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Peril or Pleasure Reward and salience neurocircuitry and the effects of intranasal oxytocin in posttraumatic stress disorder Nawijn, L.

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Citation for published version (APA): Nawijn, L. (2017). Peril or Pleasure: Reward and salience neurocircuitry and the effects of intranasal oxytocin in posttraumatic stress disorder.

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Download date:24 Sep 2021 Genetic and epigenetic association studies in traumatized police ofcers with and 6 without posttraumatic stress disorder

Izabela M. Krzyzewskaa * Judith B.M. Ensinkb,c * Laura Nawijnd * Adri N. Mula Saskia B. J. Kochd Andrea Venemaa Vinod Shankara c. De Bascule, Academic Centre for Jessie L. Frijlingd Child and Adolescent Psychiatry, Dick J. Veltmane Amsterdam, The Netherlands; Ramon J.L. Lindauerb,c Miranda Olfd,f, d. Department of Psychiatry, Marcel M.A.M. Mannensa Anxiety Disorders, Mirjam van Zuidend # Academic Medical Center, Peter Hennemana # Amsterdam, The Netherlands; a. Department of Clinical Genetics, e. Department of Psychiatry, Genome Diagnostics laboratory, VU University Medical Center, Academic Medical Center, Amsterdam, the Netherlands; Amsterdam, The Netherlands; f. Arq Psychotrauma Expert Group, b. Department of Child and Diemen, the Netherlands. Adolescent Psychiatry, Academic Medical Center, * , # Authors contributed equally Amsterdam, The Netherlands; Submitted for publication Abstract

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that may develop after a traumatic event. Here we aimed to identify epigenetic and genetic loci associated with PTSD. We included 73 traumatized police ofcers with extreme phenotypes regarding symptom severity despite similar trauma history: n=34 had PTSD and n=39 had minimal PTSD symptoms. Epigenetic and genetic profiles were based on the Illumina HumanMethylation450 BeadChip. We searched for diferentially methylated probes (DMPs) and regions (DMRs). For genetic associations we analyzed the CpG-SNPs present on the array. We detected no genome-wide significant DMPs and we did not replicate previously reported DMPs associated with PTSD. However, gene-set enrichment analysis of the top 100 DMPs showed enrichment of three genes involved the dopaminergic neurogenesis pathway. Furthermore, we observed one relatively large DMR between patients and controls which was located at the PAX8 gene and previously associated with other psychiatric disorders. Finally, we validated five PTSD-associated CpG- SNPs identified with the array using sanger sequencing. We subsequently replicated the association of one common SNP (rs1990322) in the CACNA1C locus with PTSD in an independent cohort of traumatized children. The CACNA1C locus was previously associated with other psychiatric disorders, but not yet with PTSD. Thus, despite the small sample size, inclusion of extreme symptom severity phenotypes in a highly homogenous traumatized cohort enabled detection of epigenetic and genetic loci associated with PTSD. Moreover, here we showed that genetically confounded 450K probes are informative for genetic association analysis.

129 INTRODUCTION

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that may develop after exposure to traumatic events. The disorder is characterized by recurrent, involuntary re- experiencing of the traumatic event, accompanied by avoidance of trauma-related stimuli; negative alterations in cognition and mood and in reactivity (American Psychiatric Association, 2013). Its lifetime prevalence in Western countries is estimated at 5.7-7.4% (de Vries and Olf, 2009; Kessler et al., 2012). In all, the societal impact of PTSD is high, not only because of its negative impact on quality of life of afected individuals and their families, but also due to high economic costs resulting from health care utilization and productivity loss and the high percentage of patients not adequately responding to treatment (Pietrzak et al., 2012; Schlenger et al., 2016). Additionally, PTSD is associated with increased risk for a wide range of severe chronic medical disorders and increased all- cause early mortality (Pietrzak et al., 2012; Schlenger et al., 2015). Longitudinal studies consistently show that only a subset of individuals will develop PTSD in the aftermath of apparently similar traumatic events, indicating diferential susceptibility (Santiago et al., 2013). To date, the biological mechanisms and etiology underlying diferential susceptibility for PTSD remain poorly understood, but accumulating evidence indicates that genetic and epigenetic factors are involved (Broekman et al., 2007; Thakur et al., 2015).

Genetic factors contribute substantially to PTSD development: its heritability is estimated between 0.28 – 0.46 (Ehlers et al., 2013; Stein et al., 2016, 2002). PTSD has a highly heterogeneous phenotype and can be characterized as complex trait, indicating that common genetic variation within hundreds to thousands of genes might contribute to its expression. For most of these genetic contributors, small efect sizes can be expected (Manolio et al., 2009). Moreover, PTSD development cannot be explained by genetic factors alone (Sartor et al., 2012; Stein et al., 2002; True et al., 1993). PTSD involves by definition exposure to a precipitating traumatic event suggesting an additional role for epigenetic processes that mediate gene regulation under influence of the environment (Zannas et al., 2015). Recent insights show that epigenetic associations with psychiatric disorders can not only be detected in the most obvious target tissue, i.e. the brain, but also in peripheral blood (Klengel et al., 2014). With regard to PTSD, several candidate gene studies have detected genetic and epigenetic associations. However, the number of hypothesis-free, i.e. genome wide genetic and epigenetic studies on PTSD, remains limited (Smith et al., 2011; Uddin et al., 2010). In this study we aimed to identify epigenetic and genetic loci associated with PTSD in a Dutch cohort of highly trauma exposed police ofcers with and without PTSD. We selected our cohort to be homogenous, also with respect to trauma history, but showing extreme phenotypes regarding PTSD symptom severity (i.e. highly afected vs. resilient with minimal symptoms), maximizing likelihood to detect PTSD-related associations in a relative small sample (Van Gestel et al., 2000). We used the Infinium HumanMethylation 450 BeadChip of Illumina, a commonly used

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platform for epigenome-wide association studies (EWAS). Next to approximately 430K informative epigenetic markers, this array contains a substantial number of probes biased by common genetic variation. These probes involve common single nucleotide polymorphisms at the CpG site of interest (CpG-SNPs) directly afecting the readout of the methylation array at that position. Usually these probes are discarded from epigenetic surveys, despite the fact that some of the approximately 14K probes show well defined genotypic clusters that may be used for genetic association analysis. Detailed information on promiscuous 450K probes, i.e. genetic confounded and cross hybridizing probes, and the design of the array is described by Chen et al (Chen et al., 2013). In this study, we used these 14K CpG-SNP probes for genetic association analyses and subsequently validated identified candidate PTSD- associated SNPs with additional sanger-sequencing. Consistent genetic findings between array and sequencing methods were replicated in a Dutch cohort of trauma exposed children with and without PTSD.

METHODS

Participants This study is part of a larger neuroimaging study on the efects of a single oxytocin administration on neural activity, in which trauma-exposed police ofcers with and without PTSD between 18-65 years old were included (Koch et al., 2016c). For the current study, blood samples were collected from 73 participants: 34 PTSD patients and 39 controls. PTSD patients fulfilled DSM-IV criteria for current PTSD, with a Clinician Administered PTSD Scale (CAPS) score≥45 (Blake et al., 1995). Exclusion criteria for PTSD patients were current severe MDD (with psychotic features or suicidal intent), bipolar disorder, suicidal ideation, alcohol/substance abuse and psychotic disorders, as measured with the Mini International Neuropsychiatric Interview or Structured Clinical Interview for DSM–IV (First et al., 2002; Sheehan et al., 1998). Controls reported at least one traumatic event (DSM-IV A1 criterion), with a CAPS score <15. Controls were matched to PTSD patients based on sex, age, education and years of service. Exclusion criteria for controls were lifetime MDD or PTSD or any current DSM-IV Axis-I psychiatric disorder. Exclusion criteria for all participants consisted of daily use of psychoactive medication or systemic glucocorticoids, contraindications for MRI or oxytocin administration, serious medical conditions or history of neurological disorders. All participants provided written informed consent prior to study initiation. The study was approved by the Institutional Review Board of the Academic Medical Center, Amsterdam, the Netherlands. Patients were recruited through a diagnostic outpatient center for police personnel with psychological trauma (PDC, Diemen, the Netherlands). Additionally, PTSD patients and controls were recruited through advertisements. Group diferences in demographic and clinical characteristics were tested using independent sample t-tests (log transformed in case of non-normally distributed data) for continuous variables and Chi-square tests or Fisher’s exact tests for categorical variables, performed in SPSS (version 20.0.0, IBM® SPSS® Statistics).

131 Procedures Sample preparation and quality control of the 450K DNA-methylation array are described in supplementary file 1. The 450K DNA-methylation array comprises a substantial number of SNPs located in or near CpG sites at single base extensions (SBE) sites of probes. As was previously shown by others (Chen et al., 2013), a substantial number of these CpG-SNPs show 3 or 2 well divided clusters of beta-values, reflecting genotypes. Therefore, we divided the probes into two data sets, one for epigenetic and one for genetic association analysis (see supplementary figure 1 for schematic overview). For both data sets promiscuous 450K probes, i.e. cross hybridizing probes, were excluded form analysis (Chen et al., 2013). In addition, probes located on both the X and Y chromosomes were not included in further analysis. The epigenetic probe set included 432.222 probes, excluding probes with a common SNP with a CEU minor allele frequency (maf) > 0.01 located at the SBE site. This probe set was used to identify diferentially methylated probes (DMP) and diferentially methylated regions (DMRs). The CpG-SNP genetic probe set included the 13.344 probes with a common SNP located at SBE site with a CEU maf > 0.01.

