. Epigenetics Chromatin, 8, 19.Google Scholar
Felliti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., et al. (1998). The relationship of adult health status to childhood abuse and household dysfunction. American Journal of Preventative Medicine, 14, 245–258.Google Scholar
Fernald, L. C., & Grantham-McGregor, S. M. (2002). Growth retardation is associated with changes in the stress response system and behavior in school-aged Jamaican children. Journal of Nutrition, 132, 3674–3679.Google Scholar
Ferretti, E., De Smaele, E., Miele, E., Laneve, P., Po, A., Pelloni, M., et al. (2008). Concerted microRNA control of Hedgehog signalling in cerebellar neuronal progenitor and tumour cells. EMBO Journal, 27, 2616–2627.Google Scholar
Fleming, A. S., Kraemer, G. W., Gonzalez, A., Lovic, V., Rees, S., & Melo, A. (2002). Mothering begets mothering: The transmission of behavior and its neurobiology across generations. Pharmacology, Biochemistry and Behavior, 73, 61–75.Google Scholar
Garvin, M. C., Tarullo, A. R., Van Ryzin, M., & Gunnar, M. R. (2012). Post-adoption parenting and socioemotional development in postinstitutionalized children. Development and Psychopathology, 24, 35–48.CrossRefGoogle Scholar
Gillis, J., Mistry, M., & Pavlidis, P. (2010). Gene function analysis in complex data sets using ErmineJ. Nature Protocols, 5, 1148–1159.Google Scholar
Gunnar, M. R., Bruce, J., & Grotevant, H. D. (2000). International adoption of institutionally reared children: Research and policy. Development and Psychopathology, 12, 677–693.Google Scholar
Hellerstedt, W. L., Madsen, N. J., Gunnar, M. R., Grotevant, H. D., Lee, R. M., & Johnson, D. E. (2008). The international adoption project: Population-based surveillance of Minnesota parents who adopted children internationally. Maternal and Child Health Journal, 12, 162–171.Google Scholar
Hertzman, C. (1999). The biological embedding of early experience and its effects on health in adulthood. Annals of the New York Academy of Sciences, 896, 85–95.Google Scholar
Hertzman, C., & Boyce, T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329–347.CrossRefGoogle ScholarPubMed
Hodel, A. S., Hunt, R. H., Cowell, R. A., Van Den Heuvel, S. E., Gunnar, M. R., & Thomas, K. M. (2015). Duration of early adversity and structural brain development in post-institutionalized adolescents. NeuroImage. Advance online publication.Google Scholar
Hou, Q., Barr, T., Gee, L., Vickers, J., Wymer, J., Borsani, E., et al. (2011). Keratinocyte expression of calcitonin gene-related peptide β: Implications for neuropathic and inflammatory pain mechanisms. Pain, 152, 2036–2051.CrossRefGoogle ScholarPubMed
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., et al. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13, 86.Google Scholar
Humphreys, K. L., & Zeanah, C. H. (2015). Deviations from the expectable environment in early childhood and emerging psychopathology. Neuropsychopharmacology, 40, 154–170.Google Scholar
Illingworth, R. S., & Bird, A. P. (2009). CpG islands—“A rough guide.” FEBS Letters, 583, 1713–1720.Google Scholar
Jaffe, A. E., & Irizarry, R. A. (2014). Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology, 15, R31.CrossRefGoogle ScholarPubMed
Johnson, D. E., & Gunnar, M. R. (2011). Growth failure in institutionalized children. Monograph of the Society for Child Development, 76, 92–126.Google Scholar
Johnson, J. H., & Cutcheon, S. (1980). Assessing life events in older children and adolescents: Preliminary findings with the life events checklist. In Sarason, I. G. & Spielberger, C. D. (Eds.), Stress and anxiety (Vol. 7). Washington, DC: Hemisphere.Google Scholar
