Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

A polygenic score indexing a DRD2-related co-expression network is associated with striatal dopamine function

The D2 dopamine receptor (D2R) is the primary site of the therapeutic action of antipsychotics and is involved in essential brain functions relevant to schizophrenia, such as attention, memory, motivation, and emotion processing. Moreover, the gene coding for D2R (DRD2) has been associated with schizophrenia at a genome-wide level. Recent studies have shown that a polygenic co-expression index (PCI) predicting the brain-specific expression of a network of genes co-expressed with DRD2 was associated with response to antipsychotics, brain function during working memory in patients with schizophrenia, and with the modulation of prefrontal cortex activity after pharmacological stimulation of D2 receptors. We aimed to investigate the relationship between the DRD2 gene network and in vivo striatal dopaminergic function, which is a phenotype robustly associated with psychosis and schizophrenia. To this aim, a sample of 92 healthy subjects underwent 18F-DOPA PET and was genotyped for genetic variations indexing the co-expression of the DRD2-related genetic network in order to calculate the PCI for each subject. The PCI was significantly associated with whole striatal dopamine synthesis capacity (p = 0.038). Exploratory analyses on the striatal subdivisions revealed a numerically larger effect size of the PCI on dopamine function for the associative striatum, although this was not significantly different than effects in other sub-divisions. These results are in line with a possible relationship between the DRD2-related co-expression network and schizophrenia and extend it by identifying a potential mechanism involving the regulation of dopamine synthesis. Future studies are needed to clarify the molecular mechanisms implicated in this relationship.

Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.

Patterns of Convergence and Divergence Between Bipolar Disorder Type I and Type II: Evidence From Integrative Genomic Analyses

Aim: Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about the genetic structure of BD subtypes. We aimed to investigate the genetic overlap and distinction of bipolar type I (BD I) & type II (BD II) by conducting integrative post-GWAS analyses.

Methods: We utilized single nucleotide polymorphism (SNP)–level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis, including exploring the potential causal associations between two BD subtypes and lithium responses and comparing the difference in genetic structures among four different psychiatric traits.

Results: SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184, and RPL10AP3, shared by BD I and II, and one locus (MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes affecting BD I and II through expression in specific brain regions (nucleus accumbens for BD I). Cross-phenotype analyses indicated that BD I and II share continuous genetic structures with schizophrenia and major depressive disorder, which help fill the gaps left by the dichotomy of mental disorders.

Conclusion: These combined evidences illustrate genetic convergence and divergence between BD I and II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.

A systematic review and meta-analysis of structural and functional brain alterations in individuals with genetic and clinical high-risk for psychosis and bipolar disorder

Neuroimaging findings in people at either genetic risk or at clinical high-risk for psychosis (CHR-P) or bipolar disorder (CHR-B) remain unclear. A meta-analytic review of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies in individuals with genetic risk or CHR-P or CHR-B and controls identified 94 datasets (N = 7942). Notwithstanding no significant findings were observed following adjustment for multiple comparisons, several findings were noted at a more liberal threshold. Subjects at genetic risk for schizophrenia or bipolar disorder or at CHR-P exhibited lower gray matter (GM) volumes in the gyrus rectus (Hedges' g = -0.19). Genetic risk for psychosis was associated with GM reductions in the right cerebellum and left amygdala. CHR-P was associated with decreased GM volumes in the frontal superior gyrus and hypoactivation in the right precuneus, the superior frontal gyrus and the right inferior frontal gyrus. Genetic and CHR-P were associated with small structural and functional alterations involving regions implicated in psychosis. Further neuroimaging studies in individuals with genetic or CHR-B are warranted.

Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings

Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.

Revisiting the seven pillars of RDoC

BACKGROUND

In 2013, a few years after the launch of the National Institute of Mental Health's Research Domain Criteria (RDoC) initiative, Cuthbert and Insel published a paper titled "Toward the future of psychiatric diagnosis: the seven pillars of RDoC." The RDoC project is a translational research effort to encourage new ways of studying psychopathology through a focus on disruptions in normal functions (such as reward learning or attention) that are defined jointly by observable behavior and neurobiological measures. The paper outlined the principles of the RDoC research framework, including emphases on research that acquires data from multiple measurement classes to foster integrative analyses, adopts dimensional approaches, and employs novel methods for ascertaining participants and identifying valid subgroups.

DISCUSSION

To mark the first decade of the RDoC initiative, we revisit the seven pillars and highlight new research findings and updates to the framework that are related to each. This reappraisal emphasizes the flexible nature of the RDoC framework and its application in diverse areas of research, new findings related to the importance of developmental trajectories within and across neurobehavioral domains, and the value of computational approaches for clarifying complex multivariate relations among behavioral and neurobiological systems.

CONCLUSION

The seven pillars of RDoC have provided a foundation that has helped to guide a surge of new studies that have examined neurobehavioral domains related to mental disorders, in the service of informing future psychiatric nosology. Building on this footing, future areas of emphasis for the RDoC project will include studying central-peripheral interactions, developing novel approaches to phenotyping for genomic studies, and identifying new targets for clinical trial research to facilitate progress in precision psychiatry.

Genetic analysis of the ZNF804A gene in Mexican patients with schizophrenia, schizoaffective disorder and bipolar disorder

BACKGROUND

Evidence suggests that schizophrenia (SCZ), schizoaffective disorder (SAD) and bipolar disorder (BPD) share genetic risk variants. ZNF804A gene has been associated with these disorders in different populations. GWAS and candidate gene studies have reported association between the rs1344706 A allele with SCZ, SAD and BPD in European and Asian populations. In Mexican patients, no studies have specifically analyzed ZNF804A gene variants with these disorders. The aim of the study was to analyze the rs1344706 and identify common and rare variants in a targeted region of the ZNF804A gene in Mexican patients with SCZ, BPD and SAD compared with a control group.

