Youth Psychotic Experiences: Diagnostic Associations and Evaluation of the CAPE-16.

Background: Adolescent self-reported psychotic experiences are associated with mental illness and could help guide prevention strategies. The Community Assessment of Psychic Experiences (CAPE) was developed over 20 years ago. In a rapidly changing society, where new generations of adolescents are growing up in an increasingly digital world, it is crucial to ensure high reliability and validity of the questionnaire. Methods: In this observational validation study, we used unique transgenerational questionnaire and health registry data from the Norwegian Mother, Father, and Child Cohort, a population-based pregnancy cohort. Adolescents, aged ~14 years, responded to the CAPE-16 (n = 18,835) and fathers to the CAPE-9 questionnaire (n = 28,793). We investigated the psychometric properties of CAPE-16 through factor analyses, measurement invariance testing across biological sex, response before/ during the COVID-19 pandemic, and generations (comparison with fathers), and examined associations with later psychiatric diagnoses. Outcomes: One third (33·4%) of adolescents reported lifetime psychotic experiences. We confirmed a three-factor structure (paranoia, bizarre thoughts, and hallucinations) of CAPE-16, and observed good scale reliability of the distress and frequency subscales (ω = ·86 and ·90). CAPE-16 measured psychotic experiences were invariant to biological sex and pandemic status. CAPE-9 was non-invariant across generations, with items related to understanding of the digital world (electrical influences) prone to bias. CAPE-16 sum scores were associated with a subsequent psychiatric diagnosis, particularly psychotic disorders (frequency: OR = 2·06; 97·5% CI = 1·70-2·46; distress: OR = 1·93; 97·5% CI = 1·63-2·26). Interpretation: CAPE-16 showed robust psychometric properties across sex and pandemic status, and sum scores were associated with subsequent psychiatric diagnoses, particularly psychotic disorders. These findings suggest that with certain adjustments, CAPE-16 could have value as a screening tool for adolescents in the modern, digital world. Funding: European Union's Horizon 2020 Programme, Research Council of Norway, South-Eastern Norway Regional Health Authority, NIMH, and the KG Jebsen Stiftelsen.

Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR.

Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings.

Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability.

BACKGROUND: Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. METHODS: We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. RESULTS: We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. CONCLUSION: We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.

Polygenic liability for antipsychotic dosage and polypharmacy - a real-world registry and biobank study.

Genomic prediction of antipsychotic dose and polypharmacy has been difficult, mainly due to limited access to large cohorts with genetic and drug prescription data. In this proof of principle study, we investigated if genetic liability for schizophrenia is associated with high dose requirements of antipsychotics and antipsychotic polypharmacy, using real-world registry and biobank data from five independent Nordic cohorts of a total of N = 21,572 individuals with psychotic disorders (schizophrenia, bipolar disorder, and other psychosis). Within regression models, a polygenic risk score (PRS) for schizophrenia was studied in relation to standardized antipsychotic dose as well as antipsychotic polypharmacy, defined based on longitudinal prescription registry data as well as health records and self-reported data. Meta-analyses across the five cohorts showed that PRS for schizophrenia was significantly positively associated with prescribed (standardized) antipsychotic dose (beta(SE) = 0.0435(0.009), p = 0.0006) and antipsychotic polypharmacy defined as taking ≥2 antipsychotics (OR = 1.10, CI = 1.05-1.21, p = 0.0073). The direction of effect was similar in all five independent cohorts. These findings indicate that genotypes may aid clinically relevant decisions on individual patients´ antipsychotic treatment. Further, the findings illustrate how real-world data have the potential to generate results needed for future precision medicine approaches in psychiatry.

Conduct disorder - a comprehensive exploration of comorbidity patterns, genetic and environmental risk factors.

