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The Psychiatric Genomics Consortium-Posttraumatic Stress Disorder group (PGC-PTSD) combined genome-wide case-control molecular genetic data across 11 multiethnic studies to quantify PTSD heritability, to examine potential shared genetic risk with schizophrenia, bipolar disorder, and major depressive disorder and to identify risk loci for PTSD. Examining 20 730 individuals, we report a molecular genetics-based heritability estimate (h2SNP) for European-American females of 29% that is similar to h2SNP for schizophrenia and is substantially higher than h2SNP in European-American males (estimate not distinguishable from zero). We found strong evidence of overlapping genetic risk between PTSD and schizophrenia along with more modest evidence of overlap with bipolar and major depressive disorder. No single-nucleotide polymorphisms (SNPs) exceeded genome-wide significance in the transethnic (overall) meta-analysis and we do not replicate previously reported associations. Still, SNP-level summary statistics made available here afford the best-available molecular genetic index of PTSD-for both European- and African-American individuals-and can be used in polygenic risk prediction and genetic correlation studies of diverse phenotypes. Publication of summary statistics for ∼10 000 African Americans contributes to the broader goal of increased ancestral diversity in genomic data resources. In sum, the results demonstrate genetic influences on the development of PTSD, identify shared genetic risk between PTSD and other psychiatric disorders and highlight the importance of multiethnic/racial samples. As has been the case with schizophrenia and other complex genetic disorders, larger sample sizes are needed to identify specific risk loci.

Thanks to the effective electrostatic potential generated by the ordered atomic structure, bent crystals can efficiently deflect ultra relativistic charged beams by means of planar and axial channeling phenomena as well as of the recently discovered volume reflection effect. Most of the experimental knowledge about these phenomena has been gathered with Si crystals, but it has been recently demonstrated that the steering performance can be improved by using high quality Ge materials which have a larger atomic number. In this paper, we investigate channeling and volume reflection of 400 GeV protons from (110) lattice planes in highly bent Ge strips crystals. Both production and characterization of the strips are presented. Herein, the experimental results on deflection are compared with theoretical predictions, with previous published data and with the expected performances of Si crystals in similar experimental conditions. [ABSTRACT FROM AUTHOR]

Abstract Background Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases. We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses. Results We have developed an information system mainly devoted to the storage and management of SNP genotype data produced by the Illumina platform from the raw outputs of genotyping into a relational database. The relational database can be accessed in order to import any existing data and export user-defined formats compatible with many different genetic analysis programs. After calculating family-based or case-control association study data, the results can be imported in SNPLims. One of the main features is to allow the user to rapidly identify and annotate statistically relevant polymorphisms from the large volume of data analyzed. Results can be easily visualized either graphically or creating ASCII comma separated format output files, which can be used as input to further analyses. Conclusions The proposed infrastructure allows to manage a relatively large amount of genotypes for each sample and an arbitrary number of samples and phenotypes. Moreover, it enables the users to control the quality of the data and to perform the most common screening analyses and identify genes that become “candidate” for the disease under consideration.

Abstract Background Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. Results We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. Conclusions The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.

DNA damage response (DDR) pathway is the coordinated cellular network dealing with the identification, signaling, and repair of DNA damage. It tightly regulates cell cycle progression and promotes DNA repair to minimize DNA damage to daughter cells. Key proteins involved in DDR are frequently mutated/inactivated in human cancers and promote genomic instability, a recognized hallmark of cancer. Besides being an intrinsic property of tumors, DDR also represents a unique therapeutic opportunity. Indeed, inhibition of DDR is expected to delay repair, causing persistent unrepaired breaks, to interfere with cell cycle progression, and to sensitize cancer cells to several DNA-damaging agents, such as radiotherapy and chemotherapy. In addition, DDR defects in cancer cells have been shown to render these cells more dependent on the remaining pathways, which could be targeted very specifically (synthetic lethal approach). Research over the past two decades has led to the synthesis and testing of hundreds of small inhibitors against key DDR proteins, some of which have shown antitumor activity in human cancers. In parallel, the search for synthetic lethality interaction is broadening the use of DDR inhibitors. In this review, we discuss the state-of-art of ataxia-telangiectasia mutated, ataxia-telangiectasia-and-Rad3-related protein, checkpoint kinase 1, Wee1 and Polθ inhibitors, highlighting the results obtained in the ongoing clinical trials both in monotherapy and in combination with chemotherapy and radiotherapy., Competing Interests: The authors declare they have no conflicts of interest., (© 2024 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.)

