Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

The United Kingdom Eating Disorders Genetics Initiative

OBJECTIVE

The United Kingdom Eating Disorders Genetics Initiative (EDGI UK), part of the National Institute for Health and Care Research (NIHR) Mental Health BioResource, aims to deepen our understanding of the environmental and genetic etiology of eating disorders. EDGI UK launched in February 2020 and is partnered with the UK eating disorders charity, Beat. Multiple EDGI branches exist worldwide. This article serves the dual function of providing an in-depth description of our study protocol and of describing our initial sample including demographics, diagnoses, and physical and psychiatric comorbidities.

METHOD

EDGI UK recruits via media and clinical services. Anyone living in England, at least 16 years old, with a lifetime probable or clinical eating disorder is eligible to sign up online: edgiuk.org. Participants complete online questionnaires, donate a saliva sample for genetic analysis, and consent to medical record linkage and recontact for future studies.

RESULTS

As of September 2022, EDGI UK recruited 7435 survey participants: 98% female, 93.1% white, 97.8% cisgender, 65.9% heterosexual, and 52.1% have a university degree. Over half (57.8%) of these participants have returned their saliva DNA kit. The most common diagnoses are anorexia nervosa (48.3%), purging disorder (37.8%), bulimia nervosa (37.5%), binge-eating disorder (15.8%), and atypical anorexia nervosa (7.8%).

CONCLUSION

EDGI UK is the largest UK eating disorders study and efforts to increase its diversity are underway. It offers a unique opportunity to accelerate eating disorder research. Researchers and participants with lived experience can collaborate on projects with unparalleled sample size.

PUBLIC SIGNIFICANCE STATEMENT

Eating disorders are debilitating and costly for society but are under-researched due to underfunding. EDGI UK is one of the largest eating disorder studies worldwide with ongoing recruitment. The collected data constitute a resource for secondary analysis. We will combine data from all international EDGI branches and the NIHR BioResource to facilitate research that improves our understanding of eating disorders and their comorbidities.

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS

Background

Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).

Aims

Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis.

Methods

Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses.

Results

While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD.

Conclusions

We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Genetic architecture distinguishes tinnitus from hearing loss

Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.

Genetic examination of the Mood Disorder Questionnaire and its relationship with bipolar disorder

The Mood Disorder Questionnaire (MDQ) is a common screening tool for bipolar disorder that assesses manic symptoms. Its utility for genetic studies of mania or bipolar traits has not been fully examined. We psychometrically compared the MDQ to self-reported bipolar disorder in participants from the United Kingdom National Institute of Health and Care Research Mental Health BioResource. We conducted genome-wide association studies of manic symptom quantitative traits and symptom subgroups, derived from the MDQ items (N = 11,568-19,859). We calculated genetic correlations with bipolar disorder and other psychiatric and behavioral traits. The MDQ screener showed low positive predictive value (0.29) for self-reported bipolar disorder. Neither concurrent nor lifetime manic symptoms were genetically correlated with bipolar disorder. Lifetime manic symptoms had a highest genetic correlation (rg  = 1.0) with posttraumatic stress disorder although this was not confirmed by within-cohort phenotypic correlations (rp  = 0.41). Other significant genetic correlations included attention deficit hyperactivity disorder (rg  = 0.69), insomnia (rg  = 0.55), and major depressive disorder (rg  = 0.42). Our study adds to existing literature questioning the MDQ's validity and suggests it may capture symptoms of general distress or psychopathology, rather than hypomania/mania specifically, in at-risk populations.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

OBJECTIVE

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.

METHODS

This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.

RESULTS

Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.

CONCLUSIONS

This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

General dimensions of human brain morphometry inferred from genome-wide association data

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R²  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (r_g  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

Factors associated with anxiety disorder comorbidity

BACKGROUND

Anxiety and depressive disorders often co-occur and the order of their emergence may be associated with different clinical outcomes. However, minimal research has been conducted on anxiety-anxiety comorbidity. This study examined factors associated with anxiety comorbidity and anxiety-MDD temporal sequence.

METHODS

Online, self-report data were collected from the UK-based GLAD and COPING NBR cohorts (N = 38,775). Logistic regression analyses compared differences in sociodemographic, trauma, and clinical factors between single anxiety, anxiety-anxiety comorbidity, anxiety-MDD (major depressive disorder) comorbidity, and MDD-only. Additionally, anxiety-first and MDD-first anxiety-MDD were compared. Differences in familial risk were assessed in those participants with self-reported family history or genotype data.

RESULTS

Anxiety-anxiety and anxiety-MDD had higher rates of self-reported anxiety or depressive disorder diagnoses, younger age of onset, and higher recurrence than single anxiety. Anxiety-MDD displayed greater clinical severity/complexity than MDD only. Anxiety-anxiety had more severe current anxiety symptoms, less severe current depressive symptoms, and reduced likelihood of self-reporting an anxiety/depressive disorder diagnosis than anxiety-MDD. Anxiety-first anxiety-MDD had a younger age of onset, more severe anxiety symptoms, and less likelihood of self-reporting a diagnosis than MDD-first. Minimal differences in familial risk were found.

