Erin C Dunn1, Thomas W Soare2, Yiwen Zhu3, Andrew J Simpkin4, Matthew J Suderman4, Torsten Klengel5, Andrew D A C Smith6, Kerry J Ressler7, Caroline L Relton8. 1. Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Stanley Center for Psychiatric Research, The Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts. Electronic address: edunn@mgh.harvard.edu. 2. Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Stanley Center for Psychiatric Research, The Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts. 3. Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts. 4. Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. 5. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts; Department of Psychiatry and Psychotherapy, University Medical Center Gottingen, Germany. 6. Applied Statistics Group, University of the West of England, Bristol, United Kingdom. 7. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts. 8. Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom; Institute of Genetic Medicine, University of Newcastle, Newcastle upon Tyne, United Kingdom.
Abstract
BACKGROUND: Exposure to early-life adversity is known to predict DNA methylation (DNAm) patterns that may be related to psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure. METHODS: Using a two-stage structured life course modeling approach, we tested the hypothesis that there are sensitive periods when adversity induces greater DNAm changes. We tested this hypothesis in relation to two alternatives: an accumulation hypothesis, in which the effect of adversity increases with the number of occasions exposed, regardless of timing; and a recency model, in which the effect of adversity is stronger for more proximal events. Data came from the Accessible Resource for Integrated Epigenomic Studies, a subsample of mother-child pairs from the Avon Longitudinal Study of Parents and Children (n = 691-774). RESULTS: After covariate adjustment and multiple testing correction, we identified 38 CpG sites that were differentially methylated at 7 years of age following exposure to adversity. Most loci (n = 35) were predicted by the timing of adversity, namely exposures before 3 years of age. Neither the accumulation nor recency of the adversity explained considerable variability in DNAm. A standard epigenome-wide association study of lifetime exposure (vs. no exposure) failed to detect these associations. CONCLUSIONS: The developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Very early childhood appears to be a sensitive period when exposure to adversity predicts differential DNAm patterns. Classification of individuals as exposed versus unexposed to early-life adversity may dilute observed effects. Crown
BACKGROUND: Exposure to early-life adversity is known to predict DNA methylation (DNAm) patterns that may be related to psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure. METHODS: Using a two-stage structured life course modeling approach, we tested the hypothesis that there are sensitive periods when adversity induces greater DNAm changes. We tested this hypothesis in relation to two alternatives: an accumulation hypothesis, in which the effect of adversity increases with the number of occasions exposed, regardless of timing; and a recency model, in which the effect of adversity is stronger for more proximal events. Data came from the Accessible Resource for Integrated Epigenomic Studies, a subsample of mother-child pairs from the Avon Longitudinal Study of Parents and Children (n = 691-774). RESULTS: After covariate adjustment and multiple testing correction, we identified 38 CpG sites that were differentially methylated at 7 years of age following exposure to adversity. Most loci (n = 35) were predicted by the timing of adversity, namely exposures before 3 years of age. Neither the accumulation nor recency of the adversity explained considerable variability in DNAm. A standard epigenome-wide association study of lifetime exposure (vs. no exposure) failed to detect these associations. CONCLUSIONS: The developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Very early childhood appears to be a sensitive period when exposure to adversity predicts differential DNAm patterns. Classification of individuals as exposed versus unexposed to early-life adversity may dilute observed effects. Crown
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