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[Preprint]. 2023 Sep 2:2023.09.01.23294920. [Version 1] doi: 10.1101/2023.09.01.23294920

Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders

Markos Tesfaye 1,2,*, Piotr Jaholkowski 1, Alexey A Shadrin 1,3, Guy FL Hindley 1,4, Børge Holen 1, Nadine Parker 1, Pravesh Parekh 1, Zillur Rahman 1, Shahram Bahrami 1, Gleda Kutrolli 1, Oleksandr Frei 1,5, Srdjan Djurovic 2,3,6, Anders M Dale 7,8,9,10, Olav B Smeland 1, Kevin S O’Connell 1, Ole A Andreassen 1,3,*
PMCID: PMC10491354  PMID: 37693403

Abstract

Background:

Anxiety disorders are prevalent and anxiety symptoms often co-occur with psychiatric disorders. Here, we aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

Methods:

We used the largest available GWAS of anxiety (GAD-2 score), schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Additionally, conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were carried out using OpenTargets and FUMA, respectively.

Results:

Anxiety was polygenic with 8.4k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1–7.8k variants). Both MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 154 anxiety loci (139 novel) by conditioning on the psychiatric disorders. We identified loci shared between anxiety and major depression (n = 66), bipolar disorder (n = 19), schizophrenia (n = 51), and ADHD (n = 37). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci.

Conclusions:

Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety and shared loci with psychiatric disorders. The shared genetic architecture may underlie the comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that could become potential drug targets.

Keywords: anxiety, genetic overlap, psychiatric disorder, genetic loci

Introduction

Anxiety is a human emotion and anxiety disorders encompass several diagnostic categories of mental disorders characterized by the core features of excessive fear and anxiousness, or avoidance behaviors (1). Anxiety disorders including generalized anxiety disorder, panic disorder, agoraphobia, social anxiety disorder, and specific phobias are among the leading causes of global disease burden (2). Because of the extensive phenotypic overlap and comorbidity among various anxiety disorders (3), there is a growing recognition that anxiety disorders can be better understood and measured along a continuum rather than as discrete categories (4, 5). Anxiety disorders are frequently comorbid with other psychiatric disorders (3). For example, nearly two-thirds of individuals with anxiety disorders also have concurrent depressive disorders and vice versa (6). Anxiety disorders are also common in individuals with bipolar disorder (BIP) (7, 8), schizophrenia (SCZ) (9), and attention deficit hyperactivity disorder (ADHD) (10, 11). Further, symptoms of anxiety (ANX), not meeting the criteria for anxiety disorder, frequently co-occur with other psychiatric disorders (12, 13). Anxiety disorders and ANX co-occurring with psychiatric disorders have been linked with greater symptom burden, poorer course and outcome, and lower quality of life (14–18). The clinical relevance of co-occurring ANX is highlighted by the inclusion in the diagnostic criteria of ‘with anxious distress’ as a specifier of psychiatric disorders such as major depression (MD), and BIP (1).

The etiology of anxiety disorders is not clearly understood; however, both genetic and environmental factors are involved (1, 3, 4, 19, 20). Genetic susceptibility plays a major role in anxiety disorders (20), with heritability estimates from twin studies ranging between 30 to 50% (21). Genome-wide association studies (GWAS) have identified several genetic loci for anxiety disorders (20, 22, 23) and ANX (24). Furthermore, recent evidence from genetic correlation analyses supports the notion that there is shared genetic liability between psychiatric disorders, and anxiety disorders and ANX (22, 24). However, genetic correlations do not provide a comprehensive overview of the shared genetic architecture between two phenotypes (25, 26). The identification of more genetic loci for anxiety disorders and a better understanding of the shared genetic landscape with other psychiatric disorders can unveil more information about the biological pathways underlying anxiety disorders (27, 28). This is crucial for the development of more effective treatments against anxiety disorders and comorbid conditions. Advances in statistical genetics have improved genetic discoveries for complex diseases (29, 30). The utility of these novel analytical methods in better characterizing the genetic architecture of anxiety disorders and the genetic overlap with psychiatric disorders may add to our understanding of the underlying biological mechanisms.

The standard approach to evaluate shared heritability in complex disorders is to estimate genetic correlations with linkage disequilibrium score regression (LDSC) (31). However, genetic correlation, typically a genome-wide summary measure (30, 32), may conceal shared genetic architecture involving a mixture of concordant and discordant effect directions and does not capture the specific overlapping loci and relevant genes (25, 26, 33). These characteristics can be captured by the application of the bivariate causal mixture model (MiXeR) (25) and the local analysis of covariant association (LAVA) (33). Genetic overlap can be exploited to boost the discovery of specific genetic risk loci for a phenotype (30) and loci shared with other phenotypes by applying conditional false discovery rate (condFDR) and conjunctional FDR (conjFDR) analyses respectively (29, 34).

