Epigenetics influence and are influenced by the impact of social and environmental challenges on biological outcomes. Therefore, pinpointing epigenetic factors associated with social adversity and traumatic stress enables understanding of the mechanisms underlying vulnerability and resilience. We hypothesized that micro-RNAs (miRNAs) expression may be associated with post-traumatic stress disorder symptom severity (i.e., PTSS) following exposure to social adversity. To test this hypothesis, we leveraged blood-derived RNA samples (n=632) and social adversity data from 483 unique participants in the Detroit Neighborhood Health Study, a community-based, prospective cohort of predominantly African Americans. Results identified 86 miRNAs that are associated with social adversities (financial difficulties, perceived discrimination, cumulative trauma) and PTSS. These miRNAs are primarily involved in the immune response, brain and neural function, as well as cell cycle and differentiation, and 22(25%) have previously been associated with conditions related to PTSD, including traumatic brain injury and stress response. Our findings offer a fresh perspective on understanding the epigenetic role of miRNA in the interaction between social adversity and traumatic stress.

BACKGROUND:Depression is a heterogeneous and recurrent condition, whose underlying biological mechanisms remain poorly understood. MicroRNAs (miRNAs), small non-coding RNAs that regulate post-transcriptional gene expression, are increasingly implicated in cross sectional miRNA studies of depression and depressive symptoms; however, longitudinal studies capturing miRNA changes over time in relation to depression are scarce. METHODS:We conducted small RNA sequencing of leukocyte-derived miRNAs at two timepoints in a prospective community-based cohort (n = 185) to assess associations between within-person changes in depressive symptom severity (ΔPHQ-9) and longitudinal miRNA expression. Differential expression analyses were performed using a paired limma-voom framework, adjusting for covariates (baseline PHQ-9, sex, age, DNAm-derived immune cell covariates and ancestry components derived from matched blood samples) and within-subject correlation. RESULTS:Although no miRNAs survived multiple-testing correction, 68 mature unique miRNAs showed nominal associations (p < 0.05) with depressive symptom severity change (ΔPHQ-9). Several top candidates, including miR-493-3p, miR-409-3p, and miR-323a-3p, displayed expression patterns aligning with prior reports implicating these miRNAs in stress responsivity, synaptic plasticity, and neurodevelopmental regulation. Exploratory follow-up of predicted targets of the nominally symptom-associated miRNAs converged on genes in pathways central to depression biology, including neurotransmission, HPA axis/inflammatory signaling, neuroplasticity, and circadian regulation. Enrichment analyses highlighted receptor tyrosine kinase and intracellular signaling cascades, hypothalamic-pituitary-adrenal axis feedback, and inflammatory pathways. CONCLUSIONS:These findings provide preliminary evidence that peripheral miRNA expression changes may reflect depressive symptom trajectories, highlighting potential molecular pathways involved in depression. Further studies with larger samples and broader symptom severity are warranted to validate these dynamic miRNA signatures.

The processes of labor and birth have a complex evolutionary history, with substantial variation among species showing differences in gestational length, offspring number, anatomy, and rates of fetal development. Understanding the genomic basis of pregnancy is therefore a focus of evolutionary research, given the importance of reproductive success in processes such as natural selection, mutation, genetic drift, and migration. Disruptions to normal pregnancy processes include preterm birth, which can arise from multiple factors, including infection, anatomical variation, injury, age, parity, and multiple gestation and other obstetrical syndromes as well (e.g., preeclampsia, and stillbirth). These factors each influence unique and overlapping networks of candidate genes and biological pathways. Here we synthesize evidence from comparative genomics, population genetics, and vertebrate reproductive biology to show that many PTB-relevant genes, including those involved in progesterone signaling, innate immunity, placental regulation, and chromosome 19 gene clusters, have undergone lineage- or population-specific evolutionary change. Integrating evolutionary insights with functional genomics, machine learning, and modern genome-editing technologies, we provide a principled framework to distinguish conserved, high-risk targets from evolutionarily flexible loci, guiding safer mechanistic studies and future interventions to reduce PTB risk. From an initial list of approximately 1,500 genes involved in pregnancy, we identified those that show evidence of recent evolutionary change for which functional inference is possible. We review some specific nucleotide sites that, when disrupted via CRISPR gene editing, are likely to impact the processes of labor and birth. These loci fall within protein coding genes, transposable elements, transcription factor binding sites, and non-coding RNAs. They are found in nuclear hormone receptors (e.g., PGR), genes with placenta- and uterine-specific expression patterns (e.g., LGALS13), as well as signaling molecules and immunological loci. Finally, we provide evidence that gene activity and sequence variation differ across species and provide examples of pathway differences between chimpanzees (nociception) and humans (inflammation).