METHODS

We genotyped the rs1344706 in 228 Mexican patients diagnosed with SCZ, SAD and BPD, and 295 controls. Also, an additional sample of 167 patients with these disorders and 170 controls was analyzed to identify rare and common variants using the Sanger-sequence analysis of a targeted region of ZNF804A gene.

RESULTS

Association analysis of rs1344706 observed a higher frequency of A allele in the patients compared with the control group; however, did not show statistical differences after Bonferronís correction (χ2 = 5.3, p = 0.0208). In the sequence analysis, we did not identify rare variants; however, we identified three common variants: rs3046266, rs1366842 and rs12477430. A comparison of the three identified variants between patients and controls did not show statistical differences (p > 0.0125). Finally, haplotype analysis did not show statistical differences between SCZ, SAD and BPD and controls.

CONCLUSIONS

Our findings did not support the evidence suggesting that ZNF804A gene participates in the etiology of SCZ, SAD and BPD. Future studies are needed in a larger sample size to identify the effect of this gene in psychiatric disorders.

Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis

Clinical heterogeneity is common in Mendelian disease, but small sample sizes make it difficult to identify specific contributing factors. However, if a disease represents the severely affected extreme of a spectrum of phenotypic variation, then modifier effects may be apparent within a larger subset of the population. Analyses that take advantage of this full spectrum could have substantially increased power. To test this, we developed cryptic phenotype analysis, a model-based approach that infers quantitative traits that capture disease-related phenotypic variability using qualitative symptom data. By applying this approach to 50 Mendelian diseases in two cohorts, we identify traits that reliably quantify disease severity. We then conduct genome-wide association analyses for five of the inferred cryptic phenotypes, uncovering common variation that is predictive of Mendelian disease-related diagnoses and outcomes. Overall, this study highlights the utility of computationally-derived phenotypes and biobank-scale cohorts for investigating the complex genetic architecture of Mendelian diseases.

Psychosis in Parkinson's Disease: A Lesson from Genetics

Psychosis in Parkinson's disease (PDP) represents a common and debilitating condition that complicates Parkinson's disease (PD), mainly in the later stages. The spectrum of psychotic symptoms are heterogeneous, ranging from minor phenomena of mild illusions, passage hallucinations and sense of presence to severe psychosis consisting of visual hallucinations (and rarely, auditory and tactile or gustatory) and paranoid delusions. PDP is associated with increased caregiver stress, poorer quality of life for patients and carers, reduced survival and risk of institutionalization with a significant burden on the healthcare system. Although several risk factors for PDP development have been identified, such as aging, sleep disturbances, long history of PD, cognitive impairment, depression and visual disorders, the pathophysiology of psychosis in PD is complex and still insufficiently clarified. Additionally, several drugs used to treat PD can aggravate or even precipitate PDP. Herein, we reviewed and critically analyzed recent studies exploring the genetic architecture of psychosis in PD in order to further understand the pathophysiology of PDP, the risk factors as well as the most suitable therapeutic strategies.

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

TSABL: Trait Specific Annotation Based Locus predictor

BACKGROUND

The majority of Genome Wide Associate Study (GWAS) loci fall in the non-coding genome, making causal variants difficult to identify and study. We hypothesized that the regulatory features underlying causal variants are biologically specific, identifiable from data, and that the regulatory architecture that influences one trait is distinct compared to biologically unrelated traits.

RESULTS

To better characterize and identify these variants, we used publicly available GWAS loci and genomic annotations to build 17 Trait Specific Annotation Based Locus (TSABL) predictors to identify differences between GWAS loci associated with different phenotypic trait groups. We used a penalized binomial logistic regression model to select trait relevant annotations and tested all models on a holdout set of loci not used for training in any trait. We were able to successfully build models for autoimmune, electrocardiogram, lipid, platelet, red blood cell, and white blood cell trait groups. We used these models both to prioritize variants in existing loci and to identify new genomic regions of interest.

CONCLUSIONS

We found that TSABL models identified biologically relevant regulatory features, and anticipate their future use to enhance the design and interpretation of genetic studies.

Different Frequency of Heschl’s Gyrus Duplication Patterns in Neuropsychiatric Disorders: An MRI Study in Bipolar and Major Depressive Disorders

An increased prevalence of duplicated Heschl’s gyrus (HG) has been repeatedly demonstrated in various stages of schizophrenia as a potential neurodevelopmental marker, but it remains unknown whether other neuropsychiatric disorders also exhibit this macroscopic brain feature. The present magnetic resonance imaging study aimed to examine the disease specificity of the established finding of altered HG patterns in schizophrenia by examining independent cohorts of bipolar disorder (BD) and major depressive disorder (MDD). Twenty-six BD patients had a significantly higher prevalence of HG duplication bilaterally compared to 24 age- and sex-matched controls, while their clinical characteristics (e.g., onset age, number of episodes, and medication) did not relate to HG patterns. No significant difference was found for the HG patterns between 56 MDD patients and 33 age- and sex-matched controls, but the patients with a single HG were characterized by more severe depressive/anxiety symptoms compared to those with a duplicated HG. Thus, in keeping with previous findings, the present study suggests that neurodevelopmental pathology associated with gyral formation of the HG during the late gestation period partly overlaps between schizophrenia and BD, but that HG patterns may make a somewhat distinct contribution to the phenomenology of MDD.

Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.

Motivation and Cognitive Abilities as Mediators Between Polygenic Scores and Psychopathology in Children

OBJECTIVE

Fundamental questions in biological psychiatry concern the mechanisms that mediate between genetic liability and psychiatric symptoms. Genetic liability for many common psychiatric disorders often confers transdiagnostic risk to develop a wide variety of psychopathological symptoms through yet unknown pathways. This study examined the psychological and cognitive pathways that might mediate the relationship between genetic liability (indexed by polygenic scores; PS) and broad psychopathology (indexed by p factor and its underlying dimensions).

METHOD

First, which of the common psychiatric PSs (major depressive disorder [MDD], attention-deficit/hyperactivity disorder [ADHD], anxiety, bipolar disorder, schizophrenia, autism) that were associated with p factor were identified. Then focused was shifted to 3 pathways: punishment sensitivity (reflected by behavioral inhibition system), reward sensitivity (reflected by behavioral activation system), and cognitive abilities (reflected by g factor based on 10 neurocognitive tasks). We applied structural equation modeling on the Adolescent Brain Cognitive Development (ABCD) Study dataset (n = 4,814; 2,263 girls; 9-10 years old).

RESULTS

MDD and ADHD PSs were associated with p factor. The association between MDD PS and psychopathology was partially mediated by punishment sensitivity and cognitive abilities (proportion mediated = 22.35%). Conversely, the influence of ADHD PS on psychopathology was partially mediated by reward sensitivity and cognitive abilities (proportion mediated = 30.04%). The mediating role of punishment sensitivity was specific to emotional/internalizing. The mediating role of both reward sensitivity and cognitive abilities was specific to behavioral/externalizing and neurodevelopmental dimensions of psychopathology.

CONCLUSION

This study provides a better understanding of how genetic risks for MDD and ADHD confer risks for psychopathology and suggests potential prevention/intervention targets for children at risk.

A novel longitudinal clustering approach to psychopathology across diagnostic entities in the hospital-based PsyCourse study

Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric information with the ultimate aim of identifying individuals with higher risk for severe illness. 397 participants of the PsyCourse study with schizophrenia- or bipolar-spectrum diagnoses were prospectively phenotyped over 18 months. Factor analysis of mixed data of different rating scales and subsequent longitudinal clustering were used to cluster disease trajectories. Five clusters of longitudinal trajectories were identified in the psychopathologic dimensions. Clusters differed significantly with regard to Global Assessment of Functioning, disease course, and-in some cases-diagnosis while there were no significant differences regarding sex, age at baseline or onset, duration of illness, or polygenic burden for schizophrenia. Longitudinal clustering may aid in identifying transdiagnostic homogeneous subgroups of individuals with severe psychiatric disease.

Genetics in psychiatry: Methods, clinical applications and future perspectives

Psychiatric disorders and related traits have a demonstrated genetic component, with heritability estimated by twin studies generally between 80% and 40%. Their pathogenesis is complex and multi-determined: environmental factors interact with a polygenic architecture, making difficult the development of models able to stratify patients or predict mental health outcomes. Despite this difficult challenge, relevant progress has been made in the field of psychiatric genetics in recent years. This review aims to present the main current methods in psychiatric genetics, their output, limitations, clinical applications, and possible future developments. Genome-wide association studies (GWASs) performed in increasingly large samples have led to the identification of replicated genetic loci associated with the risk of major psychiatric disorders, including schizophrenia and mood disorders. Statistical and biological approaches have been developed to improve our understanding of the etiopathogenetic mechanisms behind genome-wide significant associations, as well as for estimating the cumulative effect of risk variants at the individual level and the genetic overlap between different disorders, as pleiotropy is the rule rather than the exception. Clinical applications are available in the pharmacogenetics field. The main issues that remain to be addressed include improving ethnic diversity in genetic studies and the optimization of statistical power through methodological improvements, such as the definition of dimensional phenotypes with specific biological correlates and the integration of different types of omics data.

The effects of catechol-O-methyltransferase single nucleotide polymorphisms on positive and negative symptoms of schizophrenia: A systematic review and meta-analysis

The catechol-O-methyltransferase (COMT) gene is thought to have an important role in the etiopathogenesis of schizophrenia, but there are conflicting results regarding its role in clinical presentation. We aimed to elucidate the relationship between the single nucleotide polymorphisms (SNPs) in the COMT gene and the severity of positive and negative symptoms. In order to investigate the relationship, the PubMed, PubMed Central, Scopus, and Cochrane CENTRAL databases were screened for eligible articles. Thirty-eight studies, including 4443 adult patients with schizophrenia, were included in the quantitative analyses, and four studies were qualitatively assessed. Quantitative analyses were performed for acutely ill and clinically stable patient subgroups regarding the different genotypes of rs4680 SNP. Our results showed that the severity of negative symptoms was higher in patients who were rs4680 Met homozygous compared to Val/Met heterozygotes only in acutely ill samples. There was no other significant difference between genotypes. Meta-regression did not reveal any significant moderator effect on the difference in negative symptoms. General psychopathology, positive, negative, and total psychotic symptom levels also were similar between Val homozygotes and Met carriers. Nonetheless, there are some limitations in the study. First, SNPs except for rs4680 were under-researched because of the limited number of studies. Second, high heterogeneity across studies was the main concern. Our results suggested that the COMT rs4680 Met allele was associated with higher levels of negative symptoms within acutely ill patients. Future studies should focus on specific patient subgroups to reveal the moderating effects of SNPs.