Given the high comorbidity between ANX and psychiatric disorders, we aimed to quantify and characterize their genetic overlap. To this end, we applied univariate MiXeR to comprehensively characterize the polygenic architecture of ANX measured using a dimensional scale. We also employed bivariate MiXeR and LAVA to assess the genetic overlap with MD, SCZ, BIP, and ADHD. Additionally, we applied condFDR/conjFDR methods to identify genetic loci associated with ANX, and loci shared between ANX and the four psychiatric disorders. Finally, we tested whether polygenic liability to the psychiatric disorders predicted anxiety disorder status in an independent sample.

Methods and materials

Genome-wide association studies (GWAS) data

We used the largest GWAS available for anxiety symptoms (ANX), measured as a dimensional trait using the Generalized Anxiety Disorder 2-item scale (GAD-2), from the Million Veterans Program (MVP) (n = 175,163) (24) The ANX GWAS summary statistics were accessed through the Database of Genotype and Phenotype (dbGaP; phs001672). As the lifetime anxiety disorder GWAS meta-analysis (20) was not powerful enough as indicated by poor model fit in univariate MiXeR (25), the dataset was used for replication of identified ANX loci. We obtained GWAS summary statistics for BIP, ADHD, and SCZ from the Psychiatric Genomics Consortium (PGC) (35–37), and MD from a meta-analysis of the PGC and 23andMe, Inc. (38). All the GWAS comprised only populations of European ancestry (Table 1 and Supplementary Methods in Supplement 1). All GWAS of these datasets have been approved by their respective ethical approval committees.

Table 1:

The GWAS summary statistics data

Phenotype Number of cases Number of controls Source of data
ANX 175,163 - MVP (24)
MD 246,363 561,190 PGC-MD + 23andMe (38)
BIP 41,917 371,549 PGC-BIP (37)
SCZ 53,386 77,258 PGC-SCZ (36)
ADHD 38,691 186,843 PGC-ADHD (35)
Anxiety disordera 31,977 82,114 ANGST + UKBB + iPSYCH (20)
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GWAS: Genome-wide association study, ANX: Anxiety symptoms, MD: Major depression, BIP: Bipolar disorder, SCZ: Schizophrenia, ADHD: Attention deficit hyperactivity disorder, MVP: Million Veterans Program, PGC: Psychiatric Genomics Consortium, UKBB: UK Biobank, ANGST: Anxiety NeuroGenetics Study, iPSYCH: Initiative for Integrative Psychiatric Research

a

Replication sample

Independent sample

We obtained genotype data for a total of 95,792 mothers and fathers from the Norwegian Mother, Father, and Child Cohort Study (MoBa) (39). The details of sample collection, genotyping, and quality control are provided elsewhere (40). We obtained ICD-10 psychiatric diagnoses from the Norwegian Patient Registry until 2018. Our cases of anxiety disorder (n = 3,330) included agoraphobia (F40.0, n = 493), social phobia (F40.1, n = 874), panic disorder (F41.0, n = 913), and generalized anxiety disorder (F41.1, n = 1,050). MoBa cohort participants had provided written informed consent at enrollment. We excluded participants who withdrew their consent from the analyses. MoBa study was approved by the Regional Committees for Medical and Health Research Ethics (2016/1226).

Statistical analysis

Assessing Genetic overlap

We performed a series of casual mixture model (MiXeR) analyses (25) to investigate the genetic architecture of ANX and the genetic overlap of ANX with MD, BIP, SCZ, and ADHD. First, we conducted univariate MiXeR analyses to estimate the number of trait-influencing variants explaining 90% of SNP-based heritability after controlling for linkage disequilibrium (LD). These were followed by bivariate MiXeR analyses to estimate the number of SNPs shared between pairs of phenotypes irrespective of effect direction. We also determined the estimated proportion of shared SNPs between two phenotypes out of the total number of SNPs estimated to influence both phenotypes (Dice coefficients) and the fraction of SNPs with concordant effects in the shared component (25) (Supplementary Methods in Supplement 1).