Conduct disorder (CD), a common mental disorder in children and adolescents, is characterized by antisocial behavior. Despite similarities with antisocial personality disorder (ASPD) and possible diagnostic continuity, CD has been shown to precede a range of adult-onset mental disorders. Additionally, little is known about the putative shared genetic liability between CD and adult-onset mental disorders and the underlying gene-environment interplay. Here, we interrogated comorbidity between CD and other mental disorders from the Norwegian Mother, Father and Child Cohort Study (n = 114 500) and investigated how polygenic risk scores (PRS) for mental health traits were associated with CD/CD traits in childhood and adolescence. Gene-environment interplay patterns for CD was explored with data on bullying and parental education. We found CD to be comorbid with several child and adult-onset mental disorders. This phenotypic overlap corresponded with associations between PRS for mental disorders and CD. Additionally, our findings support an additive gene-environment model. Previously conceptualized as a precursor of ASPD, we found that CD was associated with polygenic risk for several child- and adult-onset mental disorders. High comorbidity of CD with other psychiatric disorders reflected on the genetic level should inform research studies, diagnostic assessments and clinical follow-up of this heterogenous group.

Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders.

Background: Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods: We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively. Results: Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci. Conclusions: Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.

Lipid Biomarker Research in Bipolar Disorder: A Scoping Review of Trends, Challenges, and Future Directions.

Bipolar disorder (BD) is a disabling disorder with heterogeneous symptom profiles and trajectories. Like many other neuropsychiatric disorders, clinical decision making related to diagnoses and choice of treatment is based on clinical assessments alone, and risk prediction for treatment success or resistance at an individual level remains sparse. An enormous effort to add biological markers to this risk prediction is ongoing. The role of lipids in normal brain functioning is well established, and several hypotheses about the role of lipids in the pathogenesis of neuropsychiatric disorders, including BD, have been made. The frequent comorbidity between neuropsychiatric disorders and cardiovascular disease, the genetic overlap of risk genes for severe mental disorders and genes involved in lipid regulation, and the lipid-altering effects of antipsychotics and mood stabilizers indicate that lipids could hold promise as biomarkers for neuropsychiatric disorders, including BD. To date, reviews of lipid biomarkers in schizophrenia and major depression have noted caveats for future investigations, while reviews of lipid biomarker research in BD is missing. In the current scoping review, we present a comprehensive overview of trends in previous research on lipid biomarkers in BD. The current literature varies greatly in the phenotypes investigated and study designs, leading to divergent findings. Small sample size; potential confounders related to physical activity, nutritional status, and medication use; and cross-sectional designs were frequently reported limitations. Future research may benefit from pivoting toward utilization of newer laboratory techniques such as lipidomics, but consistent use of study methods across cohorts is also needed.

Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders.

C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600-14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.

Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa.

Anorexia nervosa (AN) is a heritable eating disorder (50-60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the "signal by MST1" pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis.

BACKGROUND: Irritable bowel syndrome (IBS) often co-occurs with psychiatric and gastrointestinal disorders. A recent genome-wide association study (GWAS) identified several genetic risk variants for IBS. However, most of the heritability remains unidentified, and the genetic overlap with psychiatric and somatic disorders is not quantified beyond genome-wide genetic correlations. Here, we characterize the genetic architecture of IBS, further, investigate its genetic overlap with psychiatric and gastrointestinal phenotypes, and identify novel genomic risk loci. METHODS: Using GWAS summary statistics of IBS (53,400 cases and 433,201 controls), and psychiatric and gastrointestinal phenotypes, we performed bivariate casual mixture model analysis to characterize the genetic architecture and genetic overlap between these phenotypes. We leveraged identified genetic overlap to boost the discovery of genomic loci associated with IBS, and to identify specific shared loci associated with both IBS and psychiatric and gastrointestinal phenotypes, using the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework. We used functional mapping and gene annotation (FUMA) for functional analyses. RESULTS: IBS was highly polygenic with 12k trait-influencing variants. We found extensive polygenic overlap between IBS and psychiatric disorders and to a lesser extent with gastrointestinal diseases. We identified 132 independent IBS-associated loci (condFDR < 0.05) by conditioning on psychiatric disorders (n = 127) and gastrointestinal diseases (n = 24). Using conjFDR, 70 unique loci were shared between IBS and psychiatric disorders. Functional analyses of shared loci revealed enrichment for biological pathways of the nervous and immune systems. Genetic correlations and shared loci between psychiatric disorders and IBS subtypes were different. CONCLUSIONS: We found extensive polygenic overlap of IBS and psychiatric and gastrointestinal phenotypes beyond what was revealed with genetic correlations. Leveraging the overlap, we discovered genetic loci associated with IBS which implicate a wide range of biological pathways beyond the gut-brain axis. Genetic differences may underlie the clinical subtype of IBS. These results increase our understanding of the pathophysiology of IBS which may form the basis for the development of individualized interventions.

