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BackgroundRecent breakthroughs in psychiatric genetics have implicated biological pathways onto which genetic risk for psychiatric disorders converges. However, these studies do not reveal the developmental time point(s) at which these pathways are relevant.MethodsWe aimed to determine the relationship between psychiatric risk and developmental gene expression relating to discrete biological pathways. We used post-mortem RNA sequencing data (BrainSeq and BrainSpan) from brain tissue at multiple pre- and post-natal timepoints and summary statistics from recent genome-wide association studies of schizophrenia, bipolar disorder and major depressive disorder. We prioritised gene sets for overall enrichment of association with each disorder, and then tested the relationship between the association of each of their constituent genes with their relative expression at each developmental stage.ResultsWe observed relationships between the expression of genes involved involtage-gated cation channel activityduring Early Midfetal, Adolescence and Early Adulthood timepoints and association with schizophrenia and bipolar disorder, such that genes more strongly associated with these disorders had relatively low expression during Early Midfetal development and higher expression during Adolescence and Early Adulthood. The relationship with schizophrenia was strongest for the subset of genes related to calcium channel activity, whilst for bipolar disorder the relationship was distributed between calcium and potassium channel activity genes.ConclusionsOur results indicate periods during development when biological pathways related to the activity of calcium and potassium channels may be most vulnerable to the effects of genetic variants conferring risk to psychiatric disorders. Furthermore, they indicate key time points and potential targets for disorder-specific therapeutic interventions.

Background Cognitive impairment in schizophrenia is a major contributor to poor outcomes, yet its causes are poorly understood. Some rare copy number variants (CNVs) are associated with schizophrenia risk and affect cognition in healthy populations, but their contribution to cognitive impairment in schizophrenia has not been investigated. We examined the effect of 12 schizophrenia CNVs on cognition in those with schizophrenia. Methods General cognitive ability was measured using the Measurement and Treatment Research to Improve Cognition in Schizophrenia composite z score in 875 patients with schizophrenia and in a replication sample of 519 patients with schizophrenia using Wechsler Adult Intelligence Scale Full Scale IQ. Using linear regression, we tested for association between cognition and schizophrenia CNV status, covarying for age and sex. In addition, we tested whether CNVs hitting genes in schizophrenia-enriched gene sets (loss-of-function intolerant and synaptic gene sets) were associated with cognitive impairment. Results A total of 23 schizophrenia CNV carriers were identified. Schizophrenia CNV carriers had lower general cognitive ability than nonschizophrenia CNV carriers in discovery (β = −0.66, 95% confidence interval [CI] = −1.31 to −0.01) and replication samples (β = −0.91, 95% CI = −1.71 to −0.11) and after meta-analysis (β = −0.76, 95% CI = −1.26 to −0.25, p = .003). CNVs hitting loss-of-function intolerant genes were associated with lower cognition (β = −0.15, 95% CI = −0.29 to −0.001, p = .048). Conclusions In those with schizophrenia, cognitive ability in schizophrenia CNV carriers is 0.5–1.0 standard deviations below non-CNV carriers, which may have implications for clinical assessment and management. We also demonstrate that rare CNVs hitting genes intolerant to loss-of-function variation lead to more severe cognitive impairment, above and beyond the effect of known schizophrenia CNVs.

Highlights • Temporal autocorrelation is attenuated in the neural dynamics of schizophrenia. • Alterations are primarily observed in the alpha and beta frequency bands. • Machine-learning significantly classified patients and controls with 82% accuracy. • Brain regions showing disrupted temporal connectivity include (para)limbic areas. • Long-range temporal correlation informs on temporal dysconnectivity in schizophrenia., Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.

Genome-wide association studies (GWAS) have proved to be a powerful approach for gene discovery in schizophrenia; their findings have important implications not just for our understanding of the genetic architecture of the disorder, but for the potential applications of personalised medicine through improved classification and targeted interventions. In this article we review the current status of the GWAS literature in schizophrenia including functional annotation methods and polygenic risk scoring, as well as the directions and challenges of future research. We consider recent findings in East Asian populations and the advancements from trans-ancestry analysis, as well as the insights gained from research looking across psychiatric disorders.