The contribution of imprinted genes to neurodevelopmental and neuropsychiatric disorders

Imprinted genes are a subset of mammalian genes that are subject to germline parent-specific epigenetic modifications leading monoallelic expression. Imprinted gene expression is particularly prevalent in the brain and it is unsurprising that mutations affecting their expression can lead to neurodevelopmental and/or neuropsychiatric disorders in humans. Here I review the evidence for this, detailing key neurodevelopmental disorders linked to imprinted gene clusters on human chromosomes 15q11-q13 and 14q32, highlighting genes and possible regulatory links between these different syndromes. Similarly, rare copy number variant mutations at imprinted clusters also provide strong links between abnormal imprinted gene expression and the predisposition to severe psychiatric illness. In addition to direct links between brain-expressed imprinted genes and neurodevelopmental and/or neuropsychiatric disorders, I outline how imprinted genes that are expressed in another tissue hotspot, the placenta, contribute indirectly to abnormal brain and behaviour. Specifically, altered nutrient provisioning or endocrine signalling by the placenta caused by abnormal expression of imprinted genes may lead to increased prevalence of neurodevelopmental and/or neuropsychiatric problems in both the offspring and the mother.

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics

The emergence of genome-wide association studies (GWAS) has led to the creation of large repositories of human genetic variation, creating enormous opportunities for genetic research and worldwide collaboration. Methods that are based on GWAS summary statistics seek to leverage such records, overcoming barriers that often exist in individual-level data access while also offering significant computational savings. Such summary-statistics-based applications include GWAS meta-analysis, with and without sample overlap, and case-case GWAS. We compare performance of leading methods for summary-statistics-based genomic analysis and also introduce a novel framework that can unify usual summary-statistics-based implementations via the reconstruction of allelic and genotypic frequencies and counts (ReACt). First, we evaluate ASSET, METAL, and ReACt using both synthetic and real data for GWAS meta-analysis (with and without sample overlap) and find that, while all three methods are comparable in terms of power and error control, ReACt and METAL are faster than ASSET by a factor of at least hundred. We then proceed to evaluate performance of ReACt vs an existing method for case-case GWAS and show comparable performance, with ReACt requiring minimal underlying assumptions and being more user-friendly. Finally, ReACt allows us to evaluate, for the first time, an implementation for calculating polygenic risk score (PRS) for groups of cases and controls based on summary statistics. Our work demonstrates the power of GWAS summary-statistics-based methodologies and the proposed novel method provides a unifying framework and allows further extension of possibilities for researchers seeking to understand the genetics of complex disease.

Neurobiological and behavioral mechanisms of circadian rhythm disruption in bipolar disorder: A critical multi-disciplinary literature review and agenda for future research from the ISBD task force on chronobiology

AIM

Symptoms of bipolar disorder (BD) include changes in mood, activity, energy, sleep, and appetite. Since many of these processes are regulated by circadian function, circadian rhythm disturbance has been examined as a biological feature underlying BD. The International Society for Bipolar Disorders Chronobiology Task Force (CTF) was commissioned to review evidence for neurobiological and behavioral mechanisms pertinent to BD.

METHOD

Drawing upon expertise in animal models, biomarkers, physiology, and behavior, CTF analyzed the relevant cross-disciplinary literature to precisely frame the discussion around circadian rhythm disruption in BD, highlight key findings, and for the first time integrate findings across levels of analysis to develop an internally consistent, coherent theoretical framework.

RESULTS

Evidence from multiple sources implicates the circadian system in mood regulation, with corresponding associations with BD diagnoses and mood-related traits reported across genetic, cellular, physiological, and behavioral domains. However, circadian disruption does not appear to be specific to BD and is present across a variety of high-risk, prodromal, and syndromic psychiatric disorders. Substantial variability and ambiguity among the definitions, concepts and assumptions underlying the research have limited replication and the emergence of consensus findings.

CONCLUSIONS

Future research in circadian rhythms and its role in BD is warranted. Well-powered studies that carefully define associations between BD-related and chronobiologically-related constructs, and integrate across levels of analysis will be most illuminating.

Early deviant behaviour as a dimension trait and endophenotype in schizophrenia

Background

In psychiatry, there is still a lack of objective biological diagnostic measurements. It is important to investigate measurements or symptom dimensions that can inform diagnostic assessments and allow for a more personalised approach to patients.

Aim

To discuss how early deviant behaviour (EDB) may be seen as a possible continuous symptom dimension trait and endophenotype in schizophrenia.

Methods

Conducting a commentary review by highlighting some important findings from available literature.

Results

Findings regarding EDB in schizophrenia in a South African genetic sample point towards EDB as a progressive subtype of schizophrenia, with very early onset of illness (even prior to the psychotic symptomatology) and a genetic form of illness.

Conclusion

Valuable information can be gained by enquiring into EDB and viewing it as a continuous symptom dimension trait and endophenotype during the psychiatric diagnostic interview.

Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.

