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Sensitive Periods for the Effect of Childhood Adversity on DNA Methylation: Updated Results From a Prospective, Longitudinal Study

  • Alexandre A. Lussier
    Correspondence
    Address correspondence to Alexandre A. Lussier, Ph.D.
    Affiliations
    Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts

    Department of Psychiatry, Harvard Medical School, Boston, Massachusetts

    Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
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  • Yiwen Zhu
    Affiliations
    Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts

    Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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  • Brooke J. Smith
    Affiliations
    Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
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  • Andrew J. Simpkin
    Affiliations
    School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland
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  • Andrew D.A.C. Smith
    Affiliations
    Mathematics and Statistics Research Group, University of the West of England, Bristol, United Kingom
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  • Matthew J. Suderman
    Affiliations
    MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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  • Esther Walton
    Affiliations
    Department of Psychology, University of Bath, Bath, United Kingdom
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  • Caroline L. Relton
    Affiliations
    MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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  • Kerry J. Ressler
    Affiliations
    Department of Psychiatry, Harvard Medical School, Boston, Massachusetts

    McLean Hospital, Belmont, Massachusetts
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  • Erin C. Dunn
    Correspondence
    Erin C. Dunn, Sc.D.
    Affiliations
    Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts

    Department of Psychiatry, Harvard Medical School, Boston, Massachusetts

    Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts

    Center on the Developing Child, Harvard University, Cambridge, Massachusetts
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Open AccessPublished:April 25, 2022DOI:https://doi.org/10.1016/j.bpsgos.2022.04.002
      To the Editor:
      Childhood adversity is a potent but preventable risk factor for many physical and mental illnesses (
      • Norman R.E.
      • Byambaa M.
      • De R.
      • Butchart A.
      • Scott J.
      • Vos T.
      The long-term health consequences of child physical abuse, emotional abuse, and neglect: A systematic review and meta-analysis.
      ,
      • Slopen N.
      • Koenen K.C.
      • Kubzansky L.D.
      Cumulative adversity in childhood and emergent risk factors for long-term health.
      ,
      • Dunn E.C.
      • Soare T.W.
      • Raffeld M.R.
      • Busso D.S.
      • Crawford K.M.
      • Davis K.A.
      • et al.
      What life course theoretical models best explain the relationship between exposure to childhood adversity and psychopathology symptoms: Recency, accumulation, or sensitive periods?.
      ). Although the mechanisms underlying these associations remain unknown, DNA methylation (DNAm) and other epigenetic modifications have emerged as potential pathways for the biological embedding of early-life environments (
      • Aristizabal M.J.
      • Anreiter I.
      • Halldorsdottir T.
      • Odgers C.L.
      • McDade T.W.
      • Goldenberg A.
      • et al.
      Biological embedding of experience: A primer on epigenetics.
      ).
      In a recent publication (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ), we showed that 7 types of childhood adversity had time-dependent effects on DNAm at age 7 years. Data came from the Avon Longitudinal Study of Parents and Children (ALSPAC) and were analyzed using the structured life course modeling approach (SLCMA) (
      • Smith A.D.A.C.
      • Heron J.
      • Mishra G.
      • Gilthorpe M.S.
      • Ben-Shlomo Y.
      • Tilling K.
