Exploring the genetic heterogeneity in major depression across diagnostic criteria

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

Exploring Gender Differences in the Relationship Between Thyroid Function and Aggressive and Impulsive Behaviors in Patients with Major Depressive Disorder

Purpose

Major depressive disorder (MDD) is a widespread mental health condition with significant global impact. Exploring the gender differences in the interplay between thyroid function, aggression, and impulsivity offers valuable insights into its multifactorial nature and management.

Patients and Methods

A cross-sectional study was conducted at Anhui Mental Health Center and included 113 MDD patients (56 males, 57 females) and 102 healthy controls (45 males and 57 females). Thyroid function was assessed through serum thyroid hormone levels, and impulsivity and aggression were measured using the Buss-Perry Aggression Questionnaire (BPAQ) and Barratt's Impulsiveness Scale version 11 (BIS). Potential confounding factors such as age, education, and Hamilton Depression Rating Scale (HAMD) scores were adjusted for.

Results

Both male and female MDD patients showed significant changes in serum thyrotropin levels (F(1,213)=10.996, p=0.001), impulsivity (F(1,213)=151.521, p<0.05), and aggression (F(1,213)=44.411, p<0.05) compared to healthy controls. MANCOVA revealed significant differences in attentional impulsivity, motor impulsivity, physical aggression, anger, hostility, and self-directed aggression (all p<0.05). Moreover, significant differences between genders were observed in these areas (all p<0.05). In males, TSH levels were inversely related to several behavioral dimensions (all p<0.05), while no such correlation was found in females.

Conclusion

This study highlights the role of thyroid function, especially TSH levels, in influencing impulsivity and aggression in male MDD patients, suggesting a gender-specific physiological-behavioral relationship. The findings contribute to the development of gender-specific treatment strategies. In the future, longitudinal studies with larger sample sizes should be conducted to explore molecular mechanisms for more personalized treatments.

Major depressive disorder on a neuromorphic continuum

The heterogeneity of major depressive disorder (MDD) has hindered clinical translation and neuromarker identification. Biotyping facilitates solving the problems of heterogeneity, by dissecting MDD patients into discrete subgroups. However, interindividual variations suggest that depression may be conceptualized as a "continuum," rather than as a "category." We use a Bayesian model to decompose structural MRI features of MDD patients from a multisite cross-sectional cohort into three latent disease factors (spatial pattern) and continuum factor compositions (individual expression). The disease factors are associated with distinct neurotransmitter receptors/transporters obtained from open PET sources. Increases cortical thickness in sensory and decreases in orbitofrontal cortices (Factor 1) associate with norepinephrine and 5-HT2A density, decreases in the cingulo-opercular network and subcortex (Factor 2) associate with norepinephrine and 5-HTT density, and increases in social and affective brain systems (Factor 3) relate to 5-HTT density. Disease factor patterns can also be used to predict depressive symptom improvement in patients from the longitudinal cohort. Moreover, individual factor expressions in MDD are stable over time in a longitudinal cohort, with differentially expressed disease controls from a transdiagnostic cohort. Collectively, our data-driven disease factors reveal that patients with MDD organize along continuous dimensions that affect distinct sets of regions.

The genetics of severe depression

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have recently achieved extremely large sample sizes and yielded substantial numbers of genome-wide significant loci. Because of the approach to ascertainment and assessment in many of these studies, some of these loci appear to be associated with dysphoria rather than with MDD, potentially decreasing the clinical relevance of the findings. An alternative approach to MDD GWAS is to focus on the most severe forms of MDD, with the hope that this will enrich for loci of larger effect, rendering their identification plausible, and providing potentially more clinically actionable findings. Here we review the genetics of severe depression by using clinical markers of severity including: age of onset, recurrence, degree of impairment, and treatment with ECT. There is evidence for increased family-based and Single Nucleotide Polymorphism (SNP)-based estimates of heritability in recurrent and early-onset illness as well as severe functional impariment. GWAS have been performed looking at severe forms of MDD and a few genome-wide loci have been identified. Several whole exome sequencing studies have also been performed, identifying associated rare variants. Although these findings have not yet been rigorously replicated, the elevated heritability seen in severe MDD phenotypes suggests the value of pursuing additional genome-wide interrogation of samples from this population. The challenge now is generating a cohort of adequate size with consistent phenotyping that will allow for careful and robust classifications and distinctions to be made. We are currently pursuing such a strategy in our 50-site worldwide Gen-ECT-ics consortium.

