Background Psychotic major depressive disorder (MDD), a subtype of MDD characterised by psychotic symptoms that occur exclusively during mood episode, is clinically significant yet underexplored genetically due to its rarity. This study comprehensively examines the genetic basis of psychotic MDD and elucidates its position within the mood- psychotic spectrum. Methods This population-based cohort study used Swedish and Danish registry data for over 5.1 M individuals born between 1958 and 1993/1996. Specialist-diagnosed psychotic MDD was defined using ICD-10 sub-codes of MDD, F32.2/F32.3. We estimated familial aggregation/coaggregation using generalised estimating equations, heritability and genetic correlations using structural equation modelling. We also analysed similar to 30,000 genotyped MDD cases from the UK Biobank and a Swedish cohort to explore which polygenic risk score (PRS) may predispose individuals to psychotic MDD. Findings With over 10,000 psychotic MDD identified from the two nationwide patient registers, this study highlights the familial aggregation of psychotic MDD, co-aggregation with mood and psychotic disorders, and its stronger genetic correlation with schizophrenia compared to non-psychotic MDD. The familial risks increased with closer biological relatedness, suggesting genetic influence. Pedigree-heritability of psychotic MDD was 30.17% (95% CI 23.53-36.80%). While the genetic correlation between psychotic and non-psychotic MDD was high (0.82, 95% CI 0.73-0.92), the psychotic subgroup showed a higher genetic correlation with schizophrenia than non-psychotic MDD (0.67 vs 0.46, p-value 7.55*10-4). Within 30,000 genotyped MDD cases, individuals with psychotic MDD had higher mean PRS for schizophrenia and BD but a lower MDD PRS than non-psychotic MDD. PRS for BD type-I was associated with increased odds of psychotic MDD, while BD type-II PRS showed no significant association with psychotic MDD. Interpretation This study provides evidence for the genetic basis of psychotic MDD, underscoring its unique position bridging the spectrum of mood and psychotic disorders. These findings advance our understanding of the aetiology of psychotic MDD and contribute to the limited body of evidence on this phenotype by utilising large-scale population-based data. Copyright (c) 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Although information from real-world data can be used to identify factors that aid treatment choice, there are no guidelines for the use of such data. The aim of this Review is to summarise and evaluate definitions of treatment outcomes for antidepressants, antipsychotics, and mood stabilisers when using real-world data, and to suggest standards for the field. Given that no standards for the use of these data in estimating treatment outcomes exist, variability is high for treatment outcome definitions. We make recommendations for different scenarios of available data and highlight the importance of using other sources of information to validate proxy measures such as continued treatment, switching between medications, or polypharmacy of psychotropic medications. Well defined and validated treatment outcome measures that incorporate real-world data could facilitate the development of precision psychiatry approaches and support regulatory decision making regarding psychopharmacological agents.

Objective To provide a comprehensive analysis of initial suicide attempts, covering incidence, risk factors, outcomes, and healthcare use in the month before and the month after the attempts.Design Comprehensive analysis of the Swedish population that included three designs: a retrospective cohort study to investigate incidence and healthcare use, a nested case-control study to investigate risk factors, and a matched cohort study to examine subsequent suicide attempts and mortality.Setting Comprehensive Swedish national registers that include patient diagnoses from hospitals and specialist outpatient care, and cause of death information updated to the end of 2019.Participants 3.7 million people born in Sweden in 1963-98 and followed from age 10 to 57 years.Main outcome measure First lifetime suicide attempt identified in patient and death registers using ICD (international classification of diseases) codes for intentional self-harm, any self-harm with lethal methods or requiring hospital admission, or any self-harm resulting in death.Results The lifetime risk of an initial suicide attempt in the study population was 4.6%, with greater risk in females and highest risk between ages 18 and 24. One in 10 families in Sweden had at least one family member who attempted suicide. Overdose and poisoning were the most common methods. Previous psychiatric disorders, general medical diseases, and adverse life events were associated with increased risk of initial suicide attempt, while higher socioeconomic status was associated with decreased risk. People with an initial suicide attempt were at substantially increased risk of subsequent attempts (hazard ratio 23.4), death by suicide (16.4), and all cause mortality (7.3). At least 60% of those who made an initial suicide attempt had a healthcare contact in the month before the attempt.Conclusions This study provides comprehensive data on the incidence, risk factors, outcomes, and healthcare use of initial suicide attempts in the Swedish population, highlighting the need for systematic prevention efforts for people who have attempted suicide for the first time.