Jones, M. J., Islam, S. A., Edgar, R. D., & Kobor, M. S. (2015). Adjusting for cell type composition in DNA methylation data using a regression-based approach. Methods in Molecular Biology. Advance online publication.Google Scholar
Jones, P. A. (2012). Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nature Review Genetics, 13, 484–492.Google Scholar
Joubert, B. R., Håberg, S. E., Nilsen, R. M., Wang, X., Vollset, S. E., Murphy, S. K., et al. (2012). 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environmental Health Perspectives, 120, 1425–1431.CrossRefGoogle ScholarPubMed
Koestler, D. C., Christensen, B., Karagas, M. R., Marsit, C. J., Langevin, S. M., Kelsey, K. T., et al. (2013). Blood-based profiles of DNA methylation predict the underlying distribution of cell types: A validation analysis. Epigenetics, 8, 816–826.Google Scholar
Kohn, J. N., Howell, B. R., Guzman, D. B., Meyer, J. S., Ibegbu, C. C., & Sanchez, M. M. (2014). Early life stress and perinatal glucocorticoid exposure produce complex immune system alterations, including accelerated T cell immunosenescence, in adolescent rhesus macaques. Brain, Behavior and Immunity, 40, e50.Google Scholar
Koss, K. J., Hostinar, C. E., Donzella, B., & Gunnar, M. R. (2014). Social deprivation and the HPA axis in early development. Developmental Science, 50, 1–13.Google Scholar
Kumsta, R., Kreppner, J., Rutter, M., Beckett, C., Castle, J., Stevens, S., et al. (2010). III. Deprivation-specific psychological patterns. Monographs of the Society for Research in Child Development, 75, 48–78.Google Scholar
Lam, L. L., Emberly, E., Fraser, H. B., Neumann, S. M., Chen, E., Miller, G. E., et al. (2012). Factors underlying variable DNA methylation in a human community cohort. Proceedings of the National Academy of Sciences, 109(Suppl. 2), 17253–17260.CrossRefGoogle Scholar
Lassalle, P., Molet, S., Janin, A., Van der Heyden, J., Tavernier, J., Fiers, W., et al. (1996). ESM-1 is a novel human endothelial cell-specific molecule expressed in lung and regulated by cytokines. Journal of Biological Chemistry, 271, 20458–20464.Google Scholar
Lee, K. W. K., Ku, S. K., Kim, S. W., & Bae, J. S. (2014). Endocan elicits severe vascular inflammatory responses in vitro and in vivo. Joural of Cellular Physiology, 229, 620–630.Google Scholar
Lee, K. W. K., Richmond, R., Hu, P., French, L., Shin, J., Bourdon, C., et al. (2015). Prenatal exposure to maternal cigarette smoking and DNA methylation: Epigenome-wide association in a discovery sample of adolescents and replication in an independent cohort at birth through 17 years of age. Environmental Health Perspectives, 23, 193–199.Google Scholar
Lewis, M. H., Gluck, J. P., Petitto, J. M., Hensley, L. L., & Ozer, H. (2000). Early social deprivation in nonhuman primates: Long-term effects on survival and cell-mediated immunity. Biological Psychiatry, 47, 119–126.Google Scholar
Liu, Y., Aryee, M. J., Padyukov, L., Fallin, M. D., Hesselberg, E., Runarsson, A., et al. (2013). Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nature Biotechnology, 31, 142–147.Google Scholar
Loman, M. M., Johnson, A. E., Westerlund, A., Pollak, S. D., Nelson, C. A., & Gunnar, M. R. (2013). The effect of early deprivation on executive attention in middle childhood. Journal of Child Psychology and Psychiatry, 54, 37–45.Google Scholar
Loman, M. M., Wiik, K. L., Frenn, K. A., Pollak, S. D., & Gunnar, M. R. (2009). Post-institutionalized children's development: Growth, cognitive, and language outcomes. Developmental and Behavioral Pediatrics, 30, 426–434.Google Scholar
Lubach, G. R., Coe, C. L., & Ershler, W. B. (1995). Effects of early rearing environment on immune responses of infant rhesus monkeys. Brain, Behavior and Immunity, 9, 31–46.Google Scholar
