This plot displays 12 665 individuals and 19 225 controls. Single-nucleotide polymorphisms in green are in linkage disequilibrium with the index single-nucleotide polymorphisms (diamonds) and have a P value less than .001. Index variants located with a distance less than 400 kilobase are considered as 1 locus.
Bonferroni correction P < 2.19 × 10−4. In total, 228 traits were tested, including psychiatric traits, educational outcomes, obesity-related phenotypes, smoking, and reproductive success (eTable 4 in the Supplement). Error bars indicate 95% confidence limits. GPC indicates Genetics of Personality Consortium; PGC, Psychiatric Genomics Consortium; SSGAC, Social Science Genetic Association Consortium.
Each sample is represented by a dot superimposed on the box plot. Number of mice per group: B6 strain controls = 6 (mPFC and vHPC), resilient = 6 (mPFC) and 8 (vHPC), susceptible = 6 (mPFC) and 3 (vHPC); D2 strain controls = 6 (mPFC and vHPC), resilient = 0, susceptible = 8 (mPFC) and 5 (vHPC). CPM indicates log counts per million; mPFC, medial prefrontal cortex; vHPC, ventral hippocampus.
eFigure 1. Q-Q plot of GWAS analysis
eFigure 2. Regional association plots for index SNPs in PDE4B
eFigure 3. Forest plots for the genome-wide significant index SNP in PDE4B
eFigure 4. Manhattan plot of the results from the GWAS of anxiety disorders
eFigure 5. Manhattan plot of the results from the GWAS of stress-related disorders
eFigure 6. Manhattan plot of the results from the GWAS excluding adjustment disorders
eFigure 7. Manhattan plot of the results from the GWAS for “population” Design
eFigure 8. Manhattan plot of the results from the GWAS for the “covariate” Design
eFigure 9. Manhattan plot of the results from the GWAS for “propensity” Design
eFigure 10. Manhattan plot of the results from the GWAS excluding schizophrenia
eFigure 11. Manhattan plot of the results from the GWAS including OCD
eFigure 12. Partitioning of heritability by functional annotations
eFigure 13. Partitioning of heritability by tissue-group annotations
eFigure 14. Partitioning of heritability by tissue-specific H3K4Me1 annotations
eFigure 15. Multivariate regression of normalized polygenic risk scores on diagnostic subtypes
eFigure 16. Multivariate regression of normalized polygenic risk scores on comorbid subgroups
eTable 1. Summary of results for PDE4B
eTable 2. Gene-based test results
eTable 3. Pathway analysis results
eTable 4. Genetic correlations for “naïve” Design
eTable 5. Genetic correlations for “naïve” Design (UKBB traits)
eTable 6. Genetic correlations for “prevalence” Design
eTable 7. Genetic correlations for “prevalence” Design (UKBB traits)
eTable 8. Genetic correlations for “covariate” Design
eTable 9. Genetic correlations for “covariate” Design (UKBB traits)
eTable 10. Genetic correlations for “propensity” Design
eTable 11. Genetic correlations for “propensity” Design (UKBB traits)
eTable 12. SNP Heritability estimates across varying analytical designs
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Meier SM, Trontti K, Purves KL, et al. Genetic Variants Associated With Anxiety and Stress-Related Disorders: A Genome-Wide Association Study and Mouse-Model Study. JAMA Psychiatry. 2019;76(9):924–932. doi:10.1001/jamapsychiatry.2019.1119
Which genetic variants are associated with anxiety and stress-related disorders and do they correlate with other traits?
In this study of genome-wide association data, PDE4B variants were associated with anxiety and stress-related disorders, and their genetic signature overlapped with other psychiatric traits, educational outcomes, obesity-related phenotypes, smoking, and reproductive success.
Large samples are needed to validly identify genetic variants associated with anxiety and stress-related disorders.
Anxiety and stress-related disorders are among the most common mental disorders. Although family and twin studies indicate that both genetic and environmental factors play an important role underlying their etiology, the genetic underpinnings of anxiety and stress-related disorders are poorly understood.
To estimate the single-nucleotide polymorphism–based heritability of anxiety and stress-related disorders; to identify novel genetic risk variants, genes, or biological pathways; to test for pleiotropic associations with other psychiatric traits; and to evaluate the association of psychiatric comorbidities with genetic findings.
