Genome-wide association study identifies new loci associated with OCD.

To date, four genome-wide association studies (GWAS) of obsessive-compulsive disorder (OCD) have been published, reporting a high single-nucleotide polymorphism (SNP)-heritability of 28% but finding only one significant SNP. A substantial increase in sample size will likely lead to further identification of SNPs, genes, and biological pathways mediating the susceptibility to OCD. We conducted a GWAS meta-analysis with a 2-3-fold increase in case sample size (OCD cases: N = 37,015, controls: N = 948,616) compared to the last OCD GWAS, including six previously published cohorts (OCGAS, IOCDF-GC, IOCDF-GC-trio, NORDiC-nor, NORDiC-swe, and iPSYCH) and unpublished self-report data from 23andMe Inc. We explored the genetic architecture of OCD by conducting gene-based tests, tissue and celltype enrichment analyses, and estimating heritability and genetic correlations with 74 phenotypes. To examine a potential heterogeneity in our data, we conducted multivariable GWASs with MTAG. We found support for 15 independent genome-wide significant loci (14 new) and 79 protein-coding genes. Tissue enrichment analyses implicate multiple cortical regions, the amygdala, and hypothalamus, while cell type analyses yielded 12 cell types linked to OCD (all neurons). The SNP-based heritability of OCD was estimated to be 0.08. Using MTAG we found evidence for specific genetic underpinnings characteristic of different cohort-ascertainment and identified additional significant SNPs. OCD was genetically correlated with 40 disorders or traits-positively with all psychiatric disorders and negatively with BMI, age at first birth and multiple autoimmune diseases. The GWAS meta-analysis identified several biologically informative genes as important contributors to the aetiology of OCD. Overall, we have begun laying the groundwork through which the biology of OCD will be understood and described.

Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians.

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

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.

The selection landscape and genetic legacy of ancient Eurasians.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder.

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments.

BACKGROUND: Symptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity. METHODS: Using data from the UK Biobank (n = 41,948-109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms. RESULTS: Corresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences. CONCLUSIONS: MDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Metabolic signature of the pathogenic 22q11.2 deletion identifies carriers and provides insight into systemic dysregulation.

Large deletions at chromosome 22q11.2 are known to cause severe clinical conditions collectively known as 22q11.2 deletion syndrome. Notwithstanding the pathogenicity of these deletions, affected individuals are typically diagnosed in late childhood or early adolescence, and little is known of the molecular signaling cascades and biological consequences immediately downstream of the deleted genes. Here, we used targeted metabolomics to compare neonatal dried blood spot samples from 203 individuals clinically identified as carriers of a deletion at chromosome 22q11.2 with 203 unaffected individuals. A total of 173 metabolites were successfully identified and used to inform on systemic dysregulation caused by the genomic lesion and to discriminate carriers from non-carriers. We found 84 metabolites to be differentially abundant between carriers and non-carriers of the 22q11.2 deletion. A predictive model based on all 173 metabolites achieved high Accuracy (89%), Area Under the Curve (93%), F1 (88%), Positive Predictive Value (94%), and Negative Predictive Value (84%) with tyrosine and proline having the highest individual contributions to the model as well as the highest interaction strength. Targeted metabolomics provides insight into the molecular consequences possibly contributing to the pathology underlying the clinical manifestations of the 22q11 deletion and is an easily applicable approach to first-pass screening for carrier status of the 22q11 to prompt subsequent verification of the genomic diagnosis.

COVID-19 illness severity and 2-year prevalence of physical symptoms: an observational study in Iceland, Sweden, Norway and Denmark.

Background: Although the persistence of physical symptoms after SARS-CoV-2 infection is a major public health concern, evidence from large observational studies beyond one year post diagnosis remain scarce. We aimed to assess the prevalence of physical symptoms in relation to acute illness severity up to more than 2-years after diagnosis of COVID-19. Methods: This multinational study included 64,880 adult participants from Iceland, Sweden, Denmark, and Norway with self-reported data on COVID-19 and physical symptoms from April 2020 to August 2022. We compared the prevalence of 15 physical symptoms, measured by the Patient Health Questionnaire (PHQ-15), among individuals with or without a confirmed COVID-19 diagnosis, by acute illness severity, and by time since diagnosis. We additionally assessed the change in symptoms in a subset of Swedish adults with repeated measures, before and after COVID-19 diagnosis. Findings: During up to 27 months of follow-up, 34.5% participants (22,382/64,880) were diagnosed with COVID-19. Individuals who were diagnosed with COVID-19, compared to those not diagnosed, had an overall 37% higher prevalence of severe physical symptom burden (PHQ-15 score ≥15, adjusted prevalence ratio [PR] 1.37 [95% confidence interval [CI] 1.23-1.52]). The prevalence was associated with acute COVID-19 severity: individuals bedridden for seven days or longer presented with the highest prevalence (PR 2.25 [1.85-2.74]), while individuals never bedridden presented with similar prevalence as individuals not diagnosed with COVID-19 (PR 0.92 [0.68-1.24]). The prevalence was statistically significantly elevated among individuals diagnosed with COVID-19 for eight of the fifteen measured symptoms: shortness of breath, chest pain, dizziness, heart racing, headaches, low energy/fatigue, trouble sleeping, and back pain. The analysis of repeated measurements rendered similar results as the main analysis. Interpretation: These data suggest an elevated prevalence of some, but not all, physical symptoms during up to more than 2 years after diagnosis of COVID-19, particularly among individuals suffering a severe acute illness, highlighting the importance of continued monitoring and alleviation of these targeted core symptoms. Funding: This work was mainly supported by grants from NordForsk (COVIDMENT, grant number 105668 and 138929) and Horizon 2020 (CoMorMent, 847776). See Acknowledgements for further details on funding.

The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders.

Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.

Postpartum and non-postpartum depression: a population-based matched case-control study comparing polygenic risk scores for severe mental disorders.

It remains inconclusive whether postpartum depression (PPD) and depression with onset outside the postpartum period (MDD) are genetically distinct disorders. We aimed to investigate whether polygenic risk scores (PGSs) for major mental disorders differ between PPD cases and MDD cases in a nested case-control study of 50,057 women born from 1981 to 1997 in the iPSYCH2015 sample in Demark. We identified 333 women with first-onset postpartum depression (PPD group), who were matched with 993 women with first-onset depression diagnosed outside of postpartum (MDD group), and 999 female population controls. Data on genetics and depressive disorders were retrieved from neonatal biobanks and the Psychiatric Central Research Register. PGSs were calculated from both individual-level genetic data and meta-analysis summary statistics from the Psychiatric Genomics Consortium. Conditional logistic regression was used to calculate the odds ratio (OR), accounting for the selection-related reproductive behavior. After adjustment for covariates, higher PGSs for severe mental disorders were associated with increased ORs of both PPD and MDD. Compared with MDD cases, MDD PGS and attention-deficit/hyperactivity disorder PGS were marginally but not statistically higher for PPD cases, with the OR of PPD versus MDD being 1.12 (95% CI: 0 .97-1.29) and 1.11 (0.97-1.27) per-standard deviation increase, respectively. The ORs of PPD versus MDD did not statistically differ by PGSs of bipolar disorder, schizophrenia, or autism spectrum disorder. Our findings suggest that relying on PGS data, there was no clear evidence of distinct genetic make-up of women with depression occurring during or outside postpartum, after taking the selection-related reproductive behavior into account.

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.

Population-based Risk of Psychiatric Disorders Associated with Recurrent CNVs.

Recurrent copy number variants (rCNVs) are associated with increased risk of neuropsychiatric disorders but their pathogenic population-level impact is unknown. We provide population-based estimates of rCNV-associated risk of neuropsychiatric disorders for 34 rCNVs in the iPSYCH2015 case-cohort sample (n=120,247). Most observed significant increases in rCNV-associated risk for ADHD, autism or schizophrenia were moderate (HR:1.42-5.00), and risk estimates were highly correlated across these disorders, the most notable exception being high autism-associated risk with Prader-Willi/Angelman Syndrome duplications (HR=20.8). No rCNV was associated with significant increase in depression risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint. Comparison with published rCNV studies suggests that prevalence of some rCNVs is higher, and risk of psychiatric disorders lower, than previously estimated. In an era where genetics is increasingly being clinically applied, our results highlight the importance of population-based risk estimates for genetics-based predictions.

Identification of women at high risk of postpartum psychiatric episodes: A population-based study quantifying relative and absolute risks following exposure to selected risk factors and genetic liability.

BACKGROUND: We quantified relative and absolute risks of postpartum psychiatric episodes (PPE) following risk factors: Young age, past personal or family history of psychiatric disorders, and genetic liability. METHODS: We conducted a register-based study using the iPSYCH2012 case-cohort sample. Exposures were personal history of psychiatric episodes prior to childbirth, being a young mother (giving birth before the age of 21.5 years), having a family history of psychiatric disorders, and a high (highest quartile) polygenic score (PGS) for major depression. PPE was defined within 12 months postpartum by prescription of psychotropic medication or in- and outpatient contact to a psychiatric facility. We included primiparous women born 1981-1999, giving birth before January 1st, 2016. We conducted Cox regression to calculate hazard ratios (HRs) of PPE, absolute risks were calculated using cumulative incidence functions. RESULTS: We included 8174 primiparous women, and the estimated baseline PPE risk was 6.9% (95% CI 6.0%-7.8%, number of PPE cases: 2169). For young mothers with a personal and family history of psychiatric disorders, the absolute risk of PPE was 21.6% (95% CI 15.9%-27.8%). Adding information on high genetic liability to depression, the risk increased to 29.2% (95% CI 21.3%-38.4%) for PPE. CONCLUSIONS: Information on prior personal and family psychiatric episodes as well as age may assist in estimating a personalized risk of PPE. Furthermore, additional information on genetic liability could add even further to this risk assessment.

Disruptive mutations in the serotonin transporter associate serotonin dysfunction with treatment-resistant affective disorder.

Affective or mood disorders are a leading cause of disability worldwide. The serotonergic system has been heavily implicated in the complex etiology and serves as a therapeutic target. The serotonin transporter (SERT) is a major regulator of serotonin neurotransmission, yet the disease-relevance of impaired SERT function remains unknown. Here, we present the first identification and functional characterization of disruptive coding SERT variants found in patients with psychiatric diseases. In a unique cohort of 144 patients characterized by treatment-resistant chronic affective disorders with a lifetime history of electroconvulsive therapy, we identified two previously uncharacterized coding SERT variants: SERT-N217S and SERT-A500T. Both variants were significantly enriched in the patient cohort compared to GnomAD (SERT-N217S: OR = 151, P = 0.0001 and SERT-A500T: OR = 1348, P = 0.0022) and ethnicity-matched healthy controls (SERT-N217S: OR ≥ 17.7, P ≤ 0.013 and SERT-A500T: OR = ∞, P = 0.029). Functional investigations revealed that the mutations exert distinct perturbations to SERT function, but their overall effects converge on a partial loss-of-function molecular phenotype. Thus, the SERT-A500T variant compromises the catalytic activity, while SERT-N217S disrupts proper glycosylation of SERT with a resulting dominant-negative trafficking deficiency. Moreover, we demonstrate that the trafficking deficiency of SERT-N217S is amenable to pharmacochaperoning by noribogaine. Collectively, our findings describe the first disease-associated loss-of-function SERT variants and implicate serotonergic disturbances arising from SERT dysfunction as a risk factor for chronic affective disorders.

ADuLT: An efficient and robust time-to-event GWAS.

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores.

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.