Epigenetic association analyses A complete description of DMP and DMR analyses is provided in supplementary file 1. For both DMP and DMR analyses, we included the following covariates in the model: age, leukocyte cell distribution (CD8+ and CD4+ T cells, Natural killer cells, B cells, Monocytes and Granulocytes) and sex. For DMP association analyses lmfit (“R”package limma) function was used. We assumed a q- value <0.05 (Benjamini-Hochberg adjusted alpha) as genome-wide significant. In order to replicate previously reported epigenetic associations (DMPs) with PTSD we additionally applied a hypothesis driven approach; we performed a NCBI PubMed search using keywords “450K”, “Illumina” and “PTSD”. From the identified studies, we selected the candidate DMPs that were previously reported to be genome-wide significantly associated with PTSD, and for these DMPs data was extracted from our association data. Subsequently, we performed gene-set enrichment (GSE) analyses on our top 100 DMP hits, excluding DMPs located at intergenic regions (IGRs, > 1kb distance from the gene, 26 DMPs removed, 74 DMPs remained). Enrichment analyses were done via the publicly available web tool using the consensus pathway database web tool, developed by the Max Planck institute, www.consensuspathdb.org, version 31, 2015 (Kamburov et al., 2013).

DMR association analyses were performed using the minfi function bumphunter. DMRs were defined as including two or more probes (L>1) in the bump. Genome wide significant DMRs (adjusted P-value <0.05 according the family wise error-rate over the area of the bump, FWER) or DMRs showing consistent diferential methylation over more than 75% of the probes within the cluster were considered promising. Technical validation was performed targeting one promising DMR (PAX8) and one randomly chosen region located at the SOSTDC1 gene. The latter region was included to achieve a broader range

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of β-values that favors proper correlation analysis between the two methods. SOSTDC1 expressed average β-values < 50% within that region. Technical validation was performed through targeted amplicon sequence analysis using the Illumina MiSeq platform (details see supplementary file 1).

Genetic association analyses In order to detect genetic associations between PTSD and the 450K CpG-SNPs, we performed association analyses conform the above described statistical methodology (epigenetic association analyses). In order to ascertain appropriate genotypic clustering of beta-values we selected CpG- SNPs with a P-value <0.05 and an absolute delta diference >0.15 (visual evaluation based thresholds) for further evaluation. Associations with PTSD of these candidate SNPs were validated using Sanger sequencing in our discovery cohort. Since the amount of collected DNA was limited, we first applied a whole genome amplification for all available samples using the Qiagen REPLI-g Mini KitTM conform the manufacturers protocol, yielding >4 ug of DNA for each participant. Primer sets for candidate SNPs were designed using Primer3 (Untergasser et al., 2012), all primers were M13 extended for standardized Sanger sequencing (supplementary table 1). For validation association analyses genotypes were recoded into ordinal variables followed by Chi-square tests assessing genotype diferences between PTSD patients and controls, performed in SPSS (version 20.0.0, IBM® SPSS® Statistics). P-values at which significance was assumed were Bonferroni corrected for the number of tests performed (0.05/number of tests).

Replication analyses of validated CpG-SNPs were performed in a cohort of trauma- exposed children with (N=46) and without PTSD (N=50), see supplementary file 1 for details on participant characteristics and genotyping procedure. Since DNA of replication cohort was extracted from saliva and DNA of the discovery cohort was obtained from whole blood we considered only replication of genetic associations in the discovery cohort appropriate. Statistical analyses were performed as described above for validation analyses. P-values at which significance was assumed were Bonferroni corrected for the number of tests performed (0.05/number of tests).

RESULTS

Discovery cohort participant characteristics PTSD patients and trauma-exposed controls did not difer on demographic or trauma history characteristics. As expected, PTSD patients had significantly higher PTSD symptom severity and more co-morbid major depressive disorder (MDD) than controls (table 1).

Sample preparation and quality control 450K array High resolution melting analysis (HRMA) of the H19 locus indicated proper bisulfite conversion of all DNA samples (data not shown). Quality control of the raw Illumina

133 DNA-methylation 450K array revealed no poor quality samples, all samples showed an overall detection P-value < 0.01 (standard Illumina quality control protocol). Quantile normalization yielded proper data normalization (supplementary figure 2). Supplementary table 2 lists the estimated relative blood cell count for which we did not observe significant group diferences. Principal components (PC) one and two explained most of the variance in the 450K data set, 10.1% and 4.1% respectively (supplementary figure 3A). Pearson’s correlation analyses were performed between the first eight PCs with potential covariates/confounders. As several blood cell count estimates showed small to moderate correlations (r’s 0.2-0.5) with PCs 1 and 2, all blood cell estimates were included as covariates in further analyses. Additionally, age and sex were included as covariates in further epigenetic association analyses, although these showed low correlations with PCs 1 and 2 (supplementary figure 3B/C).

Table 1. Demographic, trauma history and clinical characteristics of discovery cohort

PTSD patients Traumatized p-value (n=34) controls (n=39)

Age 41.45 (8.96) 40.15 (10.12) 0.569a Gender (females) 16 (47.1%) 19 (48.7%) 0.887c Years of service 17.74 (13.43) 18.60 (9.93) 0.377b Educational level Low 0 (0.0%) 0 (0.0%) 0.745d Middle 29 (87.9%) 33 (84.6%) High 4 (12.1%) 6 (15.4%)

Current smoker 7 (21.9%) 8 (20.5%) 0.889c PTSD symptom severity (CAPS total score) 70.21 (13.43) 4.59 (4.73) <0.001a Current co-morbid major depressive disorder 10 (29.4%) 0 (0.0%) <0.001d Work-related traumatic events (PLES total score) 19.13 (8.65) 19.74 (6.78) 0.740a Childhood traumatic events (ETI total score) 5.64 (4.69) 3.95 (3.80) 0.116b

Continuous variables presented as mean (standard deviation); categorical variables presented as frequency (percentage). Abbreviations and symbols: PTSD=posttraumatic stress disorder; MDD = major depressive disorder; CAPS = clinician administered PTSD scale; PLES = Police Life Event Scale; ETI = Early Trauma Inventory. a= independent t-test; b = independent t-test on log-transformed data; d = Chi-square test; c = Fisher’s exact test.

Epigenetic association analyses We observed no significant global DNA-methylation group diference. Furthermore, we found no genome-wide significant PTSD-associated DMPs (supplementary table 3A, available on request, and supplementary figure 4). Subsequently, we addressed a hypothesis driven approach, evaluating previously reported PTSD associated genome-

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wide DMPs in our cohort. Our literature search yielded two genome-wide epigenetic surveys for PTSD, both using Illumina 27K arrays. Uddin et al reported only on PTSD- associated regions or nearby genes, while Smith et al reported on individual PTSD associated DMPs (Smith et al., 2011; Uddin et al., 2010). Therefore, only the latter was considered appropriate for DMP replication. None of the 21 significant loci reported by Smith et al reached significance in our study (P-values < 2.4E-3 were assumed significant, Bonferroni- corrected). Finally, we performed GSE analyses on the top 100 DMPs. DMPs located within the vicinity of 1kb of a gene, annotated according to the Illumina manifest, were included. We observed 12 enriched pathway-based sets (49 of 74 genes entered GSE) of which six reached significance according to an adjusted p-value (q) < 0.05. The lowest P-value we detected involved the dopaminergic neurogenesis pathway (q=0.012 - wikipathways database), including three DMPs located at PITX3, NEUROD1 and the RET genes (supplementary table 3B and 3B, and supplementary figure 6). A secondary GSE analysis was based on the fifth category of the Gene Ontology (GO) database, which yielded only two significant enriched GO terms, both related to molecular functions involved in DNA binding (supplementary table 3B and 3C).

There were no DMRs between PTSD patients and controls that reached genome-wide significance, however for one DMR we did observe consistent hyper methylation over more than 75% of the probes within the cluster (table 2), located at the PAX8 gene (figure 1a). Technical validation of 450K data, i.e. including PAX8 (figure 1a and SOSTDC1 figure b) using MiSEQ technology was successful (r2 = 0.85, figure 1c) and described in detail in supplementary file 1.