Cystic fibrosis (CF) is caused by mutations in the gene encoding the CF transmembrane conductance regulator (CFTR) protein. Although CF affects multiple organs, lung disease is the main cause of morbidity and mortality, and gene therapy is expected to provide a mutation-agnostic option for treatment. SP-101 is a recombinant adeno-associated virus (AAV) gene therapy vector carrying a human CFTR minigene, hCFTRΔR , and is being investigated as an inhalation treatment for people with CF. To further understand SP-101 activity, in vitro studies were performed in human airway epithelia (HAE) derived from multiple CF and non-CF donors. SP-101 restored CFTR-mediated chloride conductance, measured via Ussing chamber assay, at a multiplicity of infection (MOI) as low as 5E2 in the presence of doxorubicin, a small molecule known to augment AAV transduction. Functional correction of CF HAE increased with increasing MOI and doxorubicin concentration and correlated with increasing cell-associated vector genomes and hCFTRΔR mRNA expression. Tropism studies using a fluorescent reporter vector and single-cell mRNA sequencing of SP-101-mediated hCFTRΔR mRNA demonstrated broad expression in all cell types after apical transduction, including secretory, ciliated, and basal cells. In summary, SP-101, particularly in combination with doxorubicin, shows promise for a novel CF treatment strategy and strongly supports continued development.

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts., Competing Interests: Declaration of Competing Interest Dr. Nakahara is employed by Astellas Pharma Inc. Dr. Bustillo consulted with Novartis and Otsuka Pharmaceuticals. Dr. Mathalon is a consultant for Bristol-Myers Squibb and consulted for Roche Pharmaceuticals. Dr. Preda consulted for Boehringer-Ingelheim. Dr. Potkin has financial interests in Bristol-Myers Squibb, Eisai, Inc., Eli Lilly, Forest Laboratories, Genentech, Janssen Pharmaceutical, Lundbeck, Merck, Novartis, Organon, Pfizer, Roche, Sunovion, Takeda Pharmaceutical, Vanda Pharmaceutical, Novartis, Lundbeck, Merck, Sunovion and has received grant funding from Amgen, Baxter, Bristol-Myers Squibb, Cephalon, Inc., Eli Lilly, Forest Laboratories, Genentech, Janssen Pharmaceutical, Merck, Otsuka, Pfizer, Roche, Sunovion, Takeda Pharmaceutical, Vanda Pharmaceutical, NIAAA, NIBIB, NIH/NCRR, University of Southern California, UCSF, UCSD, Baylor College of Medicine. The remaining authors declare no potential conflict of interest. None of the authors of this manuscript are affiliated with or receive compensation from NeuroComp Systems, Inc., MATRICS Assessment, Inc., or Neurcog Trials, Inc., (Copyright © 2023. Published by Elsevier B.V.)

Transposable elements (TEs) are mobile genetic elements that constitute half of the human genome. Recent studies suggest that polymorphic non-reference TEs (nrTEs) may contribute to cognitive diseases, such as schizophrenia, through a cis-regulatory effect. The aim of this work is to identify sets of nrTEs putatively linked to an increased risk of developing schizophrenia. To do so, we inspected the nrTE content of genomes from the dorsolateral prefrontal cortex of schizophrenic and control individuals and identified 38 nrTEs that possibly contribute to the emergence of this psychiatric disorder, two of them further confirmed with haplotype-based methods. We then performed in silico functional inferences and found that 9 of the 38 nrTEs act as expression/alternative splicing quantitative trait loci (eQTLs/sQTLs) in the brain, suggesting a possible role in shaping the human cognitive genome structure. To our knowledge, this is the first attempt at identifying polymorphic nrTEs that can contribute to the functionality of the brain. Finally, we suggest that a neurodevelopmental genetic mechanism, which involves evolutionarily young nrTEs, can be key to understanding the ethio-pathogenesis of this complex disorder., (© 2023. The Author(s).)