Luo, X., Zhang, Y., Ruan, X., Jiang, X., Zhu, L., Wang, X., et al. (2011). Fasting-induced protein phosphatase 1 regulatory subunit contributes to postprandial blood glucose homeostasis via regulation of hepatic glycogenesis. Diabetes, 60, 1435–1445.CrossRefGoogle ScholarPubMed
Lutz, P. E., & Turecki, G. (2014). DNA methylation and childhood maltreatment: From animal models to human studies. Journal of Neuroscience, 264, 142–156.Google Scholar
Maksimovic, J., Gordon, L., & Oshlack, A. (2012). SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biology, 13, R44.Google Scholar
Malnic, B., Godfrey, P. A., & Buck, L. B. (2004). The human olfactory receptor gene family. Proceedings of the National Academy of Sciences, 101, 2584–2589.Google Scholar
Markunas, C. A., Xu, Z., Harlid, S., Wade, P. A., Lie, R. T., Taylor, J. A., et al. (2014). Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy. Environmental Health Perspectives, 122, 1147–1153.Google Scholar
McGowan, P. O., Sasaki, A., D'Alessio, A. C., Dymov, S., Labonté, B., Szyf, M., et al. (2009). Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nature Neuroscience, 12, 342–348.Google Scholar
McGuinness, D., McGlynn, L. M., Johnson, P. C., MacIntyre, A., Batty, G. D., Burns, H., et al. (2012). Socio-economic status is associated with epigenetic differences in the pSoBid cohort. International Journal of Epidemiology, 41, 151–160.Google Scholar
McKeown, C. R., Nowak, R. B., Gokhin, D. S., & Fowler, V. M. (2014). Tropomyosin is required for cardiac morphogenesis, myofibril assembly, and formation of adherens junctions in the developing mouse embryo. Developmental Dynamics, 243, 800–817.Google Scholar
Meaney, M. J., & Szyf, M. (2005). Environmental programming of stress responses through DNA methylation: Life at the interface between a dynamic environment and a fixed genome. Dialogues in Clinical Neuroscience, 7, 103–123.Google Scholar
Mehta, D., Klengel, T., Conneely, K. N., Smith, A. K., Altmann, A., Pace, T. W., et al. (2013). Childhood maltreatment is associated with distinct genomic and epigenetic profiles in posttraumatic stress disorder. Proceedings of the National Academy of Sciences, 110, 8302–8307.Google Scholar
Mehta, M. A., Golembo, N. I., Nosarti, C., Colvert, E., Mota, A., Williams, S. C., et al. (2009). Amygdala, hippocampal and corpus callosum size following severe early institutional deprivation: The English and Romanian Adoptees study pilot. Journal of Child Psychology and Psychiatry, 50, 943–951.Google Scholar
Melkonyan, H. S., Chang, W. C., Shapiro, J. P., Mahadevappa, M., Fitzpatrick, P. A., Kiefer, M. C., et al. (1997). SARPs: A family of secreted apoptosis-related proteins. Proceedings of the National Academy of Sciences, 94, 13636–13641.Google Scholar
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010). Enrichment map: A network-based method for gene-set enrichment visualization and interpretation. PLOS ONE, 5, e13984.Google Scholar
Miller, G. E., Chen, E., Fok, A. K., Walker, H., Lim, A., Nicholls, E. F., et al. (2009). Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. Proceedings of the National Academy of Sciences, 25, 14716–14721.Google Scholar
Moffitt, T. E., & Tank, K.-G. T. (2013). Childhood exposure to violence and lifelong health: Clinical intervention science and stress-biology research join forces. Development and Psychopathology, 25, 1619–1634.Google Scholar
Monick, M. M., Beach, S. R. H., Plume, J., Sears, R., Gerrard, M., Brody, G. H., et al. (2012). Coordinated changes in AHRR methylation in lymphoblasts and pulmonary macrophages from smokers. American Journal of Medical Genetics, 159B, 141–151.Google Scholar
Naumova, O. Y., Lee, M., Koposov, R., Szyf, M., Dozier, M., & Grigorenko, E. L. (2011). Differential patterns of whole-genome DNA methylation in institutionalized children and children raised by their biological parents. Development and Psychopathology, 24, 143–155. doi:10.1017/S0954579411000605 Google Scholar