Post-traumatic stress disorder (PTSD) is associated with increased cardiovascular disease (CVD) risk, yet the epigenetic mechanisms underlying this link remain unclear. We investigated whether DNA methylation (DNAm) within Conserved Regions of Systemic Interindividual Variation (CoRSIVs), genomic regions showing stable within-individual, but variable between-individual methylation, mediates the association between PTSD symptom severity (PTSS) and CVD. We analyzed blood-derived DNAm from three cohorts (DNHS: discovery; GTP and NHS: replication), focusing on 7,694 CoRSIV CpGs profiled with the Illumina MethylationEPIC BeadChip. Logistic regression related CpGs to PTSS and CVD, adjusting for demographic and trauma-related covariates. CpGs nominally associated (p<0.05) with both PTSS and CVD in DNHS were then tested using causal mediation analysis. In DNHS, 27 CpGs were nominally associated with both PTSS and CVD, with seven showing nominal mediation (p<0.05). Across cohorts, six of these seven displayed mediation effects in a consistent direction in at least one replication cohort, and three CpGs showed concordant mediation directions across all three cohorts. Notably, cg07941916 (C5orf56/IRF1-AS1) and cg20545458 (intergenic) exhibited positive mediation in DNHS with the same direction in GTP, implying that higher PTSS is associated with methylation changes that correspond to higher CVD risk, whereas all mediation effects in NHS were negative, consistent with its healthier, lower-risk profile. These loci map to immune and inflammatory pathways, alongside other mediators annotated to neuronal/stress-aging and autonomic processes. Overall, DNAm variation within CoRSIVs may partially mediate PTSD-related CVD risk and nominates specific CpGs as hypothesis-generating epigenetic biomarkers that require validation in larger, ancestrally diverse longitudinal cohorts.

Transcranial magnetic stimulation (TMS) is an established treatment for major depressive disorder (MDD), yet response rates remain suboptimal and biomarkers predictive of treatment outcomes are currently lacking. Recently, DNA methylation (DNAm) has shown promise as an epigenetic predictor of antidepressant and electroconvulsive therapy treatment outcomes but no study to our knowledge has characterized DNAm profiles of treatment outcomes in the context of TMS. Here, we present the first genome-scale DNAm analysis of TMS outcomes in patients with treatment-resistant depression (TRD). Peripheral blood samples from 60 TRD patients were collected prior to a standard 36-session TMS course. DNAm was profiled using the Illumina EPIC array and filtered to retain only the top 5

Rare copy number variants (CNVs) are a key component of the genetic basis of psychiatric conditions, but have not been well characterized for most. We conducted a genome-wide CNV analysis across six diagnostic categories (N = 574,965): autism (ASD), ADHD, bipolar disorder (BD), major depressive disorder (MDD), PTSD, and schizophrenia (SCZ). We identified 35 genome-wide significant associations at 18 loci, including novel associations in SCZ ( SMYD3, USP7 - HAPSTR1 ) and in the combined cross-disorder analysis ( ASTN2 ). Rare CNVs accounted for 1-3% of heritability across diagnoses. In ASD, associations were uniformly positive, consistent with autism having diverse etiologies and clinical presentations. By contrast, CNVs showed a dose-dependent relationship for other diagnoses, including SCZ and PTSD, with reciprocal deletions and duplications having inversely correlated effects and distinct genotype-phenotype relationships. Our findings suggest that genes have effects that are both dose-dependent and pleiotropic, such that a positive influence on one dimension of psychopathology may be accompanied by positive or negative effects on others.