Genetic association analyses For the genetic survey we analyzed 13.344 CpG-SNP probes (maf> 0.01), which yielded 15 CpG-SNPs potentially associated with PTSD. Since the statistical power in this study is limited due to the small sample, only the top 5 CpG-SNPs were investigated further (supplementary figure 5). These five candidate CpG-SNPs included the loci OR2V2, EIF3B, SDK1, SKA2 and CACNA1C (table 3, column discovery P-value). Next we aimed to validate these 5 candidate CpG-SNPs using sanger sequencing (supplementary table 4). We did not detect any genotype discrepancies between 450K-based and sanger- sequencing-based genotypes. Three of five candidates, located at the genes SDK1, SKA2 and CACNA1C, were significantly associated with PTSD in this validation (table 3, column validation P- value). Next we performed replication analysis for the 5 candidate CpG-SNPs in an independent cohort of trauma-exposed children with and without PTSD (see supplementary file 1 and supplementary table 5). We significantly replicated the association of one CpG-SNP (rs1990322) located at the CACNA1C locus with PTSD (table 3, column replication P-value. In addition, for this replicated CpG-SNP at the CACNA1C locus we searched for evidence of genetic interactions (cis meQTLs) regarding upstream and downstream flanking CpG sites in the discovery dataset. We observed no indications for meQTL efects of rs1990322 and its neighbouring CpG sites (supplementary figure 7).

135 A) Technical validation of 450K data of DMR PAX8 using the bsr amplicon MiSEQ method.

B) Technical validation of 450K data of SOSTDC1 using the bsr amplicon MiSEQ method.

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C) Overall correlation between 450K and MiSEQ.

Plot represents average beta-values per (ranked) CPG sites in PAX8 and SOSTDC1, detected by 450K and MiSEQ.

Figure 1. Technical validation of A. 450K data (upper plot) DMR PAX8 using the bsr amplicon MiSEQ method (lower plot). Technical validation of B. 450K data SOSTDC1 (upper plot) using the bsr amplicon MiSEQ (lower plot) method. For both A and B X-axis plots represents chromosomal position, Y-axis plots represents β-values of 450K data and MiSEQ data respectively. Pink line represents PTSD cases and blue line represents traumatized controls. Shading represent 95% C.I. C. Overall correlation plot of 450K and MiSEQ detected CpG sites.

137 Table 2. DMR association analysis PTSD vs. traumatized controls.

Gene chr: start-end value area FWER L cluster (L) % L PAX8 chr2:113992762 - 113993142 0.07 0.42 0.82 6 8 0.75 LDHC chr11:18433500 - 18433564 0.07 0.20 0.98 3 9 0.33 FAM71F1 chr7:128354862 - 128354967 -0.07 0.20 0.98 3 7 0.43

Detected DMRs (L>2) using minfi’s “bumphunter” function in whole sample, female and male stratified analyses. chr: chromosome and position; value: average diference in methylation in the bump with respect to controls, <0 represents hypo methylation in PTSD and >0 represents hypermethylation in PTSD; area: area of the bump with respect to the 0 line; FWER: Multiple test adjusted P-value with respect to the area, according the Family Wise Error Rate; L: number of probes in DMR; cluster(L): number of probes in cluster; %L: percentage of probes within the cluster contributing to the DMR – in bold ≥75%.

Table 3. Top 5 CpG-SNPs genetic association analysis PTSD vs. traumatized controls.

Discovery Validation Replication rs ID probe ID gene (P-value*) (P-value#) (P-value#) rs2546424 cg22931151 OR2V2 0.0009 >0.01 > 0.01 rs7808948 cg03122926 EIF3B 0.0040 > 0.01 > 0.01 rs13230345 cg22535849 SDK1 0.0027 0.005 > 0.01 rs7208505 cg13989295 SKA2 0.0020 0.009 > 0.01 rs1990322 cg24393317 CACNA1C 0.0006 0.006 0.001

*Detection P-value - based linear model association. # Validation and replication P-value- based on Chi-square tests, replication P <0.01 was assumed significant (Bonferroni, 5 tests).

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DISCUSSION

In this study we aimed to detect epigenetic and genetic loci associated with PTSD in a homogeneous cohort of traumatized police ofcers. Both a genome-wide and hypothesis- driven replication approach did not result in DMPs between PTSD patients and trauma- exposed controls. Gene-set enrichment analysis on the top 100 DMPs showed however a plausible association of the dopaminergic neurogenesis pathway with PTSD. Furthermore, we observed one DMR located at the PAX8 gene with consistent hypermethylation in PTSD patients. Genetic analyses yielded three CpG- SNPs significantly associated with PTSD. Of these, one CpG-SNP, located at the CACNA1C locus, was also significantly associated with PTSD in an independent replication sample of trauma-exposed children. Notably, this result shows that the Illumina 450K array is not restricted to epigenetic surveys but can provide informative genetic data as well.

Although our sample was small, it was highly homogenous as all participants were former or current police ofcers, and cases and controls were matched for sex, age, education and years of police service. All participants reported multiple prior traumatic events, without significant group diferences in reported types of traumatic experiences. PTSD patients fulfilled current diagnostic criteria for PTSD, while our trauma-exposed controls had minimal PTSD symptoms and did not report lifetime PTSD or other trauma- related psychiatric disorders. Thus our controls were apparently resilient to adverse mental health outcome of trauma. This study design, including extreme phenotypes following similar trauma load, was considered to favor detection of PTSD-associated loci, as also suggested by others (Van Gestel et al., 2000). Nevertheless, our genome- wide survey clearly remains limited in statistical power. The absence of genome-wide significant DMPs is therefore not unexpected. With regard to replication of literature reports on genome wide significant DMPs associated with PTSD, our search resulted in one previous study (Smith et al., 2011) reporting genome-wide detected DMPs, albeit in a diferent ethnic group and with substantial lower probe density of the array (27K). Such methodological diferences may explain why we were unable to replicate these DMPs.

Gene-set enrichment analysis using the top 100 DMPs revealed three genes, PITX3, NEUROD1 and RET, annotated in the pathway of dopaminergic neurogenesis pathway (wikipathways), which could very plausibly be involved in PTSD. Considering its role in stress reactivity and anhedonia, the dopamine system has previously been implicated in PTSD, and dopamine dysfunctions have been observed in PTSD patients (Hoexter et al., 2012). Interestingly, these genes are involved in neuron diferentiation and/or maturation but not neuron regionalization and specification. Diferentiation and maturation processes might be more susceptible to adaptation under influence of the environment, which is clearly relevant for PTSD. Further translational research should be performed in order to unravel the role of this pathway in PTSD.

139 Compared to DMP analyses, DMR analyses sufer less from statistical power issues, since these follow a multi probe association methodology implying a reduced multiple test penalty (Michels et al., 2013). We detected no DMRs with genome-wide significance after family-wise error rate correction. Such corrections however may be considered too conservative in genome wide screens, given that no false positives are allowed (Jafe et al., 2012). We did observe one DMR in the PAX8 gene that we consider a promising candidate for further studies on PTSD, since it involved >75% concordant diferential hypermethylated probes within the cluster window in the PTSD patients. The PAX8 gene encodes a transcription factor involved in thyroid and central nervous system (CNS) development. Interestingly, thyroid hormone alterations have been observed in PTSD patients (Friedman et al., 2005). Additionally, PAX8 has been repeatedly implicated in sleep disturbances (Lane et al., 2016), which constitute an important symptom of PTSD. Furthermore, Siegmund et al reported hypomethylation of PAX8 in schizophrenia patients, suggesting a broader role for this gene in psychiatric disorders (Siegmund et al., 2007).

To our best knowledge, we are the first to use the approximately 14K CpG-SNPs with maf >0.01, present on the 450K DNA-methylation array of Illumina for genetic association analyses. Although this CpG-SNP set is far too small for imputation and for a substantial number of these CpG- SNP genotypes cannot be reliably scored due to interfering DNA methylation efects, we were still able to detect and validate genotype calling of five CpG- SNPs potentially associated with PTSD, of which three (SDK1, SKA2 and CACNA1C) were also significantly associated with PTSD in the validation analyses.

Importantly, one of these CpG-SNPs (rs1990322, cg24393317, CACNA1C) was also significantly associated with PTSD in an independent cohort of traumatized children with and without PTSD. This locus is located in an intronic region of the CACNA1C gene. This gene encodes for an alpha-1C subunit of the L-type voltage-dependent calcium channel, crucial for generating neuronal action potentials. Several genome-wide and candidate- gene studies have reported associations between this gene and susceptibility for psychiatric disorders, including MDD (Lavebratt et al., 2010), and bipolar disorder and schizophrenia (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). Additionally, CACNA1C SNPs repeatedly have been found to afect brain structure and function, including amygdala responsivity (Sumner et al., 2015), commonly considered a biological hallmark of PTSD. However, in a candidate-gene study, another common SNP at CACNA1C locus did not moderate between hurricane exposure and subsequent PTSD in Hispanic Blacks (Dunn et al., 2014).

Although our PTSD associated SNP (rs1990322) is located in the CACNA1C gene itself, our detected variant is actually in strong linkage disequilibrium with common variants located at the promoter region of the FKBP4 gene (supplementary figure 8, acquired using webtool: http://archive.broadinstitute.org/mpg/snap, 1000 Genomes pilot1, CEU, r2=0.8, distance= 500). FKBP4 is a functional antagonist of FKBP5, modulating

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glucocorticoid receptor function. Although psychiatric studies of FKBP4 are limited, FKBP5 SNPs, methylation and gene expression have been implicated in several stress- related psychiatric disorders including PTSD (Zannas et al., 2015), and pre-trauma FKBP5 gene expression was found to predict PTSD symptom development upon military deployment (van Zuiden et al., 2012). Further studies should elucidate the actual afected gene contributing to the expression of PTSD.