Association between the functional brain network and antidepressant responsiveness in patients with major depressive disorders: a resting-state EEG study

BACKGROUND

Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.

METHODS

The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs). The source-level rsFBN was constructed using 31 sources as nodes and beta-band power envelope correlation (PEC) as edges. The degree centrality (DC) and clustering coefficients (CCs) were calculated for various sparsity levels. Network-based statistic and one-way analysis of variance models were employed for comparing PECs and network indices, respectively. The multiple comparisons were controlled by the false discovery rate.

RESULTS

Patients with trMDD were characterized by the altered dorsal attention network and salience network. Specifically, they exhibited hypoconnection between eye fields and right parietal regions (p = 0.0088), decreased DC in the right supramarginal gyrus (q = 0.0057), and decreased CC in the reward circuit (qs < 0.05). On the other hand, both MDD groups shared increased DC but decreased CC in the posterior cingulate cortex.

CONCLUSIONS

We confirmed that network topology was more severely deteriorated in patients with trMDD, particularly for the attention-regulatory networks. Our findings suggested that the altered rsFBN topologies could serve as potential pathologically interpretable biomarkers for predicting antidepressant responsiveness.

Multi-Scale Spatial-Temporal Attention Networks for Functional Connectome Classification

Many neuropsychiatric disorders are considered to be associated with abnormalities in the functional connectivity networks of the brain. The research on the classification of functional connectivity can therefore provide new perspectives for understanding the pathology of disorders and contribute to early diagnosis and treatment. Functional connectivity exhibits a nature of dynamically changing over time, however, the majority of existing methods are unable to collectively reveal the spatial topology and time-varying characteristics. Furthermore, despite the efforts of limited spatial-temporal studies to capture rich information across different spatial scales, they have not delved into the temporal characteristics among different scales. To address above issues, we propose a novel Multi-Scale Spatial-Temporal Attention Networks (MSSTAN) to exploit the multi-scale spatial-temporal information provided by functional connectome for classification. To fully extract spatial features of brain regions, we propose a Topology Enhanced Graph Transformer module to guide the attention calculations in the learning of spatial features by incorporating topology priors. A Multi-Scale Pooling Strategy is introduced to obtain representations of brain connectome at various scales. Considering the temporal dynamic characteristics between dynamic functional connectome, we employ Locality Sensitive Hashing attention to further capture long-term dependencies in time dynamics across multiple scales and reduce the computational complexity of the original attention mechanism. Experiments on three brain fMRI datasets of MDD and ASD demonstrate the superiority of our proposed approach. In addition, benefiting from the attention mechanism in Transformer, our results are interpretable, which can contribute to the discovery of biomarkers. The code is available at https://github.com/LIST-KONG/MSSTAN.

An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection

Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios can facilitate the timely detection of MDD. However, existing depression detection methods either consider only a single modality or do not fully account for the differences and similarities between modalities in multimodal approaches, potentially overlooking the latent information inherent in various modal data. To address these challenges, we propose EMO-GCN, a multimodal depression detection method based on an adaptive multi-graph neural network. By employing graph-based methods to model data from various modalities and extracting features from them, the potential correlations between modalities are uncovered. The model's performance on the MODMA dataset is outstanding, achieving an accuracy (ACC) of 96.30%. Ablation studies further confirm the effectiveness of the model's individual components.The experimental results of EMO-GCN demonstrate the application prospects of graph-based multimodal analysis in the field of mental health, offering new perspectives for future research.