Design, Setting, Participants
This genome-wide association study included individuals with various anxiety and stress-related diagnoses and controls derived from the population-based Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) study. Lifetime diagnoses of anxiety and stress-related disorders were obtained through the national Danish registers. Genes of interest were further evaluated in mice exposed to chronic social defeat. The study was conducted between June 2016 and November 2018.
Main Outcomes and Measures
Diagnoses of a relatively broad diagnostic spectrum of anxiety and stress-related disorders.
The study sample included 12 655 individuals with various anxiety and stress-related diagnoses and 19 225 controls. Overall, 17 740 study participants (55.6%) were women. A total of 7308 participants (22.9%) were born between 1981-1985, 8840 (27.7%) between 1986-1990, 8157 (25.6%) between 1991-1995, 5918 (18.6%) between 1996-2000, and 1657 (5.2%) between 2001-2005. Standard association analysis revealed variants in PDE4B to be associated with anxiety and stress-related disorder (rs7528604; P = 5.39 × 10−11; odds ratio = 0.89; 95% CI, 0.86-0.92). A framework of sensitivity analyses adjusting for mental comorbidity supported this result showing consistent association of PDE4B variants with anxiety and stress-related disorder across analytical scenarios. In mouse models, alterations in Pde4b expression were observed in those mice displaying anxiety-like behavior after exposure to chronic stress in the prefrontal cortex (P = .002; t = −3.33) and the hippocampus (P = .001; t = −3.72). We also found a single-nucleotide polymorphism heritability of 28% (standard error = 0.027) and that the genetic signature of anxiety and stress-related overlapped with psychiatric traits, educational outcomes, obesity-related phenotypes, smoking, and reproductive success.
Conclusions and Relevance
This study highlights anxiety and stress-related disorders as complex heritable phenotypes with intriguing genetic correlations not only with psychiatric traits, but also with educational outcomes and multiple obesity-related phenotypes. Furthermore, we highlight the candidate gene PDE4B as a robust risk locus pointing to the potential of PDE4B inhibitors in treatment of these disorders.
Anxiety disorders are characterized by excessive and inappropriate fear and anxiety triggered by stimuli perceived as threatening. They are among the most common mental disorders with a lifetime prevalence of more than 20%.1 Given the prevalence and the immense social and economic burden of these disorders,2 it is of strong interest to identify their risk factors.
Family and twin studies indicate that both genetic and environmental factors are of relevance in the etiology of anxiety disorders, with levels of familial aggregation and heritability at 30% to 50%.3 Although stress-related disorders share many symptom characteristics with anxiety disorders and both conditions are highly comorbid, they have recently been moved to a separate diagnostic category. Interestingly, susceptibility factors common to different anxiety and stress-related disorders seem to account for a larger proportion in heritability than factors predisposing to individual disorders.4 This indicates the potential of combining these phenotypes to identify their shared genetic underpinnings. Genome-wide association studies (GWAS) have proven to be an effective tool for the identification of common genetic variants increasing the susceptibility to complex disorders. Recently, GWAS of panic disorder,5-8 posttraumatic stress disorder,9-15 generalized anxiety disorders,16 phobias,17,18 and a composite indicator of anxiety disorders19,20 have been published. However, besides the study by Purves et al,20 most of these efforts were limited in sample size resulting in low overall power to detect significant associations.
In this study, we conducted a GWAS aggregating individuals in the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) study with varying diagnoses of anxiety and stress-related disorders to identify their common genetic factors, extending previous successful attempts.19,20 As most individuals with these disorders experience another comorbid mental disorder, especially depression,21 we explored the association of mental comorbidity with the genetics of anxiety and stress-related disorders. Genes identified through this effort were further followed up in a mouse model of chronic social defeat. Our effort represents the first genetic study of this magnitude to explicitly target comorbidity of anxiety and stress-related disorders, to our knowledge.