Schizophrenia and Bipolar Polygenic Risk Scores in Relation to Intracranial Volume

Schizophrenia and bipolar disorder are neurodevelopmental disorders with overlapping symptoms and a shared genetic background. Deviations in intracranial volume (ICV)—a marker for neurodevelopment—differ between schizophrenia and bipolar disorder. Here, we investigated whether genetic risk for schizophrenia and bipolar disorder is related to ICV in the general population by using the UK Biobank data (n = 20,196). Polygenic risk scores for schizophrenia (SZ-PRS) and bipolar disorder (BD-PRS) were computed for 12 genome wide association study P-value thresholds (PT) for each individual and correlations with ICV were investigated. Partial correlations were performed at each PT to investigate whether disease specific genetic risk variants for schizophrenia and bipolar disorder show different relationships with ICV. ICV showed a negative correlation with SZ-PRS at PT ≥ 0.005 (r < −0.02, p < 0.005). ICV was not associated with BD-PRS; however, a positive correlation between BD-PRS and ICV at PT = 0.2 and PT = 0.4 (r = +0.02, p < 0.005) appeared when the genetic overlap between schizophrenia and bipolar disorder was accounted for. Despite small effect sizes, a higher load of schizophrenia risk genes is associated with a smaller ICV in the general population, while risk genes specific for bipolar disorder are correlated with a larger ICV. These findings suggest that schizophrenia and bipolar disorder risk genes, when accounting for the genetic overlap between both disorders, have opposite effects on early brain development.

Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder

BACKGROUND

Substance use occurs at a high rate in persons with a psychiatric disorder. Genetically informative studies have the potential to elucidate the etiology of these phenomena. Recent developments in genome-wide association studies (GWAS) allow new avenues of investigation.

METHOD

Using results of GWAS meta-analyses, we performed a factor analysis of the genetic correlation structure, a genome-wide search of shared loci, and causally informative tests for six substance use phenotypes (four smoking, one alcohol, and one cannabis use) and five psychiatric disorders (ADHD, anorexia, depression, bipolar disorder, and schizophrenia).

RESULTS

Two correlated externalizing and internalizing/psychosis factor were found, although model fit was beneath conventional standards. Of 458 loci reported in previous univariate GWAS of substance use and psychiatric disorders, about 50% (230 loci) were pleiotropic with additional 111 pleiotropic loci not reported from past GWAS. Of the 341 pleiotropic loci, 152 were associated with both substance use and psychiatric disorders, implicating neurodevelopment, cell morphogenesis, biological adhesion pathways, and enrichment in 13 different brain tissues. Seventy-five and 114 pleiotropic loci were specific to either psychiatric disorders or substance use phenotypes, implicating neuronal signaling pathway and clathrin-binding functions/structures, respectively. No consistent evidence for phenotypic causation was found across different Mendelian randomization methods.

CONCLUSIONS

Genetic etiology of substance use and psychiatric disorders is highly pleiotropic and involves shared neurodevelopmental path, neurotransmission, and intracellular trafficking. In aggregate, the patterns are not consistent with vertical pleiotropy, more likely reflecting horizontal pleiotropy or more complex forms of phenotypic causation.

New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork

Gene-by-environment interactions are important for all facets of biology, especially behaviour. Families of isogenic strains of mice, such as the BXD strains, are excellently placed to study these interactions, as the same genome can be tested in multiple environments. BXD strains are recombinant inbred mouse strains derived from crossing two inbred strains—C57BL/6J and DBA/2J mice. Many reproducible genometypes can be leveraged, and old data can be reanalysed with new tools to produce novel insights. We obtained drug and behavioural phenotypes from Philip et al. Genes, Brain and Behaviour 2010, and reanalysed their data with new genotypes from sequencing, as well as new models (Genome-wide Efficient Mixed Model Association (GEMMA) and R/qtl2). We discovered QTLs on chromosomes 3, 5, 9, 11, and 14, not found in the original study. We reduced the candidate genes based on their ability to alter gene expression or protein function. Candidate genes included Slitrk6 and Cdk14. Slitrk6, in a Chromosome14 QTL for locomotion, was found to be part of a co-expression network involved in voluntary movement and associated with neuropsychiatric phenotypes. Cdk14, one of only three genes in a Chromosome5 QTL, is associated with handling induced convulsions after ethanol treatment, that is regulated by the anticonvulsant drug valproic acid. By using families of isogenic strains, we can reanalyse data to discover novel candidate genes involved in response to drugs of abuse.

Facial Emotion Recognition in Psychosis and Associations With Polygenic Risk for Schizophrenia: Findings From the Multi-Center EU-GEI Case-Control Study

BACKGROUND AND HYPOTHESIS

Facial Emotion Recognition is a key domain of social cognition associated with psychotic disorders as a candidate intermediate phenotype. In this study, we set out to investigate global and specific facial emotion recognition deficits in first-episode psychosis, and whether polygenic liability to psychotic disorders is associated with facial emotion recognition.

STUDY DESIGN

828 First Episode Psychosis (FEP) patients and 1308 population-based controls completed assessments of the Degraded Facial Affect Recognition Task (DFAR) and a subsample of 524 FEP and 899 controls provided blood or saliva samples from which we extracted DNA, performed genotyping and computed polygenic risk scores for schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MD).

STUDY RESULTS

A worse ability to globally recognize facial emotion expressions was found in patients compared with controls [B= -1.5 (0.6), 95% CI -2.7 to -0.3], with evidence for stronger effects on negative emotions (fear [B = -3.3 (1.1), 95% CI -5.3 to -1.2] and anger [B = -2.3 (1.1), 95% CI -4.6 to -0.1]) than on happiness [B = 0.3 (0.7), 95% CI -1 to 1.7]. Pooling all participants, and controlling for confounds including case/control status, facial anger recognition was associated significantly with Schizophrenia Polygenic Risk Score (SZ PRS) [B = -3.5 (1.7), 95% CI -6.9 to -0.2].