      Model selection of the effect of binary exposures over the life course.
      ,
      • Smith A.D.A.C.
      • Hardy R.
      • Heron J.
      • Joinson C.J.
      • Lawlor D.A.
      • Macdonald-Wallis C.
      • et al.
      A structured approach to hypotheses involving continuous exposures over the life course.
      ). We identified potential sensitive periods, largely occurring in the first 3 years of life, when childhood adversity exposure appeared to have greater effects on DNAm (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ).
      Since the publication of this research, several improvements were made to increase the accuracy and robustness of the results. First, the ALSPAC investigators revised the individual-level DNAm data available to researchers. DNAm data are now derived from processing pipelines that use functional normalization and preprocessing approaches to reduce technical variation and false positives, increase statistical power, and reduce heterogeneity in downstream meta-analyses (
      • Min J.L.
      • Hemani G.
      • Davey Smith G.
      • Relton C.
      • Suderman M.
      Meffil: Efficient normalization and analysis of very large DNA methylation datasets.
      ). Second, we refined the statistical framework of the SLCMA (
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Lussier A.A.
      • Walton E.
      • Dunn E.C.
      • et al.
      A structured approach to evaluating life course hypotheses: Moving beyond analyses of exposed versus unexposed in the -omics context.
      ), as it had not been applied to high-dimensional data prior to our work in the ALSPAC (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ). Specifically, we improved the estimation methods that the SLCMA uses in high-dimensional settings to reduce the likelihood of false positives and obtain higher confidence associations. Elsewhere, we describe how these updates shifted the original results, with an emphasis on how even modest changes to epigenetic data and analytic approaches can shape the replicability of epigenome-wide associations (
      • Lussier A.A.
      • Zhu Y.
      • Smith B.J.
      • Simpkin A.J.
      • Smith A.D.A.C.
      • Suderman M.J.
      • et al.
      Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies [published online ahead of print Feb 14].
      ). In brief, the updated results recapitulate the main finding by Dunn et al. (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ) in showing that sensitive periods in development play an important role in the biological embedding of childhood adversity. There were other similarities: the magnitude and direction of adversity-to-DNAm associations were stable across analyses, as were the selected life course hypotheses for most CpGs. However, and as expected, different top loci were identified at conventional p-value thresholds. For these reasons, we urged investigators performing the SLCMA or a traditional epigenome-wide association study to extend their replication analyses beyond traditional metrics of significance alone (
      • Lussier A.A.
      • Zhu Y.
      • Smith B.J.
      • Simpkin A.J.
      • Smith A.D.A.C.
      • Suderman M.J.
      • et al.
      Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies [published online ahead of print Feb 14].
      ).
      In this report, we focus on the biological relevance of this updated set of top loci. Our updated analyses revealed 46 loci showing time-dependent associations between childhood adversity and DNAm levels at a 5% false discovery rate (FDR) (Figure 1; Table 1) (
      • Lussier A.A.
      • Zhu Y.
      • Smith B.J.
      • Simpkin A.J.
      • Smith A.D.A.C.
      • Suderman M.J.
      • et al.
      Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies [published online ahead of print Feb 14].