LIMITATIONS

Self-report, retrospective measures may introduce recall bias. The familial risk analyses were likely underpowered.

CONCLUSIONS

Anxiety-anxiety comorbidity displayed a similarly severe and complex profile of symptoms as anxiety-MDD but distinct features. For anxiety-MDD, first-onset anxiety had an earlier age of onset and greater severity than MDD-first. Anxiety disorders and comorbidity warrant further investigation and attention in research and practice.

Common and rare variant associations with latent traits underlying depression, bipolar disorder, and schizophrenia

Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants' responses. We calculated participants' scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05-1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case-control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10^-9) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.

Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans

Importance

Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown.

Objective

To identify novel, replicable genomic risk loci for SITB.

Design, Setting, and Participants

This genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022.

Main Outcome and Measures

SITB.

Results

A total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 GWS (P < 5 × 10-8) cross-ancestry risk single-nucleotide variants for SITB concentrated in 7 regions on chromosomes 2, 6, 9, 11, 14, 16, and 18. Top single-nucleotide variants were largely intronic in nature; 5 were independently replicated in ISGC, including rs6557168 in ESR1, rs12808482 in DRD2, rs77641763 in EXD3, rs10671545 in DCC, and rs36006172 in TRAF3. Associations for FBXL19 and AC018880.2 were not replicated. Gene-based analyses implicated 24 additional GWS cross-ancestry risk genes, including FURIN, TSNARE1, and the NCAM1-TTC12-ANKK1-DRD2 gene cluster. Cross-ancestry enrichment analyses revealed significant enrichment for expression in brain and pituitary tissue, synapse and ubiquitination processes, amphetamine addiction, parathyroid hormone synthesis, axon guidance, and dopaminergic pathways. Seven other unique European ancestry-specific GWS loci were identified, 2 of which (POM121L2 and METTL15/LINC02758) were replicated. Two additional GWS ancestry-specific loci were identified within the African ancestry (PET112/GATB) and Hispanic ancestry (intergenic locus on chromosome 4) subsets, both of which were replicated. No GWS loci were identified within the Asian ancestry subset; however, significant enrichment was observed for axon guidance, cyclic adenosine monophosphate signaling, focal adhesion, glutamatergic synapse, and oxytocin signaling pathways across all ancestries. Within the European ancestry subset, genetic correlations (r > 0.75) were observed between the SITB phenotype and a suicide attempt-only phenotype, depression, and posttraumatic stress disorder. Additionally, polygenic risk score analyses revealed that the Million Veteran Program polygenic risk score had nominally significant main effects in 2 independent samples of veterans of European and African ancestry.

Conclusions and Relevance

The findings of this analysis may advance understanding of the molecular genetic basis of SITB and provide evidence for ESR1, DRD2, TRAF3, and DCC as cross-ancestry candidate risk genes. More work is needed to replicate these findings and to determine if and how these genes might impact clinical care.

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.

Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts

BACKGROUND

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.

AIMS

To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.

METHOD

We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.

RESULTS

Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including 'minimally affected', 'inactive restless', active restless', 'focused creative' and 'extensively affected' individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.

CONCLUSIONS

Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Patient characteristics associated with retrospectively self-reported treatment outcomes following psychological therapy for anxiety or depressive disorders - a cohort of GLAD study participants

BACKGROUND

Progress towards stratified care for anxiety and depression will require the identification of new predictors. We collected data on retrospectively self-reported therapeutic outcomes in adults who received psychological therapy in the UK in the past ten years. We aimed to replicate factors associated with traditional treatment outcome measures from the literature.

METHODS

Participants were from the Genetic Links to Anxiety and Depression (GLAD) Study, a UK-based volunteer cohort study. We investigated associations between retrospectively self-reported outcomes following therapy, on a five-point scale (global rating of change; GRC) and a range of sociodemographic, clinical and therapy-related factors, using ordinal logistic regression models (n = 2890).

RESULTS

Four factors were associated with therapy outcomes (adjusted odds ratios, OR). One sociodemographic factor, having university-level education, was associated with favourable outcomes (OR = 1.37, 95%CI: 1.18, 1.59). Two clinical factors, greater number of reported episodes of illness (OR = 0.95, 95%CI: 0.92, 0.97) and higher levels of personality disorder symptoms (OR = 0.89, 95%CI: 0.87, 0.91), were associated with less favourable outcomes. Finally, reported regular use of additional therapeutic activities was associated with favourable outcomes (OR = 1.39, 95%CI: 1.19, 1.63). There were no statistically significant differences between fully adjusted multivariable and unadjusted univariable odds ratios.

CONCLUSION

Therapy outcome data can be collected quickly and inexpensively using retrospectively self-reported measures in large observational cohorts. Retrospectively self-reported therapy outcomes were associated with four factors previously reported in the literature. Similar data collected in larger observational cohorts may enable detection of novel associations with therapy outcomes, to generate new hypotheses, which can be followed up in prospective studies.

Risk factor profiles for depression following childbirth or a chronic disease diagnosis: case-control study

BACKGROUND

Progress towards understanding the aetiology of major depression is compromised by its clinical heterogeneity. The variety of contexts underlying the development of a major depressive episode may contribute to such heterogeneity.