Glucocorticoids are a key component to the cellular response to stress. Glucocorticoids act via glucocorticoid receptors found ubiquitously in the brain and body. Glucocorticoid receptors can bind to response elements throughout the genome to elicit changes in transcription, an adaptation observed at the cellular level. Yet, the transcriptional changes as a consequence of glucocorticoid receptor activation are variable across brain regions, stress conditions and recurrent bouts of glucocorticoid exposure. Here we describe a non-coding RNA, B2 SINE, which is regulated by glucocorticoids and can in turn regulate glucocorticoid receptor transcriptional activity. We show that activated glucocorticoid receptors interact directly with B2 SINE RNA via a decoy response element contained within the transcript sequence and alter receptor binding to response elements in the genome and, subsequently, changes in loci expression., Competing Interests: No authors have any conflict of interest to disclose, and there has been no financial support that might influence this work., (© 2023 Published by Elsevier Inc.)

Objective: Individuals with posttraumatic stress disorder (PTSD) are significantly more likely to be diagnosed with cardiovascular disease (CVD) (e.g., myocardial infarction, stroke). The evidence for this link is so compelling that the National Institutes of Health convened a working group to determine gaps in the literature, including the need for large-scale genomic studies to identify shared genetic risk. The aim of the present study was to address some of these gaps by utilizing PTSD and CVD genome-wide association study (GWAS) summary statistics in a large biobank sample to determine the shared genetic risk of PTSD and CVD., Methods: A large health care biobank data set was used (N=36,412), combined with GWAS summary statistics from publicly available large-scale PTSD and CVD studies. Disease phenotypes (e.g., PTSD) were collected from electronic health records. De-identified genetic data from the biobank were genotyped using Illumina SNP array. Summary statistics data sets were processed with the following quality-control criteria: 1) SNP heritability h 2 >0.05, 2) compute z-statistics (z=beta/SE or z=log(OR)/SE), 3) filter nonvariable SNPs (0G =0.24, SE=0.06), and Mendelian randomization analyses indicated a potential causal link from PTSD to hypertension (β=0.20, SE=0.04), but not the reverse. PTSD summary statistics significantly predicted PTSD diagnostic status (R 2 =0.27), and this was significantly improved by incorporating summary statistics from CVD and major depressive disorder (R 2 =1.30). Further, pathway enrichment analyses indicated that genetic variants involved in shared PTSD-CVD risk included those involved in postsynaptic structure, synapse organization, and interleukin-7-mediated signaling pathways., Conclusions: The results from this study suggest that PTSD and CVD may share genetic risk. Further, these results implicate PTSD as a risk factor leading to the development of hypertension and coronary artery disease. Additional research is needed to determine the clinical utility of these findings.

Recent reports have suggested that the reactivation of otherwise transcriptionally silent transposable elements (TEs) might induce brain degeneration, either by dysregulating the expression of genes and pathways implicated in cognitive decline and dementia or through the induction of immune-mediated neuroinflammation resulting in the elimination of neural and glial cells. In the work we present here, we test the hypothesis that differentially expressed TEs in blood could be used as biomarkers of cognitive decline and development of AD. To this aim, we used a sample of aging subjects (age > 70) that developed late-onset Alzheimer's disease (LOAD) over a relatively short period of time (12-48 months), for which blood was available before and after their phenoconversion, and a group of cognitive stable subjects as controls. We applied our developed and validated customized pipeline that allows the identification, characterization, and quantification of the differentially expressed (DE) TEs before and after the onset of manifest LOAD, through analyses of RNA-Seq data. We compared the level of DE TEs within more than 600,000 TE-mapping RNA transcripts from 25 individuals, whose specimens we obtained before and after their phenotypic conversion (phenoconversion) to LOAD, and discovered that 1790 TE transcripts showed significant expression differences between these two timepoints (logFC ± 1.5, logCMP > 5.3, nominal p value < 0.01). These DE transcripts mapped both over- and under-expressed TE elements. Occurring before the clinical phenoconversion, this TE storm features significant increases in DE transcripts of LINEs, LTRs, and SVAs, while those for SINEs are significantly depleted. These dysregulations end with signs of manifest LOAD. This set of highly DE transcripts generates a TE transcriptional profile that accurately discriminates the before and after phenoconversion states of these subjects. Our findings suggest that a storm of DE TEs occurs before phenoconversion from normal cognition to manifest LOAD in risk individuals compared to controls, and may provide useful blood-based biomarkers for heralding such a clinical transition, also suggesting that TEs can indeed participate in the complex process of neurodegeneration., (© 2022. The Author(s).)

Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs)., Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms., Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program., Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods., (Copyright © 2021 Society of Biological Psychiatry. All rights reserved.)

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD., (© 2020. The Author(s).)

Biomarkers that predict symptom trajectories after trauma can facilitate early detection or intervention for posttraumatic stress disorder (PTSD) and may also advance our understanding of its biology. Here, we aimed to identify trajectory-based biomarkers using blood transcriptomes collected in the immediate aftermath of trauma exposure. Participants were recruited from an Emergency Department in the immediate aftermath of trauma exposure and assessed for PTSD symptoms at baseline, 1, 3, 6, and 12 months. Three empirical symptom trajectories (chronic-PTSD, remitting, and resilient) were identified in 377 individuals based on longitudinal symptoms across four data points (1, 3, 6, and 12 months), using latent growth mixture modeling. Blood transcriptomes were examined for association with longitudinal symptom trajectories, followed by expression quantitative trait locus analysis. GRIN3B and AMOTL1 blood mRNA levels were associated with chronic vs. resilient post-trauma symptom trajectories at a transcriptome-wide significant level (N = 153, FDR-corrected p value = 0.0063 and 0.0253, respectively). We identified four genetic variants that regulate mRNA blood expression levels of GRIN3B. Among these, GRIN3B rs10401454 was associated with PTSD in an independent dataset (N = 3521, p = 0.04). Examination of the BrainCloud and GTEx databases revealed that rs10401454 was associated with brain mRNA expression levels of GRIN3B. While further replication and validation studies are needed, our data suggest that GRIN3B, a glutamate ionotropic receptor NMDA type subunit-3B, may be involved in the manifestation of PTSD. In addition, the blood mRNA level of GRIN3B may be a promising early biomarker for the PTSD manifestation and development., (© 2021. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Background: Cross-sectional studies have found that individuals with posttraumatic stress disorder (PTSD) exhibit deficits in autonomic functioning. While PTSD rates are twice as high in women compared to men, sex differences in autonomic functioning are relatively unknown among trauma-exposed populations. The current study used a prospective design to examine sex differences in posttraumatic autonomic functioning., Methods: 192 participants were recruited from emergency departments following trauma exposure ( Mean age = 35.88, 68.2% female). Skin conductance was measured in the emergency department; fear conditioning was completed two weeks later and included measures of blood pressure (BP), heart rate (HR), and high frequency heart rate variability (HF-HRV). PTSD symptoms were assessed 8 weeks after trauma., Results: 2-week systolic BP was significantly higher in men, while 2-week HR was significantly higher in women, and a sex by PTSD interaction suggested that women who developed PTSD demonstrated the highest HR levels. Two-week HF-HRV was significantly lower in women, and a sex by PTSD interaction suggested that women with PTSD demonstrated the lowest HF-HRV levels. Skin conductance response in the emergency department was associated with 2-week HR and HF-HRV only among women who developed PTSD., Conclusions: Our results indicate that there are notable sex differences in autonomic functioning among trauma-exposed individuals. Differences in sympathetic biomarkers (BP and HR) may have implications for cardiovascular disease risk given that sympathetic arousal is a mechanism implicated in this risk among PTSD populations. Future research examining differential pathways between PTSD and cardiovascular risk among men versus women is warranted., Competing Interests: In the last three years GDS has received research funding from the NSF, NIH and LifeBell AI, and unrestricted donations from AliveCor, Amazon Research, the Center for Discovery, the Gordon and Betty Moore Foundation, MathWorks, Microsoft Research, the Gates Foundation, Google, One Mind Foundation, and Samsung Research. GDS has financial interest in AliveCor and receives unrestricted funding from the company. He is also the CTO of MindChild Medical and CSO of LifeBell AI and has ownership in both companies. These relationships are unconnected to the current work. In the past three years, LTG has served on the Scientific Advisory Board of Sage Bionetworks, for which she received a small honorarium. SLR reports grants from NIH during the conduct of the study; personal fees from SOBP (Society of Biological Psychiatry) paid role as secretary, other from Oxford University Press royalties, other from APP (American Psychiatric Publishing Inc.) royalties, other from VA (Veterans Administration) per diem for oversight committee, and other from Community Psychiatry paid board service, including equity outside the submitted work; and Leadership roles on Board or Council for SOBP, ADAA (Anxiety and Depression Association of America), and NNDC (National Network of Depression Centers). SS has received funding from the Florida Medical Malpractice Joint Underwriter's Association Dr. Alvin E. Smith Safety of Healthcare Services Grant; Allergan Foundation; the NIH/NIA-funded Jacksonville Aging Studies Center (JAX-ASCENT; R33AG05654); and the Substance Abuse and Mental Health Services Administration (1H79TI083101-01); and the Florida Blue Foundation. CWJ reports no direct conflicts related to this paper. He has been an investigator on studies funded by Hologic Inc, Janssen, AstraZeneca, and Vapotherm, for which his department has received research funding. JJ receives consulting payments from Janssen Pharmaceuticals. Over the past three years, DAP has received consulting fees from Albright Stonebridge Group, BlackThorn Therapeutics, Boehringer Ingelheim, Compass Pathway, Concert Pharmaceuticals, Engrail Therapeutics, Neurocrine Biosciences, Otsuka Pharmaceuticals, and Takeda Pharmaceuticals as well as one honorarium from Alkermes. In addition, he has received stock options from BlackThorn Therapeutics, and research support from National Institute of Mental Health, Dana Foundation, Brain and Behavior Research Foundation, and Millennium Pharmaceuticals. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors. JME reports support from the National Institutes of Health (NIH) through Grant Numbers R01HD079076 & R03HD094577, Eunice Kennedy Shriver National Institute of Child Health & Human Development, and National Center for Medical Rehabilitation Research. He also reports funding from the New South Wales Health Spinal Cord Injury Research Grants Program and consulting fees (<$15,000 per annum) from Orofacial Therapeutics, LLC. In the past 3 years, RCK was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., and Sage Pharmaceuticals. He has stock options in Mirah, PYM, and Roga Sciences. KJR has received consulting income from Alkermes, research support from NIH, Genomind and Brainsway, and he is on scientific advisory boards for Janssen and Verily, all of which is unrelated to the present work. All other authors have no conflicts of interest to disclose., (© 2021 The Authors.)