      ). As previously shown (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ), we continued to find evidence in support of sensitive periods among this new set of loci. However, exposure to adversity during early childhood, meaning between ages 3 and 5, was most frequently associated with DNAm differences (39 of 46 top loci), rather than exposure to adversity between ages 0 and 3, as previously reported (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ). Exposure to adversity during other sensitive periods before age 7 was associated with DNAm differences at 7 loci (1 for very early childhood and 6 for middle childhood). An accumulation model (i.e., the cumulative number of exposed time points) or a recency model (i.e., the cumulative number of exposed time points from ages 0 to 7 weighted by exposure timing) was not associated with any DNAm differences. Childhood adversity was mostly associated with decreased DNAm levels (89.1% negative effect estimates; χ21 = 28.2; p = 1.1 × 10−7). On average, children exposed to adversity showed a 3.1% absolute difference in DNAm levels (range, 0.4%–9.6%).
      Figure thumbnail gr1
      Figure 1Frequency of life course model selection for each type of childhood adversity. The number of loci for which adversity predicted DNA methylation levels at 5% false discovery rate (FDR) is shown here. Sensitive period hypotheses, or theoretical models, included very early (0–3 years), early (3–5 years), or middle childhood (6–7 years). Additive hypotheses included recency and accumulation (not shown, as there were no associations at an FDR < .05).
      Table 1Time-Dependent Associations Between Childhood Adversity and DNAm at 7 Years of Age
      AdversityCpGHypothesis (Age in Years)DNAm (Unexposed)
      Mean DNAm levels in individuals unexposed to adversity.
      DNAm (Exposed)
      Mean DNAm levels in individuals exposed to adversity.
      DNAm Difference
      Differences in DNAm levels between exposed and unexposed individuals.
      Effect Estimate
      Effect estimates from linear regression of exposure to adversity and DNAm, adjusting for cell types, child sex, race/ethnicity, birth weight, maternal education, maternal age at birth, number of previous pregnancies, and maternal smoking during pregnancy.
      SEp ValueFDRNearest GenepLI
      Probability of intolerance to loss of function from the Exome Aggregation Consortium for the gene nearest to the significant locus (12).
      Caregiver physical or emotional abuse (n = 698)cg12023170
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Middle childhood (6)0.0910.1250.0340.0340.0051.25 × 10−95.48 × 10−4TCEA39.66 × 10−4
      Physical or sexual abuse (n = 681)cg20369299Early childhood (4.75)0.7240.628−0.096−0.0910.0182.12 × 10−74.67 × 10−2MIR4776-1NA
      cg13817046
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Early childhood (4.75)0.4830.411−0.072−0.0750.0135.07 × 10−82.23 × 10−2PRR154.47 × 10−1
      Family instability (n = 681)cg04079399
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Very early childhood (2.5)0.8910.870−0.021−0.0200.0046.15 × 10−88.79 × 10−3LINC00398NA
      cg01407460
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Early childhood (4.75)0.0250.021−0.004−0.0040.0013.10 × 10−86.82 × 10−3CORO7-PAM161.63 × 10−12
      cg01587190Early childhood (4.75)0.0670.0780.0100.0110.0021.11 × 10−62.53 × 10−2ERCC23.14 × 10−13
      cg22346081Early childhood (4.75)0.8630.844−0.019−0.0190.0043.48 × 10−63.92 × 10−2SORT11.00
      cg14948379Early childhood (4.75)0.8550.822−0.034−0.0350.0064.10 × 10−72.07 × 10−2MRPS93.36 × 10−6
      cg17134302Early childhood (4.75)0.8510.834−0.017−0.0180.0042.91 × 10−63.92 × 10−2FBXO363.23 × 10−6
      cg22060367Early childhood (4.75)0.8870.867−0.020−0.0200.0041.84 × 10−71.62 × 10−2TANK9.44 × 10−1
      cg01100868Early childhood (4.75)0.9030.885−0.018−0.0180.0032.07 × 10−63.37 × 10−2SLIT39.92 × 10−1
      cg16338178Early childhood (4.75)0.8300.800−0.030−0.0300.0063.22 × 10−63.92 × 10−2LINC01845NA
      cg27639644Early childhood (4.75)0.8580.827−0.031−0.0310.0065.64 × 10−72.07 × 10−2RAB9BP1NA
      cg00943585Early childhood (4.75)0.8340.785−0.049−0.0480.0103.48 × 10−63.92 × 10−2LOC154449NA
      cg27061903Early childhood (4.75)0.0520.0700.0180.0180.0032.23 × 10−63.51 × 10−2COX7A23.76 × 10−1
      cg01023798Early childhood (4.75)0.8650.839−0.026−0.0260.0051.54 × 10−62.82 × 10−2SDK15.02 × 10−3
      cg02886132Early childhood (4.75)0.8850.868−0.017−0.0170.0041.18 × 10−62.53 × 10−2TYW1B8.12 × 10−9
      cg10571837Early childhood (4.75)0.9010.888−0.014−0.0140.0035.35 × 10−72.07 × 10−2ZNF7131.12 × 10−3