AIMS

To compare risk factor profiles for three subgroups of major depression according to episode context.

METHOD

Using self-report questionnaires and administrative records from the UK Biobank, we characterised three contextual subgroups of major depression: postpartum depression (3581 cases), depression following diagnosis of a chronic disease (409 cases) and a more typical (named heterogeneous) major depression phenotype excluding the two other contexts (34 699 cases). Controls with the same exposure were also defined. We tested each subgroup for association with the polygenic risk scores (PRS) for major depression and with other risk factors previously associated with major depression (bipolar disorder PRS, neuroticism, reported trauma in childhood and adulthood, socioeconomic status, family history of depression, education).

RESULTS

Major depression PRS was associated with all subgroups, but postpartum depression cases had higher PRS than heterogeneous major depression cases (OR = 1.06, 95% CI 1.02-1.10). Relative to heterogeneous depression, postpartum depression was more weakly associated with adulthood trauma and neuroticism. Depression following diagnosis of a chronic disease had weaker association with neuroticism and reported trauma in adulthood and childhood relative to heterogeneous depression.

CONCLUSIONS

The observed differences in risk factor profiles according to the context of a major depressive episode help provide insight into the heterogeneity of depression. Future studies dissecting such heterogeneity could help reveal more refined aetiological insights.

Genetic and early environmental predictors of adulthood self-reports of trauma

BACKGROUND

Retrospective self-reports of childhood trauma are associated with a greater risk of psychopathology in adulthood than prospective measures of trauma. Heritable reporter characteristics are anticipated to account for part of this association, whereby genetic predisposition to certain traits influences both the likelihood of self-reporting trauma and of developing psychopathology. However, previous research has not considered how gene-environment correlation influences these associations.

AIMS

To investigate reporter characteristics associated with retrospective self-reports of childhood trauma and whether these associations are accounted for by gene-environment correlation.

METHOD

In 3963 unrelated individuals from the Twins Early Development Study, we tested whether polygenic scores for 21 psychiatric, cognitive, anthropometric and personality traits were associated with retrospectively self-reported childhood emotional and physical abuse. To assess the presence of gene-environment correlation, we investigated whether these associations remained after controlling for composite scores of environmental adversity across development.

RESULTS

Retrospectively self-reported childhood trauma was associated with polygenic scores for autism spectrum disorder (ASD), body mass index (BMI), post-traumatic stress disorder (PTSD) and risky behaviours. When composite scores of environmental adversity were controlled for, only associations with the polygenic scores for ASD and PTSD remained significant.

CONCLUSIONS

Genetic predisposition to ASD and PTSD may increase liability to experiencing or interpreting events as traumatic. Associations between genetic predisposition for risky behaviour and BMI with self-reported childhood trauma may reflect gene-environment correlation. Studies of the association between retrospectively self-reported childhood trauma and later-life outcomes should consider that genetically influenced reporter characteristics may confound associations, both directly and through gene-environment correlation.

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Editorial: Genome-wide Association Studies of Internalizing Symptoms: A Big Step on a Long Road

Anxiety and depression are collectively the most common mental illnesses, affecting 15% of the world's population in any given year.¹ Together, they account for the greatest global burden of ongoing disability of any disorder, mental or physical.² They frequently emerge early in life as internalizing disorders in childhood or adolescence, and have long-lasting effects on mental wellbeing, acting as risk factors for mental illnesses in adulthood.³ As such, understanding the causes of these disorders is imperative. Internalizing disorders are influenced by multiple environmental and genetic factors, and research from twin studies has indicated that they have a genetic contribution (heritability) of 40% to 50%.⁴ However, implicating specific genetic variants through genome-wide association studies (GWAS) has been challenging, in part due to the need to obtain large sample sizes and the logistical difficulty of doing so. In this issue of the Journal, Jami et al. present an innovative meta-analysis that is a major step toward an understanding of specific variants.⁵.

Feasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells

Genome-wide association studies have identified thousands of significant associations between genetic variants and complex traits. Inferring biological insights from these associations has been challenging. One approach attempted has been to examine the effects of individual variants in cellular models. Here, I demonstrate the feasibility of examining the aggregate effect of many variants on cellular phenotypes. I examine the effects of polygenic scores for cross-psychiatric disorder risk, schizophrenia, body mass index and height on cellular morphology, using 1.5 million induced pluripotent stem cells (iPSC) from 60 European-ancestry donors from the Human iPSC Initiative dataset. I show that measuring multiple cells per donor provides sufficient power for polygenic score analyses, and that cross-psychiatric disorder risk is associated with cell area (p = 0.004). Combined with emerging methods of high-throughput iPSC phenotyping, cellular polygenic scoring is a promising method for understanding potential biological effects of the polygenic component of complex traits.

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND

Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders.

METHODS

We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors.

RESULTS

Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged.

CONCLUSIONS

Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.

Using major depression polygenic risk scores to explore the depressive symptom continuum

BACKGROUND

Major depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression.

METHODS

Factor analysis on symptom-level data from the UK Biobank (N = 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (N = 119 692).

RESULTS

Confirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised β range: 0.057-0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p value range: 2.23 × 10^-3-3.94 × 10^-7).

CONCLUSIONS

An association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

Association Between Genetic Risk for Psychiatric Disorders and the Probability of Living in Urban Settings

IMPORTANCE

Urban residence has been highlighted as an environmental risk factor for schizophrenia and, to a lesser extent, several other psychiatric disorders. However, few studies have explored genetic effects on the choice of residence.

OBJECTIVE

To investigate whether individuals with genetic predisposition to a range of psychiatric disorders have an increased likelihood to live in urban areas.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional retrospective cohort study including genotypes, address history, and geographic distribution of population density in the UK based on census data from 1931-2011 was conducted. Polygenic risk score (PRS) analyses, genome-wide association studies, genetic correlation, and 2-sample mendelian randomization analyses were applied to 385 793 UK Biobank participants with self-reported or general practitioner registration-based address history. The study was conducted from February 2018 to May 2021, and data analysis was performed from April 2018 to May 2021.

MAIN OUTCOMES AND MEASURES

Population density of residence at different ages and movement during the life span between urban and rural environments.

RESULTS

In this cohort study of 385 793 unrelated UK Biobank participants (207 963 [54%] were women; age, 37-73 years; mean [SD], 56.7 [8] years), PRS analyses showed significant associations with higher population density across adult life (age 25 to >65 years) reaching highest significance at the 45- to 55-year age group for schizophrenia (88 people/km2; 95% CI, 65-98 people/km2), bipolar disorder (44 people/km2; 95% CI, 34-54 people/km2), anorexia nervosa (36 people/km2; 95% CI, 22-50 people/km2), and autism spectrum disorder (35 people/km2; 95% CI, 25-45 people/km2). The schizophrenia PRS was also significantly associated with higher birthplace population density (37 people/km2; 95% CI, 19-55 people/km2; P = 8 × 10-5). Attention-deficit/hyperactivity disorder PRS was significantly associated with reduced population density in adult life (-31 people/km2; 95% CI, -42 to -20 people/km2 at age 35-45 years). Individuals with higher PRS for schizophrenia, bipolar disorder, anorexia nervosa, and autism spectrum disorder and lower PRS for attention-deficit/hyperactivity disorder preferentially moved from rural environments to cities (difference in PRS with Tukey pairwise comparisons for schizophrenia: 0.05; 95% CI, 0.03 to 0.60; bipolar disorder: 0.10; 95% CI, 0.08 to 0.13; anorexia nervosa: 0.05; 95% CI, 0.03 to 0.07; autism spectrum disorder: 0.04; 95% CI 0.03 to 0.06; and attention-deficit/hyperactivity disorder: -0.09, 95% CI, -0.12 to -0.06). Genetic correlation results were largely consistent with PRS analyses, whereas mendelian randomization provided support for associations between schizophrenia and bipolar disorder and living in high population-density areas.

CONCLUSIONS AND RELEVANCE

These findings suggest that a high genetic risk for a variety of psychiatric disorders may affect an individual's choice of residence. This result supports the hypothesis of genetic selection of an individual's environment, which intersects the traditional gene-environment dichotomy.

Sociodemographic factors associated with treatment-seeking and treatment receipt: cross-sectional analysis of UK Biobank participants with lifetime generalised anxiety or major depressive disorder

Background

Anxiety and depressive disorders can be chronic and disabling. Although there are effective treatments, only a fraction of those impaired receive treatment. Predictors of treatment-seeking and treatment receipt could be informative for initiatives aiming to tackle the burden of untreated anxiety and depression.

Aims

To investigate sociodemographic characteristics associated with treatment-seeking and treatment receipt.

Method

Two binary retrospective reports of lifetime treatment-seeking (n = 44 810) and treatment receipt (n = 37 346) were regressed on sociodemographic factors (age, gender, UK ethnic minority background, educational attainment, household income, neighbourhood deprivation and social isolation) and alternative coping strategies (self-medication with alcohol/drugs and self-help) in UK Biobank participants with lifetime generalised anxiety or major depressive disorder. Analyses were also stratified by gender.

Results

Treatment access was more likely in those who reported use of self-help strategies, with university-level education and those from less economically advantaged circumstances (household income <£30 000 and greater neighbourhood deprivation). Treatment access was less likely in those who were male, from a UK ethnic minority background and with high household incomes (>£100 000). Men who self-medicated and/or had a vocational qualification were also less likely to seek treatment.

Conclusions

This work on retrospective reports of treatment-seeking and treatment receipt at any time of life replicates known associations with treatment-seeking and treatment receipt during time of treatment need. More work is required to understand whether improving rates of treatment-seeking improves prognostic outcomes for individuals with anxiety or depression.

The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare

BACKGROUND

Cardiorespiratory fitness (CRF) and physical activity (PA) are well-established predictors of morbidity and all-cause mortality. However, CRF is not routinely measured and PA not routinely prescribed as part of standard healthcare. The American Heart Association (AHA) recently presented a scientific case for the inclusion of CRF as a clinical vital sign based on epidemiological and clinical observation. Here, we leverage genetic data in the UK Biobank (UKB) to strengthen the case for CRF as a vital sign and make a case for the prescription of PA.

METHODS

We derived two CRF measures from the heart rate data collected during a submaximal cycle ramp test: CRF-vo2max, an estimate of the participants' maximum volume of oxygen uptake, per kilogram of body weight, per minute; and CRF-slope, an estimate of the rate of increase of heart rate during exercise. Average PA over a 7-day period was derived from a wrist-worn activity tracker. After quality control, 70,783 participants had data on the two derived CRF measures, and 89,683 had PA data. We performed genome-wide association study (GWAS) analyses by sex, and post-GWAS techniques to understand genetic architecture of the traits and prioritise functional genes for follow-up.

RESULTS

We found strong evidence that genetic variants associated with CRF and PA influenced genetic expression in a relatively small set of genes in the heart, artery, lung, skeletal muscle and adipose tissue. These functionally relevant genes were enriched among genes known to be associated with coronary artery disease (CAD), type 2 diabetes (T2D) and Alzheimer's disease (three of the top 10 causes of death in high-income countries) as well as Parkinson's disease, pulmonary fibrosis, and blood pressure, heart rate, and respiratory phenotypes. Genetic variation associated with lower CRF and PA was also correlated with several disease risk factors (including greater body mass index, body fat and multiple obesity phenotypes); a typical T2D profile (including higher insulin resistance, higher fasting glucose, impaired beta-cell function, hyperglycaemia, hypertriglyceridemia); increased risk for CAD and T2D; and a shorter lifespan.

CONCLUSIONS

Genetics supports three decades of evidence for the inclusion of CRF as a clinical vital sign. Given the genetic, clinical and epidemiological evidence linking CRF and PA to increased morbidity and mortality, regular measurement of CRF as a marker of health and routine prescription of PA could be a prudent strategy to support public health.

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

BACKGROUND

Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.

METHODS

The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.

RESULTS

Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.

CONCLUSIONS

Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

The Genetic Architecture of Depression in Individuals of East Asian Ancestry: A Genome-Wide Association Study

Importance

Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations.

Objective

To investigate the genetics of depression among individuals of East Asian and European descent living in different geographic locations, and with different outcome definitions for depression.

Design, Setting, and Participants

Genome-wide association analyses followed by meta-analysis, which included data from 9 cohort and case-control data sets comprising individuals with depression and control individuals of East Asian descent. This study was conducted between January 2019 and May 2021.

Exposures

Associations of genetic variants with depression risk were assessed using generalized linear mixed models and logistic regression. The results were combined across studies using fixed-effects meta-analyses. These were subsequently also meta-analyzed with the largest published GWAS for depression among individuals of European descent. Additional meta-analyses were carried out separately by outcome definition (clinical depression vs symptom-based depression) and region (East Asian countries vs Western countries) for East Asian ancestry cohorts.

Main Outcomes and Measures

Depression status was defined based on health records and self-report questionnaires.

Results

There were a total of 194 548 study participants (approximate mean age, 51.3 years; 62.8% women). Participants included 15 771 individuals with depression and 178 777 control individuals of East Asian descent. Five novel associations were identified, including 1 in the meta-analysis for broad depression among those of East Asian descent: rs4656484 (β = -0.018, SE = 0.003, P = 4.43x10-8) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (β = 0.028, SE = 0.005, P = 6.48x10-9 for rs10240457). The lead variants of these 2 novel loci were not associated with depression risk in European ancestry cohorts (β = -0.003, SE = 0.005, P = .53 for rs4656484 and β = -0.005, SE = 0.004, P = .28 for rs10240457). Only 11% of depression loci previously identified in individuals of European descent reached nominal significance levels in the individuals of East Asian descent. The transancestry genetic correlation between cohorts of East Asian and European descent for clinical depression was r = 0.413 (SE = 0.159). Clinical depression risk was negatively genetically correlated with body mass index in individuals of East Asian descent (r = -0.212, SE = 0.084), contrary to findings for individuals of European descent.

Conclusions and Relevance

These results support caution against generalizing findings about depression risk factors across populations and highlight the need to increase the ancestral and geographic diversity of samples with consistent phenotyping.

Self-reported medication use as an alternate phenotyping method for anxiety and depression in the UK Biobank

The requirement for large sample sizes for psychiatric genetic analyses necessitates novel approaches to derive cases. Anxiety and depression show substantial genetic overlap and share pharmacological treatments. Data on prescribed medication could be effective for inferring case status when other indicators of mental health are unavailable. We investigated self-reported current medication use in UK Biobank participants of European ancestry. Medication Status cases reported using antidepressant or anxiolytic medication (n = 22,218), controls did not report psychotropic medication use (n = 168,959). A subset, "Medication Only," additionally did not meet criteria for any other mental health indicator (case n = 2,643, control n = 107,029). We assessed genetic overlap between these phenotypes and two published genetic association studies of anxiety and depression, and an internalizing disorder trait derived from symptom-based questionnaires in UK Biobank. Genetic correlations between Medication Status and the three anxiety and depression phenotypes were significant (r_g  = 0.60-0.73). In the Medication Only subset, the genetic correlation with depression was significant (r_g  = 0.51). The three polygenic scores explained 0.33% - 0.80% of the variance in Medication Status and 0.07% - 0.19% of the variance in Medication Only. This study provides evidence that self-reported current medication use offers an alternate or supplementary anxiety or depression phenotype in genetic studies where diagnostic information is sparse or unavailable.

Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression

BACKGROUND

Treatment resistant depression (TRD) poses a significant clinical challenge, despite a range of efficacious specialist treatments. Accurately predicting response a priori may help to alleviate the burden of TRD. This study sought to determine whether outcome prediction can be achieved in a specialist inpatient setting.

METHODS

Patients at the Affective Disorders Unit of the Bethlam Royal Hospital, with current depression and established TRD were included (N = 174). Patients were treated with an individualised combination of pharmacotherapy and specialist psychological therapies. Predictors included clinical and sociodemographic characteristics, and polygenic risk scores for depression and related traits. Logistic regression models examined associations with outcome, and predictive potential was assessed using elastic net regularised logistic regressions with 10-fold nested cross-validation.

RESULTS

47% of patients responded (50% reduction in HAMD-21 score at discharge). Age at onset and number of depressive episodes were positively associated with response, while degree of resistance was negatively associated. All elastic net models had poor performance (AUC<0.6). Illness history characteristics were commonly retained, and the addition of genetic risk scores did not improve performance.

LIMITATIONS

The patient sample was heterogeneous and received a variety of treatments. Some variable associations may be non-linear and therefore not captured.

CONCLUSIONS

This treatment may be most effective for recurrent patients and those with a later age of onset, while patients more severely treatment resistant at admission remain amongst the most difficult to treat. Individual level prediction remains elusive for this complex group. The assessment of homogenous subgroups should be one focus of future investigations.

Examining Individual and Synergistic Contributions of PTSD and Genetics to Blood Pressure: A Trans-Ethnic Meta-Analysis

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European (n = 70,870) and African (n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study - United Kingdom Biobank - PTSD symptoms were negatively associated with SBP levels (β = -1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.

Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits

BACKGROUND

The origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits.

METHODS

Using European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (rg < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations.

RESULTS

We observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior).

CONCLUSIONS

Sex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences.

Psychological trauma and the genetic overlap between posttraumatic stress disorder and major depressive disorder

BACKGROUND

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are commonly reported co-occurring mental health consequences of psychological trauma exposure. The disorders have high genetic overlap. Trauma is a complex phenotype but research suggests that trauma sensitivity has a heritable basis. We investigated whether sensitivity to trauma in those with MDD reflects a similar genetic component in those with PTSD.

METHODS

Genetic correlations between PTSD and MDD in individuals reporting trauma and MDD in individuals not reporting trauma were estimated, as well as with recurrent MDD and single-episode MDD, using genome-wide association study (GWAS) summary statistics. Genetic correlations were replicated using PTSD data from the Psychiatric Genomics Consortium and the Million Veteran Program. Polygenic risk scores were generated in UK Biobank participants who met the criteria for lifetime MDD (N = 29 471). We investigated whether genetic loading for PTSD was associated with reporting trauma in these individuals.

RESULTS

Genetic loading for PTSD was significantly associated with reporting trauma in individuals with MDD [OR 1.04 (95% CI 1.01-1.07), Empirical-p = 0.02]. PTSD was significantly more genetically correlated with recurrent MDD than with MDD in individuals not reporting trauma (rg differences = ~0.2, p < 0.008). Participants who had experienced recurrent MDD reported significantly higher rates of trauma than participants who had experienced single-episode MDD (χ2 > 166, p < 0.001).

CONCLUSIONS

Our findings point towards the existence of genetic variants associated with trauma sensitivity that might be shared between PTSD and MDD, although replication with better powered GWAS is needed. Our findings corroborate previous research highlighting trauma exposure as a key risk factor for recurrent MDD.

Investigating Pleiotropy Between Depression and Autoimmune Diseases Using the UK Biobank

Background

Epidemiological studies report increased comorbidity between depression and autoimmune diseases. The role of shared genetic influences in the observed comorbidity is unclear. We investigated the evidence for pleiotropy between these traits in the UK Biobank (UKB).

Methods

We defined autoimmune and depression cases using hospital episode statistics, self-reported conditions and medications, and mental health questionnaires. Pairwise comparisons of depression prevalence between autoimmune cases and controls, and vice versa, were performed. Cross-trait polygenic risk score (PRS) analyses tested for pleiotropy, i.e., whether PRSs for depression could predict autoimmune disease status, and vice versa.

Results

We identified 28,479 cases of autoimmune diseases (pooling across 14 traits) and 324,074 autoimmune controls, and 65,075 cases of depression and 232,552 depression controls. The prevalence of depression was significantly higher in autoimmune cases than in controls, and similarly, the prevalence of autoimmune disease was higher in depression cases than in controls. PRSs for myasthenia gravis and psoriasis were significantly higher in depression cases than in controls (p < 5.2 × 10^-5, R ² ≤ 0.04%). PRSs for depression were significantly higher in inflammatory bowel disease, psoriasis, psoriatic arthritis, rheumatoid arthritis, and type 1 diabetes cases than in controls (p < 5.8 × 10^-5, R ² range = 0.06%-0.27%), and lower in celiac disease cases than in controls (p < 5.4 × 10^-7, R ² range = 0.11%-0.15%).

Conclusions

Consistent with the literature, depression was more common in individuals with autoimmune diseases than in controls, and vice versa. PRSs showed some evidence for involvement of shared genetic factors, but the modest R ² values suggest that shared genetic architecture accounts for a small proportion of the increased risk across traits.

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Elevated C-Reactive Protein in Patients With Depression, Independent of Genetic, Health, and Psychosocial Factors: Results From the UK Biobank

OBJECTIVE

The authors investigated the pathways (genetic, environmental, lifestyle, medical) leading to inflammation in major depressive disorder using C-reactive protein (CRP), genetic, and phenotypic data from the UK Biobank.

METHODS

This was a case-control study of 26,894 participants with a lifetime diagnosis of major depressive disorder from the Composite International Diagnostic Interview and 59,001 control subjects who reported no mental disorder and had not reported taking any antidepressant medication. Linear regression models of log CRP level were fitted to regress out the effects of age, sex, body mass index (BMI), and smoking and to test whether the polygenic risk score (PRS) for major depression was associated with log CRP level and whether the association between log CRP level and major depression remained after adjusting for early-life trauma, socioeconomic status, and self-reported health status.

RESULTS

CRP levels were significantly higher in patients with depression relative to control subjects (2.4 mg/L compared with 2.1 mg/L, respectively), and more case than control subjects had CRP levels >3 mg/L (21.2% compared with 16.8%, respectively), indicating low-grade inflammation. The PRS for depression was positively and significantly associated with log CRP levels, but this association was no longer significant after adjustment for BMI and smoking. The association between depression and increased log CRP level was substantially reduced, but still remained significant, after adjustment for the aforementioned clinical and sociodemographic factors.

CONCLUSIONS

The data indicate that the "genetic" contribution to increased inflammation in depression is due to regulation of eating and smoking habits rather than an "autoimmune" genetic predisposition. Moreover, the association between depression and increased inflammation even after full adjustment indicates either the presence of yet unknown or unmeasured psychosocial and clinical confounding factors or that a core biological association between depression and increased inflammation exists independently from confounders.

Imputed gene expression risk scores: a functionally informed component of polygenic risk

Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

Evaluation of polygenic prediction methodology within a reference-standardized framework

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Associations and limited shared genetic aetiology between bipolar disorder and cardiometabolic traits in the UK Biobank

BACKGROUND

People with bipolar disorder (BPD) are more likely to die prematurely, which is partly attributed to comorbid cardiometabolic traits. Previous studies report cardiometabolic abnormalities in BPD, but their shared aetiology remains poorly understood. This study examined the phenotypic associations and shared genetic aetiology between BPD and various cardiometabolic traits.

METHODS

In a subset of the UK Biobank sample (N = 61 508) we investigated phenotypic associations between BPD (ncases = 4186) and cardiometabolic traits, represented by biomarkers, anthropometric traits and cardiometabolic diseases. To determine shared genetic aetiology in European ancestry, polygenic risk scores (PRS) and genetic correlations were calculated between BPD and cardiometabolic traits.

RESULTS

Several traits were significantly associated with increased risk for BPD, namely low total cholesterol, low high-density lipoprotein cholesterol, high triglycerides, high glycated haemoglobin, low systolic blood pressure, high body mass index, high waist-to-hip ratio; and stroke, coronary artery disease and type 2 diabetes diagnosis. BPD was associated with higher polygenic risk for triglycerides, waist-to-hip ratio, coronary artery disease and type 2 diabetes. Shared genetic aetiology persisted for coronary artery disease, when correcting PRS associations for cardiometabolic base phenotypes. Associations were not replicated using genetic correlations.

CONCLUSIONS

This large study identified increased phenotypic cardiometabolic abnormalities in BPD participants. It is found that the comorbidity of coronary artery disease may be based on shared genetic aetiology. These results motivate hypothesis-driven research to consider individual cardiometabolic traits rather than a composite metabolic syndrome when attempting to disentangle driving mechanisms of cardiometabolic abnormalities in BPD.

Multiple measures of depression to enhance validity of major depressive disorder in the UK Biobank

BACKGROUND

The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders.

AIMS

To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD).

METHOD

In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study.

RESULTS

The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons.

CONCLUSIONS

Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.

Exploring the genetic heterogeneity in major depression across diagnostic criteria

Major depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components-(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence-to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h²_SNP range: 0.102-0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651-0.895 (PGC); 0.652-0.837 (23andMe); 0.699-0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.