We observed a significant association between PTSD and the SKA2 gene (cg13989295, rs7208505) in the discovery cohort, which was not replicated. Nevertheless, this observation supports the accumulating evidence for the role of this gene in PTSD and PTSD-related clinical and biological phenotypes. SKA2 modulates glucocorticoid receptor function (Rice et al., 2008), which was previously found to predict PTSD symptom development after military deployment (van Zuiden et al., 2012). Within that same cohort it was recently observed that decreasing SKA2 methylation was associated with PTSD symptom development during deployment, depending on the exact genotype carried (Boks et al., 2016). In addition, in another military cohort, genotype carrier status and genotype-adjusted methylation status of this locus was also associated with PTSD (Sadeh et al., 2015). In highly trauma-exposed civilians, similar associations emerged for this locus after controlling for childhood trauma (Boks et al., 2016). SKA2 genotype status, genotype-unadjusted and adjusted methylation, and gene and protein expression have also been associated with suicide risk, either directly or in interaction with trauma- exposure or anxiety level (Guintivano et al., 2014; Pandey et al., 2016). As several studies reported diferential SKA2-methylation between (prospective) psychiatric patients and controls when adjusting for genotype, we additionally investigated this in the discovery cohort conform methods described by Guintivano et al (Boks et al., 2016; Guintivano et al., 2014). We found no evidence for epigenetic efects additional to the CpG-SNP association (data not shown). This may be explained by the fact that epigenetic efects at the SKA2 locus can only occur in C allele carriers and our sample includes a limited number of homozygous SKA2 C allele carriers, limiting statistical power to detect such an epigenetic efect.

We also observed a significant association between PTSD and CpG-SNP rs13230345 (cg22535849) in the discovery cohort only. This SNP is located in an intronic region (c.3203- 511) of the SDK1 gene (Kaut et al., 2015), encoding for the sidekick cell adhesion molecule 1. In silico evaluation of this variant showed that it might introduce a novel donor and acceptor splice site (Alamut Visual version 2.7, Interactive Biosoftware, Rouen, France), which in turn might afect transcription, leading to the absence or a dysfunctional protein. Interestingly, the SDK1 gene has been implicated in schizophrenia (Sakai et al., 2015) and autism (Connolly et al., 2013), and altered SDK1 methylation was observed in post-mortem brain tissue of MDD patients (Kaut et al., 2015). Furthermore, Bagot et al investigated transcriptional regulation of stress-susceptibility in rats and found that SDK1 was associated with social interaction and anxiety-behaviours behaviours (Bagot et al., 2016).

141 Besides age, our replication cohort also difers from our adult discovery cohort in terms of trauma-exposure, which may partially explain why we replicated only one CpG-SNP identified in the discovery cohort. While our controls in the discovery cohort typically reported multiple prior traumatic experiences (including various types of potentially traumatic experiences during childhood), the majority of controls in the replication sample reported a single traumatic experience. As PTSD risk increases dose-dependently with increasing number of traumatic experiences (Kolassa et al., 2010), it could be that the non-replicated SNPs are involved in PTSD susceptibility in the context of increasing trauma load. Such an interaction between SNPs and increasing trauma load was previously observed by Kolassa et al (Kolassa et al., 2010).

CONCLUSIONS

In summary, while our statistical power to detect genome-wide significant DMPs was low, we detected an enriched gene set in the dopaminergic neurogenesis pathway that plausibly plays a role in PTSD. In addition, we detected one DMR located at the PAX8 gene that seems a good candidate for future studies in PTSD. Furthermore, genetic association analyses based on 450k methylation data, and subsequent validation and replication analyses revealed that the CpG-SNP located at the CACNA1C locus was consistently associated with PTSD both in our discovery cohort and replication cohort. Despite the fact that we were unable to replicate the CpG-SNPs located at SDK1 and SKA2 loci, previously reported findings of others and our data do suggest a role of these genes in PTSD.

Additional research has to be performed to elucidate whether the identified DMRs and SNPs are consistently and causally associated with development or expression of PTSD, and importantly with which specific symptoms and biological mechanisms they are associated. For this purpose, transdiagnostic approaches among diferent types of psychiatric disorders seem especially fruitful. If replicated, these findings may eventually contribute to the development of PTSD biomarker sets that can potentially support or improve an early clinical diagnosis of PTSD or aid in identifying individuals at risk for PTSD.

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AUTHOR DISCLOSURES

Funding This research was supported by grants from ZonMw, the Netherlands organization for Health Research and Development (grant no. 91210041, discovery cohort), the Academic Medical Center Research Council (grant no. 110614, discovery cohort), and Augeo Foundation, Driebergen- Rijsenburg, The Netherlands, a Foundation dedicated to the prevention of adverse childhood experiences (genetic and epigenetic analyses).

Conflict of interest All authors declare that they have no conflict of interest.

Acknowledgements The authors would like to thank all participants for their contribution to this study. We additionally would like to thank Renée Hutter, Gré Westerveld, Marthe Hoofwijk and colleagues of the PDC police outpatient clinic and Els van Meijel, Maj Gigengack, Jasper Zantvoord and Rosanne op den Kelder for their assistance in participant recruitment and data collection; and Peter Boorsma and Karin van der Lip from the department of Clinical Genetics, Genome Diagnostics laboratory, Amsterdam Medical Center, Amsterdam, The Netherlands for their technical assistance.

143 SUPPLEMENTARY INFORMATION – CHAPTER 6

Supplementary file 1

Sample preparation and quality control 450K DNA-methylation array (Illumina) DNA was isolated from whole blood. DNA samples were randomized followed by bisulfite conversion using the EZ DNA MethylationTM kit of Zymo Research, according to the manufacturers protocol. Quality of converted DNA was analyzed using high resolution melting analysis (HRMA) of the H19 locus (Alders et al). Subsequently, converted DNA was submitted for 450K DNA-Methylation array analysis. Quality control of the 450K data set was performed using the control probes present on the array and conform the standard protocol of Illumina. Arrays showing a detection P-value < 0.01 were assumed as passed QC. Samples of 67 participants had sufcient amounts of DNA, passed QC and were included in subsequent association analyses (30 patients; 37 controls). Next, normalization of the 450K dataset was performed using Quantile normalization (Aryee et al.). Subsequently normalization quality and concordance between sex chromosome probes and self- reported sex were evaluated. In order to elucidate potential phenotypic confounding efects, principal component analysis was performed. Principal components 1-8 were evaluated using Pearson’s correlation analyses with age, sex, ethnicity, presence of childhood trauma(s), current MDD, current smoking and all blood cell estimates. Prior to association analyses, probes located on sex chromosomes X and Y were excluded in both probe sets (genetic and epigenetic probe sets) to prevent sex biased results. Moreover, for both probe sets we excluded promiscuous probes as well, as determined from the database of Chen et al.

DMP and DMR analyses DMP and DMR association analyses were performed using the “R” (version 3.3.0) packages minfi (version 1.18.2) (Aryee et al) and limma (version 3.28.5). In order to adjust for diferences in blood cell type distribution we estimated the distribution of CD8+ and CD4+ T cells, Natural killer cells, B cells, Monocytes and Granulocytes according to the method of Houseman et al (2012) and used these estimates as covariates in our model (Houseman et al). Cell estimate diferences between groups were analyzed using T-tests. DMR association analyses were performed using the minfi function bumphunter (parameters: coefcient=2, nullmethod, boostrap =500 iterations, cutofs 0.01 -0.07). DMRs were defined as including two or more probes (L>1). Gene-set enrichment analyses were done via the publicly available webtool using the consensus pathway database web tool, developed by the Max Planck institute, www.consensuspathdb.org, version 31, 2015 (Kamburov et al). Overrepresentation of genes were analyzed without feeding the algorithm a background list of genes. We used all available pathways as defined by the pathway-based databases with a minimum overlap of 2 and a p-value cutof of 0.01. Secondly we applied the gene ontology (GO) category 5 on biological processes (BP), molecular function (MF) and cellular component (CC), with a p-value cutof of 0.01.