The GWAS sample under analysis included 12 655 individuals with anxiety and stress-related diagnoses as well as 19 225 controls from Denmark. All study participants were enrolled in iPSYCH, a study designed to unravel risk factors of severe mental disorders, including individuals with schizophrenia, autism, attention-deficit/hyperactivity disorder, anorexia nervosa, and affective disorders (referred to in this article as design phenotypes) as wells as population-based controls. More information on iPSYCH can be found elsewhere.22 Of these design individuals, 4584 individuals were diagnosed as having an anxiety disorder and 9831 as having a stress-related disorder, of which 1760 received both diagnoses. DNA samples for iPSYCH were taken from the Danish Neonatal Screening Biobank.23 Following protocols for DNA extraction and amplification (described elsewhere24), all samples were genotyped using Illumina’s PsychChip (Illumina). Through the national research registers,25,26 we identified individuals with an anxiety and stress-related diagnosis assigned by a psychiatrist during routine clinical care according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (F40.0-F41.9; F43.0-F43.9). Individuals with comorbid autism were excluded. Although patients with autism experience anxiety, their anxiety is often reflecting their core autistic symptomatology and lacks the social component central to many anxiety and stress-related diagnoses.27 Exclusion criteria for control individuals were International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnoses of anxiety, stress-related disorders, and mood disorders. Characteristics of the sample are displayed in Table 1. The study was conducted between June 2016 and November 2018. This study was approved by the Danish Data Protection Agency. By Danish law, registry-based studies do not require informed consent.
Quality control, imputation, and primary association analyses in iPSYCH have been described elsewhere.28 We used the bioinformatics pipeline Ricopili29 developed by the Psychiatric Genomics Consortium.30 To avoid potential study effects of the 23 genotyping batches within the iPSYCH cohort, each batch was processed separately. Standard procedures for stringent quality control included filters for call rate, Hardy-Weinberg equilibrium, and heterozygosity rates. Each batch was phased and imputed using the 1000 Genomes Project phase 3 imputation reference panel31 using SHAPEIT32 and IMPUTE2,33 respectively. Cryptic relatedness and population structure were assessed on high-quality single-nucleotide polymorphisms (SNPs) with low linkage disequilibrium (LD).
Genome-wide association studies for the 23 batches in iPSYCH were performed using logistic regression models with the imputed marker dosages including the first 4 principal components to control for remaining population stratification.34 Subsequently, the results were meta-analyzed using an inverse variance–weighted fixed-effects model, implemented in METAL.35 Additionally, the analyses were stratified for anxiety disorders and stress-related disorders and different subtypes. Most individuals were diagnosed as having an additional mental disorder owing to their comorbidity with 1 of the design phenotypes in iPSYCH. We therefore aimed to adjust for mental comorbidity in a framework of sensitivity analyses (eMethods in the Supplement). There is currently no other study available with the same phenotype definition, to our knowledge; however, we sought for replication of our index SNP and correlated genome-wide significant variants (0.2 < r2 < 0.5, rs7528604 and rs7539350) in related phenotypes, such as anxiety20 and neuroticism.36 Finally, as obsessive-compulsive disorder has in previous classifications systems been categorized under anxiety disorders, analyses were supplemented by also including individuals with obsessive-compulsive disorder.
Gene-based associations were calculated with MAGMA37 using the summary statistics from the main GWAS analyses. Association was tested using the SNP-wise mean model, in which the sum of –log (SNP P value) for SNPs located within the transcribed region was used as test statistic. MAGMA37 controls for gene size, number of SNPs in a gene, and LD between markers estimated from 1000 Genomes Project phase 3 samples.
Linkage disequilibrium score regression was used to dissect the relative contribution of polygenic effects and confounders (eg, cryptic relatedness, sample overlap, and population stratification) to deviation from the null in the genome-wide distribution of GWAS χ2 statistics.38,39 Prevalence was specified at 20%.40,41 Using LD score regression, SNP heritability was also partitioned by functional category and tissue association.38,39 Partitioning was performed for 53 overlapping functional categories as well as 220 cell-type–specific annotations grouped into 10 cell-type categories.42 Genetic correlations were tested for 228 phenotypes with publicly available GWAS summary statistics and 596 traits from the UK Biobank study43 using LD Hub.44 In addition, polygenic risk scores45 were constructed in our sample to explore polygenic heterogeneity across diagnostic subtypes and comorbidities (eMethods in the Supplement).
