Cannabis use is associated with increased risk of later psychotic disorder but whether it affects incidence of the disorder remains unclear. We aimed to identify patterns of cannabis use with the ...strongest effect on odds of psychotic disorder across Europe and explore whether differences in such patterns contribute to variations in the incidence rates of psychotic disorder.
We included patients aged 18–64 years who presented to psychiatric services in 11 sites across Europe and Brazil with first-episode psychosis and recruited controls representative of the local populations. We applied adjusted logistic regression models to the data to estimate which patterns of cannabis use carried the highest odds for psychotic disorder. Using Europe-wide and national data on the expected concentration of Δ9-tetrahydrocannabinol (THC) in the different types of cannabis available across the sites, we divided the types of cannabis used by participants into two categories: low potency (THC <10%) and high potency (THC ≥10%). Assuming causality, we calculated the population attributable fractions (PAFs) for the patterns of cannabis use associated with the highest odds of psychosis and the correlation between such patterns and the incidence rates for psychotic disorder across the study sites.
Between May 1, 2010, and April 1, 2015, we obtained data from 901 patients with first-episode psychosis across 11 sites and 1237 population controls from those same sites. Daily cannabis use was associated with increased odds of psychotic disorder compared with never users (adjusted odds ratio OR 3·2, 95% CI 2·2–4·1), increasing to nearly five-times increased odds for daily use of high-potency types of cannabis (4·8, 2·5–6·3). The PAFs calculated indicated that if high-potency cannabis were no longer available, 12·2% (95% CI 3·0–16·1) of cases of first-episode psychosis could be prevented across the 11 sites, rising to 30·3% (15·2–40·0) in London and 50·3% (27·4–66·0) in Amsterdam. The adjusted incident rates for psychotic disorder were positively correlated with the prevalence in controls across the 11 sites of use of high-potency cannabis (r = 0·7; p=0·0286) and daily use (r = 0·8; p=0·0109).
Differences in frequency of daily cannabis use and in use of high-potency cannabis contributed to the striking variation in the incidence of psychotic disorder across the 11 studied sites. Given the increasing availability of high-potency cannabis, this has important implications for public health.
Medical Research Council, the European Community's Seventh Framework Program grant, São Paulo Research Foundation, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King's College London and the NIHR BRC at University College London, Wellcome Trust.
Abstract
Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, ...genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.
Abstract
Aims
In long QT syndrome (LQTS) patients, modifier genes modulate the arrhythmic risk associated with a disease-causing mutation. Their recognition can improve risk stratification and ...clinical management, but their discovery represents a challenge. We tested whether a cellular-driven approach could help to identify new modifier genes and especially their mechanism of action.
Methods and results
We generated human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) from two patients carrying the same KCNQ1-Y111C mutation, but presenting opposite clinical phenotypes. We showed that the phenotype of the iPSC-CMs derived from the symptomatic patient is due to impaired trafficking and increased degradation of the mutant KCNQ1 and wild-type human ether-a-go-go-related gene. In the iPSC-CMs of the asymptomatic (AS) patient, the activity of an E3 ubiquitin-protein ligase (Nedd4L) involved in channel protein degradation was reduced and resulted in a decreased arrhythmogenic substrate. Two single-nucleotide variants (SNVs) on the Myotubularin-related protein 4 (MTMR4) gene, an interactor of Nedd4L, were identified by whole-exome sequencing as potential contributors to decreased Nedd4L activity. Correction of these SNVs by CRISPR/Cas9 unmasked the LQTS phenotype in AS cells. Importantly, the same MTMR4 variants were present in 77% of AS Y111C mutation carriers of a separate cohort. Thus, genetically mediated interference with Nedd4L activation seems associated with protective effects.
Conclusion
Our finding represents the first demonstration of the cellular mechanism of action of a protective modifier gene in LQTS. It provides new clues for advanced risk stratification and paves the way for the design of new therapies targeting this specific molecular pathway.
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to ...unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing ...these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.
Depression is the world's leading cause of disability. Greater understanding of the neurobiological basis of depression is necessary for developing novel treatments with improved efficacy and ...acceptance. Recently, major advances have been made in the search for genetic variants associated with depression which may help to elucidate etiological mechanisms. The present review has two major objectives. First, we offer a brief review of two major biological systems with strong evidence for involvement in depression pathology: neurotransmitter systems and the stress response. Secondly, we provide a synthesis of the functions of the 269 genes implicated by the most recent genome-wide meta-analysis, supporting the importance of these systems in depression and providing insights into other possible mechanisms involving neurodevelopment, neurogenesis, and neurodegeneration. Our goal is to undertake a broad, preliminary stock-taking of the most recent hypothesis-free findings and examine the weight of the evidence supporting these existing theories and highlighting novel directions. This qualitative review and accompanying gene function table provides a valuable resource and guide for basic and translational researchers, with suggestions for future mechanistic research, leveraging genetics to prioritize studies on the neurobiological processes involved in depression etiology and treatment.
As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from ...genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.
The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising 94 genome-wide ...profiles from 17 individuals. To begin with, protein modules defining key directional trends spanning the lateral and anteroposterior axes were derived from high-resolution spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific profiles of regulatory activities and displayed potential paths of deconstruction in the level- and location-matched aged cadaveric discs. Machine learning methods predicted a 'hydration matrisome' that connects extracellular matrix with MRI intensity. Importantly, the static proteome used as point-references can be integrated with dynamic proteome (SILAC/degradome) and transcriptome data from multiple clinical samples, enhancing robustness and clinical relevance. The data, findings, and methodology, available on a web interface (http://www.sbms.hku.hk/dclab/DIPPER/), will be valuable references in the field of IVD biology and proteomic analytics.
Maternal infection during pregnancy increases risk of severe neuropsychiatric disorders, including schizophrenia and autism, in the offspring. The most consistent brain structural abnormality in ...patients with schizophrenia is enlarged lateral ventricles. However, it is unknown whether the aetiology of ventriculomegaly in schizophrenia involves prenatal infectious processes. The present experiments tested the hypothesis that there is a causal relationship between prenatal immune challenge and emergence of ventricular abnormalities relevant to schizophrenia in adulthood.
We used an established mouse model of maternal immune activation (MIA) by the viral mimic PolyI:C administered in early (day 9) or late (day 17) gestation. Automated voxel-based morphometry mapped cerebrospinal fluid across the whole brain of adult offspring and the results were validated by manual region-of-interest tracing of the lateral ventricles. Parallel behavioral testing determined the existence of schizophrenia-related sensorimotor gating abnormalities.
PolyI:C-induced immune activation, in early but not late gestation, caused marked enlargement of lateral ventricles in adulthood, without affecting total white and grey matter volumes. This early exposure disrupted sensorimotor gating, in the form of prepulse inhibition. Identical immune challenge in late gestation resulted in significant expansion of 4(th) ventricle volume but did not disrupt sensorimotor gating.
Our results provide the first experimental evidence that prenatal immune activation is an environmental risk factor for adult ventricular enlargement relevant to schizophrenia. The data indicate immune-associated environmental insults targeting early foetal development may have more extensive neurodevelopmental impact than identical insults in late prenatal life.
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