CONCLUSIONS

Psychosis is associated with impaired recognition of fear and anger, and higher SZ PRS is associated with worse facial anger recognition. Our findings provide evidence that facial emotion recognition of anger might play a role as an intermediate phenotype for psychosis.

Different patterns of white matter microstructural alterations between psychotic and non-psychotic bipolar disorder

This study aimed to investigate alterations in white matter (WM) microstructure in patients with psychotic and non-psychotic bipolar disorder (PBD and NPBD, respectively). We used 3T-magnetic resonance imaging to examine 29 PBD, 23 NPBD, and 65 healthy control (HC) subjects. Using tract-based spatial statistics for diffusion tensor imaging data, we compared fractional anisotropy (FA) and mean diffusion (MD) pairwise among the PBD, NPBD, and HC groups. We found several WM areas of decreased FA or increased MD in the PBD and NPBD groups compared to HC. PBD showed widespread FA decreases in the corpus callosum as well as the bilateral internal capsule and fornix. However, NPBD showed local FA decreases in a part of the corpus callosum body as well as in limited regions within the left cerebral hemisphere, including the anterior and posterior corona radiata and the cingulum. In addition, both PBD and NPBD shared widespread MD increases across the posterior corona radiata, cingulum, and sagittal stratum. These findings suggest that widespread WM microstructural alterations might be a common neuroanatomical characteristic of bipolar disorder, regardless of being psychotic or non-psychotic. Particularly, PBD might involve extensive inter-and intra-hemispheric WM connectivity disruptions.

Precision medicine in mood disorders

The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

Putting Genetics to Work in the Psychiatric Clinic

The rapid progress in psychiatric genetics over the past 10 years, while exciting from a research perspective, has not yet had an impact on clinical practice. How will we really be able to put genetics to work in the psychiatric clinic? This overview will attempt to answer this question. A survey of widely used methods and major study designs highlights key findings that have emerged so far. These findings inform a broad conceptual model of how genetic risk may act to influence dimensions of psychopathology and clinical presentations. The overview concludes with highlights of some of the most clinically relevant findings to date and their implications for psychiatric practice in the near future.

Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia

Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.

Epigenome-wide association study of alcohol use disorder in five brain regions

Alcohol use disorder (AUD) is closely linked to the brain regions forming the neurocircuitry of addiction. Postmortem human brain tissue enables the direct study of the molecular pathomechanisms of AUD. This study aims to identify these mechanisms by examining differential DNA-methylation between cases with severe AUD (n = 53) and controls (n = 58) using a brain-region-specific approach, in which sample sizes ranged between 46 and 94. Samples of the anterior cingulate cortex (ACC), Brodmann Area 9 (BA9), caudate nucleus (CN), ventral striatum (VS), and putamen (PUT) were investigated. DNA-methylation levels were determined using the Illumina HumanMethylationEPIC Beadchip. Epigenome-wide association analyses were carried out to identify differentially methylated CpG-sites and regions between cases and controls in each brain region. Weighted correlation network analysis (WGCNA), gene-set, and GWAS-enrichment analyses were performed. Two differentially methylated CpG-sites were associated with AUD in the CN, and 18 in VS (q < 0.05). No epigenome-wide significant CpG-sites were found in BA9, ACC, or PUT. Differentially methylated regions associated with AUD case-/control status (q < 0.05) were found in the CN (n = 6), VS (n = 18), and ACC (n = 1). In the VS, the WGCNA-module showing the strongest association with AUD was enriched for immune-related pathways. This study is the first to analyze methylation differences between AUD cases and controls in multiple brain regions and consists of the largest sample to date. Several novel CpG-sites and regions implicated in AUD were identified, providing a first basis to explore epigenetic correlates of AUD.

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach

BACKGROUND

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

AIMS

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

METHOD

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

RESULTS

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

CONCLUSIONS

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90-98% and 65-70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.

Dissecting the genetic architecture of suicide attempt and repeated attempts in Korean patients with bipolar disorder using polygenic risk scores

BACKGROUND

Bipolar disorder (BD) has the greatest suicide risk among mental and physical disorders. A recent genome-wide association study (GWAS) of European ancestry (EUR) samples revealed that the genetic etiology of suicide attempt (SA) was not only polygenic but also, in part, diagnosis-specific. The authors aimed to examine whether the polygenic risk score (PRS) for SA derived from that study is associated with SA or repeated attempts in Korean patients with BD. This study also investigated the shared heritability of SA and mental disorders which showed an increased risk of SA and a high genetic correlation with BD.

METHODS

The study participants were 383 patients with BD. The history of SA was assessed on a lifetime basis. PRSs for reference disorders were calculated using the aforementioned GWAS data for SA and the Psychiatric Genomics Consortium data of BD, schizophrenia, major depressive disorder (MDD), and obsessive-compulsive disorder (OCD).

RESULTS

The PRS for SA was significantly associated with lifetime SA in the current subjects (Nagelkerke's R2 = 2.73%, odds ratio [OR] = 1.36, p = 0.007). Among other PRSs, only the PRS for OCD was significantly associated with lifetime SA (Nagelkerke's R2 = 2.72%, OR = 1.36, p = 0.007). The PRS for OCD was higher in multiple attempters than in single attempters (Nagelkerke's R2 = 4.91%, OR = 1.53, p = 0.043).

CONCLUSION

The PRS for SA derived from EUR data was generalized to SA in Korean patients with BD. The PRS for OCD seemed to affect repeated attempts. Genetic studies on suicide could benefit from focusing on specific psychiatric diagnoses and refined sub-phenotypes, as well as from utilizing multiple PRSs for related disorders.

Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.

Genetic and Epigenetic Markers of Lithium Response

The mood stabilizer lithium represents a cornerstone in the long term treatment of bipolar disorder (BD), although with substantial interindividual variability in clinical response. This variability appears to be modulated by genetics, which has been significantly investigated in the last two decades with some promising findings. In addition, recently, the interest in the role of epigenetics has grown significantly, since the exploration of these mechanisms might allow the elucidation of the gene-environment interactions and explanation of missing heritability. In this article, we provide an overview of the most relevant findings regarding the pharmacogenomics and pharmacoepigenomics of lithium response in BD. We describe the most replicated findings among candidate gene studies, results from genome-wide association studies (GWAS) as well as post-GWAS approaches supporting an association between high genetic load for schizophrenia, major depressive disorder or attention deficit/hyperactivity disorder and poor lithium response. Next, we describe results from studies investigating epigenetic mechanisms, such as changes in methylation or noncoding RNA levels, which play a relevant role as regulators of gene expression. Finally, we discuss challenges related to the search for the molecular determinants of lithium response and potential future research directions to pave the path towards a biomarker guided approach in lithium treatment.

Alternative Diagnostic Models of the Psychotic Disorders: Evidence-Based Choices

Standard diagnostic systems, the predominantly categorical DSM-5 and ICD-11, have limitations in validity, utility, and predictive and descriptive power. For psychotic disorders, these issues were partly addressed in current versions, but additional modifications are thought to be needed. Changes should be evidence based. We reviewed categorical, modified-categorical, and continuum-based models versus factor-based models of psychosis. Factors are clusters of symptoms or single prominent aspects of illness. Consistent evidence from studies of the genetics, pathobiology, and clinical presentation of psychotic disorders all support an underlying structure of factors, not categories, as best characterizing psychoses. Factors are not only the best fit but also comprehensive, as they can encompass any key feature of illness, including symptoms and course, as well as determinants of risk or response. Factors are inherently dimensional, even multidimensional, as are the psychoses themselves, and they provide the detail needed for either grouping or distinguishing patients for treatment decisions. The tools for making factor-based diagnoses are available, reliable, and concordant with actual practices used for clinical assessments. If needed, factors can be employed to create categories similar to those in current use. In addition, they can be used to define unique groupings of patients relevant to specific treatments or studies of the psychoses. Lastly, factor-based classifications are concordant with other comprehensive approaches to psychiatric nosology, including personalized (precision treatment) models and hierarchical models, both of which are currently being explored. Factors might be considered as the right primary structural choice for future versions of standard diagnostic systems, both DSM and ICD.

Investigating genetic overlaps of the genetic factor differentiating schizophrenia from bipolar disorder with cognitive function and hippocampal volume

Schizophrenia and bipolar disorder display clinical similarities and dissimilarities. We investigated whether the genetic factor differentiating schizophrenia from bipolar disorder is genetically associated with cognitive phenotypes and hippocampal volumes. We revealed genetic overlaps of the genetic differentiating factor with low general cognitive ability, low childhood IQ, low educational attainment and reduced hippocampal volumes. The genetic correlations with low general cognitive ability and reduced hippocampal volumes were associated with risk of schizophrenia, whereas the genetic correlations with high childhood IQ and educational attainment were associated with risks of bipolar disorder. These findings suggest these disorders have disorder-specific genetic factors related to clinical phenotypes.

Lack of Support for the Genes by Early Environment Interaction Hypothesis in the Pathogenesis of Schizophrenia

Ursini et al reported recently that the liability of schizophrenia explained by a polygenic risk score (PRS) derived from the variants most associated with schizophrenia was increased 5-fold in individuals who experienced complications during pregnancy or birth. Follow-up gene expression analysis showed that the genes mapping to the most associated genetic variants are highly expressed in placental tissues. If confirmed, these findings will have major implications in our understanding of the joint effect of genes and environment in the pathogenesis of schizophrenia. We examined the interplay between PRS and obstetric complications (OCs) in 5 independent samples (effective N = 2110). OCs were assessed with the full or modified Lewis-Murray scale, or with birth weight < 2.5 kg as a proxy. In a large cohort we tested whether the pathways from placenta-relevant variants in the original report were associated with case-control status. Unlike in the original study, we did not find significant effect of PRS on the presence of OCs in cases, nor a substantial difference in the association of PRS with case-control status in samples stratified by the presence of OCs. Furthermore, none of the PRS by OCs interactions were significant, nor were any of the biological pathways, examined in the Swedish cohort. Our study could not support the hypothesis of a mediating effect of placenta biology in the pathway from genes to schizophrenia. Methodology differences, in particular the different scales measuring OCs, as well as power constraints for interaction analyses in both studies, may explain this discrepancy.

Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Classifying major mental disorders genetically

Typically, mental disorders are defined and classified based on clinical symptoms and syndromes. Although clinically useful, current diagnostic systems for psychiatry cause concerns due to the lack of biological mechanisms. Deciphering the relationships among psychiatric traits according to their genetic basis may facilitate understanding the biological mechanisms of psychiatric disorders. Ten mental disorders were classified by genomic structural equation modeling (SEM), which leverages summary results of genome-wide association studies. Attention-deficit/hyperactivity disorder (ADHD), anorexia nervosa (AN), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), schizophrenia (SZ), and Tourette syndrome (TS) were included. The analysis indicates that they are genetically inter-correlated with one another and can be separated based on their general psychopathology. Most disorders have a close partner, forming pairs of traits; only TS is a relatively distinctive condition. At a higher level, MDD, ANX, ADHD, ASD, and PTSD cluster together, while OCD, AN, and TS cluster together. Together, the ten traits constitute a hierarchical classificatory system. This study allows inference of genetically determined classification of the ten mental disorders, which may biologically inform the current diagnostic framework and treatment regimens for mental disorders.