Given that stress is known to increase the risk of anxiety and stress-related disorders, we aimed to establish the association of chronic psychosocial stress with the brain gene expression levels of significant genes. We used the chronic social defeat stress (CSDS) model46 including 2 inbred strains: C57/BL6NCrl (B6, innately nonanxious strain) and DBA/2NCrl (D2, innately anxious strain). Twenty-four hours after the last CSDS session, we tested all mice for social aversion comparing their explorative behavior in the area around a cylinder with and without a Clr-CD1 mouse. One week after the end of CSDS, we dissected the medial prefrontal cortex (mPFC) and ventral hippocampus (vHPC) for RNA sequencing. All animal procedures were approved by the Regional State Administration Agency for Southern Finland (license numbers ESAVI-3801-041003-2011 and ESAVI/2766/04.10.07/2014) and carried out according to directive 2010/63/EU of the European Parliament and of the Council and the Finnish Act on the Protection of Animals Used for Science or Educational Purposes (497/2013).
Total RNA was extracted using TriReagent. For RNA sequencing, we carried out ribosomal RNA depletion using Ribo-Zero (Illumina) followed by fragmentation with an S2 ultrasonicator (Covaris Inc). Messenger RNA sequencing libraries were prepared with Nextera (Illumina; vHPC samples) or ScriptSeq version 2 (Epicenter; mPFC samples) kits. Whole transcriptome level multiple testing correction was done with the Benjamini-Hochberg method,47 after which the expression levels of significant genes were extracted from the data set (eMethods in the Supplement). A 2-sided P value less than .05 was considered statistically significant.
At a single locus on chromosome 1, 68 genetic variants exceeded the threshold for genome-wide significance in this GWAS for anxiety and stress-related disorders (ie, without correction for the study design). The locus overlaps with 1 gene (PDE4B) with the lead SNP rs7528604 (P = 5.39 × 10−11; odds ratio [OR] = 0.89; 95% CI, 0.86-0.92) (Figure 1, Table 230,48-51; eFigures 1 and 2 in the Supplement). We found no evidence for significant heterogeneity between genotyping batches for this marker (eFigure 3 in the Supplement). Stratified analyses focusing on anxiety and stress-related disorder phenotype definitions separately supported the identified locus (eFigures 4 and 5 in the Supplement), permutation tests indicated that the weaker signals can be attributed to a loss in sample size. Permutation tests also showed the difference in significance levels across diagnostic subgroups could be attributed to their sample size (for exclusion of adjustment disorders see eFigure 6 in the Supplement). In addition, results from analyses mimicking the comorbidity pattern of population-based samples (rs7528604; P = 1.20 × 10−11; OR = 0.88; 95% CI, 0.85-0.91) and including psychiatric phenotypes as covariates (rs7528604; P = 2.32 × 10−8; OR = 0.90; 95% CI, 0.87-0.93) were in line with our initial GWAS results (eFigures 7 and 8 in the Supplement). Even in the stringent sensitivity analyses, associations within the PDE4B gene were among the top signals although with a different lead SNP (rs17128482; P = 1.43 × 10−5; OR = 1.12; 95% CI, 1.07-1.17; eFigure 9 in the Supplement). Resampling analyses by removing individuals with comorbid depression from our data did not show a significant association of depression with outcomes of our result at the PDE4B locus (P = .46). To assess whether our PDE4B signal might have been based on comorbid schizophrenia, we excluded individuals with schizophrenia and still observed genome-wide significance and the same OR for that locus (P = 3.58 × 10−10; OR = 0.89; 95% CI, 0.86-0.91; eFigure 10 in the Supplement). Analyses including individuals with obsessive-compulsive disorder are displayed in eFigure 11 in the Supplement. A summary of all analyses can be found in eTable 1 in the Supplement. Genome-wide analyses using MAGMA37 identified 7 genome-wide significant genes, whereas no pathways were significant after correction for multiple testing (eTables 2 and 3 in the Supplement).
