Shared genetic loci between depression and cardiometabolic traits.

Epidemiological and clinical studies have found associations between depression and cardiovascular disease risk factors, and coronary artery disease patients with depression have worse prognosis. The genetic relationship between depression and these cardiovascular phenotypes is not known. We here investigated overlap at the genome-wide level and in individual loci between depression, coronary artery disease and cardiovascular risk factors. We used the bivariate causal mixture model (MiXeR) to quantify genome-wide polygenic overlap and the conditional/conjunctional false discovery rate (pleioFDR) method to identify shared loci, based on genome-wide association study summary statistics on depression (n = 450,619), coronary artery disease (n = 502,713) and nine cardiovascular risk factors (n = 204,402-776,078). Genetic loci were functionally annotated using FUnctional Mapping and Annotation (FUMA). Of 13.9K variants influencing depression, 9.5K (SD 1.0K) were shared with body-mass index. Of 4.4K variants influencing systolic blood pressure, 2K were shared with depression. ConjFDR identified 79 unique loci associated with depression and coronary artery disease or cardiovascular risk factors. Six genomic loci were associated jointly with depression and coronary artery disease, 69 with blood pressure, 49 with lipids, 9 with type 2 diabetes and 8 with c-reactive protein at conjFDR < 0.05. Loci associated with increased risk for depression were also associated with increased risk of coronary artery disease and higher total cholesterol, low-density lipoprotein and c-reactive protein levels, while there was a mixed pattern of effect direction for the other risk factors. Functional analyses of the shared loci implicated metabolism of alpha-linolenic acid pathway for type 2 diabetes. Our results showed polygenic overlap between depression, coronary artery disease and several cardiovascular risk factors and suggest molecular mechanisms underlying the association between depression and increased cardiovascular disease risk.

Identification of novel genomic risk loci shared between common epilepsies and psychiatric disorders.

Psychiatric disorders and common epilepsies are heritable disorders with a high comorbidity and overlapping symptoms. However, the causative mechanisms underlying this relationship are poorly understood. Here we aimed to identify overlapping genetic loci between epilepsy and psychiatric disorders to gain a better understanding of their comorbidity and shared clinical features. We analysed genome-wide association study data for all epilepsies (n = 44 889), genetic generalized epilepsy (n = 33 446), focal epilepsy (n = 39 348), schizophrenia (n = 77 096), bipolar disorder (n = 406 405), depression (n = 500 199), attention deficit hyperactivity disorder (n = 53 293) and autism spectrum disorder (n = 46 350). First, we applied the MiXeR tool to estimate the total number of causal variants influencing the disorders. Next, we used the conjunctional false discovery rate statistical framework to improve power to discover shared genomic loci. Additionally, we assessed the validity of the findings in independent cohorts, and functionally characterized the identified loci. The epilepsy phenotypes were considerably less polygenic (1.0 K to 3.4 K causal variants) than the psychiatric disorders (5.6 K to 13.9 K causal variants), with focal epilepsy being the least polygenic (1.0 K variants), and depression having the highest polygenicity (13.9 K variants). We observed cross-trait genetic enrichment between genetic generalized epilepsy and all psychiatric disorders and between all epilepsies and schizophrenia and depression. Using conjunctional false discovery rate analysis, we identified 40 distinct loci jointly associated with epilepsies and psychiatric disorders at conjunctional false discovery rate <0.05, four of which were associated with all epilepsies and 39 with genetic generalized epilepsy. Most epilepsy risk loci were shared with schizophrenia (n = 31). Among the identified loci, 32 were novel for genetic generalized epilepsy, and two were novel for all epilepsies. There was a mixture of concordant and discordant allelic effects in the shared loci. The sign concordance of the identified variants was highly consistent between the discovery and independent datasets for all disorders, supporting the validity of the findings. Gene-set analysis for the shared loci between schizophrenia and genetic generalized epilepsy implicated biological processes related to cell cycle regulation, protein phosphatase activity, and membrane and vesicle function; the gene-set analyses for the other loci were underpowered. The extensive genetic overlap with mixed effect directions between psychiatric disorders and common epilepsies demonstrates a complex genetic relationship between these disorders, in line with their bi-directional relationship, and indicates that overlapping genetic risk may contribute to shared pathophysiological and clinical features between epilepsy and psychiatric disorders.

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools.

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine's polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100-12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of 'pleiotropic' variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation.

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.