Research by the Psychiatric Genomics Consortium (PGC) has advanced the discovery of common and rare genetic variations that contribute to the susceptibility to many psychiatric disorders and neurodevelopmental conditions. This Review reflects on major findings from the past 5 years of research by the PGC in five priority areas: discovery of common variants using genome-wide association studies; rare variation and its interplay with polygenic risk; using genetics to go beyond diagnostic boundaries; ascribing functional attributes to genomic discoveries; and developing and implementing processes for data sharing, outreach to various communities, and training. The insights gained in these domains frame the agenda for the next phase of PGC research. In addition to accelerating integrative findings of common and rare variants within, and across, multiple psychiatric disorders and neurodevelopmental conditions, the next phase will use multiple populations to elucidate genetic causes, integrate results with rapidly accumulating multimodal functional genomics data to gain mechanistic understanding, convert genetic findings to clinically actionable phenotypes, such as treatment response, and address the emerging use of polygenic scores. Together, these next steps will highlight the biological underpinnings of psychiatric disorders and neurodevelopmental conditions, which continue to contribute to global morbidity and mortality.

Eating disorders -including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

IMPORTANCE:Individuals with psychiatric disorders have increased risk of cardiometabolic diseases (CMDs). Evaluating how psychiatric genetic liability relates to CMD may clarify mechanisms. OBJECTIVE:Identify genetic overlap between psychiatric disorders and CMDs independent of cross-disorder pleiotropy, BMI, and smoking. DESIGN SETTING AND PARTICIPANTS:Three Northern European cohorts (the Swedish Twin Registry, the Estonian Biobank, and the Norwegian Mother, Father and Child Cohort Study [MoBa]) totaling 355,159 individuals. Associations with CMDs were estimated as adjusted odds ratios (AORs) from logistic models mutually adjusted for all psychiatric PRSs and in models additionally adjusting for body mass index (BMI) and smoking. Cohort-specific AORs were pooled by inverse-variance weighting. MAIN OUTCOMES AND MEASURES:Exposures were PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety disorder, posttraumatic stress disorder (PTSD), bipolar disorder, and schizophrenia. Outcomes were diagnoses of CMDs (hyperlipidemia, obesity, type 2 diabetes, hypertensive diseases, arteriosclerosis, ischemic heart disease, heart failure, thromboembolic disease, cerebrovascular disease, and arrhythmias), ascertained from electronic health records. RESULTS:The MDD PRS was associated with increased risk of all CMDs across analyses (AORs ranged from 1.13 [95% CI, 1.10-1.15] for heart failure to 1.02 [95% CI, 1.00-1.05] for arrhythmias). The ADHD PRS was associated with increased risk of all CMDs (AOR ranged from 1.11 [95% CI, 1.09-1.12] for obesity to 1.02 [95% CI, 1.01-1.03] for hyperlipidemia), however associations where attenuated when adjusting for BMI and smoking (lifestyle adjusted AOR for obesity: 1.03 [95% CI, 1.02-1.05]). When not mutually adjusting for all psychiatric PRSs, anxiety disorder and PTSD PRSs were associated with all CMDs; these associations diminished after adjustment. The bipolar and schizophrenia PRSs were inversely associated with most CMDs (AOR for schizophrenia PRS and obesity, 0.93 [95% CI, 0.92-0.94]). CONCLUSIONS AND RELEVANCE:Associations between psychiatric PRSs and CMDs diverged: ADHD, MDD, anxiety disorder, and PTSD PRSs were positively associated with CMDs, whereas bipolar and schizophrenia PRSs were inversely associated. Genetic liability to MDD showed robust associations with CMDs independent of cross-disorder pleiotropy, BMI, and smoking status, whereas associations between the ADHD PRS and CMDs were largely attenuated after adjustment for BMI and smoking.

Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.

The aim is to investigate the evidence for shared genetic architecture between each of asthma, allergic rhinitis and eczema with gastro-esophageal reflux disease (GERD). Structural equation models (SEM) and polygenic risk score (PRS) analyses are applied to three Swedish twin cohorts (n = 46,582) and reveal a modest genetic correlation between GERD and asthma of 0.18 and bidirectional PRS and phenotypic associations ranging between OR 1.09-1.14 and no correlations for eczema and allergic rhinitis. Linkage disequilibrium score regression is applied to summary statistics of recently published GERD and asthma/allergic disease genome wide association studies and reveals a genetic correlation of 0.48 for asthma and GERD, and Genomic SEM supports a single latent factor. A gene-/gene-set analysis using MAGMA reveals six pleiotropic genes (two at 12q13.2) associated with asthma and GERD. This study provides evidence that there is a common genetic architecture unique to asthma and GERD that may explain comorbidity and requires further investigation. Structural equation models, polygenic risk score analysis of twin data and linkage disequilibrium score regression of GWAS studies indicate genetic architecture of asthma and GERD. Gene-/gene-set analysis revealed six pleiotropic genes.

Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.

ImportanceAdolescent depression is characterized by diverse symptom trajectories over time and has a strong genetic influence. Research has determined genetic overlap between depression and other psychiatric conditions; investigating the shared genetic architecture of heterogeneous depression trajectories is crucial for understanding disease etiology, prediction, and early intervention.ObjectiveTo investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries.Design, Setting, and ParticipantsThis cohort study entailed longitudinal growth modeling followed by polygenic risk score (PRS) association testing for individual and multitrait genetic models. Two longitudinal cohorts from the US and UK were used: the Adolescent Brain and Cognitive Development (ABCD; N = 11 876) study and the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8787) study. Included were adolescents with genetic information and depression measures at up to 8 and 4 occasions, respectively. Study data were analyzed January to July 2023.Main Outcomes and MeasuresTrajectories were derived from growth mixture modeling of longitudinal depression symptoms. PRSs were computed for depression, anxiety, neuroticism, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism in European ancestry. Genomic structural equation modeling was used to build multitrait genetic models of psychopathology followed by multitrait PRS. Depression PRSs were computed in African, East Asian, and Hispanic ancestries in the ABCD cohort only. Association testing was performed between all PRSs and trajectories for both cohorts.ResultsA total sample size of 14 112 adolescents (at baseline: mean [SD] age, 10.5 [0.5] years; 7269 male sex [52%]) from both cohorts were included in this analysis. Distinct depression trajectories (stable low, adolescent persistent, increasing, and decreasing) were replicated in the ALSPAC cohort (6096 participants; 3091 female [51%]) and ABCD cohort (8016 participants; 4274 male [53%]) between ages 10 and 17 years. Most univariate PRSs showed significant uniform associations with persistent trajectories, but fewer were significantly associated with intermediate (increasing and decreasing) trajectories. Multitrait PRSs—derived from a hierarchical factor model—showed the strongest associations for persistent trajectories (ABCD cohort: OR, 1.46; 95% CI, 1.26-1.68; ALSPAC cohort: OR, 1.34; 95% CI, 1.20-1.49), surpassing the effect size of univariate PRS in both cohorts. Multitrait PRSs were associated with intermediate trajectories but to a lesser extent (ABCD cohort: hierarchical increasing, OR, 1.27; 95% CI, 1.13-1.43; decreasing, OR, 1.23; 95% CI, 1.09-1.40; ALSPAC cohort: hierarchical increasing, OR, 1.16; 95% CI, 1.04-1.28; decreasing, OR, 1.32; 95% CI, 1.18-1.47). Transancestral genetic risk for depression showed no evidence for association with trajectories.Conclusions and RelevanceResults of this cohort study revealed a high multitrait genetic loading of persistent symptom trajectories, consistent across traits and cohorts. Variability in univariate genetic association with intermediate trajectories may stem from environmental factors. Multitrait genetics may strengthen depression prediction models, but more diverse data are needed for generalizability.