      cg23184756Early childhood (4.75)0.8470.822−0.025−0.0250.0054.89 × 10−72.07 × 10−2ZNF735NA
      cg01654242Early childhood (4.75)0.8020.752−0.050−0.0500.0091.95 × 10−63.31 × 10−2FBXO431.50 × 10−4
      cg16231917Early childhood (4.75)0.2110.2680.0570.0570.0124.09 × 10−64.39 × 10−2PVT1NA
      cg27457457
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Early childhood (4.75)0.6950.623−0.073−0.0750.0132.77 × 10−86.82 × 10−3RIPK25.54 × 10−1
      cg13876553Early childhood (4.75)0.8120.775−0.037−0.0390.0083.88 × 10−64.27 × 10−2DOCK81.35 × 10−4
      cg21172807Early childhood (4.75)0.1040.1280.0240.0240.0053.14 × 10−63.92 × 10−2BRINP19.95 × 10−1
      cg05886789Early childhood (4.75)0.8450.817−0.028−0.0290.0052.57 × 10−71.89 × 10−2PLXDC26.13 × 10−1
      cg07206497Early childhood (4.75)0.8760.854−0.022−0.0230.0041.16 × 10−62.53 × 10−2USP6NL1.02 × 10−1
      cg08971940Early childhood (4.75)0.7840.741−0.043−0.0450.0091.73 × 10−63.05 × 10−2FZD8NA
      cg01504589Early childhood (4.75)0.8510.809−0.043−0.0420.0088.53 × 10−72.53 × 10−2ZC3H12C9.99 × 10−1
      cg22011436Early childhood (4.75)0.8550.826−0.029−0.0300.0051.21 × 10−62.53 × 10−2SYT132.20 × 10−1
      cg26997966Early childhood (4.75)0.8530.829−0.025−0.0250.0053.14 × 10−71.97 × 10−2RNF2148.30 × 10−1
      cg00967695Early childhood (4.75)0.8850.847−0.038−0.0390.0071.30 × 10−62.56 × 10−2CHFR6.37 × 10−1
      cg01267076Early childhood (4.75)0.8650.841−0.024−0.0240.0041.34 × 10−62.56 × 10−2
      cg13706680
      Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Early childhood (4.75)0.8810.859−0.021−0.0220.0047.98 × 10−88.79 × 10−3KITLG5.09 × 10−1
      cg14637285Early childhood (4.75)0.8550.828−0.027−0.0270.0052.82 × 10−63.92 × 10−2
      cg12188526Early childhood (4.75)0.8890.873−0.016−0.0160.0033.24 × 10−63.92 × 10−2SNORD56BNA
      cg01841772Early childhood (4.75)0.8060.772−0.035−0.0360.0094.52 × 10−64.63 × 10−2NOB18.61 × 10−8
      cg09305491Early childhood (4.75)0.9130.899−0.015−0.0150.0031.02 × 10−62.53 × 10−2PRKCB1.00
      cg27567416Early childhood (4.75)0.8910.876−0.015−0.0150.0031.12 × 10−62.53 × 10−2ADCY99.74 × 10−1
      cg05353659Early childhood (4.75)0.8970.881−0.016−0.0170.0031.02 × 10−62.53 × 10−2G6PC1.81 × 10−3
      cg11438065Early childhood (4.75)0.9010.886−0.015−0.0160.0035.06 × 10−72.07 × 10−2ZNF780B3.90 × 10−13
      cg14401897Early childhood (4.75)0.8300.784−0.046−0.0440.0092.34 × 10−63.56 × 10−2SBNO22.44 × 10−2
      cg06770536Middle childhood (5.75)0.7400.688−0.052−0.0510.0104.31 × 10−64.51 × 10−2C1orf1271.33 × 10−5
      cg26848593Middle childhood (5.75)0.0250.0300.0050.0050.0011.10 × 10−62.53 × 10−2UBOX51.79 × 10−3
      cg17719337Middle childhood (5.75)0.0360.0450.0090.0090.0022.48 × 10−63.65 × 10−2CNPY18.75 × 10−3
      cg19569074Middle childhood (6.75)0.6730.594−0.079−0.0790.0173.03 × 10−63.92 × 10−2SDK15.02 × 10−3
      cg10940545Middle childhood (6.75)0.8190.750−0.069−0.0730.0153.47 × 10−63.92 × 10−2BRI3BP1.25 × 10−1
      DNAm, DNA methylation; FDR, false discovery rate; NA, nearest gene was not in the Exome Aggregation Consortium list.
      a Mean DNAm levels in individuals unexposed to adversity.
      b Mean DNAm levels in individuals exposed to adversity.
      c Differences in DNAm levels between exposed and unexposed individuals.
      d Effect estimates from linear regression of exposure to adversity and DNAm, adjusting for cell types, child sex, race/ethnicity, birth weight, maternal education, maternal age at birth, number of previous pregnancies, and maternal smoking during pregnancy.
      e Probability of intolerance to loss of function from the Exome Aggregation Consortium for the gene nearest to the significant locus (
      • Huang da W.
      • Sherman B.T.
      • Lempicki R.A.
      Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
      ).
      f Significant at a genome-wide Bonferroni-corrected threshold p < 1.13 × 10−7.