Treatment-resistant depression: molecular mechanisms and management

Due to the heterogeneous nature of depression, the underlying etiological mechanisms greatly differ among individuals, and there are no known subtype-specific biomarkers to serve as precise targets for therapeutic efficacy. The extensive research efforts over the past decades have not yielded much success, and the currently used first-line conventional antidepressants are still ineffective for close to 66% of patients. Most clinicians use trial-and-error treatment approaches, which seem beneficial to only a fraction of patients, with some eventually developing treatment resistance. Here, we review evidence from both preclinical and clinical studies on the pathogenesis of depression and antidepressant treatment response. We also discuss the efficacy of the currently used pharmacological and non-pharmacological approaches, as well as the novel emerging therapies. The review reveals that the underlying mechanisms in the pathogenesis of depression and antidepressant response, are not specific, but rather involve an interplay between various neurotransmitter systems, inflammatory mediators, stress, HPA axis dysregulation, genetics, and other psycho-neurophysiological factors. None of the current depression hypotheses sufficiently accounts for the interactional mechanisms involved in both its etiology and treatment response, which could partly explain the limited success in discovering efficacious antidepressant treatment. Effective management of treatment-resistant depression (TRD) requires targeting several interactional mechanisms, using subtype-specific and/or personalized therapeutic modalities, which could, for example, include multi-target pharmacotherapies in augmentation with psychotherapy and/or other non-pharmacological approaches. Future research guided by interaction mechanisms hypotheses could provide more insights into potential etiologies of TRD, precision biomarker targets, and efficacious therapeutic modalities.

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up

BACKGROUND

The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.

METHODS

Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).

RESULTS

Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).

CONCLUSIONS

This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

Investigating the role of the relaxin-3/RXFP3 system in neuropsychiatric disorders and metabolic phenotypes: A candidate gene approach

The relaxin-3/RXFP3 system has been implicated in the modulation of depressive- and anxiety-like behaviour in the animal literature; however, there is a lack of human studies investigating this signalling system. We seek to bridge this gap by leveraging the large UK Biobank study to retrospectively assess genetic risk variants linked with this neuropeptidergic system. Specifically, we conducted a candidate gene study in the UK Biobank to test for potential associations between a set of functional, candidate single nucleotide polymorphisms (SNPs) pertinent to relaxin-3 signalling, determined using in silico tools, and several outcomes, including depression, atypical depression, anxiety and metabolic syndrome. For each outcome, we used several rigorously defined phenotypes, culminating in subsample sizes ranging from 85,881 to 386,769 participants. Across all outcomes, there were no associations between any candidate SNP and any outcome phenotype, following corrections for multiple testing burden. Regression models comprising several SNPs per relevant candidate gene as exploratory variables further exhibited no prediction of outcome. Our findings corroborate conclusions from previous literature about the limitations of candidate gene approaches, even when based on firm biological hypotheses, in the domain of genetic research for neuropsychiatric disorders.

[Movement as a Neuromodulator: How Physical Activity Influences the Physiology of Adolescent Depression]

Movement as a Neuromodulator: How Physical Activity Influences the Physiology of Adolescent Depression Abstract: In the context of adolescent depression, physical activity is becoming increasingly recognized for its positive effects on neuropathology. Current scientific findings indicate that physical training affects the biological effects of depression during adolescence. Yet the pathophysiology of adolescent depression is not yet fully understood. Besides psychosocial and genetic influences, various neurobiological factors are being discussed. One explanation model describes a dysfunction of the hypothalamus-pituitary-adrenal axis (HPA axis) with a sustained elevation in cortisol concentration. Recent studies highlight neuroimmunological processes and a reduced concentration of growth factors as causative factors. These changes appear to lead to a dysregulation of the excitation and inhibition balance of the cerebral cortex as well as to cerebral morphological alterations. Regular physical training can potentially counteract the dysregulation of the HPA axis and normalize cortisol levels. The release of proinflammatory cytokines is inhibited, and the expression of growth factors involved in adult neurogenesis is stimulated. One should ensure the synergistic interaction of biological and psychosocial factors when designing the exercise schedule (endurance or strength training, group or individual sports, frequency, duration, and intensity). Addressing these open questions is essential when integrating physical activity into the guidelines for treating depressive disorders in children and adolescents.