In this chapter we summarize a broad range of research studies that aim to improve our understanding of the genetic risk and etiology of psychosis. We begin by discussing early, mainly unsuccessful, attempts at identifying causal genetic factors through candidate gene and linkage studies. We then highlight the major advances achieved over the last decade by genome-wide association studies, including identification of common risk loci, quantifying genetic correlations between psychiatric disorders, and use of polygenic scores to explore phenotypic manifestation of genetic risk and for development of disease prediction models. We then summarize the main findings from copy number and rare variant studies and end with a discussion of the use of genetic factors in Mendelian randomization to improve causal inference.

Around 30% of individuals with schizophrenia remain symptomatic and markedly impaired despite standard antipsychotic treatment and are considered to be treatment-resistant. The only licensed medication for treatment-resistant schizophrenia (TRS) is the antipsychotic clozapine, which has been shown to improve symptoms and functioning in the majority of patients. Early identification of TRS patients is an important goal for personalized psychiatry, with the aim to facilitate timely access to effective treatment. Previous studies have strongly implicated several clinical features as predictive of TRS, such as a younger age at onset of psychosis and poor premorbid functioning. However, to date, robust biomarker or genetic studies have been scarce. This chapter provides a summary of our current understanding of the genetic etiology of TRS, discusses the key challenges facing this area of research, and assesses the potential for treatment response prediction in the future.

Background Anxiety and depression are common, debilitating and costly. These disorders are influenced by multiple risk factors, from genes to psychological vulnerabilities and environmental stressors, but research is hampered by a lack of sufficiently large comprehensive studies. We are recruiting 40,000 individuals with lifetime depression or anxiety and broad assessment of risks to facilitate future research. Methods The Genetic Links to Anxiety and Depression (GLAD) Study (www.gladstudy.org.uk) recruits individuals with depression or anxiety into the NIHR Mental Health BioResource. Participants invited to join the study (via media campaigns) provide demographic, environmental and genetic data, and consent for medical record linkage and recontact. Results Online recruitment was effective; 42,531 participants consented and 27,776 completed the questionnaire by end of July 2019. Participants’ questionnaire data identified very high rates of recurrent depression, severe anxiety, and comorbidity. Participants reported high rates of treatment receipt. The age profile of the sample is biased toward young adults, with higher recruitment of females and the more educated, especially at younger ages. Discussion This paper describes the study methodology and descriptive data for GLAD, which represents a large, recontactable resource that will enable future research into risks, outcomes, and treatment for anxiety and depression., Highlights • Online recruitment of 40,000 individuals with lifetime depression or anxiety. • Detailed online phenotyping combined with genetic and clinical data. • The study sample is severe, highly comorbid, with chronic psychopathology. • The study protocol enables recall of participants for future research and trials.

IntroductionStudies assessing premature mortality in people with severe mental illness (SMI) are often based in one setting, hospital (secondary care inpatients and/or outpatients) or community (primary care). This may lead to ascertainment bias. Objectives and ApproachThis study aimed to estimate standardised mortality ratios (SMRs) for all-cause and cause-specific mortality in people with SMI drawn from linked primary and secondary care populations compared to the general population. Standardised mortality ratios (SMRs) were calculated using the indirect method for a United Kingdom population of almost four million between 2004-2013. ResultsThe all-cause SMR was higher in the cohort identified from secondary care hospital admissions (SMR: 2.9; 95% CI: 2.8-3.0) than from primary care (SMR: 2.2; 95% CI: 2.1-2.3) when compared to the general population. The SMR for the combined cohort was 2.6 (95% CI: 2.5-2.6). Solely hospital admission based studies may somewhat over-estimate premature mortality in those with SMI. However, there is no doubt this remains a major health inequality. Cause specific SMRs in the combined cohort were particularly elevated in those with SMI relative to the general population for ill-defined and unknown causes, suicide, and substance abuse, as well as a number of other causes. Conclusion/ImplicationsThe ability to combine cohorts electronically from primary and secondary care is more representative of the whole population. Comprehensive characterisation of mortality is important to inform policy and practice and to discriminate settings to allow for proportionate interventions to address this health injustice.

Background: Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. Methods: We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. Results: Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p < 1 × 10− 10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. Conclusions: The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of > 10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia. We are grateful for the support of all study staff and participants. Acknowledgements for each sample are provided in the Supplementary Materials. Data processing and analyses (of the legacy data) at the central site was supported by the National Institute of Mental Health (NIMH) of the National Institutes of Health (NIH) grant number R01MH092380 to T.L.P. supporting the Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium, and NIMH grant R21MH109819 to E.D.R. Appendix A