Increased circulating IL-18 levels in severe mental disorders indicate systemic inflammasome activation.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental illnesses (SMI) that are part of a psychosis continuum, and dysregulated innate immune responses have been suggested to be involved in their pathophysiology. However, disease-specific immune mechanisms in SMI are not known yet. Recently, dyslipidemia has been linked to systemic inflammasome activation, and elevated atherogenic lipid ratios have been shown to correlate with circulating levels of inflammatory biomarkers in SMI. It is, however, not yet known if increased systemic cholesterol load leads to inflammasome activation in these patients. METHODS: We tested the hypothesis that patients with SCZ and BD display higher circulating levels compared to healthy individuals of key members of the IL-18 system using a large patient cohort (n = 1632; including 737 SCZ and 895 BD), and healthy controls (CTRL; n = 1070). In addition, we assessed associations with coronary artery disease risk factors in SMI, focusing on relevant inflammasome-related, neuroendocrine, and lipid markers. RESULTS: We report higher baseline levels of circulating IL-18 system components (IL-18, IL-18BPA, IL-18R1), and increased expression of inflammasome-related genes (NLRP3 and NLRC4) in the blood of patients relative to CTRL. We demonstrate a cholesterol dyslipidemia pattern in psychotic disorders, and report correlations between levels of blood cholesterol types and the expression of inflammasome system elements in SMI. CONCLUSIONS: Based on these results, we suggest a role for inflammasome activation/dysregulation in SMI. Our findings further the understanding of possible underlying inflammatory mechanisms and may expose important therapeutic targets in SMI.

Identification of genetic overlap and novel risk loci for attention-deficit/hyperactivity disorder and bipolar disorder.

Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainly due to overlapping symptoms and high rates of comorbidity. Despite this, genetic correlation reported for these disorders is low and non-significant. Here we aimed to better characterize the genetic architecture of these disorders utilizing recent large genome-wide association studies (GWAS). We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 controls) and BD (20,352 cases and 31,358 controls) applying the conditional/conjunctional false discovery rate (condFDR/conjFDR) statistical framework that increases the power to detect novel phenotype-specific and shared loci by leveraging the combined power of two GWAS. We observed cross-trait polygenic enrichment for ADHD conditioned on associations with BD, and vice versa. Leveraging this enrichment, we identified 19 novel ADHD risk loci and 40 novel BD risk loci at condFDR <0.05. Further, we identified five loci jointly associated with ADHD and BD (conjFDR < 0.05). Interestingly, these five loci show concordant directions of effect for ADHD and BD. These results highlight a shared underlying genetic risk for ADHD and BD which may help to explain the high comorbidity rates and difficulties in differentiating between ADHD and BD in the clinic. Improving our understanding of the underlying genetic architecture of these disorders may aid in the development of novel stratification tools to help reduce these diagnostic difficulties.

The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis.

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg = 0.10-0.62). Of 10.4 K genomic variants influencing MOOD, 4 K-9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, "synapse organization." The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR.

MOTIVATION: Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. RESULTS: Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/precimed/mixer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic variants associated with cardiometabolic abnormalities during treatment with selective serotonin reuptake inhibitors: a genome-wide association study.

Selective serotonin reuptake inhibitors (SSRIs) are prescribed both to patients with schizophrenia and bipolar disorder. Previous studies have shown associations between SSRI treatment and cardiometabolic alterations. The aim of the present study was to investigate genetic variants associated with cardiometabolic adverse effects in patients treated with SSRIs in a naturalistic setting, using a genome-wide cross-sectional approach in a genetically homogeneous sample. We included and genotyped 1981 individuals with schizophrenia or bipolar disorder, of whom 1180 had information available on the outcomes low-density lipoprotein cholesterol (LDL-cholesterol), high-density lipoprotein cholesterol (HDL-cholesterol), triglycerides, and body mass index (BMI) and investigated interactions between SNPs and SSRI use (N = 246) by conducting a genome-wide GxE analysis. We report 13 genome-wide significant interaction effects of SNPs and SSRI serum concentrations on LDL-cholesterol, HDL-cholesterol, and BMI, located in four distinct genomic loci. This study provides new insight into the pharmacogenetics of SSRI but warrants replication in independent populations.

Shared Genetics of Multiple System Atrophy and Inflammatory Bowel Disease.

BACKGROUND: Multiple system atrophy (MSA) is a rare neurodegenerative disease characterized by intracellular accumulations of α-synuclein and nerve cell loss in striatonigral and olivopontocerebellar structures. Epidemiological and clinical studies have reported potential involvement of autoimmune mechanisms in MSA pathogenesis. However, genetic etiology of this interaction remains unknown. We aimed to investigate genetic overlap between MSA and 7 autoimmune diseases and to identify shared genetic loci. METHODS: Genome-wide association study summary statistics of MSA and 7 autoimmune diseases were combined in cross-trait conjunctional false discovery rate analysis to explore overlapping genetic background. Expression of selected candidate genes was compared in transgenic MSA mice and wild-type mice. Genetic variability of candidate genes was further investigated using independent whole-exome genotyping data from large cohorts of MSA and autoimmune disease patients and healthy controls. RESULTS: We observed substantial polygenic overlap between MSA and inflammatory bowel disease and identified 3 shared genetic loci with leading variants upstream of the DENND1B and RSP04 genes, and in intron of the C7 gene. Further, the C7 gene showed significantly dysregulated expression in the degenerating midbrain of transgenic MSA mice compared with wild-type mice and had elevated burden of protein-coding variants in independent MSA and inflammatory bowel disease cohorts. CONCLUSION: Our study provides evidence of shared genetic etiology between MSA and inflammatory bowel disease with an important role of the C7 gene in both phenotypes, with the implication of immune and gut dysfunction in MSA pathophysiology. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Genetic contributions to bipolar disorder: current status and future directions.