No Evidence for Passive Gene-Environment Correlation or the Influence of Genetic Risk for Psychiatric Disorders on Adult Body Composition via the Adoption Design

The relationship between genetic and environmental risk is complex and for many traits, estimates of genetic effects may be inflated by passive gene-environment correlation. This arises because biological offspring inherit both their genotypes and rearing environment from their parents. We tested for passive gene-environment correlation in adult body composition traits using the 'natural experiment' of childhood adoption, which removes passive gene-environment correlation within families. Specifically, we compared 6165 adoptees with propensity score matched non-adoptees in the UK Biobank. We also tested whether passive gene-environment correlation inflates the association between psychiatric genetic risk and body composition. We found no evidence for inflation of heritability or polygenic scores in non-adoptees compared to adoptees for a range of body composition traits. Furthermore, polygenic risk scores for anorexia nervosa, attention-deficit/hyperactivity disorder and schizophrenia did not differ in their influence on body composition traits in adoptees and non-adoptees. These findings suggest that passive gene-environment correlation does not inflate genetic effects for body composition, or the influence of psychiatric disorder genetic risk on body composition. Our design does not look at passive gene-environment correlation in childhood, and does not test for 'pure' environmental effects or the effects of active and evocative gene-environment correlations, where child genetics directly influences home environment. However, these findings suggest that genetic influences identified for body composition in this adult sample are direct, and not confounded by the family environment provided by biological relatives.

Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

A major role for common genetic variation in anxiety disorders

Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30-60%. We conducted a genome-wide association study of Lifetime Anxiety Disorder (n_case = 25 453, n_control = 58 113) and an additional analysis of Current Anxiety Symptoms (n_case = 19 012, n_control = 58 113). The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%. Five novel genome-wide significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed significant positive genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies. We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.

Multivariable G-E interplay in the prediction of educational achievement

Polygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement. Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance. Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .38; 95% CIs = .30, .45). We estimated that 40% (95% CIs = 31%, 50%) of the polygenic scores effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs = 12%, 25%) of environmental effects were accounted for by the polygenic model, indicating genetic confounding. Lastly, we did not find evidence that GxE effects significantly contributed to multivariable prediction. Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

Our study investigates the complex interplay between genetic and environmental contributions underlying educational achievement (EA). Polygenic scores are becoming increasingly powerful predictors of EA. While emerging evidence indicates that polygenic scores are not pure measures of genetic predisposition, previous quantitative genetics findings indicate that measures of the environment are themselves heritable. In this regard it is unclear how such measures of individual predisposition jointly combine to predict EA. We investigate this question in a representative UK sample of 7,026 16-year-olds where we provide substantive results on gene-environment correlation and interaction underlying variation in EA. We show that polygenic score and environmental prediction models of EA overlap substantially. Polygenic scores effects on EA are partly accounted for by their correlation with environmental effects; similarly, environmental effects on EA are linked to polygenic scores effects. Nonetheless, jointly considering polygenic scores and measured environments significantly improves prediction of EA. We also find that, although correlation between polygenic scores and measured environments is substantial, interactions between them do not play a significant role in the prediction of EA. Our findings have relevance for genomic and environmental prediction models alike, as they show the way in which individuals’ genetic predispositions and environmental effects are intertwined. This suggests that both genetic and environmental effects must be taken into account in prediction models of complex behavioral traits such as EA.

Genome-wide Meta-analysis Finds the ACSL5-ZDHHC6 Locus Is Associated with ALS and Links Weight Loss to the Disease Genetics

We meta-analyze amyotrophic lateral sclerosis (ALS) genome-wide association study (GWAS) data of European and Chinese populations (84,694 individuals). We find an additional significant association between rs58854276 spanning ACSL5-ZDHHC6 with ALS (p = 8.3 × 10⁻⁹), with replication in an independent Australian cohort (1,502 individuals; p = 0.037). Moreover, B4GALNT1, G2E3-SCFD1, and TRIP11-ATXN3 are identified using a gene-based analysis. ACSL5 has been associated with rapid weight loss, as has another ALS-associated gene, GPX3. Weight loss is frequent in ALS patients and is associated with shorter survival. We investigate the effect of the ACSL5 and GPX3 single-nucleotide polymorphisms (SNPs), using longitudinal body composition and weight data of 77 patients and 77 controls. In patients’ fat-free mass, although not significant, we observe an effect in the expected direction (rs58854276: −2.1 ± 1.3 kg/A allele, p = 0.053; rs3828599: −1.0 ± 1.3 kg/A allele, p = 0.22). No effect was observed in controls. Our findings support the increasing interest in lipid metabolism in ALS and link the disease genetics to weight loss in patients.

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Cross-ethnic meta-analysis finds an association between the ACSL5-ZDHHC6 locus and ALS

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The ACSL5-ZDHHC6 association is replicated in an independent Australian cohort

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ACSL5-ZDHHC6 lead SNP is in ACSL5 and is an eQTL of ZDHHC6 in brain tissues

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ACSL5 SNPs might have an effect on fat-free mass in ALS patients

Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression

It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic etiology irrespective of the age depression first presents.

The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls

BACKGROUND

Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction.

METHODS

To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).

RESULTS

Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.

CONCLUSIONS

The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.