A meta-analysis comparing cognitive function across the mood/psychosis diagnostic spectrum

BACKGROUND

The nature and degree of cognitive impairments in schizoaffective disorder is not well established. The aim of this meta-analysis was to characterise cognitive functioning in schizoaffective disorder and compare it with cognition in schizophrenia and bipolar disorder. Schizoaffective disorder was considered both as a single category and as its two diagnostic subtypes, bipolar and depressive disorder.

METHODS

Following a thorough literature search (468 records identified), we included 31 studies with a total of 1685 participants with schizoaffective disorder, 3357 with schizophrenia and 1095 with bipolar disorder. Meta-analyses were conducted for seven cognitive variables comparing performance between participants with schizoaffective disorder and schizophrenia, and between schizoaffective disorder and bipolar disorder.

RESULTS

Participants with schizoaffective disorder performed worse than those with bipolar disorder (g = -0.30) and better than those with schizophrenia (g = 0.17). Meta-analyses of the subtypes of schizoaffective disorder showed cognitive impairments in participants with the depressive subtype are closer in severity to those seen in participants with schizophrenia (g = 0.08), whereas those with the bipolar subtype were more impaired than those with bipolar disorder (g = -0.23) and less impaired than those with schizophrenia (g = 0.29). Participants with the depressive subtype had worse performance than those with the bipolar subtype but this was not significant (g = 0.25, p = 0.05).

CONCLUSION

Cognitive impairments increase in severity from bipolar disorder to schizoaffective disorder to schizophrenia. Differences between the subtypes of schizoaffective disorder suggest combining the subtypes of schizoaffective disorder may obscure a study's results and hamper efforts to understand the relationship between this disorder and schizophrenia or bipolar disorder.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Genetic risk for psychiatric illness is associated with the number of hospitalizations of bipolar disorder patients

OBJECTIVES

Bipolar disorder (BD) has a highly heterogeneous clinical course that is characterized by relapses and increased health care utilization in a significant fraction of patients. A thorough understanding of factors influencing illness course is essential for predicting disorder severity and developing targeted therapies.

METHODS

We performed polygenic score analyses in four cohorts (N = 954) to test whether the genetic risk for BD, schizophrenia, or major depression is associated with a severe course of BD. We analyzed BD patients with a minimum illness duration of five years. The severity of the disease course was assessed by using the number of hospitalizations in a mental health facility and a composite measure of longitudinal illness severity (OPCRIT item 90).

RESULTS

Our analyses showed that higher polygenic scores for BD (β = 0.11, SE = 0.03, p = 1.17 × 10-3) and schizophrenia (β = 0.09, SE = 0.03, p = 4.24 × 10-3), but not for major depression, were associated with more hospitalizations. None of the investigated polygenic scores was associated with the composite measure of longitudinal illness severity (OPCRIT item 90).

LIMITATIONS

We could not account for non-genetic influences on disease course. Our clinical sample contained more severe cases.

CONCLUSIONS

This study demonstrates that the genetic risk burden for psychiatric illness is associated with increased health care utilization, a proxy for disease severity, in BD patients. The findings are in line with previous observations made for patients diagnosed with schizophrenia or major depression. Therefore, in the future psychiatric disorder polygenic scores might become helpful for stratifying patients with high risk of a chronic manifestation and predicting disease course.

Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders

Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.

Identification of a Risk Locus at 7p22.3 for Schizophrenia and Bipolar Disorder in East Asian Populations

Background: Shared psychopathological features and mechanisms have been observed between schizophrenia (SZ) and bipolar disorder (BD), but their common risk genes and full genetic architectures remain to be fully characterized. The genome-wide association study (GWAS) datasets offer the opportunity to explore this scientific question using combined genetic data from enormous samples, ultimately allowing a better understanding of the onset and development of these illnesses.

Methods: We have herein performed a genome-wide meta-analysis in two GWAS datasets of SZ and BD respectively (24,600 cases and 40,012 controls in total, discovery sample), followed by replication analyses in an independent sample of 4,918 SZ cases and 5,506 controls of Han Chinese origin (replication sample). The risk SNPs were then explored for their correlations with mRNA expression of nearby genes in multiple expression quantitative trait loci (eQTL) datasets.

Results: The single nucleotide polymorphisms (SNPs) rs1637749 and rs3800908 at 7p22.3 region were significant in both discovery and replication samples, and exhibited genome-wide significant associations when combining all East Asian SZ and BD samples (29,518 cases and 45,518 controls). The risk SNPs were also significant in GWAS of SZ and BD among Europeans. Both risk SNPs significantly predicted lower expression of MRM2 in the whole blood and brain samples in multiple datasets, which was consistent with its reduced mRNA level in the brains of SZ patients compared with normal controls. The risk SNPs were also associated with MAD1L1 expression in the whole blood sample.

Discussion: We have identified a novel genome-wide risk locus associated with SZ and BD in East Asians, adding further support for the putative common genetic risk of the two illnesses. Our study also highlights the necessity and importance of mining public datasets to explore risk genes for complex psychiatric diseases.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.