Linkage disequilibrium–score regression38,39 was used to calculate SNP heritability of our naive anxiety and stress-related disorders GWAS and the genetic correlation with other phenotypes. Assuming a population prevalence of 20% for anxiety and stress-related disorders, we estimate the liability-scale SNP heritability at 0.28 (standard error [SE] = 0.027). This estimate is comparable with those reported in previous studies for posttraumatic stress disorder52 and anxiety disorders.20 Partitioning heritability based on functional annotations revealed significant enrichment in the heritability for SNPs located in conserved regions (enrichment = 2.01; SE = 0.32; P < .001), supporting the general biological importance of conserved regions and their potential association with susceptibility of anxiety and stress-related disorders (eFigure 12 in the Supplement). Cell-type–specific analyses revealed a significant enrichment in the heritability by SNPs located in central nervous system specific enhancers and promoters (enrichment = 2.93; SE = 0.50; P = 4.32 × 10−4; eFigures 13 and 14 in the Supplement).
Using LDhub,44 31 phenotypes displayed significant genetic correlation with anxiety and stress-related disorders after Bonferroni correction (P = 2.19 × 10−4), including psychiatric traits, educational outcomes, obesity-related phenotypes, smoking, and reproductive success (Figure 2). An overview of all genetic correlations can be found in eTables 4 and 5 in the Supplement. Linkage disequilibrium–score regression also revealed a strong genetic correlation (rfor genetic correlation = 0.55; P = 6.79 × 10−17) with the largest anxiety GWAS20 to date. We performed the same LD score regression–based analyses also for the results from our framework of sensitivity analyses. The association of the analyses mimicking the comorbidity pattern of population-based samples on SNP heritability and the genetic correlations with other traits was marginal (eTables 6 and 7 in the Supplement). Owing to the overrepresentation of psychiatric phenotypes in the control sample, the design including psychiatric phenotypes as covariates and the propensity score matched design resulted in lower SNP heritability and genetic correlations (eTables 8 to 11 in the Supplement). An overview with regard to SNP heritability reflecting the increased prevalence of anxiety and stress-related disorders in psychiatric phenotypes can be found in eTable 12 in the Supplement. Using polygenic risk scores trained on different phenotypes, we only observed limited heterogeneity across diagnostic and comorbidity groups (eFigures 15 and 16 in the Supplement).
We sought for replication of our index SNP and correlated genome-wide significant variants (rs7528604; P = 5.39 × 10−11; OR = 0.89; 95% CI, 0.86-0.92 and rs7539350; P = 1.46 × 10−8; OR = 1.10; 95% CI, 1.06-1.14) in related phenotypes.20,36PDE4B was associated with anxiety disorders in the largest GWAS to date20 (rs7528604; P = .18; OR = 0.97; 95% CI, 0.93-1.02 and rs7539350; P = .02; OR = 1.05; 95% CI, 1.01-1.09) and neuroticism36 (rs7528604; P = 9.01 × 10−3; OR = 0.99; 95% CI, 0.99-0.99 and rs7539350; P = 2.59 × 10−4; OR = 1.01; 95% CI, 1.01-1.01), which is often used as a proxy for anxiety and stress-related disorders.
We determined the gene expression levels of Pde4b in mice exposed to CSDS using RNA sequencing (Figure 3). Stress-susceptible mice from the B6 strain had lower expression levels of Pde4b in the mPFC compared with both control (P < .01; t = −3.33; adjusted P = .06) and stress-resilient mice (P = .01; t = −2.97; adjusted P = .10), as well as in the vHPC compared with control (P < .01; t = −3.72; adjusted P < .01) and stress-resilient mice (P = .003; t = −3.29; adjusted P = .01). D2 mice are highly susceptible to CSDS and therefore, we were only able to compare stress-susceptible mice with controls. There were no differences in Pde4b expression levels between these groups in either brain region. Interestingly, the innately highly anxious D2 control mice had lower Pde4b expression levels compared with the nonanxious B6 control mice in the vHPC (P = 4.6 × 10−5; t = −4.81; adjusted P = .00016) but not in the mPFC (P = .13; t = −1.57; adjusted P = .26), suggesting that especially in the vHPC Pde4b expression levels may also contribute to innate anxiety levels.
To our knowledge, we conducted the largest GWAS on anxiety and stress-related disorders to date and extend previous findings about shared genetic associations with other phenotypes. Specifically, we aggregated 12 655 individuals and 19 225 control individuals with the aim of identifying common variants underlying the etiology of these disorders. We tried to capture the clinical complexity of anxiety and stress-related phenotypes in a sample enriched for individuals with comorbid mental illnesses and identified genetic variants that were associated with disease susceptibility.