The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis.

While neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.

Psychometric Properties and Diagnostic Associations of the Short-Form Community Assessment of Psychic Experiences in a Population-Based Sample of 29 021 Adult Men.

BACKGROUND AND HYPOTHESIS: Around 5%-7% of the adult population are estimated to have lifetime psychotic experiences (PEs), which are associated with psychosis risk. PEs assessed with Community Assessment of Psychic Experiences (CAPE) are associated with psychosis but also non-psychotic disorders, which could be partly explained by CAPE indirectly capturing emotional symptoms. We investigated the psychometric properties of a shorter version, CAPE-9, and whether CAPE-9 scores are associated with lifetime psychotic or non-psychotic mental disorders after controlling for current anxiety and depressive symptoms. DESIGN: CAPE-9 questionnaire data were obtained from 29 021 men (42.4 ± 5.6 yrs.) from the Norwegian Mother, Father, and Child Cohort Study. We investigated CAPE-9 reliability and factor structure. Logistic regression was used to test effects of current anxiety and depressive symptoms (SCL-12) on associations between CAPE-9 scores and psychiatric diagnoses. RESULTS: CAPE-9 fit a previously reported 3-factor structure and showed good reliability. Twenty-six percent reported at least one lifetime PE. CAPE-9 scores were significantly associated with most psychiatric disorders (schizophrenia, depression, bipolar disorder, substance abuse, anxiety, trauma-related disorders, and ADHD). After controlling for concurrent emotional symptoms, only associations with schizophrenia (OR = 1.29; 95% CI = 1.18-1.38) and trauma-related disorders (OR = 1.09; CI = 1.02-1.15) remained significant. CONCLUSIONS: CAPE-9 showed good psychometric properties in this large population-based adult male sample, and PEs were more clearly associated with psychotic disorders after controlling for current emotional symptoms. These results support the use of the short CAPE-9 as a cost-effective tool for informing public health initiatives and advancing our understanding of the dimensionality of psychosis.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system.

BACKGROUND: Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. METHODS: We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. RESULTS: MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. CONCLUSIONS: Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study.