Importance Schizophrenia and bipolar disorder are highly heritable psychiatric disorders with strong genetic and phenotypic overlap. Twin and molecular methods can be leveraged to predict the shared genetic liability to these disorders. Objective To investigate whether twin concordance for psychosis depends on the level of polygenic risk score (PRS) for psychosis and zygosity and compare PRS from cases and controls from several large samples and estimate the twin heritability of psychosis. Design, Setting, and Participants In this case-control study, psychosis PRS were generated from a genome-wide association study (GWAS) combining schizophrenia and bipolar disorder into a single psychosis phenotype and compared between cases and controls from the Schizophrenia and Bipolar Twin Study in Sweden (STAR) project. Further tests were conducted to ascertain if twin concordance for psychosis depended on the mean PRS for psychosis. Structural equation modeling was used to estimate heritability. This study constituted an analysis of existing clinical and population datasets with genotype and/or twin data. Included were twins from the STAR cohort and from the Swedish Twin Registry. Data were collected during the 2006 to 2013 period and analyzed from March 2023 to June 2024. Exposures PRS for psychosis based on the most recent GWAS of combined schizophrenia/bipolar disorder. Main Outcomes and Measures Psychosis case status was assessed by clinical interviews and/or Swedish National Register data. Results The final cohort comprised 87 pairs of twins with 1 or both affected and 59 unaffected pairs from the STAR project (for a total of 292 twins) as well as 443 pairs with 1 or both affected and 20 913 unaffected pairs from the Swedish Twin Registry. Among the 292 twins (mean [SD] birth year, 1960 [10.8] years; 158 female [54.1%]; 134 male [45.9%]), 134 were monozygotic twins, and 158 were dyzygotic twins. PRS for psychosis was higher in cases than in controls and associated with twin concordance for psychosis (1-SD increase in PRS, odds ratio [OR], 2.12; 95% CI, 1.23-3.87 on case status in monozygotic twins and OR, 2.74; 95% CI, 1.56-5.30 in dizygotic twins). The association between PRS for psychosis and concordance was not modified by zygosity. The twin heritability was estimated at 0.73 (95% CI, 0.30-1.00), which overlapped with the estimate in the full Swedish Twin Registry (0.69; 95% CI, 0.43-0.85). Conclusions and Relevance In this case-control study, using the natural experiment of twins, results suggest that twins with greater inherited liability for psychosis were more likely to have an affected co-twin. Results from twin and molecular designs largely aligned. Even as illness vulnerability is not solely genetic, PRS carried predictive power for psychosis even in a modest sample size.

Background Major depressive disorder (MDD) is a highly polygenic, highly heterogeneous psychiatric illness. To explain this heterogeneity, many potential subgroups of MDD have been proposed. MDD with atypical energy-related symptoms (AERS) is a subgroup arising from recent research. AERS includes symptoms of the atypical depression spectrum, such as the “reversed neurovegetative symptoms” of increased appetite, weight and sleep during worst depressive episode. These symptoms are associated with earlier age of onset, increased episode frequency and severity of MDD, and with several immune and metabolic traits. There is now emerging evidence of a causal effect of body mass index on MDD with AERS. This suggests there may be differences in the aetiology of AERS compared to other subgroups of MDD. However, studies examining the underlying genetic architecture of AERS have thus far been limited by small sample sizes. The previous largest genome-wide association study (GWAS) of AERS includes only around 2,900 cases. The aim of this study is to identify and analyse cohorts with MDD symptom-level data and perform a meta-analysis of AERS in the largest sample to date. Methods Thus far participating cohorts include: UK Biobank, Australian Genetics of Depression Study (AGDS), Estonian Biobank, Genetic Links to Anxiety and Depression Study (GLAD), Biobanks Netherlands Internet Collaboration (BIONIC) Project, Generation Scotland: Scottish Family Health Study (GS:SFHS), and PGC MDD3, resulting in an estimated AERS sample of around 15,000 cases. AERS+ is defined as: MDD with increased appetite or weight gain and increased sleep symptoms during worst episode. Analyses will also examine the subgroup of MDD with opposing symptoms of decreased appetite or weight loss and decreased sleep (AERS-), and the individuals not in either of these subgroups (uncategorised). A case-control GWAS will be performed for each subgroup, as well as a case-only GWAS directly comparing AERS+ with AERS-. Results across cohorts will then be meta-analysed to increase the power to detect significant results. Results Analyses in participating cohorts are ongoing. A preliminary meta-analysis of the UK Biobank and AGDS cohorts (AERS+ N = 4,420, AERS- N = 11,039, uncategorised MDD N = 23,921, controls N = 63,285) suggested a SNP-heritability of 0.10 for AERS+ (se = 0.02, k = 0.03, p = 7.22 × 10-7), 0.07 for AERS- (se = 0.01, k = 0.05, p = 1.09 × 10-10) for and 0.08 for uncategorised MDD (se = 0.008, k = 0.09, p = 2.60 × 10-19). Genetic correlation analyses suggest there are significant differences between AERS+ and AERS- (rg = 0.44, se = 0.13, p = 9.00 × 10-4) and AERS+ and uncategorised MDD (rg = 0.74, se = 0.11, p = 3.41 × 10-12), but not between AERS- and uncategorised MDD (rg = 0.93, se = 0.09, p = 5.49 × 10-24). Discussion The preliminary results from the UK Biobank and AGDS meta-analysis, along with previous work examining MDD with AERS, suggest there are differences in the genetics and aetiology of MDD with AERS compared with other subgroups of MDD. These differences could explain some of the heterogeneity observed in MDD and offer alternate targets for treatment in this subgroup. A sufficiently powered meta-analysis of AERS is crucial to furthering the understanding of the mechanisms underlying this subgroup. This study will be the largest meta-analysis of AERS to date and should provide valuable insight into its underlying genetic architecture.

Background Genome-wide association studies (GWAS) have been a remarkable success in terms of detecting robust and replicable associations between human phenotype and genotype. Sharing GWAS summary statistics instead of individual-level data alleviates privacy concerns and facilities a wide array of downstream analyses. However, no standardized format exists for summary statistics, resulting in inconsistencies in column names, datatypes and field separators. As a result, data wrangling or “munging” has become a staple in analysis utilizing summary statistics. To address this, we developed the R package tidyGWAS, with features such as reparation and validation of chromosome, position and rsID, reparation of missing statistical columns, harmonization of datatypes and identification of multi-allelic variants, indel variants and duplicated variants. Methods We use dbSNP (version 155) as reference data to allow verification and updating of chromosome, position and RSID. Utilizing the Apache arrow parquet format, we make the alignment of a typical summary statistics file (∼10 million rows) to both dbSNP 155 GRCh37 and GRCh38 computationally efficient? We implement functions that convert common data-formats to a harmonized format (for example, converting ‘chr1’ to 1). Filters are applied to remove nonsensical values (for example, standard error < 0). Results Aligning a typical summary statistics file of ∼7 million rows against two billion rows (GRCh37 and GRCh38) required approximately 5 minutes, with some variation due to computational architecture. Across 250 summary statistics downloaded from different sources online, we showcase the most common sources of errors such as missing or non-numeric effect sizes, duplicated variants and p-values represented as a non-numeric type. By storing the summary statistics in the Apache arrow parquet format, we demonstrate how analysis across hundreds of summary statistics can be done with minimal memory and CPU usage. Discussion Summary statistics are the principal results of a GWAS, and a large ecosystem of downstream analysis tools has been developed to tackle various analytical demands. Here we introduce tidyGWAS, an R package that automates the process of “cleaning” summary statistics, reducing the risk of errors and making the use of summary statistics more efficient. We demonstrate how using the Parquet data format can optimize analysis, effortlessly scaling common tasks (such as meta-analysis) to hundreds of summary statistics. Lastly, we show how automatic pipelines can be built for downstream analysis when datatypes across all summary statistics are identical.