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Protein complex-based gene sets nor Network neighborhood-based entity sets were considered in the analyses. packages for 450K DMP and DMR analyses: The following “R” packages and their main application in processing the Illumina DNA-methylation beadchip were used in this study: minfi version 1.18.2 - DMP/DMR detection (Kamburov et al) limma version 3.28.5 - DMP detection (Ritchie et al) bumphunter version 1.12.0 - DMR detection (Jafe et al) FlowSorted.Blood.450k version 1.10.0 - DMP/DMR detection (Houseman et al)

Sample preparation and quality control MiSEQ validation analysis Technical validation of promising DMRs was performed through targeted amplicon sequence analysis using the Illumina MiSeq platform. Primers were designed with a bisulfite converted reference sequence, human genome build 19 (hg19), using Primer3 (Untergasser et al). PAX8 was covered by two amplicons and SOSTDC1 by one amplicon, see supplementary table 1 for Primer sequences. Amplicons were amplified through PCR and subsequently pooled per subject. Pooled amplicons were elongated using TruSEQ indices and adapter sequences. Next quality of the pools was evaluated using Agilent 2100 Bioanalyzer. All DNA purification steps were performed using the Agencourt AMPure PCR purification kit (Beckman Coulter). DNA concentration was measured using Qubit 2.0 Fluorometer (ThermoFisher) and equalized to equimolar concentrations for all subject pools. MiSeq amplicon sequencing was performed according to the standard protocol. Raw sequence data was mapped, aligned and analyzed using GATK (DePristo et al), BWA and Integrative Genomics viewer (version 2.3.57), respectively, against the bisulfite converted hg19. A coverage of minimally 100 reads per patient amplicon was deemed successful. Technical validation was based on Pierson’s coefcient of determination (R2) between 450K and MiSEQ data using all overlapping CpG sites. A coefcient > 0.80 was assumed successful.

Results technical validation For technical validation of our 450K analyses we selected two regions: (i) the promising DMR located at the PAX8 gene showing diferential DNA methylation in > 75% of the total number of probes within the cluster and (ii) a randomly selected region located at the SOSTDC1 gene. The PAX8 DMR represented β-values ranging between approximately 60% to 80%. In order to test for a broader methylation index range, the small region located at SOSTDC1 was selected since this locus represented β-values ranging between approximately 30% to 40%. For both the PAX8 (figure 1a) and SOSTDC1 (figure 1b) genes the average 450K β-values and direction of efects was in concordance with MiSEQ data. Pierson’s coefcient of determination between 450K and MiSEQ data using all overlapping CpG sites of the two genes was high (r2 = 0.85, figure 1c), validation was therefore assumed successful.

145 References Alders M, Bliek J, vd Lip K, vd Bogaard Jafe AE, Murakami P, Lee H, Leek JT, Fallin R, Mannens M (2009). Determination of MD, Feinberg AP, et al (2012). Bump hunting KCNQ1OT1 and H19 methylation levels in BWS to identify diferentially methylated regions and SRS patients using methylation-sensitive in epigenetic epidemiology studies. Int J high-resolution melting analysis. Eur J Hum Epidemiol 41(1): 200-209. Genet 17(4): 467-473. Kamburov A, Stelzl U, Lehrach H, Herwig R Aryee MJ, Jafe AE, Corrada-Bravo H, Ladd- (2013). The ConsensusPathDB interaction Acosta C, Feinberg AP, Hansen KD, et al database: 2013 update. Nucleic Acids Res (2014). Minfi: a flexible and comprehensive 41(Database issue): D793-800. Bioconductor package for the analysis of Infinium DNA methylation microarrays. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Bioinformatics 30(10): 1363-1369. Shi W, et al (2015). limma powers diferential expression analyses for RNA-sequencing and DePristo MA, Banks E, Poplin R, Garimella KV, microarray studies. Nucleic Acids Res 43(7): Maguire JR, Hartl C, et al (2011). A framework e47. for variation discovery and genotyping using next-generation DNA sequencing data. Nat Untergasser A, Cutcutache I, Koressaar T, Ye J, Genet 43(5): 491-498. Faircloth BC, Remm M, et al (2012). Primer3-- new capabilities and interfaces. Nucleic Acids Houseman EA, Accomando WP, Koestler DC, Res 40(15): e115. Christensen BC, Marsit CJ, Nelson HH, et al (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13: 86.

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Descriptives and genetic association analyses replication cohort Methods replication cohort Participants and procedure The replication cohort comprises trauma-exposed children who were enrolled as part of a study investigating biomarkers of development and treatment of PTSD in children. We included 46 children with PTSD (16 boys) and 50 children without PTSD (29 boys), age ranging 8-18 years. Children were recruited at the academic centre for child and adolescent psychiatry de Bascule (Amsterdam, the Netherlands). All children experienced one or more traumatic events, meeting the DSM-IV PTSD criterion A definition. Exposure to trauma was determined by a trained clinician, using the Dutch version of the life-events checklist of the Clinician-Administered PTSD Scale for Children and adolescents (Nader et al).

A two-step approach was used in diagnosing PTSD: After reporting a traumatic event all children were screened with the The Children’s Revised Impact of Event Scale questionnaire (CRIES- 13). If the child had a score of 30 or higher, or a score of 17 or higher on the subscales intrusions and avoidance the child was interviewed with the CAPS-CA to measure the clinician-rated severity of the PTSD (Perrin et al). Based on the CRIES-13 questionnaire and the outcome of the CAPS-CA we defined three PTSD diagnostic groups. The first group met all DSM-IV criteria for PTSD and was categorized as “full PTSD”. The second group consisted of children not meeting full PTSD criteria but with at least one symptom of criterion B, criterion C, and criterion D and meeting all other DSM-IV PTSD criteria; they were categorized as “partial PTSD”. The third group consisted of children who met neither full PTSD nor partial PTSD criteria. For the current study the children with PTSD and partial PTSD were pooled in the PTSD group, the children without PTSD constituted the traumatized control group. Exclusion criteria for participating in the study were suicidality, alcohol/substance abuse and psychotic disorders as measured with the Anxiety Disorders Interview Schedule for children (ADIS) for DSM–IV disorders. Exclusion criteria for controls were PTSD or any other current DSM-IV Axis-I psychiatric disorder. Exclusion criteria for all participants consisted of daily use of psychoactive medication. All children (if above 12 years of age) and their parents provided written informed consent prior to study initiation. The study was approved by the Institutional Review Board of the Academic Medical Center in Amsterdam, the Netherlands.

Psychometric measures The Dutch CRIES-13 (Children and War Foundation, Olf et al) and the CAPS-ca are both valid instruments with good psychometric properties (Diehle et al, Verlinden et al). The CRIES-13 (range from 0 to 65) is based on the Impact of Event Scale questionnaire, and the CAPS-CA is based on the CAPS interview, as both used in our adult discovery cohort.

147 Genotyping DNA in this replication cohort was obtained from saliva samples (DNA genotek, OG-500® kit) and isolated conform the manufacturers protocol. Genotypes were analysed using sanger sequencing conform the method described for the validation of the CpG-SNPs detected in the discovery cohort. Genotypes were called using CodonCode Aligner (version 5.1.5) and association analysis of genotypes in the validation and replication analyses were performed in SPSS (version 20.0.0, IBM® SPSS® Statistics). We applied linear by linear association on ordinal recorded genotypes in order to detect genotypic diferences between PTSD patients and controls. P-values < 0.01 were assumed significant, based on Bonferroni multiple test penalty applying 5 independent tests. Since both the discovery and replication cohort do not represent the general population, Hardy-Weinberg quality control for the genotypes was assumed inappropriate.

Table S1: Demographic, trauma history and clinical characteristics of replication cohort

PTSD Patients Traumatized p-value (n=46) controls (n=50)

Age 13.28 (3.250) 15.82 (3.40) 0.001a Gender (females) 30 (65.2%) 22 (44.0%) 0.030b Ethnicity

West-European 30 (65.2%) 44 (88.0%) 0.008c Other 16 (34.8%) 6 (12.0%) PTSD symptom severity (CAPS-CA total score) 58.77 (25.30)

PTSD symptom severity (CRIES-13) 37.40 (16.71) 11.18 (10.6) 0.000a Current co-morbid depressive symptoms (RCADS) 14 (30.4%) 0 (0.0%) 0.000c Type of childhood traumatic events (CAPS-ca)

Severe accident 4 (8.7%) 47 (94.0%) 0.000c Interpersonal trauma 33 (71.7%) 3 (6.0%) Other* 9 (19.6%) 0 (0.0%)

Continuous variables presented as mean (standard deviation); categorical variables presented as frequency (percentage). a= Mann-Whitney U-test; b = Fisher’s exact test c = Pearson Chi square test. PTSD=posttraumatic stress disorder; CAPS-CA= clinician administered PTSD scale for children; CRIES-13= Children’s Revised Impact of Event Scale. RCADS; the Revised Child Anxiety and Depression Scale. * children who have experienced the following traumatic events: poisoning, witness of a community violence, having a life-threating disease and death of a family member.

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Results replication cohort Demographic characteristics PTSD patients and trauma-exposed controls did difer on demographic characteristics. Ethnicity was significantly diferent between PTSD and trauma-exposed controls, i.e. the trauma- exposed control group were more often of a West-European ethnicity compared to the PTSD group. The type of experienced childhood traumatic events was significantly diferent between PTSD patients and controls. The children with PTSD experienced more interpersonal or other traumatic events. Children in the trauma-exposed control group often experienced a severe accident. As expected, children with PTSD had significantly higher PTSD symptom severity and significantly more co-morbid depression than controls (see supplementary table 1 on the left).