      Most associations in the updated results came from family instability (43 loci), followed by sexual/physical abuse (2 loci) and caregiver physical/emotional abuse (1 locus). Exposures to maternal psychopathology, neighborhood disadvantage, or 1 adult in the household were not associated with any DNAm differences (FDR < .05). We did not detect any of these adversity-DNAm associations in DNAm measured from cord blood at birth, suggesting that our observed differences in DNAm likely resulted from postnatal exposures. Similar to our earlier study, we observed more associations than a traditional epigenome-wide association study comparing ever with never-exposed individuals (
      • Lussier A.A.
      • Zhu Y.
      • Smith B.J.
      • Simpkin A.J.
      • Smith A.D.A.C.
      • Suderman M.J.
      • et al.
      Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies [published online ahead of print Feb 14].
      ).
      From a biological standpoint, FDR-significant loci were more often located in predicted enhancer regions (χ21 = 10.6; p = 1.1 × 10−3) and slightly less often located in gene promoters (χ21 = 1.82; p = .18). Top loci were also more likely to be away from, rather than inside or near, CpG islands compared with all loci tested (χ25 = 22.1; p = 5.0 × 10−4). These findings suggest that enhancers and regions of low CpG density may be more responsive to childhood adversity than CpG-dense regions.
      To probe the biological relevance of FDR-significant loci, we examined the correlation between DNAm levels in blood and brain using publicly available data (
      • 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.
      ). Two-thirds of loci (30 of 46) showed small but positive correlations between blood and brain region DNAm (prefrontal cortex: ravg_positive = 0.13; entorhinal cortex: ravg_positive = 0.13; superior temporal gyrus: ravg_positive = 0.15; cerebellum: ravg_positive = 0.1), providing some evidence that adversity-induced changes in blood could reflect parallel changes in the brain.
      We next assessed the biological functions of genes near FDR-significant loci (n = 42 genes) using the DAVID gene ontology tool, identifying 25 clusters of processes involved in metabolism, cell death, and epigenetic regulation (
      • Huang da W.
      • Sherman B.T.
      • Lempicki R.A.
      Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
      ,
      • Huang da W.
      • Sherman B.T.
      • Lempicki R.A.
      Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists.
      ). Only 1 cluster related to hemopoeisis and immune development was enriched in our top loci (p = .024), highlighting a potential relationship between childhood adversity and immune function, consistent with prior literature (
      • Elwenspoek M.M.C.
      • Kuehn A.
      • Muller C.P.
      • Turner J.D.
      The effects of early life adversity on the immune system.
      ).
      To further understand the broader epigenetic context of FDR-significant loci, we investigated their likely chromatin context (
      • Breeze C.E.
      • Paul D.S.
      • van Dongen J.
      • Butcher L.M.
      • Ambrose J.C.
      • Barrett J.E.
      • et al.
      eFORGE: A tool for identifying cell type-specific signal in epigenomic data.
      ). There was no overrepresentation of DNase I–hypersensitive regions or coincident histone H3 marks in top loci (FDR < .05). However, top loci were slightly overrepresented within H3 marks in primary T cells from cord blood (p = .007), further linking childhood adversity to immune functioning.
      Finally, 7 FDR-significant loci were located in genes with high probabilities of intolerance to loss-of-function variation (pLI > 0.9) (
      • Lek M.
      • Karczewski K.J.
      • Minikel E.V.
      • Samocha K.E.
      • Banks E.
      • Fennell T.
      • et al.
      Analysis of protein-coding genetic variation in 60,706 humans.
      ), suggesting that certain genes associated with childhood adversity may be under higher evolutionary constraint for human survival and reproduction (Table 1).
      Overall, our latest results parallel the primary finding from our original article (
      • Dunn E.C.
      • Soare T.W.
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Klengel T.
      • et al.
      Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal Study.