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European ( n = 70,870) and African ( n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study - United Kingdom Biobank - PTSD symptoms were negatively associated with SBP levels (β = -1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Sumner, Maihofer, Michopoulos, Rothbaum, Almli, Andreassen, Ashley-Koch, Baker, Beckham, Bradley, Breen, Coleman, Dale, Dennis, Feeny, Franz, Garrett, Gillespie, Guffanti, Hauser, Hemmings, Jovanovic, Kimbrel, Kremen, Lawford, Logue, Lori, Lyons, Maples-Keller, Mavissakalian, McGlinchey, Mehta, Mellor, Milberg, Miller, Morris, Panizzon, Ressler, Risbrough, Rothbaum, Roy-Byrne, Seedat, Smith, Stevens, van den Heuvel, Voisey, Young, Zoellner, PGC-PTSD Physical Health Working Group, Nievergelt and Wolf.)

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Epigenetic differences may help to distinguish between PTSD cases and trauma-exposed controls. Here, we describe the results of the largest DNA methylation meta-analysis of PTSD to date. Ten cohorts, military and civilian, contribute blood-derived DNA methylation data from 1,896 PTSD cases and trauma-exposed controls. Four CpG sites within the aryl-hydrocarbon receptor repressor (AHRR) associate with PTSD after adjustment for multiple comparisons, with lower DNA methylation in PTSD cases relative to controls. Although AHRR methylation is known to associate with smoking, the AHRR association with PTSD is most pronounced in non-smokers, suggesting the result was independent of smoking status. Evaluation of metabolomics data reveals that AHRR methylation associated with kynurenine levels, which are lower among subjects with PTSD. This study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to (1) identify genetic variation associated with self-reported childhood maltreatment, (2) estimate SNP-based heritability (h 2 snp ), (3) assess predictive value of polygenic risk scores (PRS) for childhood maltreatment, and (4) quantify genetic overlap of childhood maltreatment with mental and physical health-related phenotypes, and condition the top hits from our analyses when such overlap is present. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n = 124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder group (PGC-PTSD) (n = 26,290). h 2 snp for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression. PRS was calculated using PRSice and mtCOJO was used to perform conditional analysis. Two genome-wide significant loci associated with childhood maltreatment (rs142346759, p = 4.35 × 10 -8 , FOXP1; rs10262462, p = 3.24 × 10 -8 , FOXP2) were identified in the discovery dataset but were not replicated in PGC-PTSD. h 2 snp for childhood maltreatment was ~6% and the PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC-PTSD (r 2  = 0.0025; p = 1.8 × 10 -15 ). The most significant genetic correlation of childhood maltreatment was with depressive symptoms (r g  = 0.70, p = 4.65 × 10 -40 ), although we show evidence that our top hits may be specific to childhood maltreatment. This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Speculatively, FOXP genes might influence externalizing traits and so be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of phenotypes, may ultimately be useful in in developing targeted treatment and prevention strategies.