BACKGROUND: The relationship between psychotic disorders and cannabis use is heavily debated. Shared underlying genetic risk is one potential explanation. We investigated the genetic association between psychotic disorders (schizophrenia and bipolar disorder) and cannabis phenotypes (lifetime cannabis use and cannabis use disorder). METHODS: We used genome-wide association summary statistics from individuals with European ancestry from the Psychiatric Genomics Consortium, UK Biobank, and International Cannabis Consortium. We estimated heritability, polygenicity, and discoverability of each phenotype. We performed genome-wide and local genetic correlations. Shared loci were identified and mapped to genes, which were tested for functional enrichment. Shared genetic liabilities to psychotic disorders and cannabis phenotypes were explored using causal analyses and polygenic scores, using the Norwegian Thematically Organized Psychosis cohort. FINDINGS: Psychotic disorders were more heritable than cannabis phenotypes and more polygenic than cannabis use disorder. We observed positive genome-wide genetic correlations between psychotic disorders and cannabis phenotypes (range 0·22-0·35) with a mixture of positive and negative local genetic correlations. Three to 27 shared loci were identified for the psychotic disorder and cannabis phenotype pairs. Enrichment of mapped genes implicated neuronal and olfactory cells as well as drug-gene targets for nicotine, alcohol, and duloxetine. Psychotic disorders showed a causal effect on cannabis phenotypes, and lifetime cannabis use had a causal effect on bipolar disorder. Of 2181 European participants from the Norwegian Thematically Organized Psychosis cohort applied in polygenic risk score analyses, 1060 (48·6%) were females and 1121 (51·4%) were males (mean age 33·1 years [SD 11·8]). 400 participants had bipolar disorder, 697 had schizophrenia, and 1044 were healthy controls. Within this sample, polygenic scores for cannabis phenotypes predicted psychotic disorders independently and improved prediction beyond the polygenic score for the psychotic disorders. INTERPRETATION: A subgroup of individuals might have a high genetic risk of developing a psychotic disorder and using cannabis. This finding supports public health efforts to reduce cannabis use, particularly in individuals at high risk or patients with psychotic disorders. Identified shared loci and their functional implications could facilitate development of novel treatments. FUNDING: US National Institutes of Health, the Research Council Norway, the South-East Regional Health Authority, Stiftelsen Kristian Gerhard Jebsen, EEA-RO-NO-2018-0535, European Union's Horizon 2020 Research and Innovation Programme, the Marie Sklodowska-Curie Actions, and University of Oslo Life Science.

Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression.

Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways.

Associations Between Symptoms of Premenstrual Disorders and Polygenic Liability for Major Psychiatric Disorders.

Importance: Premenstrual disorders are heritable, clinically heterogenous, with a range of affective spectrum comorbidities. It is unclear whether genetic predispositions to affective spectrum disorders or other major psychiatric disorders are associated with symptoms of premenstrual disorders. Objective: To assesss whether symptoms of premenstrual disorders are associated with the genetic liability for major psychiatric disorders, as indexed by polygenic risk scores (PRSs). Design, Setting, and Participants: Women from the Norwegian Mother, Father and Child Cohort Study were included in this genetic association study. PRSs were used to determine whether genetic liability for major depression, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism spectrum disorder were associated with the symptoms of premenstrual disorders, using the PRS for height as a somatic comparator. The sample was recruited across Norway between June 1999 and December 2008, and analyses were performed from July 1 to October 14, 2022. Main Outcomes and Measures: The symptoms of premenstrual disorders were assessed at recruitment at week 15 of pregnancy with self-reported severity of depression and irritability before menstruation. Logistic regression was applied to test for the association between the presence of premenstrual disorder symptoms and the PRSs for major psychiatric disorders. Results: The mean (SD) age of 56 725 women included in the study was 29.0 (4.6) years. Premenstrual disorder symptoms were present in 12 316 of 56 725 participants (21.7%). The symptoms of premenstrual disorders were associated with the PRSs for major depression (ß = 0.13; 95% CI, 0.11-0.15; P = 1.21 × 10-36), bipolar disorder (ß = 0.07; 95% CI, 0.05-0.09; P = 1.74 × 10-11), attention deficit/hyperactivity disorder (ß = 0.07; 95% CI, 0.04-0.09; P = 1.58 × 10-9), schizophrenia (ß = 0.11; 95% CI, 0.09-0.13; P = 7.61 × 10-25), and autism spectrum disorder (ß = 0.03; 95% CI, 0.01-0.05; P = .02) but not with the PRS for height. The findings were confirmed in a subsample of women without a history of psychiatric diagnosis. Conclusions: The results of this genetic association study show that genetic liability for both affective spectrum disorder and major psychiatric disorders was associated with symptoms of premenstrual disorders, indicating that premenstrual disorders have overlapping genetic foundations with major psychiatric disorders.

Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci.

Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.

Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia.

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.