The interplay of family history of depression and early trauma: associations with lifetime and current depression in the German national cohort (NAKO)

Introduction

Family history of depression and childhood maltreatment are established risk factors for depression. However, how these factors are interrelated and jointly influence depression risk is not well understood. The present study investigated (i) if childhood maltreatment is associated with a family history of depression (ii) if family history and childhood maltreatment are associated with increased lifetime and current depression, and whether both factors interact beyond their main effects, and (iii) if family history affects lifetime and current depression via childhood maltreatment.

Methods

Analyses were based on a subgroup of the first 100,000 participants of the German National Cohort (NAKO), with complete information (58,703 participants, mean age = 51.2 years, 53% female). Parental family history of depression was assessed via self-report, childhood maltreatment with the Childhood Trauma Screener (CTS), lifetime depression with self-reported physician's diagnosis and the Mini-International Neuropsychiatric Interview (MINI), and current depressive symptoms with the depression scale of the Patient Health Questionnaire (PHQ-9). Generalized linear models were used to test main and interaction effects. Mediation was tested using causal mediation analyses.

Results

Higher frequencies of the childhood maltreatment measures were found in subjects reporting a positive family history of depression. Family history and childhood maltreatment were independently associated with increased depression. No statistical interactions of family history and childhood maltreatment were found for the lifetime depression measures. For current depressive symptoms (PHQ-9 sum score), an interaction was found, with stronger associations of childhood maltreatment and depression in subjects with a positive family history. Childhood maltreatment was estimated to mediate 7%-12% of the effect of family history on depression, with higher mediated proportions in subjects whose parents had a depression onset below 40 years. Abuse showed stronger associations with family history and depression, and higher mediated proportions of family history effects on depression than neglect.

Discussion

The present study confirms the association of childhood maltreatment and family history with depression in a large population-based cohort. While analyses provide little evidence for the joint effects of both risk factors on depression beyond their individual effects, results are consistent with family history affecting depression via childhood maltreatment to a small extent.

Structural and functional features of treatment-resistant depression: A systematic review and exploratory coordinate-based meta-analysis of neuroimaging studies

OBJECTIVES

A third of people suffering from major depressive disorder do not experience a significant improvement in their symptoms even after adequate treatment with two different antidepressant medications. This common condition, termed treatment-resistant depression (TRD), severely affects the quality of life of millions of people worldwide, causing long-lasting interpersonal problems and social costs. Given its epidemiological and clinical relevance and the little consensus on whether the neurobiological underpinnings of TRD differ from treatment-sensitive depression (TSD), we sought to highlight the convergent morphometric and functional neuroimaging correlates of TRD.

METHODS

We systematically reviewed the published literature on structural and resting-state functional neuroimaging of TRD compared to TSD and healthy controls (HC) and performed exploratory coordinate-based meta-analyses (CBMA) of significant results separately for each modality and multimodally ("all-effects"). CBMAs were also performed for each direction and combining both directions of group contrasts.

RESULTS

Out of the initial 1929 studies, only eight involving 555 participants (189 patients with TRD, 156 with TSD, and 210 HC) were included. In all-effects CBMA, precentral/superior frontal gyrus showed a significant difference between TRD and HC. Functional and structural imaging meta-analyses did not yield statistically significant results. A marginally significant cluster of altered intrinsic activity was found between TRD and HC in the cerebellum/pons.