The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5-20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations.

Objective: The clinical and nosological significance of grief reactions in youth exposed to a shared trauma (9/11, the September 11, 2001 terrorist attacks on the United States) was tested by examining whether the predictors (ie, non-loss-related trauma versus traumatic bereavement), clinical correlates, factorial structure, and phenomenology of grief reactions are distinct from those of major depressive disorder (MDD) and 9/11-related posttraumatic stress disorder (PTSD)., Method: In a representative sample of New York City schoolchildren (N = 8,236; grades 4-12; n = 1,696 bereaved), assessed 6 months post-9/11, multivariate regressions examined predictors of grief, PTSD, and MDD, as well as the incremental validity of grief in predicting health problems and functional impairment. Factor analysis and latent class analysis determined, respectively, the factorial and the syndromic distinctiveness of grief, PTSD, and MDD., Results: Four types of evidence supporting the distinctiveness of grief emerged. (1) Bereavement was associated with grief independently of PTSD and MDD, but not with PTSD and MDD after adjusting for grief; conversely, non-loss related trauma was associated primarily with PTSD. (2) Grief contributed uniquely to functional impairment. (3) Grief reactions loaded on a separate factor. (4) Youth with elevated grief reactions fell into two classes characterized by only moderate and negligible probability of co-occurring PTSD and MDD symptoms, respectively., Conclusion: A multifaceted approach provided convergent evidence that grief reactions are independent of other common types of postdisaster child and adolescent psychopathology, and capture a unique aspect of bereavement-related distress. These findings suggest that grief reactions in traumatically bereaved youth merit separate clinical attention, informing tailored interventions., (Copyright © 2019 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Investigations of pathophysiological mechanisms implicated in vulnerability to depression have been negatively impacted by the significant heterogeneity characteristic of psychiatric syndromes. Such challenges are also reflected in numerous null findings emerging from genome-wide association studies (GWAS) of depression. Bolstered by increasing sample sizes, recent GWAS studies have identified genetics variants linked to MDD. Among them, Okbay and colleagues (Nat. Genet. 2016 Jun;48(6):624-33) identified genetic variants associated with three well-validated depression-related phenotypes: subjective well-being, depressive symptoms, and neuroticism. Despite this progress, little is known about psychopathological and neurobiological mechanisms underlying such risk. To fill this gap, a genetic risk score (GRS) was computed from the Okbay's study for a sample of 88 psychiatrically healthy females. Across two sessions, participants underwent two well-validated psychosocial stressors, and performed two separate tasks probing reward learning both before and after stress. Analyses tested whether GRS scores predicted anhedonia-related phenotypes across three units of analyses: self-report (Snaith Hamilton Pleasure Scale), behavior (stress-induced changes in reward learning), and circuits (stress-induced changes in striatal reward prediction error; striatal volume). GRS scores were negatively associated with anhedonia-related phenotypes across all units of analyses but only circuit-level variables were significant. In addition, the amount of explained variance was systematically larger as variables were putatively closer to the effects of genes (self-report < behavior < neural circuitry). Collectively, findings implicate anhedonia-related phenotypes and neurobiological mechanisms in increased depression vulnerability, and highlight the value of focusing on fundamental dimensions of functioning across different units of analyses.

Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R 2  = 4.68%, P = 0.003) and severity (R 2  = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R 2  = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder.