Overlapping common genetic architecture between major depressive disorders and anxiety and stress-related disorders

Polygenic overlap between subjective well-being and psychiatric disorders and cross-ancestry validation

Subjective well-being (SWB) is important for understanding human behaviour and health. Although the connection between SWB and psychiatric disorders has been studied, common genetic mechanisms remain unclear. This study aimed to explore the genetic relationship between SWB and psychiatric disorders. Bivariate causal mixture modelling (MiXeR), polygenic risk score (PRS) and Mendelian randomization (MR) analyses showed substantial polygenic overlap and associations between SWB and the psychiatric disorders. Subsequent replication studies in East Asian populations confirmed the polygenic overlap between schizophrenia and SWB. The conditional and conjunctional false discovery rate analyses identified additional or shared genetic loci associated with SWB or psychiatric disorders. Functional annotation revealed enrichment of specific brain tissues and genes associated with SWB. The identified genetic loci showed cross-ancestry transferability between the European and Korean populations. Our findings provide valuable insights into the common genetic mechanisms underlying SWB and psychiatric disorders.

Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders

AIMS

Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

METHODS

We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders.

RESULTS

Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k-11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MDn = 47 , BIPn = 33 , SCZn = 71 , ADHDn = 20 , and ASDn = 5 . Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci.

CONCLUSIONS

Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

Sex-specific gene expression differences in the prefrontal cortex of major depressive disorder individuals

Major depressive disorder (MDD) is a leading global cause of disability, being more prevalent in females, possibly due to molecular and neuronal pathway differences between females and males. However, the connection between transcriptional changes and MDD remains unclear. We identified transcriptionally altered genes (TAGs) in MDD through gene and transcript expression analyses, focusing on sex-specific differences. Analyzing 263 brain samples from both sexes, we conducted differential gene expression, differential transcript expression, and differential transcript usage analyses, revealing 1169 unique TAGs, primarily in the prefrontal areas, with nearly half exhibiting transcript-level alterations. Females showed notable RNA splicing and export process disruptions in the orbitofrontal cortex, alongside altered DDX39B gene expression in five of the six brain regions in both sexes. Our findings suggest that disruptions in RNA processing pathways may play a vital role in MDD.

PDE4B Missense Variant Increases Susceptibility to Post-traumatic Stress Disorder-Relevant Phenotypes in Mice

Large-scale genome-wide association studies (GWASs) have associated intronic variants in PDE4B, encoding cAMP-specific phosphodiesterase-4B (PDE4B), with increased risk for post-traumatic stress disorder (PTSD), as well as schizophrenia and substance use disorders that are often comorbid with it. However, the pathophysiological mechanisms of genetic risk involving PDE4B are poorly understood. To examine the effects of PDE4B variation on phenotypes with translational relevance to psychiatric disorders, we focused on PDE4B missense variant M220T, which is present in the human genome as rare coding variant rs775201287. When expressed in HEK-293 cells, PDE4B1-M220T exhibited an attenuated response to a forskolin-elicited increase in the intracellular cAMP concentration. In behavioral tests, homozygous Pde4b M220T male mice with a C57BL/6JJcl background exhibited increased reactivity to novel environments, startle hyperreactivity, prepulse inhibition deficits, altered cued fear conditioning, and enhanced spatial memory, accompanied by an increase in cAMP signaling pathway-regulated expression of BDNF in the hippocampus. In response to a traumatic event (10 tone-shock pairings), neuronal activity was decreased in the cortex but enhanced in the amygdala and hippocampus of Pde4b M220T mice. At 24 h post-trauma, Pde4b M220T mice exhibited increased startle hyperreactivity and decreased plasma corticosterone levels, similar to phenotypes exhibited by PTSD patients. Trauma-exposed Pde4b M220T mice also exhibited a slower decay in freezing at 15 and 30 d post-trauma, demonstrating enhanced persistence of traumatic memories, similar to that exhibited by PTSD patients. These findings provide substantive mouse model evidence linking PDE4B variation to PTSD-relevant phenotypes and thus highlight how genetic variation of PDE4B may contribute to PTSD risk.

cAMP-PKA signaling pathway and anxiety: Where do we go next?

Cyclic adenosine monophosphate (cAMP) is an intracellular second messenger that is derived from the conversion of adenosine triphosphate catalysed by adenylyl cyclase (AC). Protein kinase A (PKA), the main effector of cAMP, is a dimeric protein kinase consisting of two catalytic subunits and two regulatory subunits. When cAMP binds to the regulatory subunits of PKA, it leads to the dissociation and activation of PKA, which allows the catalytic subunit of PKA to phosphorylate target proteins, thereby regulating various physiological functions and metabolic processes in cellular function. Recent researches also implicate the involvement of cAMP-PKA signaling in the pathologenesis of anxiety disorder. However, there are still debates on the prevention and treatment of anxiety disorders from this signaling pathway. To review the function of cAMP-PKA signaling in anxiety disorder, we searched the publications with the keywords including "cAMP", "PKA" and "Anxiety" from Pubmed, Embase, Web of Science and CNKI databases. The results showed that the number of publications on cAMP-PKA pathway in anxiety disorder tended to increase. Bioinformatics results displayed a close association between the cAMP-PKA pathway and the occurrence of anxiety. Mechanistically, cAMP-PKA signaling could influence brain-derived neurotrophic factor and neuropeptide Y and participate in the regulation of anxiety. cAMP-PKA signaling could also oppose the dysfunctions of gamma-aminobutyric acid (GABA), intestinal flora, hypothalamic-pituitary-adrenal axis, neuroinflammation, and signaling proteins (MAPK and AMPK) in anxiety. In addition, chemical agents with the ability to activate cAMP-PKA signaling demonstrated therapy potential against anxiety disorders. This review emphasizes the central roles of cAMP-PKA signaling in anxiety and the targets of the cAMP-PKA pathway would be potential candidates for treatment of anxiety. Nevertheless, more laboratory investigations to improve the therapeutic effect and reduce the adverse effect, and continuous clinical research will warrant the drug development.

Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements

Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.

Shared genetic architecture highlights the bidirectional association between major depressive disorder and fracture risk

Background

There is limited evidence suggesting that osteoporosis might exacerbate depressive symptoms, while more studies demonstrate that depression negatively affects bone density and increases fracture risk.

Aims

To explore the relationship between major depressive disorder (MDD) and fracture risk.

Methods

We conducted a nested case-control analysis (32 670 patients with fracture and 397 017 individuals without fracture) and a matched cohort analysis (16 496 patients with MDD and 435 492 individuals without MDD) in the same prospective UK Biobank data set. Further, we investigated the shared genetic architecture between MDD and fracture with linkage disequilibrium score regression and the MiXeR statistical tools. We used the conditional/conjunctional false discovery rate approach to identify the specific shared loci. We calculated the weighted genetic risk score for individuals in the UK Biobank and logistic regression was used to confirm the association observed in the prospective study.

Results

We found that MDD was associated with a 14% increase in fracture risk (hazard ratio (HR) 1.14, 95% CI 1.14 to 1.15, p<0.001) in the nested case-control analysis, while fracture was associated with a 72% increase in MDD risk (HR 1.72, 95% CI 1.64 to 1.79, p<0.001) in the matched cohort analysis, suggesting a longitudinal and bidirectional relationship. Further, genetic summary data suggested a genetic overlap between MDD and fracture. Specifically, we identified four shared genomic loci, with the top signal (rs7554101) near SGIP1. The protein encoded by SGIP1 is involved in cannabinoid receptor type 1 signalling. We found that genetically predicted MDD was associated with a higher risk of fracture and vice versa. In addition, we found that the higher expression level of SGIP1 in the spinal cord and muscle was associated with an increased risk of fracture and MDD.

Conclusions

The genetic pleiotropy between MDD and fracture highlights the bidirectional association observed in the epidemiological analysis. The shared genetic components (such as SGIP1) between the diseases suggest that modulating the endocannabinoid system could be a potential therapeutic strategy for both MDD and bone loss.