References Nader, K. O., Kriegler, J. A., Blake, D. D., Pynoos, R. Olf M. Dutch version of the children’s revised S., Newman, E., & Weathers, F. W. (1996). Clinician impact of event scale (CRIES-13) 2005. Retrieved Administered PTSD scale for children and adolescents. June 2, 2008, from http://www.childrenandwar.org/ National Center for PTSD. wp-content/uploads/2010/04/CRIES-13-inclusief- handleiding-maart-2013.pdf. Perrin, S., Meiser-Stedman, R., & Smith, P. (2005). The Children’s Revised Impact of Event Scale Diehle, J., de Roos, C., Boer, F., & Lindauer, R. J. (CRIES): Validity as a screening instrument for PTSD. (2013). A cross-cultural validation of the Clinician Behavioural and Cognitive Psychotherapy, 33(4): 487. Administered PTSD Scale for Children and Adolescents in a Dutch population. European journal Children and War Foundation. The children’s revised of psychotraumatology, 4. impact of event scale (13): CRIES-13. 1998. Retrieved June 2, 2008, from http://www.childrenandwar.org/ Verlinden, E., Meijel, E. P., Opmeer, B. C., Beer, R., wp-content/uploads/2010/04/cries_13_UK.doc. Roos, C., Bicanic, I. A., ... & Lindauer, R. J. (2014). Characteristics of the Children’s Revised Impact of Event Scale in a clinically referred Dutch sample. Journal of traumatic stress, 27(3): 338-344.

149 Supplementary Figures

Supplementary figure 1. Flow scheme of study design and analyses

Flow scheme study design and performed genetic and epigenetic analyses, red boxes represent primary outcomes; * filter steps include P<0.05 and absolute delta mean diference between groups > 0.15; T: technical validation.

Supplementary figure 2. Density plots raw and normalized 450K data set

Density plots of (A) raw 450K data and (B) quantile normalized 450K data. Green lines represent control samples, orange lines represent PTSD samples. X-axis represent β-values, Y-axis represent the genome wide density.

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Supplementary figure 3. Pearson’s correlation analyses of first eight Principal Components and potential confounders/covariates

A. Explained variance by all principal components.

B. Pearson’s correlation analyses of first eight principal components with potential confounders/covariates: age (categorical), gender, childhood trauma, ethnicity, major depres- sion disorder and smoking

C. Pearson’s correlation analyses of first eight principal components with potential confounders/covariates: blood cell estimates - CD8+ and CD4+ T cells, Natural killer cells, B cells, Monocytes and Granulo- cytes. X-axis represent PC 1 to 8, Y-axis represents correla- tion coefcient (r) with above described traits.

151 Supplementary figure 4. Volcano plot DMPs

Volcano plot DMPs. X-axis represent delta diference (beta-values) and Y-axis represent –log10 (P-value). Horizontal dotted line represents genome wide significance level q-values < 0.05; Vertical line represents an absolute delta diferences > 0.1.

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Supplementary figure 5. β-values top 5 CpG-SNPs

(A) cg22931151, rs2546424 - OR2V2 (B) cg03122926, rs7808948 - EIF3B

(C) cg22535849, rs13230345 - SDK1 (D) cg13989295, rs7208505 - SKA2

(E) cg24393317, rs1990322 - CACNA1C

Methylation levels of 5 CpG-SNPs which were nominal significant associated with PTSD in discovery cohort. X-axis represents DNA-methylation index expressed in beta; X-axis represents control or PTSD.

153 Supplementary figure 6. Dopaminergic neurogenesis pathway (wikipathways)

Dopaminergic neurogenesis pathway according wikipathways database. Pathway represents four categories within the pathway: regionalization; specialization, diferentiation and maturation. Green boxes (diferentiation and maturation) represent three top100 DMPs located at the PITX3, NEUROD1 and the RET genes.

Supplementary figure 7. rs1990322; cg24393317; CACNA1C

Methylation levels of flanking CpG sites of the replicated CpG-SNPs located at the CACNA1C locus in the discovery set. Y-axis represents DNA- methylation index expressed in beta; X-axis represents control or PTSD.

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Supplementary figure 8. Regional linkage disequilibrium plot of rs1990322

Regional linkage disequilibrium plot of common variant in CACNA1C (rs1990322)

155 Supplementary Tables

Supplementary table 1. Descriptives (A) CpG-SNP validation and replication primers and (B) Miseq targeted bisulfite sequence primers A Name Probe chr-position* SNP Sequence OR2V2_F cg22931151 chr5:180581761 rs2546424 GTCCGAAGGTCTCTGACACA OR2V2_R GCTCCTGGTCACCTGAGAAT EIF3B_F cg03122926 chr7:2425741 rs7808948 TGGACAGAAGATCAGGTGCA EIF3B_R TATGAAGCCACCGTCCTCTC SDK1_F cg22535849 chr7:4118583 rs13230345 CCCACCTTCCACCTTATCCA SDK1_R TCGGAGGGATGGAGGGAAG SKA2_F cg13989295 chr17:57187728 rs7208505 CCACAAAGAGGCTTGAGAAACA SKA2_R TGGCTCTGGGATGTGATGAT CACNA1C_F cg24393317 chr12:2760969 rs1990322 CTAACTGCACCTCCTGTTGC CACNA1C_R ACAGGGAGCCAGGCAGAGCG

B Name chr: start position – end position (N CpG) sequence PAX8_ 1_F chr2:113992699-113993196 (4CpG) GGTTTTGGGGTGGTGTATTTT PAX8_ 1_R AACCAAACCCTCCTCTCAAA PAX8_2_F chr2:113992843-113993443 (2CpG) TTTGAGAGGAGGGTTTGGTT PAX8_2_R TCTCACACCCCTCTCTCCTT SOSTDC1_F chr7:16505267-16505924 (4CpG) TTGTTTGAAATTTTAGAGTTGGATTTT SOSTDC1_R AAAAACTCCCTTTCTAACCCAAA

Chr-position: Chromosomal position (H19) of CpG position

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Supplementary table 2. Average estimated Blood cell type distribution among cases and controls

Blood cell type PTSD (Mean) PTSD (SD) Control (Mean) Control (SD) P-value * CD8+T Cells 0.069 0.040 0.077 0.042 0.4 CD4+T Cells 0.167 0.054 0.149 0.051 0.2 Natural Killer 0.066 0.032 0.059 0.034 0.4

B cell 0.057 0.020 0.056 0.022 0.9 Monocyte 0.078 0.024 0.083 0.023 0.4 Granulocyte 0.585 0.080 0.597 0.104 0.6