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

Association between complement component 4A expression, cognitive performance and brain imaging measures in UK Biobank.

Abstract. BACKGROUND: Altered expression of the complement component C4A gene is a known risk factor for schizophrenia. Further, predicted brain C4A expression has also been associated with memory function highlighting that altered C4A expression in the brain may be relevant for cognitive and behavioral traits. METHODS: We obtained genetic information and performance measures on seven cognitive tasks for up to 329 773 individuals from the UK Biobank, as well as brain imaging data for a subset of 33 003 participants. Direct genotypes for variants (n = 3213) within the major histocompatibility complex region were used to impute C4 structural variation, from which predicted expression of the C4A and C4B genes in human brain tissue were predicted. We investigated if predicted brain C4A or C4B expression were associated with cognitive performance and brain imaging measures using linear regression analyses. RESULTS: We identified significant negative associations between predicted C4A expression and performance on select cognitive tests, and significant associations with MRI-based cortical thickness and surface area in select regions. Finally, we observed significant inconsistent partial mediation of the effects of predicted C4A expression on cognitive performance, by specific brain structure measures. CONCLUSIONS: These results demonstrate that the C4 risk locus is associated with the central endophenotypes of cognitive performance and brain morphology, even when considered independently of other genetic risk factors and in individuals without mental or neurological disorders.

Quantifying the Polygenic Architecture of the Human Cerebral Cortex: Extensive Genetic Overlap between Cortical Thickness and Surface Area.

The thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remain unknown. Our ability to identify causal genetic variants can be improved by employing brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, that is, lower polygenicity and higher discoverability. Using Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area, as well as the polygenicity and discoverability of regional measures. We made use of UK Biobank data from 30 880 healthy White European individuals (mean age 64.3, standard deviation 7.5, 52.1% female). We found large genetic overlap between total surface area and mean thickness, sharing 4016 out of 7941 causal variants. Regional surface area was more discoverable (P = 2.6 × 10-6) and less polygenic (P = 0.004) than regional thickness measures. These findings may serve as a roadmap for improved future GWAS studies; knowledge of which measures are most discoverable may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

Variation within voltage-gated calcium channel genes and antipsychotic treatment response in a South African first episode schizophrenia cohort.

Voltage-gated calcium channels have been implicated in schizophrenia aetiology; however, little is known about their involvement in antipsychotic treatment response. This study investigated variants within the calcium channel subunit genes for association with antipsychotic treatment response in a first episode schizophrenia cohort. Twelve regulatory variants within seven genes were shown to be significantly associated with treatment outcome. Most notably, the CACNA1B rs2229949 CC genotype was associated with improved negative symptomology, where the C allele was predicted to abolish a miRNA-binding site (has-mir-5002-3p), suggesting a possible mechanism of action through which this variant may have an effect. These results implicate the calcium channel subunits in antipsychotic treatment response and suggest that increased activation of these channels may be explored to enhance or predict antipsychotic treatment outcome.

The genetic architecture of schizophrenia, bipolar disorder, obsessive-compulsive disorder and autism spectrum disorder.

Considerable evidence suggests that autism spectrum disorders (ASD), schizophrenia (SCZ), bipolar disorder (BD) and obsessive-compulsive disorder (OCD) share a common molecular aetiology, despite their unique clinical diagnostic criteria. The aim of this study was therefore to determine and characterise the common and unique molecular architecture of ASD, SCZ, BD and OCD. Gene lists were obtained from previously published studies for ASD, BD, SCZ and for OCD. Genes identified to be common to all disorders, or unique to one specific disorder, were included for enrichment analyses using the web-server tool Enrichr. Ten genes were identified to be commonly associated with the aetiology of ASD, SCZ, BD and OCD. Enrichment analyses determined that these genes are predominantly involved in the dopaminergic and serotonergic pathways, the voltage-gated calcium ion channel gene network, folate metabolism, regulation of the hippo signaling pathway, and the regulation of gene silencing and expression. In addition to well-characterised and previously described pathways, regulation of the hippo signaling pathway was commonly associated with ASD, SCZ, BD and OCD, implicating neural development and neuronal maintenance as key in neuropsychiatric disorders. In contrast, a large number of previously associated genes were shown to be disorder-specific. And unique disorder-specific pathways and biological processes were presented for ASD, BD, SCZ and OCD aetiology. Considering the current global incidence and prevalence rates of mental health disorders, focus should be placed on cross-disorder commonalities in order to realise actionable and translatable results to combat mental health disorders.