Nishi, M., Horii-Hayashi, N., & Sasagawa, T. (2014). Effects of early life adverse experiences on the brain: Implications from maternal separation models in rodents. Frontiers in Neuroscience, 8, 166.Google Scholar
Novakovic, B., Ryan, J., Pereira, N., Boughton, B., Craig, J. M., & Saffery, R. (2014). Postnatal stability, tissue, and time specific effects of AHRR methylation change in response to maternal smoking in pregnancy. Epigenetics, 9, 377–386.Google Scholar
Pérez-de-Heredia, F., Gómez-Martínez, S., Díaz, L. E., Veses, A. M., Nova, E., Wärnberg, J., et al. (2015). Influence of sex, age, pubertal maturation and body mass index on circulating white blood cell counts in healthy European adolescents—The HELENA study. European Journal of Pediatrics. Advance online publication.Google Scholar
Perry, A. S., O'Hurley, G., Raheem, O. A., Brennan, K., Wong, S., O'Grady, A., et al. (2013). Gene expression and epigenetic discovery screen reveal methylation of SFRP2 in prostate cancer. International Journal of Cancer, 132, 1771–1780.Google Scholar
Pickard, B. S., Malloy, M. P., Clark, L., Lehellard, S., Ewald, H. L., Mors, O., et al. (2005). Candidate psychiatric illness genes identified in patients with pericentric inversions of chromosome 18. Psychiatric Genetics, 15, 37–44.Google Scholar
Price, M. E., Cotton, A. M., Lam, L. L., Farré, P., Emberly, E., Brown, C. J., et al. (2013). Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin, 6, 4.Google Scholar
Richmond, R. C., Simpkin, A. J., Woodward, G., Gaunt, T. R., Lyttleton, O., McArdle, W. L., et al. (2015). Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Human Molecular Genetics, 24, 2201–2217.CrossRefGoogle ScholarPubMed
Riordan, J. R., Rommens, J. M., Kerem, B., Alon, N., Rozmahel, R., Grzelczak, Z., et al. (1989). Identification of the cystic fibrosis gene: Cloning and characterization of complementary DNA. Science, 245, 1066–1073.Google Scholar
Romano, E., Babchishin, L., Marquis, R., & Fréchette, S. (2014). Childhood maltreatment and educational outcomes. Trauma, Violence and Abuse. Advance online publication.Google Scholar
Saito, T., Mitomi, H., Imamhasan, A., Hayashi, T., Mitani, K., Takahashi, M., et al. (2014). Downregulation of sFRP-2 by epigenetic silencing activates the β-catenin/Wnt signaling pathway in esophageal basaloid squamous cell carcinoma. Virchows Archives, 464, 135–143.Google Scholar
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498–2504.Google Scholar
Shenker, N. S., Polidoro, S., van Veldhoven, K., Sacerdote, C., Ricceri, F., Birrell, M. A., et al. (2013). Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking. Human Molecular Genetics, 22, 843–851.Google Scholar
Sheridan, M. A., Fox, N. A., Zeanah, C., McLaughlin, K. A., & Nelson, C. A. (2012). Variation in neural development as a result of exposure to institutionalization early in childhood. Proceedings of the National Academy of Sciences, 109, 12927–12932.Google Scholar
Shirtcliff, E. A., Coe, C. L., & Pollak, S. D. (2009). Early childhood stress is associated with elevated antibody levels to herpes simplex virus type 1. Proceedings of the National Academy of Sciences, 106, 2963–2967.Google Scholar
Shonkoff, J., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. Journal of the American Medical Association, 301, 2252–2259.Google Scholar
Smith, A. K., Kilaru, V., Klengel, T., Mercer, K. B., Bradley, B., Conneely, K. N., et al. (2015). DNA extracted from saliva for methylation studies of psychiatric traits: Evidence tissue specificity and relatedness to brain. American Journal of Medical Genetics, 168B, 36–44.Google Scholar