We employed LAVA to estimate local genetic correlations between ANX, and MD, BIP, SCZ, and ADHD (33). LAVA estimates local genetic correlations across 2,495 semi-independent genetic regions of approximately 1Mb and identifies shared genetic regions with their effect directions (33). It takes sample overlap into account by using the genetic covariance intercept from LDSC (31). LAVA estimates the heritability of each of the genetic regions for each of the phenotypes followed by estimation of local genetic covariance between the pairs of phenotypes. We corrected the p-values for local genetic correlations for multiple testing using the Bonferroni method.

Conditional and conjunctional false discovery rates

We generated quantile-quantile (Q-Q) plots where the p-values of single nucleotide polymorphisms (SNPs) in ANX were plotted conditional on three different cut-offs of p-values in the secondary phenotypes (i.e., one of MD, BIP, SCZ, and ADHD). Q-Q plots with successive leftward and upward deviation compared to the null were considered to exhibit cross-trait enrichment (29). We performed condFDR analyses to identify loci associated with ANX. Next, conjFDR analyses identified loci shared between ANX and each of the secondary phenotypes respectively (29, 34). In condFDR analysis, the SNP p-values in the ANX GWAS summary statistics were re-ranked based on their p-values in the GWAS summary statistics of the secondary phenotype. CondFDR leverages the SNPs’ association with the secondary phenotype to boost the power to identify novel SNPs associated with the primary phenotype (i.e., anxiety) (29). This boost in power is contingent on the extent of genetic overlap between the two phenotypes (29, 41).

We then performed inverse condFDR analyses whereby MD, BIP, SCZ, and ADHD were primary phenotypes, and ANX was the secondary phenotype and used both pairs of condFDR results for conjFDR analyses. The conjFDR value for a SNP is defined by taking the maximum of the condFDR and inverse condFDR values for a given pair of phenotypes (29). A threshold of 5% was used as statistically significant for both condFDR and conjFDR p-values. We excluded SNPs within the extended major histocompatibility complex (MHC) region and chromosome 8p23.1 inversion (genome build 19 positions of chr6:25119106 – 33854733 and chr8:7200000 – 12500000, respectively) from the condFDR model fit procedure, but not from the discovery analyses (42). We randomly pruned SNPs in 500 iterations to minimize inflation in fold enrichment. All the p-values were corrected for inflation using a genomic inflation control procedure as described previously (34) (Supplementary Methods in Supplement 1). The cond/conjFDR code is available online (https://github.com/precimed/pleiofdr).

Definition of genomic loci

We designated independent genomic loci according to the functional mapping and gene annotation (FUMA) protocol (43). We specified candidate SNPs as any SNP with condFDR or conjFDR < 0.05, and candidate SNPs with LD r2 < 0.6 with each other as independent significant SNPs. Lead SNPs were defined as independent SNPs with LD r2 < 0.1. The candidate SNPs in LD r2 ≥ 0.6 with a lead SNP delineated the boundaries of a genomic locus. We defined all candidate SNPs positioned within the boundaries of a genomic locus to correspond to a single independent genomic locus. We obtained LD information from the 1000 Genomes Project European reference panel (44). In conjFDR, we interpreted the effect directions by comparing the Z-scores of lead SNPs for each locus in the GWAS summary statistics corresponding to the phenotype. We defined novel risk loci as genomic loci not identified in the GWAS catalog for ANX and anxiety disorder (accessed in October 2022) and in ANX or anxiety disorders GWAS (20, 23, 24).

Consistency of genetic effects in an independent sample

We performed a left-sided binomial test of lead SNPs for concordant effect directions in the discovery (GWAS used for condFDR) and replication datasets (independent samples). For replication, we used independent GWAS summary statistics of lifetime anxiety disorders (20). If the statistics for a lead SNP were not available in the replication GWAS dataset, we used a candidate SNP with the next lowest FDR value.

Polygenic risk scores

In MoBa, we restricted the polygenic risk score (PRS) analyses to individuals of European ancestry selected based on genotype principal components (PCs) as described elsewhere (45). We used a kinship coefficient greater than 0.05 to exclude one of the related pairs of study participants while prioritizing individuals with anxiety disorders. When two related individuals had an anxiety disorder diagnosis, one of them was selected. We used PRSice (46) to calculate PRSs at different p-value thresholds (i.e., 5e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 5e-1, 1) using the GWAS summary statistics for ANX, MD, BIP, ADHD, and SCZ (Table 1). Subsequently, we extracted the first PC for each PRS across all p-value thresholds (47). Next, we used logistic regression to estimate PRS association with anxiety disorder using models that included PRS of the disorder, and age, sex, and the first 10 genotype PCs as covariates. The combined model included the PRSs for all the psychiatric disorders and the covariates.

Functional annotations and gene set analyses

We performed functional gene mapping for lead SNPs from cond/conjFDR using the OpenTarget platform (https://genetics.opentargets.org/) (48). For each lead SNP, we selected the gene with the highest overall score. We used genes mapped to the lead SNPs using the OpenTarget platform for gene set analyses conducted using the GENE2FUNC analyses in FUMA. We also obtained Combined Annotation Dependent Depletion (CADD) scores which show how deleterious the SNP is on protein function (49), and RegulomeDB scores which predicted the regulatory function of the SNP from FUMA (50). We performed gene set analyses for genes mapped to lead SNPs identified condFDR and conjFDR analyses. We obtained the expression of genes identified in 54 different human tissues using Genotype-Tissue Expression (GTEx) (51).

Results

Polygenicity and genetic overlap

The MiXeR analyses showed that ANX is a polygenic trait with 8.4k ± 0.8k (mean ± SD) trait-influencing variants contributing to 90% of its heritability. We estimated the SNP heritability of ANX to be 5.6 ± 0.4% (Table 2). The other psychiatric disorders were also polygenic with the following estimated numbers of trait-influencing variants: MD 13.9k ± 0.4k, BIP 8.6k ± 0.2k, SCZ 9.6k ± 0.2k, and ADHD 7.7k ± 0.4k (Supplement 2: Tables S1 – S5), as reported previously (26, 35).

Table 2:

SNP heritability and genetic correlation parameters between anxiety and psychiatric disorders from LD score regression analyses

Phenotypes SNP heritability (SE) Genetic correlation (rg, SE) p-value (rg)
ANX 0.056 (0.004) 1.00 -
MD 0.065 (0.002) 0.63 (0.03) 8.97e-94
BIP 0.197 (0.007) 0.23 (0.04) 4.53e-11
SCZ 0.384 (0.013) 0.31 (0.03) 7.61e-28
ADHD 0.168 (0.008) 0.47 (0.04) 1.03e-38
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SNP: Single nucleotide polymorphism, LD: Linkage disequilibrium, SE: Standard error, rg: Genetic correlation, ANX: Anxiety symptoms, MD: Major depression, BIP: Bipolar disorder, SCZ: Schizophrenia, ADHD: Attention deficit hyperactivity disorder.

In the bivariate MiXeR analyses, ANX exhibited a huge genetic overlap with psychiatric disorders as demonstrated by the estimated number of shared variants with MD (7.8k ± 0.6k), BIP (7.6k ± 0.6k), SCZ (7.1k ± 0.2k), and ADHD (4.1k ± 0.3k). The Dice coefficients also indicated a substantial overlap with MD (69.6%), BIP (89.6%), SCZ (79.6%), and a lesser extent with ADHD (51.3%). The estimates for genetic overlap between ANX and MD may be unreliable due to suboptimal model fit based on the Akaike Information Criterion. The majority of trait-influencing variants shared between ANX and each of the psychiatric disorders had concordant effect directions, with 92% for ADHD, 86% for MD, 60% for BIP, and 68% for SCZ (Figure 1, Supplement 2: Tables S6 – S9).

Figure 1.

Figure 1.

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A – D: Bivariate MiXeR – Genome-wide genetic overlap between anxiety symptoms (ANX), and major depression (MD)*, bipolar disorder (BIP), schizophrenia (SCZ), and attention deficit hyperactivity disorder (ADHD). rg: genetic correlation. The numbers indicate estimates of trait-influencing variants in thousands. * Unreliable model fit, interpret with caution.

Local genetic correlation estimates from LAVA showed mixed directions with the majority of regions with nominal p < 0.05 having positive genetic correlations between ANX and all four psychiatric disorders (Supplement 1: Figure S1). A few positively correlated regions remained significant after Bonferroni correction of the p-values with five positively correlated loci between ANX and MD (Figure 2). Genome-wide genetic correlations using LDSC also showed a significant positive genetic correlation (rg) between ANX and all four psychiatric disorders.

Figure 2.

Figure 2.

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A – D: LAVA – Volcano plots of local genetic correlation coefficients (rho) with −log10 p-values for each locus. Dark red dots represent significantly correlated loci after Bonferroni correction. ANX: Anxiety symptoms, ADHD: Attention deficit hyperactivity disorder, BIP: Bipolar disorder, MD: Major depression, SCZ: Schizophrenia.

Cross-trait polygenic enrichment

We examined the Q-Q plots for cross-trait polygenic enrichment between ANX and psychiatric disorders. The Q-Q plots SNP p-values for ANX exhibited upward and leftward deviation when conditioned on progressively smaller p-value strata from each of MD, BIP, SCZ, and ADHD (Supplement 1: Figure S2). The pattern of Q-Q plots was consistent with the presence of polygenic enrichment between ANX and each of the psychiatric disorders.

Identification of genetic loci for anxiety

We leveraged the cross-trait enrichment and genetic overlap between ANX and psychiatric disorders to identify genomic risk loci for ANX. The condFDR analyses identified 154 unique loci associated with ANX (condFDR < 0.05), and 139 of these loci (90%) were novel (Supplement 3: Tables S10 – S13). The lead SNPs of the identified ANX-loci showed a significant en masse concordance of effect directions in the independent GWAS of lifetime anxiety disorders (Table 3).

Table 3:

En masse test of concordance of effect directions between genetic variants identified for ANX (condFDR) and corresponding variants in independent GWAS of anxiety disorders

CondFDR Phenotype pairs ANX-loci (n) SNPs (n) in the replication dataset SNPs (n) with concordant effects p-value*
ANX | MD 83 79 69 2.77e-12
ANX | BIP 41 38 34 3.02e-07
ANX | SCZ 70 66 54 8.47e-08
ANX | ADHD 59 59 47 2.56e-06
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GWAS: Genome-wide association study, SNP: Single nucleotide polymorphism, FDR: False discovery rate, ANX: Anxiety symptoms, MD: Major depression, BIP: Bipolar disorder, SCZ: Schizophrenia, ADHD: Attention deficit hyperactivity disorder,

*

one-sided binomial test

Genomic loci shared between anxiety and psychiatric disorders

We identified 113 genomic loci shared between ANX and psychiatric disorders. Notably, ANX and MD had 66 jointly associated loci (conjFDR < 0.05) of which 62 had a concordant effect direction (94%), and 33 were novel loci for MD. There were 51 loci shared between ANX and SCZ with 46 loci having concordant effect direction (90%). Fourteen of the 19 shared loci between ANX and BIP (74%), and 35 of the 37 loci (95%) shared between ANX and ADHD had concordant effect directions (Supplement 3: Tables S14 – S17). Many of the joint loci were shared across different pairs of traits in conjFDR analysis (Supplement 1: Figure S3).

Polygenic risk scores

The PRS for each of the psychiatric disorders and ANX positively associated with anxiety disorders Bonferroni corrected p < 1.0e-12, MD PRS had a larger effect estimate than all other PRSs. Nagelkerke’s R2 showed that MD PRS had a stronger association with anxiety disorders (1.28%) than ANX PRS (0.29%) (Figure 3A–B).

Figure 3.

Figure 3.

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A – B: Logistic regression – The association between polygenic risk scores (PRS) of various psychiatric disorders and anxiety disorders in MoBa parents. A – models for the PRS of anxiety symptoms (ANX), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BIP), major depression (MD), and covariates. B – Nagelkerke’s R2 shows the difference in the percentage of prediction of anxiety traits by each PRS over a base model that includes age, sex, and genotype principal components.

Functional annotations and gene set analyses

Most of the lead SNPs in the ANX-loci were either intronic or intergenic while five of the lead SNPs were exonic: rs61740399 (YTHDF3), rs1792 (FARP1), rs473267 (DOPEY1; PGM3), rs11507683 (EXD3), and rs9988 (NUP85) (Supplement 3: Tables S10 – S13). Similarly, most lead SNPs in shared loci were intronic or intergenic except a SNP shared between ANX and MDD - rs4434138 (STAB1), and another shared between ANX and SCZ - rs473267 (DOPEY1; PGM3) were also exonic. A few SNPs shared between ANX, and MDD (rs4434138, rs17149653, rs5030889), ANX and SCZ (rs473267, rs7858, rs17288409), and ANX and ADHD (rs56403421) had a CADD score >12.37 (Supplement 3: Tables S14 – S17).

Gene set analyses of genes annotated to loci identified for ANX with condFDR revealed enrichment of biological processes relevant to neurodevelopment and synaptic biology including neuron development, synaptic signaling, synapse organization, and regulation of trans-synaptic signaling. Other enriched biological processes included fear response, and glutamatergic and GABAergic synaptic transmissions (Supplement 4: Table S18). Genes annotated to loci shared between ANX and psychiatric disorders were enriched for biological processes such as neuron development, synaptic signaling, and regulation of neuron differentiation (Supplement 1: Figure S4, Supplement 4: Table S19). The top enriched cellular components for genes annotated to loci identified for ANX as well as those shared with psychiatric disorders converge to synapse, synaptic membrane, and synapse part (Supplement 4: Tables S20 and S21).

Enrichment analysis of the genes annotated to ANX loci showed that they are differentially expressed in various tissues of the nervous system, cardiovascular system, gastrointestinal system, and genitourinary system (Figure 4). In contrast, enrichment analysis of the genes annotated to the loci shared between ANX and the psychiatric disorders showed differential tissue expression in parts of the brain and minor salivary glands (Supplement 1: Figure S5).

Figure 4.

Figure 4.

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FUMA functional annotation – Tissue enrichment for differential gene expression (DEG) in 54 GTEx tissue types of genes mapped to genomic loci associated with anxiety symptoms (condFDR < 0.05; Supplement 3)

Discussion

Here, we showed that ANX is a highly polygenic phenotype with over eight thousand trait-influencing genetic variants, with extensive genetic overlap with other psychiatric disorders beyond their positive genetic correlations. We also demonstrated that the polygenic liabilities for ANX, MD, SCZ, and ADHD predicted a lifetime clinical diagnosis of anxiety disorders in an independent population-based sample. Overlapping trait-influencing variants were the largest for ANX and MD, BIP, and SCZ (70 – 90%), and a relatively smaller overlap between ANX and ADHD (51%). The proportion of variants with concordant effects within the shared component was the highest for ADHD (92%) and lowest for BIP (59%). Local genetic correlations revealed a mixture of effect directions with predominantly positively correlated loci. We identified 139 novel genetic loci associated with ANX, and unique 113 genetic loci shared between ANX and psychiatric disorders and a similar pattern of effect directions, with a predominance of concordant effects. Downstream analyses revealed genes annotated to ANX loci exhibited enrichment for a wider range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci.

We found that ANX is a highly polygenic phenotype, and this is important as common genetic variants contribute to a large portion of the heritability of anxiety disorders (20). We also found that common genetic variants in ANX exhibit low discoverability and hence require very large sample size studies for genome-wide discoveries. This may partly explain the missing heritability – the huge difference between the SNP heritability we found or reported in other GWASs (20, 24), and heritability reported in twins (21). Our comprehensive characterization of the genetic overlap between ANX, and MD, BIP, SCZ, and ADHD disorders using methods agnostic to effect directions such as bivariate MiXeR expand our understanding beyond genetic correlations (24). Genetic correlations alone show strong overlap of ANX with internalizing disorders (24) whereas our results from MiXeR revealed extensive overlap with externalizing disorders as well. Further, the higher genetic correlation between ANX and ADHD despite a smaller genetic overlap compared to that of ANX and BIP is probably accounted for by the similar effect directions for the majority of shared genetic variants with ADHD (25).

By leveraging genetic overlap with psychiatric disorders, we obtained an over 30-fold boost in the identification of genetic loci for ANX. The identification of a larger number of loci and the annotated genes revealed a wide range of biological pathways potentially involved in the pathophysiology of ANX (43). Genes annotated to ANX loci are enriched for pathways of neurodevelopment and synaptic signaling. In particular, enrichment for ‘regulation of glutamatergic synaptic transmission’ and ‘GABAergic synaptic transmission’ support the role of the specific neurotransmitters in the pathophysiology of ANX (52, 53). Conceivably, the large number of shared genetic loci identified between ANX, and psychiatric disorders suggest shared biological pathways related to neurodevelopment. These highlight the role of neurodevelopmental factors in the pathophysiology of anxiety (54). We speculate that such shared biological pathways may underlie the higher prevalence of anxiety among individuals with psychiatric disorders than in the general population.

Notably, the genes annotated to ANX loci showed differential tissue expression in much broader range of tissues including the brain, gastrointestinal, cardiovascular, and endocrine tissues while those of the shared loci were enriched mainly in brain tissue (Figure 4 and Figure S5). While these may be due to comorbidity between anxiety disorders and medical conditions (55, 56), we argue that ANX has stronger somatic component involving various organ systems than other psychiatric disorders. Also, the genetic risk for ANX may influence risk through a more diverse set of tissues than other psychiatric disorders as demonstrated in animal models (57).

Five of the novel loci identified for ANX rs11507683 [EXD3], rs61740399 [YTHDF3], rs1792 [FARP1], rs9988 [NUP85], rs473267 [PGM3; DOPEY1] are in the exonic region of the respective genes. In particular, FARP1 is a protein-coding gene that has been reported to play a role in the regulation of excitatory synapse development and dendritic extensions (58, 59). Several of the identified genetic loci are associated with high deleteriousness as indicated by CADD scores (Supplement 3: Tables S10 – S13). Notably, four of these loci have previously been linked to neurodevelopmental processes rs7858 [NFIB] and rs78642714 [SYT3] (60, 61), and psychiatric disorders such as SCZ rs61687445 (DRD2) and rs11062166 (CACNA1C) (62, 63).

We found five shared loci, rs12023347 [RP4–598G3.1], rs7585730 [AC068490.2], rs55789728 [MAD1L1], rs7155889 [TRAF3], and rs375241 [ASIC2 /ACCN1] common to all four conjFDR analyses. Notably, the locus annotated to ASIC2 is novel for all five psychiatric phenotypes (Supplement 3: Tables S14 – S17). ASIC2 is a coding gene for a sodium ion channel protein located on the cell membranes and has biased expression in parts of the brain such as the hippocampus, amygdala, striatum, cortex, and cerebellum (64). In preclinical studies, ASIC2 was shown to be involved in neurotransmission and plays a role in behavioral changes of drug abuse (65, 66). An intronic variant of ASIC2 was associated with response to treatment with lithium in patients with BIP (67). Furthermore, genetic studies in humans of a closely related gene ASIC1a have previously demonstrated a link between variants, panic disorder (68), and amygdala volume (69). Although ASIC2 is less well studied or lacked adequate power (70) compared to ASIC1a, there is evidence suggesting that ASIC2 works in concert and regulates neuroplasticity in the brain (71, 72). ASICs are considered potential drug targets in various somatic and psychiatric conditions (64, 72, 73).

The tool used to measure ANX i.e., GAD-2 as a dimensional score in the primary GWAS has the advantage of capturing several of the anxiety disorders as well as subsyndromal ANX (74) both of which have important clinical implications. The recognition of concomitant symptoms of anxiety is highlighted by the addition of a diagnostic specifier - anxious distress - in DSM-5 (1, 75, 76). Anxious distress can have a negative impact on the severity and clinical outcome of primary psychiatric disorders (75, 77). Our findings on the shared genetic architecture of ANX and MD, BIP, SCZ, and ADHD provide insight into the underlying molecular pathways and may assist in improving the treatment of not only comorbid anxiety disorders but also for optimizing treatment in co-occurring ANX. The demonstration that polygenic liability for psychiatric disorders predicting anxiety disorders lends further evidence for a shared genetic risk. Our results suggest the genetic risk for anxiety may involve pathophysiology in neurodevelopment and neurotransmission, and the former might be common to anxiety and other psychiatric disorders.

We acknowledge that the primary phenotype ANX GWAS (24) was a dimensional trait defined based on self-reported GAD-2 and referred to symptoms experienced in the preceding two weeks rather than a lifetime clinical diagnosis of specific anxiety disorders. The vast majority of the study participants were males which may limit the identification of female-specific genetic loci of ANX. Nevertheless, we replicated the genetic loci identified for ANX in an independent GWAS of a predominantly female study population (20). Since individuals with a history of diagnosis of depression were not excluded from the ANX GWAS, the genetic overlap between ANX and MD could partly be due to comorbidity. We applied our analyses to GWAS data from populations of European ancestry and therefore, generalizations cannot be made to other ancestries. As multi-ancestry GWAS data become available, our methods can be applied to improve genetic discoveries for anxiety.

In conclusion, our investigation of the genetic architecture of symptoms of anxiety revealed a high polygenicity and low discoverability which partly explains the missing heritability of anxiety. There was also a large genetic overlap between anxiety and psychiatric disorders, which enabled the identification of 139 novel anxiety loci and 113 shared loci. The shared genetic architecture may underlie the high burden of anxiety symptoms in individuals with other psychiatric disorders. The genes annotated to anxiety loci implicated a broader range of biological pathways as well as differential tissue expression in more diverse tissues than the shared loci. Our findings advance our understanding of the pathophysiology of anxiety that occurs alone or concomitantly with other psychiatric disorders and may help in the identification of potential drug targets. Further research is needed to investigate the genetic underpinnings of specific types of anxiety disorders.

Supplementary Material

Supplement 1
media-1.docx (1.9MB, docx)
Supplement 2
media-2.xlsx (25KB, xlsx)
Supplement 3
media-3.xlsx (124KB, xlsx)
Supplement 4
media-4.xlsx (33.8KB, xlsx)

Acknowledgments

We are grateful to all the families who participated in MoBa. MoBa is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We would like to thank the research participants and employees of 23andMe, the Psychiatric Genomic Consortium (Schizophrenia, Bipolar Disorder, and Depression Working Groups), the UK Biobank, the Million Veteran Program, and MoBa for making this work possible. This work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) (tsd-drift@usit.uio.no).

Funding

We gratefully acknowledge support from the American National Institutes of Health (NS057198, EB00790), the Research Council of Norway (RCN) (296030, 324252, 324499, 300309, 273291, 223273), the South-East Norway Regional Health Authority (2022–073), and KG Jebsen Stiftelsen (SKGJ-MED-021). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 847776, 964874, and 801133 under the Marie Skłodowska-Curie grant.

List of acronyms

ADHD

Attention Deficit Hyperactivity Disorder

ANX

Anxiety symptoms

BIP

Bipolar Disorder

CADD

Combined Annotation Dependent Depletion

CondFDR

Conditional FDR

ConjFDR

Conjunctional FDR

dbGAP

Database of Genotype and Phenotype

FDR

False Discovery Rate

FUMA

Functional Mapping and Annotations

GO

Gene Ontology

GAD-2

Generalized Anxiety Disorder 2-item scale

GTEx

Genotype-Tissue Expression

GWAS

Genome-Wide Association Study

LD

Linkage Disequilibrium

LDSC

Linkage Disequilibrium Score Regression

MiXeR

bivariate causal mixture model

MD

Major Depression

MHC

major histocompatibility complex

MVP

Million Veterans Program

MoBa

Mor og Barn (Norwegian Mothers and Children Cohort

PGC

Psychiatric Genomics Consortium

Q-Q

Quantile-Quantile

SCZ

Schizophrenia

SNP

Single Nucleotide Polymorphism

Footnotes

Competing interests

Ole A. Andreassen is a consultant for cortechs.ai and has received speaker’s honoraria from Lundbeck, Janssen, and Sunovion. Srdjan Djurovic has received speaker’s honoraria from Lundbeck. Anders M. Dale is a founder of and holds equity in CorTechs Labs and serves on its scientific advisory board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc., and receives research funding from General Electric Healthcare and Medtronic, Inc. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. The other authors have no conflicts of interest to declare.

Ethics approval and consent to participate

The MoBa cohort has initially been approved by the Norwegian Data Protection Agency and The Regional Committees for Medical and Health Research Ethics (REK) in Norway and is currently regulated by the Norwegian Health Registry Act. Previously published datasets have been approved by their respective ethical approval committees. This research was conducted according to the Helsinki Declaration.

Availability of data and materials

GWAS of anxiety can be accessed at https://www.ncbi.nlm.nih.gov/gap/, dbGaP Study Accession phs001672, All PGC data are available at https://www.med.unc.edu/pgc/download-results/, Full GWAS summary statistics for the 23andMe DEP dataset will be available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Interested investigators should email dataset-request@23andme.com and reference this paper for more information. Access to the MoBa data can be obtained by applying to the Norwegian Institute of Public Health (NIPH). Restrictions apply regarding the availability of the MoBa data, and therefore, it is not publicly available. Access can be given after approval provided that the applications are consistent with the consent provided by participants. Detailed information on the application can be found on the NIPH website at https://www.fhi.no/en/studies/moba/. The cond/conjFDR code is available online at https://github.com/precimed/pleiofdr.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1
media-1.docx (1.9MB, docx)
Supplement 2
media-2.xlsx (25KB, xlsx)
Supplement 3
media-3.xlsx (124KB, xlsx)
Supplement 4
media-4.xlsx (33.8KB, xlsx)

Data Availability Statement

GWAS of anxiety can be accessed at https://www.ncbi.nlm.nih.gov/gap/, dbGaP Study Accession phs001672, All PGC data are available at https://www.med.unc.edu/pgc/download-results/, Full GWAS summary statistics for the 23andMe DEP dataset will be available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Interested investigators should email dataset-request@23andme.com and reference this paper for more information. Access to the MoBa data can be obtained by applying to the Norwegian Institute of Public Health (NIPH). Restrictions apply regarding the availability of the MoBa data, and therefore, it is not publicly available. Access can be given after approval provided that the applications are consistent with the consent provided by participants. Detailed information on the application can be found on the NIPH website at https://www.fhi.no/en/studies/moba/. The cond/conjFDR code is available online at https://github.com/precimed/pleiofdr.

Articles from medRxiv are provided here courtesy of Cold Spring Harbor Laboratory Preprints

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