The most consistent association signal across our different analyses was observed for genetic variants located within the PDE4B gene, which regulates intracellular cyclic adenosine monophosphate signaling and is strongly expressed in the human brain. PDE4B has been proposed as a candidate gene for anxiety, in particular, panic disorder53; however, replication has so far been lacking. Pharmacologic profiles of selective PDE4B inhibitors have demonstrated clear antidepressant and anxiolytic benefits.54 Furthermore, mice deficient in Pde4b exhibit behavioral changes in a range of tests sensitive to anxiolytic drugs.55 We found the expression of Pde4b to be altered in B6 mice susceptible to chronic psychosocial stress compared with controls and stress-resilient mice. Lower expression levels were observed in brain regions (mPFC and vHPC), which are known to regulate emotional and social behavior in mice and humans. In innately anxious (D2 inbred) mice, Pde4b expression did not differ from controls after chronic psychosocial stress exposure indicating a genetic background effect.
In line with other GWAS,19,52 our SNP heritability estimate of 28% for anxiety and stress-related disorders indicates a substantial role for common genetic variation, accounting for a sizable portion of twin-based heritability.3 Conserved regions and regions containing enhancers and promoters of expression in the central nervous system tissues were found to be enriched for associations with anxiety disorders, consistent with findings for schizophrenia, bipolar disorder, and depression.42 More work is needed to unravel the nature of the genetic correlations described in this article and how different designs in our analytical framework affected these findings. Nevertheless, the range of genetic correlations with psychiatric traits, educational outcomes, obesity-related phenotypes, and smoking helps to broaden our conceptualization of anxiety and stress-related disorders. First, the strong positive genetic correlations of these disorders with depression and neuroticism in our naive GWAS reinforce clinical and epidemiologic observations. Anxiety and stress-related disorders are commonly comorbid with depression, often precede depression, and even affect the course of the depression.56 Second, the positive genetic correlations with schizophrenia and the cross–psychiatric disorder phenotype firmly anchor anxiety and stress-related disorders with other psychiatric traits and reflect the substantial evidence for partially shared genetic susceptibility across many psychiatric disorders.57 Third, negative associations between these disorders and educational attainment have been reported,58 and our results suggest that genetic factors may partially account for these reported associations.
A major strength of this study is the aim to identify genetic variants that play a central but nonspecific role in the susceptibility of anxiety and stress-related disorders. This contrasts with the approach taken in most psychiatric genetic studies, which generally focus on specific clinical diagnoses. However, it has long been recognized that clinical diagnoses poorly reflect etiological mechanisms, as both genetic and environmental factors have been found to have nonspecific effects across a wide range of diagnoses.59 Given how critical fear and anxiety are for human survival, it is very likely that conserved genes common to a range of anxiety and stress-related disorder regulate these basic biological processes.
Making use of registry-based diagnoses as a proxy for mental disorders in a research study that was primarily ascertained for closely related traits constitute the major limitation, but it is also a strength of our study enabling the characterization of anxiety and stress-related disorders susceptibility in the context of mental comorbidity. Similar to previous studies, the generalizability to milder forms of anxiety and stress-related disorders or truly population-based samples is difficult to assess.
Through different sensitivity analyses, we aimed to address some of the limitations (to the extent possible with the data at hand) and gained new insights that probably would not have been possible otherwise. Importantly, despite this lack in generalizability, these limitations are unlikely to lead to false positive associations in a narrow sense. However, they do ask for a reflection on the tested hypothesis in the sensitivity analyses under consideration. However, we cannot exclude the possibility that our results rather reflect the genetic underpinnings of stress-related disorders than anxiety or specific diagnostic subgroups (ie, adjustment disorders) given their differences in sample sizes, although our permutation analyses did not suggest so. As a final point, we would like to stress that our replication efforts were limited by the fact that our study is including a wider range of both anxiety and stress-related disorders than previous efforts.19,20,52
In summary, our results highlight anxiety and stress-related disorders to be a complex heritable phenotype. We highlight the candidate gene PDE4B as a robust risk locus (through studies in mice and humans), pointing to the potential of PDE4B inhibitors in treatment of these disorders. Future studies are needed to confirm these findings via independent replication and to detect additional loci, not only identifying potential pleiotropic effects across the full spectrum of anxiety and stress-related disorders, but also loci associated specifically with individual disorders.
Corresponding Author: Sandra M. Meier, PhD, Department of Psychiatry, Dalhousie University, 5850/5980 University Ave, PO Box 9700, Halifax, NS B3K 6R8, Canada (email@example.com).
Accepted for Publication: March 20, 2019.
Published Online: May 22, 2019. doi:10.1001/jamapsychiatry.2019.1119
Author Contributions: Drs Meier and Mattheisen had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Hovatta, Mattheisen, and Mors contributed equally.
Concept and design: Meier, Hougaard, Werge, Nordentoft, Breen, Børglum, Mattheisen, Mors.
Acquisition, analysis, or interpretation of data: Meier, Trontti, Purves, Als, Grove, Laine, Pedersen, Bybjerg-Grauholm, Bækved-Hansen, Sokolowska, Mortensen, Hougaard, Werge, Breen, Børglum, Eley, Hovatta, Mattheisen, Mors.
Drafting of the manuscript: Meier, Trontti, Laine, Breen, Hovatta, Mattheisen.
Critical revision of the manuscript for important intellectual content: Purves, Als, Grove, Pedersen, Bybjerg-Grauholm, Bækved-Hansen, Sokolowska, Mortensen, Hougaard, Werge, Nordentoft, Breen, Børglum, Eley, Mors.
Statistical analysis: Meier, Trontti, Purves, Als, Grove, Bybjerg-Grauholm, Bækved-Hansen, Breen, Mattheisen.
Obtained funding: Mortensen, Werge, Nordentoft, Breen, Børglum, Eley, Hovatta, Mors.
Administrative, technical, or material support: Bybjerg-Grauholm, Bækved-Hansen, Mortensen, Hougaard, Werge, Nordentoft, Breen, Børglum, Mattheisen, Mors.
Supervision: Sokolowska, Mortensen, Werge, Nordentoft, Breen, Børglum, Eley, Hovatta, Mattheisen, Mors.
Conflict of Interest Disclosures: Dr Mortensen reports grants from the Lundbeck Foundation during the conduct of the study and outside the submitted work. Dr Hougaard reports grants from the Lundbeck Foundation during the conduct of the study and personal fees from the Statens Serum Institut outside the submitted work. Dr Nordentoft reports grants from the Lundbeck Foundation during the conduct of the study. Dr Breen reports grants from the UK National Institute for Health Research during the conduct of the study. Dr Børglum reports grants from the Lundbeck Foundation during the conduct of the study and outside the submitted work. Dr Eley reports grants from the UK Medical Research Council, UK National Institute for Health Research, and Fondation Peters during the conduct of the study. Dr Hovatta reports grants from European Research Council, Sigrid Juselius Foundation, and University of Helsinki during the conduct of the study. Dr Mattheisen reports grants from the Lundbeck Foundation during the conduct of the study. Dr Mors reports grants from the Lundbeck Foundation during the conduct of the study. No other disclosures were reported.
Funding/Support: This article was funded by the Lundbeck Foundation (grants R102-A9118 and R155-2014-1724); the Novo Nordisk Foundation, who provided support for the Danish National Biobank resource; the European Research Council Starting Grant GenAnx; and the Sigrid Jusélius Foundation. The research in this study was partly funded by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Ms Purves’s work was funded by the Alexander von Humboldt Foundation. Dr Eley is partly funded by a program grant from the UK Medical Research Council (MR/M021475/1).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health.
Additional Contributions: We thank Naomi Wray, PhD (University of Queensland, Brisbane), and Jack Hettema, MD, PhD (Virginia Commonwealth University), for helpful comments and suggestions during the preparation of the manuscript. We also thank Ingrid Balcells, PhD; Natalia Kulesskaya, PhD; Zuzanna Misiewicz, PhD; Suvi Saarnio, MSc; Jenni Lahtinen, MSc; and Sanna Kängsep, PhD, from the Hovatta Lab for technical help and discussions on mouse experiments. None of these individuals received compensation for their work.
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