BACKGROUND:Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points. METHODS:We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367). RESULTS:Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals. CONCLUSIONS:Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

BACKGROUND:Epigenetic modifications, including DNA methylation (DNAm), can change in response to traumatic stress exposure, and may help to distinguish between individuals with and without PTSD. Here, we examine the DNAm patterns specific to immune cell types and inflammation in those with PTSD. METHODS:This study includes 3,277 participants from 11 cohorts participating in the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. DNAm was assayed from blood with the MethylationEPIC BeadChip. A standardized QC pipeline was applied and used to impute cell composition. Within each cohort, we identified cell-type-specific DNAm patterns associated with PTSD, controlling for sex (if applicable), age, and ancestry. Meta-analyses were performed from summary statistics. RESULTS:PTSD cases had lower proportions of B cells and NK cells as well as higher proportions of neutrophils when compared to trauma-exposed controls. Overall, we identified 96 PTSD-associated CpGs across six types of immune cells. Most of these differences were identified in B cells, with 95 % exhibiting lower methylation levels in those with PTSD. Interestingly, the PTSD-associated CpGs annotated to a gene in B cells were enriched in a recent GWAS of PTSD (p < 0.0001). CONCLUSIONS:This study identifies novel PTSD-associated CpGs in individual immune cell types and supports the role of immune dysregulation and inflammation in PTSD.

Thirty years after the 1994 genocide against the Tutsi in Rwanda, children of survivors are being increasingly documented to be at higher risk compared to their peers for adverse mental health outcomes. However, no studies in Rwanda have empirically explored family psychosocial factors underlying this intergenerational transmission of trauma. We investigated family psychosocial factors that could underlie this transmission in 251 adult Rwandan children of survivors (mean age = 23.31, SD = 2.40; 50.2% female) who completed a crosssectional online survey. For participants with survivor mothers (n = 187), we found that both offspringreported maternal trauma exposure and maternal PTSD were indirectly associated with children's PTSD via maternal trauma communication (specifically, nonverbal and guilt-inducing communication), and that maternal PTSD was indirectly associated with children's PTSD, anxiety, and depression symptoms through family communication styles. For participants with survivor fathers (n = 170), we found that paternal PTSD symptoms were indirectly associated with children's anxiety and depression symptoms via paternal parenting styles (specifically, abusive and indifferent parenting). Although replication is needed in longitudinal research with parentchild dyads, these results reaffirm the importance of looking at mass trauma in a family context and suggest that intergenerational trauma interventions should focus on addressing family communication, trauma communication, and parenting.

Studies of DNA methylation (DNAm) provide biological insight into posttraumatic stress disorder (PTSD) risk and symptoms. We present the results of the largest epigenome-wide association study (EWAS) of PTSD to date and the use of multi-omics data from multiple tissues to interpret the results.

The link between childhood adversity and adulthood depression is well-established; however, the underlying mechanisms are still being explored. Recent research suggests biological age may mediate the relationship between childhood adversity and depression in later life. This study examines if biological age mediates the relationship between childhood adversity and depression symptoms using an expanded set of biological age measures in an urban population-based cohort. Data from waves 1-3 of the Detroit Neighborhood Health Study (DNHS) were used in this analysis. Questions about abuse during childhood were coded to form a childhood adversity score similar to the Adverse Childhood Experience measure. Multiple dimensions of biological age, defined as latent variables, were considered, including systemic biological age (GrimAge, PhenoAge), epigenetic age (Horvath, SkinBlood), and immune age (cytomegalovirus, herpes simplex virus type 1, C-reactive protein, interleukin-6). Depression symptoms, modeled as a latent variable, were captured through the Patient Health Questionnaire-9 (PHQ-9). Models were adjusted for age, gender, race, parent education, and past depressive symptoms. Total and direct effects of childhood adversity on depression symptoms and indirect effects mediated by biological age were estimated. For total and direct effects, we observed a dose-dependent relationship between cumulative childhood adversity and depression symptoms, with emotional abuse being particularly influential. However, contrary to prior studies, in this sample, we found few direct effects of childhood adversity on biological age or biological age on depression symptoms and no evidence of mediation through the measures of biological age considered in this study. Further research is needed to understand how childhood maltreatment experiences are embodied to influence health and wellness.

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation. Multi-ancestry genome-wide analyses identify 95 loci associated with post-traumatic stress disorder and implicate candidate genes, pathways and neurobiological systems underlying its pathophysiology.

Background:Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods:Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results:The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. Conclusion:Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Abstract Background The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using Illumina HumanMethylation450 or MethylationEPIC (850 K) BeadChips. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e − 09 < p < 5.30e − 08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood–brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Out of 9 CpGs annotated to a gene expressed in blood, methylation levels at 5 CpGs showed significant correlations with the expression levels of their respective annotated genes. Conclusions This study identifies 11 PTSD-associated CpGs and leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.