      ) in showing that sensitive periods appear to play an important role in the DNAm differences associated with exposure to postnatal adversity. However, current findings—for the FDR-significant loci specifically—emphasize the salience of adversity exposure during early childhood (between ages 3 and 5), rather than exposure during very early childhood (before age 3) as previously identified. We think that this shift likely reflects a more robust and reproducible set of loci thanks to the implementation of methods explicitly designed to reduce false positives (
      • Min J.L.
      • Hemani G.
      • Davey Smith G.
      • Relton C.
      • Suderman M.
      Meffil: Efficient normalization and analysis of very large DNA methylation datasets.
      ,
      • Zhu Y.
      • Simpkin A.J.
      • Suderman M.J.
      • Lussier A.A.
      • Walton E.
      • Dunn E.C.
      • et al.
      A structured approach to evaluating life course hypotheses: Moving beyond analyses of exposed versus unexposed in the -omics context.
      ). However, it is also possible that both early and very early childhood are sensitive periods with partially overlapping effects that may be difficult to disentangle in a single analysis. The analysis of sensitive periods for the biological embedding of early-life environments is a relatively novel and emerging field. Thus, the evolution of methods and findings is not unsurprising. It is also reminiscent of the early days of genome-wide association studies, when analyses of psychiatric disorders yielded few associations, a problem eventually remedied by larger sample sizes and methodological advances (
      • Sullivan P.F.
      Don't give up on GWAS.
      ,
      • Ormel J.
      • Hartman C.A.
      • Snieder H.
      The genetics of depression: Successful genome-wide association studies introduce new challenges.
      ).
      Perhaps most importantly, the partial dependence of some findings on technical details is not at all new to epidemiology. Such findings emphasize the importance of cross-examining evidence (for all findings) relative to existing literature, complementary statistical methods, diverse populations, and alternative measurements of exposures and outcomes. Our findings also point to the urgent need for meta-analyses that combine data across multiple cohorts to increase statistical power to detect associations and replicate findings of sensitive periods. Further studies are also needed to assess the persistence of effects we have identified into adolescence and adulthood, as well as their role in psychiatric disease risk. Ultimately, continued efforts will help determine when and how early-life experiences influence epigenetic mechanisms and health across the life course.

      Acknowledgments and Disclosures

      This work was supported by the National Institute of Mental Health of the National Institutes of Health (Grant No. R01MH113930 [to ECD]). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The UK Medical Research Council and Wellcome Trust (Grant No. 217065/Z/19/Z ) and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC). A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This research was also supported by grants from the Biotechnology and Biological Sciences Research Council (Grant Nos. BBI025751/1 , BB/I025263/1 ), Integrative Epidemiology Unit (Grant Nos. MC_UU_12013/1, MC_UU_12013/2, MC_UU_12013/8), National Institute of Child Health and Human Development (Grant No. R01HD068437 ), National Institutes of Health (Grant No. 5RO1AI121226-02 ), and CONTAMED EU (Grant No. 212502 ). EW is funded by the European Union’s Horizon 2020 research and innovation programme (Grant No. 848158) and by CLOSER ( Economic and Social Research Council Grant No. ES/K000357/1 ). The funders took no role in the design, execution, analysis or interpretation of the data, or writing up of the findings. This publication is the work of the authors, who will serve as guarantors for the contents of this article.
      We are extremely grateful to all the families who took part in ALSPAC, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.
      The authors report no biomedical financial interests or potential conflicts of interest.

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