Rapid Metabolism Underlying Subtherapeutic Serum Levels of Atypical Antipsychotics Preceding Clozapine Treatment: A Retrospective Analysis of Real-World Data.

INTRODUCTION: Adequate antipsychotic treatment intensity is required before diagnosing resistant schizophrenia and initiating clozapine treatment. We aimed to investigate potential rapid drug metabolism underlying low dose-adjusted serum concentration (CD) of non-clozapine atypical antipsychotics preceding clozapine treatment. METHODS: Patients using non-clozapine, atypical antipsychotics (aripiprazole, risperidone, olanzapine, or quetiapine) within 1 year before starting clozapine were included in this study from a therapeutic drug monitoring service in Oslo, Norway, between 2005 and 2023. Patients were assigned into low CD (LCD) and normal CD (NCD) subgroups. Using a reference sample with 147,964 antipsychotic measurements, LCD was defined as CDs below the 25th percentile, while patients with NCD exhibited CDs between the 25th and 75th percentile of the respective reference measurements. Metabolic ratios, doses, and frequency of subtherapeutic levels of non-clozapine antipsychotics were compared between LCD and NCD groups. RESULTS: Preceding clozapine treatment, 110 out of 272 included patients (40.4%) were identified with LCD. Compared with the NCD group, LCD patients exhibited higher metabolic ratios of olanzapine (1.5-fold; p < 0.001), quetiapine (3.0-fold; p < 0.001), and risperidone (6.0-fold; p < 0.001). Metabolic ratio differences were independent of smoking and CYP2D6 genotype for olanzapine (p = 0.008) and risperidone (p = 0.016), respectively. Despite higher doses of olanzapine (1.25-fold; p = 0.054) and quetiapine (1.6-fold; p = 0.001) in LCD versus NCD patients, faster metabolism among the former was accompanied by higher frequencies of subtherapeutic levels of olanzapine (3.3-fold; p = 0.044) and quetiapine (1.8-fold; p = 0.005). CONCLUSION: LCD and associated rapid metabolism of non-clozapine antipsychotics is frequent before starting clozapine treatment. For olanzapine and quetiapine, this is associated with significantly increased risk of having subtherapeutic concentrations.

Effect of the NFIB rs28379954 T>C polymorphism on CYP2D6-catalyzed metabolism of solanidine.

Cytochrome P450 2D6 (CYP2D6) is important for metabolism of 20%-25% of all clinically used drugs. Many known genetic variants contribute to the large interindividual variability in CYP2D6 metabolism, but much is still unexplained. We recently described that nuclear factor 1B (NFIB) regulates hepatic CYP2D6 expression with the minor allele of NFIB rs28379954 T>C significantly increasing CYP2D6-mediated risperidone metabolism. In this study, we investigated the effect of NFIB T>C on metabolism of solanidine, a dietary CYP2D6 substrate. Analyses of solanidine and metabolites (M414, M416, and M444) were performed by ultra-high performance liquid chromatography-high-resolution mass spectrometry in a cohort of 463 CYP2D6-genotyped patients of which with 58 (12.5%) carried NFIB TC (n = 56) or CC (n = 2). Increased metabolism of solanidine was found in CYP2D6 normal metabolizers (NMs; n = 258, 55.7%) carrying the NFIB C variant (n = 27, 5.8%) with 2.83- and 3.38-fold higher M416-to-solanidine (p = 0.039) and M444-to-solanidine (p = 0.046) ratios, respectively, whereas this effect was not significant among intermediate metabolizers (n = 166, 35.9%) (p ≥ 0.09). Importantly, no effect of the NFIB polymorphism on solanidine metabolism was seen in TC or CC carriers lacking CYP2D6 activity (poor metabolizers, n = 30, 6.5%, p ≥ 0.74). Furthermore, the NFIB polymorphism significantly explained variability in solanidine metabolism (M414 p = 0.013, M416 p = 0.020, and M416 and M444 p = 0.009) in multiple linear regression models for each metabolic ratio in the entire population, correcting for covariates (including CYP2D6 genotypes). Thus, the study confirms the effect of NFIB in regulating CYP2D6 activity, suggesting an about 200% increase in CYP2D6-mediated clearance in NMs being NFIB CT or CC carriers, comprising around 6% of Europeans.

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.

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia.

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.