Background Schizophrenia is a strongly heritable psychiatric disorder marked by significant morbidity. Despite ongoing advances in genetics, developing effective treatment strategies for schizophrenia remains difficult due to its extensive polygenicity and population heterogeneity. This study seeks to explore if individuals with highly treatment-resistant schizophrenia (HTRS) carry a higher burden of common genetic variants associated with schizophrenia or cognitive ability, in contrast to those with treatment-resistant schizophrenia (TRS), treatment-responsive schizophrenia, or neurotypical controls. Methods HTRS cases were recruited from state psychiatric hospitals in Pennsylvania (US), diagnosed with DSM-IV schizophrenia and with ≥5 years of continuous hospitalization and poor response to antipsychotic medications despite receiving adequate dosages and durations. Controls- treatment-resistant schizophrenia, treatment-responsive schizophrenia, and neurotypical individuals were obtained from the Sweden Schizophrenia Study, CardiffCOGS, CLOZUK, WTCCC2, and Molecular Genetics of Schizophrenia-2 (MGS2) study. Quality control procedures adhered to standard protocols with genotype data imputed using the Haplotype Reference Consortium (r1.1). The schizophrenia and cognitive ability polygenic scores (PGS) were calculated utilizing the latest genome-wide association studies and PRSice2, standardized to a mean of 0 and standard deviation of 1. Logistic regression was used to test the association of PGS with HTRS vs. the control groups adjusting for 10 genetic ancestry principal components by wave and then meta-analyzed in R. Results We used samples from 37,162 individuals of European ancestry (N=368 HTRS, 12,233 TRS, 1,208 treatment-responsive schizophrenia, and 23,353 neurotypical controls). Meta-analysis demonstrated that for each 1-SD increase in the schizophrenia-PGS, the odds of HTRS increased by 50-80% compared to treatment-responsive and TRS (schizophrenia-PGS odds ratio per 1-SD: 1.80, 95%CI: 1.03-3.15 and 1.53, 95%CI: 0.92-2.55). The schizophrenia-associated common variant burden was highest in this sequence: HTRS> TRS> Treatment responsive schizophrenia> Neurotypical controls. The cognitive ability PGS was significantly lower in HTRS compared to neurotypical controls (odds ratio=0.82 [95%CI: 0.76-0.88]), while the other comparisons were not statistically significant. Discussion The meta-analysis indicates that higher schizophrenia PGS is strongly associated with an increased likelihood of having HTRS. Additionally, individuals with HTRS also exhibited significantly lower cognitive ability PGS compared to neurotypical controls suggests a potential link between genetic risk for schizophrenia, treatment resistance, and cognitive impairment. Disclosure Nothing to disclose.

Background Genome-wide association studies (GWAS) of schizophrenia have identified 287 loci and sample sizes of >70,000 schizophrenia cases. Previous GWAS of schizophrenia have identified enrichment of common variants concentrated in genes expressed in excitatory and inhibitory neurons. The Sweden Schizophrenia Study (S3) samples have been part of most major genetic studies in the past 15 years of schizophrenia research, and we have now doubled our prior collection of cases and most of these samples are reported here for the first time. Our overall goal was to identify common genetic variation associated with schizophrenia. Methods Using multiple sample sources, including neonatal dried blood spots, we assayed samples on Illumina GSA genotyping arrays. Phenotypes included psychiatric specialist hospitalizations (for primary GWAS of schizophrenia case/control), redeemed drug prescriptions, and family linkages. Genotype data were subject to standard RICOPILI quality control and imputation using the Haplotype Reference Consortium (r1.1). We conducted a GWAS using the European ancestry samples and then meta-analyzed with the latest PGC-Schizophrenia European genetic ancestry GWAS (Sweden-free N=47,248 schizophrenia cases). We used LD-score regression to compute the SNP-heritability (h2SNP), and genetic correlations with many external traits and then compared any significant loci (p LESS THAN 5e-8) with that of the latest PGC-schizophrenia GWAS (2022) to identify novel loci and genes using multiple bioinformatic tools. Results Following quality control, we analyzed N=10,365 cases (of which 5,674 have never been reported) and 15,835 controls. SNP heritability for schizophrenia (h2SNP=0.25, standard error [SE]=0.02) and genetic correlations were as expected: rg SCZ=0.92 [SE=0.04], bipolar-1=0.70 [SE=0.04], major depressive disorder=0.37 [SE=0.03], anorexia nervosa=0.22 [SE=0.05], educational attainment=0.08 [SE=0.02], cognitive ability= -0.21 [SE=0.03], body mass index= -0.12 [SE=0.02]. PLINK2 logistic/Firth regression analyses of cases vs. controls in S3 identified 4 genome-wide significant loci, which all overlapped with the latest PGC-SCZ GWAS. Meta-analyzing along with PGC-SCZ European cohorts identified 18 novel genome-wide significant loci mapping to 32 protein-coding genes including 6 synaptic genes in the curated SynGO. Discussion This study highlights the contribution of richly phenotyped samples to identifying common genetic variation associated with schizophrenia. Further analyses will be presented at WCPG. Disclosure Nothing to disclose.

Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants (25,255 non-responders and 110,216 responders). We performed genome-wide association meta-analyses, genetic correlation analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci (rs1106260 and rs60847828) associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. Genetic correlation analyses show positive associations between non-response to antidepressants and most psychiatric traits, and negative associations with cognitive traits and subjective well-being. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.

Background Treatment-resistant depression (TRD) accounts for a large share of the disease burden of depression. It has been suggested that individuals with TRD experience higher rates of comorbidities, but there remains a lack of comprehensive investigation of its individual and familial risk with psychiatric and cardiometabolic comorbidities. This study aimed to investigate psychiatric and cardiometabolic disorders associated with TRD, the familial aggregation of TRD, and the familial coaggregation between TRD and these disorders. Methods Leveraging comprehensive data from Swedish registers with ∼2.4 million individuals born between 1970-1996, we defined TRD phenotypes with three definitions: one based on ECT treatment and previous antidepressant use, the other two based on the number of antidepressants used or antidepressants switch. Using logistic regression, we investigated the association between TRD and individual risks of psychiatric and cardiometabolic comorbidities. Subsequently, we estimated familial aggregation of TRD and its co-aggregation with other psychiatric and cardiometabolic disorders using generalized estimating equations. Results Compared to non-TRD and healthy controls, TRD patients showed higher risk of comorbid psychiatric disorders, with anxiety, severe stress response disorders and obsessive-compulsive disorder exhibiting the strongest associations (OR ranged from 7.1-65.5 and 1.2-3.7 compared to healthy controls and non-TRD, respectively). Additionally, TRD patients showed increased risk of cardiovascular diseases, obesity, hypertension, hyperlipidemia, and type 2 diabetes (OR ranged from 1.9-4.5 and 1.1-2.1 compared to healthy controls and non-TRD, respectively). Familial aggregation analysis revealed that full siblings, half-siblings, and cousins of TRD patients also had a higher risk of TRD compared to relatives of individuals without TRD, with the strength of association reduced with decreasing genetic relatedness. Furthermore, psychiatric and cardiometabolic disorders displayed co-aggregation with TRD within families. The patterns of associations were consistent across different TRD definitions, except that TRD cases with ECT showed lower comorbidity risk with attention deficit hyperactivity disorder compared to those based on antidepressant use. Discussion The study underscores the substantial burden of psychiatric and cardiometabolic comorbidities among individuals with TRD. These findings indicate the complexity of TRD with other health conditions, highlighting the importance of comprehensive evaluation and management of comorbidities for TRD patients.

AbstractRecent advances in technology have made possible to quantify fine-grained individual differences at many levels, such as genetic, genomics, organ level, behavior, and clinical. The wealth of data becoming available raises great promises for research on brain disorders as well as normal brain function, to name a few, systematic and agnostic study of disease risk factors (e.g., genetic variants, brain regions), the use of natural experiments (e.g., evaluate the effect of a genetic variant in a human population), and unveiling disease mechanisms across several biological levels (e.g., genetics, cellular gene expression, organ structure and function). However, this data revolution raises many challenges such as data sharing and management, the need for novel analysis methods and software, storage, and computing.Here, we sought to provide an overview of some of the main existing human datasets, all accessible to researchers. Our list is far from being exhaustive, and our objective is to publicize data sharing initiatives and help researchers find new data sources.