Automated anxiety detection using probabilistic binary pattern with ECG signals

BACKGROUND AND AIM

Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals.

MATERIALS AND METHODS

We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm.

RESULTS

Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction.

CONCLUSIONS

The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people

BACKGROUND

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

METHODS

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

RESULTS

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity.

CONCLUSIONS

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Beyond monoamines: I. Novel targets and radiotracers for Positron emission tomography imaging in psychiatric disorders

With the emergence of positron emission tomography (PET) in the late 1970s, psychiatry had access to a tool capable of non-invasive assessment of human brain function. Early applications in psychiatry focused on identifying characteristic brain blood flow and metabolic derangements using radiotracers such as [¹⁵ O]H₂ O and [¹⁸ F]FDG. Despite the success of these techniques, it became apparent that more specific probes were needed to understand the neurochemical bases of psychiatric disorders. The first neurochemical PET imaging probes targeted sites of action of neuroleptic (dopamine D₂ receptors) and psychoactive (serotonin receptors) drugs. Based on the centrality of monoamine dysfunction in psychiatric disorders and the measured success of monoamine-enhancing drugs in treating them, the next 30 years witnessed the development of an armamentarium of PET radiopharmaceuticals and imaging methodologies for studying monoamines. Continued development of monoamine-enhancing drugs over this time however was less successful, realizing only modest gains in efficacy and tolerability. As patent protection for many widely prescribed and profitable psychiatric drugs lapsed, drug development pipelines shifted away from monoamines in search of novel targets with the promises of improved efficacy, or abandoned altogether. Over this period, PET radiopharmaceutical development activities closely paralleled drug development priorities resulting in the development of new PET imaging agents for non-monoamine targets. Part one of this review will briefly survey novel PET imaging targets with relevance to the field of psychiatry, which include the metabotropic glutamate receptor type 5 (mGluR5), purinergic P₂ X₇ receptor, type 1 cannabinoid receptor (CB₁ ), phosphodiesterase 10A (PDE10A), and describe radiotracers developed for these and other targets that have matured to human subject investigations. Current limitations of the targets and techniques will also be discussed.

Rehman A, Morris DW. Major Depressive Disorder: Existing Hypotheses about Pathophysiological Mechanisms and New Genetic Findings

Major depressive disorder (MDD) is a common mental disorder generally characterized by symptoms associated with mood, pleasure and effectiveness in daily life activities. MDD is ranked as a major contributor to worldwide disability. The complex pathogenesis of MDD is not yet understood, and this is a major cause of failure to develop new therapies and MDD recurrence. Here we summarize the literature on existing hypotheses about the pathophysiological mechanisms of MDD. We describe the different approaches undertaken to understand the molecular mechanism of MDD using genetic data. Hundreds of loci have now been identified by large genome-wide association studies (GWAS). We describe these studies and how they have provided information on the biological processes, cell types, tissues and druggable targets that are enriched for MDD risk genes. We detail our understanding of the genetic correlations and causal relationships between MDD and many psychiatric and non-psychiatric disorders and traits. We highlight the challenges associated with genetic studies, including the complexity of MDD genetics in diverse populations and the need for a study of rare variants and new studies of gene-environment interactions.

A new paradigm shift in antidepressant therapy? From dual-action to multitarget-directed ligands

Major Depressive Disorder is a chronic, recurring, and potentially fatal disease affecting up to 20% of the global population. Since the monoamine hypothesis was proposed more than 60 years ago, only a few relevant advances have been achieved, with very little disease course changing, from a pharmacological perspective. Moreover, since negative efficacy studies with novel molecules are frequent, many pharmaceutical companies have put new studies on hold. Fortunately, relevant clinical studies are currently being performed, and extensive striving is being developed by universities, research centers, and other public and private institutions. Depression is no longer considered a simple disease but a multifactorial one. New research fields are emerging in what could be a paradigm shift: the multitarget approach beyond monoamines. In this review, we summarize the present and the past of antidepressant drug discovery, with the aim to shed some light on the current state of the art in clinical and preclinical advances to face this increasingly devastating disease.