* T-test, 2 sided, assumed equal variances

157 0 1 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 DISTANCE >1kb FEATURE 5'UTR IGR IGR Body Body IGR 5'UTR Body IGR IGR Body TSS1500 5'UTR TSS1500 Body Body IGR TSS1500 TSS1500 IGR TSS1500 Body IGR Body TSS1500 IGR Body TSS1500 TSS1500 IGR TSS1500 TSS200 Body Body IGR IGR TSS200 TSS200 Body Body Body TSS1500 IGR Body TSS1500 Body Body Body Body IGR IGR IGR TSS200 -633 -958 -8401 -2331 -2161 -8227 55946 11196 30163 -63624 -63304 461877 -106996 -130878 -143822 -137207 DISTANCE NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA GENE TDRD5 UCHL3 MIR4634 SLC35B4 DOK6 INHBB CHL1 CAMTA1 IFFO2 SOX21 RIPK1 CACYBP AGPAT3 ZFYVE28 ACOT7 CRAMP1L SLC45A1 MYEOV RAB3D MTX3 C8orf86 ECE2 SLCO4C1 CLEC16A OXTR MIR1244-3 SCD5 EIF3I KIAA1632 C9orf62 UNC119B FAM190B ABCB8 JAG2 CANT1 RBAK-RBAKDN FAM59B TREML2 KCNK2 NUAK1 ZNF511 NEUROD1 COBL LPHN3 DTHD1 RET HMOX1 FAM13A DST NR2F2 HAR1A TMEM72-AS1 MCM6 Delta 0,03151092 -0,05071326 -0,02108251 0,024516889 0,010457612 0,018226288 0,066307836 0,026339483 0,011615133 0,016573049 0,012850454 0,054237238 -0,04024562 0,036812125 0,010125811 0,013862558 0,011512944 0,024032938 0,019243944 0,009508461 0,016733107 0,008885013 0,019497996 0,040025234 0,035189295 -0,01797351 0,084289707 0,010774203 0,014700209 0,053620642 0,016849356 0,015131435 0,015439326 -0,052017802 -0,024416088 -0,015545662 -0,036765197 -0,044402705 -0,018616761 -0,041114117 -0,041768986 -0,017159936 -0,022535596 -0,012970771 -0,033336664 -0,011137635 -0,018358589 -0,015957802 -0,036865178 -0,033955169 -0,039850088 -0,018840337 -0,015911161 BH 0,255604112 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 B -0,3863751 1,74627129 0,27426616 0,02571603 0,39807288 4,936736267 2,118009955 2,060326966 1,998320553 1,771292848 1,390210911 1,084899389 1,018060843 1,002336657 0,801152326 0,780632857 0,755347219 0,556752861 0,415106103 0,389261291 0,371217222 0,340696819 0,339833433 0,185818541 0,108362081 0,081852069 0,051033348 0,038439348 0,007255579 -0,20860945 -0,000502126 -0,046969247 -0,107714872 -0,117930984 -0,148683901 -0,152543214 -0,189780046 -0,234254343 -0,242301782 -0,288772971 -0,300325497 -0,332954783 -0,337360234 -0,419724776 -0,490299058 -0,515505076 -0,550014475 -0,558368759 -0,574067528 -0,594586373 -0,610218895 -0,625341606 -0,323317924 q-value 0,255604112 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 P.Value 5,91E-07 1,05E-05 1,11E-05 1,18E-05 1,49E-05 1,53E-05 2,20E-05 3,02E-05 3,23E-05 3,28E-05 4,04E-05 4,13E-05 4,24E-05 5,20E-05 6,02E-05 6,18E-05 6,30E-05 6,50E-05 6,50E-05 6,96E-05 7,62E-05 8,26E-05 8,49E-05 8,77E-05 8,88E-05 9,00E-05 9,17E-05 9,25E-05 9,70E-05 6,12E-05 0,0001712 0,00010331 0,00011877 0,000104409 0,000107789 0,000108221 0,000112478 0,000114694 0,000117784 0,000124633 0,000126135 0,000130477 0,000131075 0,000137912 0,000142769 0,000153622 0,000157697 0,000163452 0,000164877 0,000167588 0,000174004 0,000176761 0,000129179 t 4,32064116 4,30747032 4,01828188 4,801027955 4,494942962 4,435617012 4,422054594 4,363007484 4,312887796 4,298298885 4,228139781 -4,21073252 4,203032847 4,197413725 4,180884767 4,162325799 4,159200139 4,149783385 4,148600809 4,137181106 4,131399935 4,123519016 4,121044244 4,106737126 4,091748813 4,066270362 4,044367374 4,036528311 4,023177805 -4,01187759 4,006994477 -4,09608376 -5,595729626 -4,817458082 -4,783338779 -4,718322114 -4,711131771 -4,608257912 -4,519177834 -4,499563446 -4,429543987 -4,298039287 -4,278300757 -4,251597845 -4,220095093 -4,206903298 -4,195051166 -4,103176142 -4,093109079 -4,076597435 -4,025781844 -4,002267366 -4,315532061 DMPs: available at the author on request. DMPs: available at the author on request. Top 100-DMPs Top AveExpr 0,54808027 0,81070625 0,89128743 0,24065965 0,15567529 0,12252565 0,07434529 0,652280964 0,248798603 0,639588241 0,835090269 0,231634817 0,727509001 0,767363318 0,856957354 0,131671297 0,797742122 0,602175091 0,871075143 0,198591361 0,507825493 0,099654735 0,892015749 0,654504567 0,772958456 0,817985752 0,332288426 0,609104819 0,874322647 0,795170314 0,155278942 0,131375662 0,164952369 0,854556404 0,722119745 0,082325542 0,906993818 0,880751395 0,936529536 0,888470302 0,240554028 0,811728185 0,299678833 0,932872253 0,901682436 0,470696057 0,468138809 0,809133464 0,887098889 0,442766831 0,528076506 0,871207353 0,106631508 logFC 0,0229986 -0,0510963 0,01493179 0,03031704 0,01680354 0,01618553 -0,03587138 0,038654055 -0,02488893 0,027766232 0,020042561 0,060773971 0,018299974 0,013020224 0,057165313 0,040399446 0,010450744 0,017124454 0,013305529 0,023566894 0,009705186 0,022831856 0,010998676 0,019437187 0,047205535 0,032880021 -0,01890007 0,098115887 0,011269239 0,058499604 0,015665537 0,017059943 0,015763961 -0,060291811 -0,016054206 -0,035891533 -0,041625286 -0,021530313 -0,046084176 -0,042213887 -0,018878033 -0,022350004 -0,057702155 -0,016981347 -0,035662014 -0,014335393 -0,018395162 -0,016904789 -0,037935699 -0,035814967 -0,040649457 -0,021527914 -0,016283267 Probe cg09656934 cg17183174 cg11649981 cg07718324 cg20169576 cg21840697 cg14188441 cg09182435 cg14656657 cg03637815 cg13420934 cg24317406 cg16531348 cg13979305 cg05045598 cg18208185 cg27624753 cg07113230 cg26548122 cg15604242 cg22172610 cg24909309 cg08154107 cg12549858 cg01512138 cg00247334 cg05172956 cg11188666 cg26219182 cg26966455 cg21203775 cg14380013 cg13381360 cg09884940 cg23169652 cg18761997 cg04385765 cg17129645 cg18297196 cg13916421 cg27305063 cg24295424 cg03290677 cg01863682 cg07064592 cg24087039 cg05897210 cg06632098 cg12151446 cg13640730 cg17759095 cg20889818 cg03733979 Supplementary table 3A. Supplementary table 3B. Supplementary

158 6 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 26 74 d e t t i m d b e u v s o

E m S e IGR TSS1500 Body 1stExon Body TSS200 Body IGR Body TSS200 5'UTR 1stExon TSS1500 Body 1stExon 5'UTR IGR IGR 1stExon Body IGR Body TSS1500 TSS1500 5'UTR TSS200 Body 1stExon TSS200 IGR Body Body TSS1500 IGR IGR Body Body 1stExon Body TSS1500 TSS1500 Body 5'UTR Body TSS200 IGR IGR IGR R G 9453 -8227 -1375 -49910 -36314 -45577 -75539 -27989 -146373 -285757 -851801 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA HAR1A RPS19 DPF1 MFAP3 DGKB FKBP2 ARHGEF3 IRX4 WDR37 KLF11 CD300A ZDHHC11 BUB3 C7orf50 TEKT5 PITX3 SETD3 MAN2B2 LBX2 SLC22A18 NPTX2 TMEM65 GP1BA C10orf128 RHOJ GOLPH3L FBXO7 RPRM MKX POTEA EMX2OS PARD3B VPS18 OSBPL3 TMEM161B SOX2OT PDK2 SKIV2L ACSL3 TNFRSF19 ZNF589 KIF26B CDC7 DCLK2 C2orf54 SALL3 GALNT5 ARFGAP3 0,0282638 -0,0375427 0,04173234 0,02006377 0,17553482 0,027154774 0,033189496 0,018237398 0,012619809 0,052862218 0,047084286 0,053927482 0,013009758 0,012919704 0,029805382 0,013764534 0,024692562 0,008985484 0,012893465 0,012796963 0,013055086 0,014320902 0,014253579 0,051944944 -0,01199126 0,011000353 -0,039850088 -0,036451384 -0,009281311 -0,017308951 -0,011771843 -0,057187782 -0,014514197 -0,017231149 -0,009728548 -0,004805002 -0,050525163 -0,010466014 -0,023935612 -0,054739176 -0,040608268 -0,012366447 -0,052363936 -0,032500411 -0,012457041 -0,011728696 -0,020461515 -0,009253303 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 -0,92230176 -0,92299875 -1,01233384 -0,625341606 -0,642725677 -0,652408251 -0,689271012 -0,711619834 -0,744355276 -0,749150235 -0,777191723 -0,792423316 -0,800384331 -0,844526867 -0,847268678 -0,868182579 -0,870242153 -0,880613907 -0,881064301 -0,888422668 -0,901080607 -0,904905702 -0,912785095 -0,913194107 -0,948313773 -0,954873535 -0,958175561 -0,977066923 -0,993650189 -1,003247306 -1,009752064 -1,011687475 -1,019929031 -1,027651033 -1,034114251 -1,037862986 -1,043568269 -1,047529449 -1,082781729 -1,089389009 -1,097609346 -1,125352619 -1,127658195 -1,132847683 -1,143137304 -1,162050922 -1,171641673 -1,000685883 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,994219968 0,0002355 0,00026445 0,00028461 0,00030311 0,000176761 0,000179984 0,000181805 0,000188909 0,000193351 0,000200049 0,000201049 0,000207002 0,000210309 0,000212059 0,000222031 0,000222666 0,000227568 0,000228057 0,000230533 0,000230641 0,000232416 0,000236441 0,000238389 0,000238491 0,000240764 0,000240939 0,000247379 0,000249076 0,000249934 0,000254904 0,000259349 0,000261956 0,000263739 0,000264271 0,000266552 0,000268707 0,000270524 0,000271583 0,000273204 0,000274335 0,000286578 0,000289047 0,000297536 0,000298253 0,000299873 0,000309154 0,000312265 0,000261258 3,96344509 3,996829428 3,993798763 3,975236496 3,954621634 3,949824102 3,947315251 3,933387143 -3,93252108 -3,92591127 3,925259991 3,921836769 3,919508194 -3,91428914 3,911792826 3,911663219 3,908555584 3,900525101 3,898442607 3,886118899 3,883065238 3,880172721 3,877753961 3,875293895 3,857703299 -3,85296406 3,844087292 3,883880384 -4,002267366 -3,982248504 -3,964952716 -3,921979271 -3,915500652 -3,908776547 -3,897394081 -3,891392099 -3,880994708 -3,880378521 -3,873234137 -3,872039157 -3,870220067 -3,868956773 -3,855591895 -3,843349044 -3,841687053 -3,838390409 -3,832326315 -3,829249115 0,22453742 0,528076506 0,267572354 0,280673497 0,157690232 0,320904118 0,049787081 0,139386728 0,193736064 0,874633609 0,238845388 0,234274022 0,879068439 0,465049217 0,333980598 0,901902332 0,443405234 0,856171956 0,743005744 0,157874301 0,860558712 0,112298938 0,823008943 0,174675292 0,654699434 0,056497808 0,044626344 0,101586403 0,112778294 0,396154638 0,156533781 0,834328088 0,721834478 0,833302977 0,781890638 0,667156805 0,309643128 0,049814583 0,905215013 0,442996576 0,739351292 0,575257792 0,059604818 0,895939727 0,844746614 0,781675488 0,923139766 0,854502965 0,02378998 0,01339698 0,05495283 0,029342962 0,044992324 -0,03754862 0,043836363 0,022063028 0,012197491 0,051708996 0,041515653 0,022099368 0,054583834 0,014183328 0,029738186 0,015828325 0,022564813 0,169683383 -0,01084716 0,010422619 0,014470884 0,021805872 0,014695549 0,015707656 0,017053879 -0,06449136 0,011884683 -0,040649457 -0,009649006 -0,018263955 -0,033386828 -0,012480761 -0,061270543 -0,014342507 -0,019942578 -0,005387449 -0,055341875 -0,011411394 -0,024081941 -0,058938497 -0,044153892 -0,011705427 -0,035843725 -0,013078708 -0,014348434 -0,013358616 -0,021562576 -0,010681061 cg03733979 cg07940585 cg26950531 cg13950403 cg03229767 cg04446112 cg08052292 cg14062050 cg10492716 cg04278225 cg24375215 cg02540781 cg23423227 cg09937438 cg23514672 cg23095743 cg01022455 cg19562439 cg08490768 cg22833478 cg11107212 cg08621846 cg21934015 cg03370951 cg07157030 cg01139966 cg07911523 cg22278296 cg03207265 cg17389504 cg11430197 cg07833262 cg17350544 cg18388559 cg09165414 cg23970546 cg04599356 cg18156543 cg17319109 cg09708286 cg06150803 cg02219026 cg25683662 cg18127191 cg21118689 cg20140201 cg14642773 cg25492875