CONCLUSIONS

Frontal, cerebellar, and brainstem functions can be involved in the pathophysiology of TRD. However, the design and heterogeneity of the (scarce) published literature hinder the generalizability of the findings.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Air pollution, genetic factors and the risk of depression

Both genetics and ambient air pollutants contribute to depression, but the degree to which genetic susceptibility modifies the effect of air pollution on depression remains unknown. We aimed to investigate the effect of the modification of genetic susceptibility on depression. Notably, 490,780 participants who were free of depression at baseline in the UK Biobank study were recruited from 2006 to 2010. A land use regression (LUR) model was performed to estimate the concentrations of particulate matter with diameters ranging from ≤2.5-≤10 μm (PM_2.5, PM_2.5-10 and PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x). The International Classification of Diseases 10th Revision (ICD-10) code was used to identify depression cases. Cox proportional hazard models adjusted for covariates were used to investigate the association between ambient air pollutants and depression. Moreover, the polygenic risk score (PRS) was calculated to evaluate cumulative genetic effects, and additive interaction models were established to explore whether genetic susceptibility modified the effects of air pollutants on depression. PM_2.5, PM₁₀, NO₂ and NO_x exposure were significantly positively associated with the risk of depression, and the hazard ratios and 95 % confidence intervals for a 10-μg/m³ increase in PM_2.5, PM₁₀, NO₂ and NO_x concentrations were 2.12 (1.82, 2.47), 1.12 (1.03, 1.23), 1.07 (1.05, 1.10) and 1.04 (1.03, 1.05), respectively. Air pollutants and genetic variants exerted significant additive effects on the risk of depression (relative excess risk due to the interaction [RERI]: 0.15 for PM_2.5, 0.12 for PM₁₀, 0.10 for NO₂, and 0.12 for NO_x; attributable proportion due to the interaction [AP]: 0.12 for PM_2.5, 0.10 for PM₁₀, 0.08 for NO₂, and 0.09 for NO_x). Air pollution exposure was significantly associated with the risk of depression, and participants with a higher genetic risk were more likely to develop depression when exposed to high levels of air pollution.

Genetic similarities and differences among distinct definitions of depression

Depression is a common and complex psychiatric illness with considerable heritability. Genome-wide association studies (GWAS) have been conducted among different definitions of depression based on different diagnostic criteria. However, the heritability explained by different depression GWAS and the identified loci varied widely. To understand the genetic architectures of different definitions of depression, we conducted a series of genetic analyses including linkage disequilibrium score regression (LDSC), Mendelian randomization, and polygenic overlap quantification and identification. Different definitions of depression and other common psychiatric traits were included in this analysis. We found that although genetic correlations between different definitions of depression were relatively high, they showed substantially different genetic correlation and causality with other psychiatric traits. Using bivariate causal mixture mode (MiXeR) and conjunctional false discovery rate (conjFDR) approach, we observed both shared and unique risk loci across different definitions of depression. Further functional mapping with expression quantitative trait loci (eQTL) information from multiple brain tissues and single cell types indicated distinct genes underlying different definitions of depression, and pathways associated with synapses were significantly enriched in the illness. Our study showed that the genetic architectures of different definitions of depression were distinct and genetic studies of depression should be conducted more cautious.

Genome-by-Trauma Exposure Interactions in Adults With Depression in the UK Biobank

Importance

Self-reported trauma exposure has consistently been found to be a risk factor for major depressive disorder (MDD), and several studies have reported interactions with genetic liability. To date, most studies have examined gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance.

Objective

To reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance.

Design, Setting, and Participants

The UK Biobank study was conducted from April 2007 to May 1, 2016 (follow-up mental health questionnaire). The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021.

Exposures

Trauma and genome-by-trauma exposure interactions.

Main Outcomes and Measures

Measures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism.

Results

Analyses were conducted on 148 129 participants (mean [SD] age, 56 [7] years) of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure (catastrophic, adult, childhood, and full trauma) accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]).

Conclusions and Relevance

This cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.