Smyth, G. K. (2005). Limma: Linear models for microarray data. In Gentleman, R., Carey, V. J., Huber, W., Irizarry, R. A., & Dudoit, S. (Eds.), Bioinformatics and computational biology solutions using R and Bioconductor (pp. 397–420). New York: Springer–Verlag.Google Scholar
Stefanski, V., & Engler, H. (1998). Effects of acute and chronic social stress on blood cellular immunity in rats. Physiology & Behavior, 64, 733–741.Google Scholar
Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31, 2013–2035.Google Scholar
Stovall, K. C., & Dozier, M. (2000). The development of attachment in new relationships: Single subject analyses for 10 foster infants. Development and Psychopathology, 12, 133–156.Google Scholar
Sun, Y. V., Smith, A. K., Conneely, K. N., Chang, Q., Li, W., Lazarus, A., et al. (2013). Epigenomic association analysis identifies smoking-related DNA methylation sites in African Americans. Human Genetics, 132, 1027–1037.Google Scholar
R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: Author.Google Scholar
Teh, A. L., Pan, H., Chen, L., Ong, M. L., Dogra, S. Wong, J., et al. (2014) The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Research, 24, 1064–1074.Google Scholar
Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, K., Nurse, M., Gilhooly, T., et al. (2010). Prolonged institutional rearing is associated with atypically larger amygdala volume and difficulties in emotion regulation. Developmental Science, 13, 46–61.Google Scholar
Tsaprouni, L. G., Yang, T.-P., Bell, J., Dick, K. J., Kanoni, S., Nisbet, J., et al. (2014). Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics, 9, 1382–1396.Google Scholar
Vanderwert, R. E., Marshall, P. J., Nelson, C. A., Zeanah, C. H., & Fox, N. A. (2010). Timing of intervention affects brain electrical activity in children exposed to severe psychosocial neglect. PLOS ONE, 5, e11415.Google Scholar
Wagner, J. R., Busche, S., Ge, B., Kwan, T., Pastinen, T., & Blanchette, M. (2014). The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biology, 15, R37.Google Scholar
Waluk, D. P., Schultz, N., & Hunt, M. C. (2010). Identification of glycine N-acyltransferase-like 2 (GLYATL2) as a transferase that produces N-acyl glycines in humans. Journal of the American Federation of Societies of Experimental Biology, 24, 2795–2803.Google Scholar
Waluk, D. P., Sucharski, F., Sipos, L., Silberring, J., & Hunt, M. C. (2012). Reversible lysine acetylation regulates activity of human glycine N-acyltransferase-like 2 (hGLYATL2): Implications for production of glycine-conjugated signaling molecules. Journal of Biological Chemistry, 287, 16158–16167.Google Scholar
Wang, G., He, Q., Feng, C., Liu, Y., Deng, Z., Qi, X., et al. (2014). The atomic resolution structure of human AlkB homolog 7 (ALKBH7), a key protein for programmed necrosis and fat metabolism. Journal of Biological Chemistry, 289, 2792–2736.Google Scholar
Weber, M., Hellmann, I., Stadler, M. B., Ramos, L., Pääbo, S., Rebhan, M., et al. (2007). Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nature Genetics, 39, 457–466.Google Scholar
Zeanah, C. H., Gunnar, M. R., McCall, R. B., Kreppner, J. M., & Fox, N. A. (2011). Sensitive periods. Monographs of the Society for Research in Child Development, 74, 147–162.Google Scholar
Zeanah, C. H., Nelson, C. A., Fox, N. A., Smyke, A. T., Marshall, P. M., Parker, S. W., et al. (2003). Designing research to study the effects of institutionalization on brain and behavioral development: The Bucharest Early Intervention Project. Development and Psychopathology, 15, 885–907.Google Scholar
Zhang, Y., Xu, D., Huang, H., Chen, S., Wang, L., Zhu, L., et al. (2014). Regulation of glucose homeostasis and lipid metabolism by PPP1R3G-mediated hepatic glycogenesis. Molecular Endocrinology, 28, 116–126.Google Scholar