159 30 82 13 54 23 32 37 37 37 39 15 152 103 135 171 1833 1531 e f ective_size e f ective_size members_input_overlap PITX3; NEUROD1; RET ACOT7; AGPAT3; ACSL3; SCD5 ACOT7; ACSL3 ACOT7; ACSL3; SCD5 ACOT7; SCD5; AGPAT3; ACSL3 ACOT7; SCD5 MCM6; CDC7 MCM6; BUB3; CDC7 MCM6; CDC7 MCM6; CDC7 MCM6; CDC7 DOK6; RET members_input_overlap_geneids ACSL3; SCD5; PDK2; ACOT7 JAG2; NEUROD1 RHOJ; ACSL3; RET; DCLK2; RIPK1; MCM6; PDK2; KIF26B; SKIV2L; NUAK1; DGKB; ABCB8; RAB3D; CDC7 CD300A; NEUROD1; GP1BA; RET ACSL3; RET; DCLK2; RIPK1; MCM6; PDK2; KIF26B; SKIV2L; NUAK1; DGKB; ABCB8; CDC7 source Wikipathways Reactome HumanCyc Reactome Reactome KEGG Reactome Wikipathways Reactome Reactome Reactome PID term_goid_category GO:0009108 _ bp GO:0072148 _ bp GO:0032550 _ mf GO:0007259 _ bp GO:0030554 _ mf

pathway Dopaminergic Neurogenesis Triglyceride Biosynthesis stearate biosynthesis Fatty Acyl-CoA Biosynthesis Fatty acid, triacylglycerol, and ketone body metabolism Biosynthesis of unsaturated fatty acids - Homo sapiens (human) Activation of the pre-replicative complex Cell Cycle Activation of ATR in response to replication stress DNA Replication Pre-Initiation M/G1 Transition Signaling events regulated by Ret tyrosine kinase term_name coenzyme biosynthetic process epithelial cell fate commitment purine ribonucleoside binding JAK-STAT cascade adenyl nucleotide binding 0,012 0,012 0,024 0,024 0,046 0,049 0,068 0,068 0,068 0,068 0,068 0,069 0,075 0,075 0,026 0,077 0,026 GSE-analyses q-value q-value 2,06E-04 2,78E-04 1,11E-03 1,18E-03 2,79E-03 3,53E-03 6,76E-03 7,36E-03 8,97E-03 8,97E-03 8,97E-03 9,93E-03 9,59E-04 1,05E-03 1,82E-03 2,29E-03 3,29E-03 p-value Enriched pathway-bases sets - 49 genes (76.6%) Enriched GO-based sets - 61 genes (95.3%) p-value Table 3C. Table

160 6 31 33 35 35 39 41 102 349 688 232 121 RPS19; CD300A; HMOX1 CD300A; HMOX1 CD300A; HMOX1 ZFYVE28; CD300A; GP1BA; BUB3; FBXO7 MCM6; CDC7 NEUROD1; OXTR NEUROD1; ACSL3; HMOX1; RAB3D; GOLPH3L; OXTR; CD300A MCM6; KLF11; FBXO7; CDC7 CD300A; HMOX1 RPS19; CD300A; HMOX1 CD300A; HMOX1 GO:0002698 _ bp GO:0033006 _ bp GO:0002686 _ bp GO:0051348 _ bp GO:0006270 _ bp GO:0044058 _ bp GO:0051046 _ bp GO:0000082 _ bp GO:0033003 _ bp GO:0050777 _ bp GO:0043300 _ bp negative regulation of immune e f ector process regulation of mast cell activation involved in immune response negative regulation of leukocyte migration negative regulation of transferase activity DNA replication initiation regulation of digestive system process regulation of secretion G1/S transition of mitotic cell cycle regulation of mast cell activation negative regulation of immune response regulation of leukocyte degranulation 0,077 0,077 0,077 0,077 0,077 0,077 0,077 0,077 0,077 0,077 0,079 4,42E-03 4,51E-03 5,10E-03 5,40E-03 5,73E-03 5,73E-03 6,63E-03 6,76E-03 7,08E-03 7,09E-03 7,80E-03

161 Supplementary table 4. Association of 5 CpG-SNP in the discovery cohort

Group rs number Probe Gene Genotype (freq %)* LHR p-value# PTSD rs2546424 cg22931151 OR2V2 CC(54.8) CT(38.7) TT(6.6) 7.27 0.014 Non PTSD CC(84.2) CT(13.2) TT(2.6) PTSD rs7808948 cg03122926 EIF3B CC(41.9) CT(22.6) TT(35.5) 3.92 0.053

Non PTSD CC(21.1) CT(23.7) TT(55.3) PTSD rs13230345 cg22535849 SDK1 AA(9.7) AG(48.4) GG(41.9) 8.75 0.005 Non PTSD AA(2.6) AG(21.1) GG(76.3) PTSD rs7208505 cg13989295 SKA2 AA(48.4) AG(41.9) GG(9.7) 7.75 0.009 Non PTSD AA(18.4) AG(57.9) GG(23.7) PTSD rs1990322 cg24393317 CACNA1C AA(45.2) AG(38.7) GG(16.1) 7.94 0.006 Non PTSD AA(75.5) AG(21.6) GG(2.7)

*Genotypes according sanger sequencing. # P-value Chi-square (linear-by-linear) test, LHR: likelihood ratio.

Supplementary table 5. Association of 5 CpG-SNPs in the replication cohort

Group Probe rs number Gene Genotype (freq %)* LHR P-value# PTSD rs2546424 cg22931151 OR2V2 CC(61.1) CT(33.3) TT(5.6) 0.87 0.413 Non PTSD CC(67.4) CT(30.2) TT(2.3) PTSD rs7808948 cg03122926 EIF3B CC(24.1) CT(46.3) TT(29.6) 0.77 0.385 Non PTSD CC(18.6) CT(44.2) TT(37.2) PTSD rs13230345 cg22535849 SDK1 AA(7.4) GA(38.9) GG(53.7) 4.51 0.06 Non PTSD AA(4.7) GA(20.9) GG(74.4) PTSD rs7208505 cg13989295 SKA2 AA(25.9) GA(57.4) GG(16.7) 0.15 0.721 Non PTSD AA(27.9) GA(58.1) GG(14) PTSD rs1990322 cg24393317 CACNA1C AA(34) GA(40) GG(26) 10.95 0.001 Non PTSD AA(65.1) GA(27.9) GG(7)

*Genotypes according sanger sequencing. # P-value Chi-square

162