A cross-disorder analysis of CNVs finds novel loci and dose-dependent relationships of genes to psychiatric traits.

Rare copy number variants (CNVs) are a key component of the genetic basis of psychiatric conditions, but have not been well characterized for most. We conducted a genome-wide CNV analysis across six diagnostic categories (N = 574,965): autism (ASD), ADHD, bipolar disorder (BD), major depressive disorder (MDD), PTSD, and schizophrenia (SCZ). We identified 35 genome-wide significant associations at 18 loci, including novel associations in SCZ ( - ) and in the combined cross-disorder analysis ( ).

Psychiatric disorders converge on common pathways but diverge in cellular context, spatial distribution, and directionality of genetic effects.

Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses remain unclear. We present a multidimensional framework for functional analysis of rare copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ), autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965). Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL) burden across 2,645 functional gene sets defined by the intersections of pathways, cell types, and cortical regions.

Immune, Developmental, and Synaptic Pathways Define Bipolar Disorder Clinical Heterogeneity.

Importance: The clinical heterogeneity of bipolar disorder (BD) is a major obstacle to improving diagnosis, predicting patient outcomes, and developing personalized treatments. A genetic approach is needed to deconstruct the disorder and uncover its fundamental biology. Previous genetic studies focusing on broad diagnostic categories have been limited in their ability to parse this complexity.

GWAS meta-analysis of CSF Alzheimer's disease biomarkers 18,948 individuals reveal novel loci and genes regulating lipid metabolism, brain volume and autophagy.

Cerebrospinal fluid (CSF) amyloid beta (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau181) are well accepted markers of Alzheimer's disease. We performed a GWAS meta-analysis including 18,948 individuals of European and 416 non-European ancestry. We identified 12 genome-wide significant loci across all three biomarkers, eight of them novel.

Symptom network connectivity indices as predictors of relapse in major depressive disorder.

Major Depressive Disorder (MDD) demonstrates heterogeneous symptom profiles and a high relapse risk. Understanding how symptom interactions relate to relapse in MDD may enhance maintenance strategies. We thus investigated how connectivity in depressive symptom networks relates to relapse in MDD.

Body fluid biomarkers and psychosis risk in The Accelerating Medicines Partnership® Schizophrenia Program: design considerations.

Advances in proteomic assay methodologies and genomics have significantly improved our understanding of the blood proteome. Schizophrenia and psychosis risk are linked to polygenic scores for schizophrenia and other mental disorders, as well as to altered blood and saliva levels of biomarkers involved in hormonal signaling, redox balance, and chronic systemic inflammation. The Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ) aims to ascertain biomarkers that both predict clinical outcomes and provide insights into the biological processes driving clinical outcomes in persons meeting CHR criteria.

Predicting Relapse of Depressive Episodes During Maintenance Treatment: The Canadian Biomarker Integration Network in Depression (CAN-BIND) Wellness Monitoring in Major Depressive Disorder Study: Prédire la rechute d'épisodes dépressifs pendant le traitement d'entretien : Une étude de suivi du bien-être dans les troubles dépressifs majeurs du Réseau canadien d'intégration des biomarqueurs pour la dépression (CAN-BIND).

BackgroundRelapse rates in major depressive disorder (MDD) remain high even after treatment to remission. Identifying predictors of relapse is, therefore, crucial for improving maintenance strategies and preventing future episodes. Remote data collection and sensing technologies may allow for more comprehensive and longitudinal assessment of potential predictors.

Recommendations for defining treatment outcomes in major psychiatric disorders using real-world data.

Although information from real-world data can be used to identify factors that aid treatment choice, there are no guidelines for the use of such data. The aim of this Review is to summarise and evaluate definitions of treatment outcomes for antidepressants, antipsychotics, and mood stabilisers when using real-world data, and to suggest standards for the field. Given that no standards for the use of these data in estimating treatment outcomes exist, variability is high for treatment outcome definitions.

Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.

The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project assesses a large sample of individuals at clinical high-risk for developing psychosis (CHR) and community controls. Subjects are enrolled in 43 sites across 5 continents. The assessments include domains similar to those acquired in previous CHR studies along with novel domains that are collected longitudinally across a period of 2 years.

Genomics yields biological and phenotypic insights into bipolar disorder.

Bipolar disorder is a leading contributor to the global burden of disease. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.

Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.

Background: Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

Aims: We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Production of lactulose from lactose using a novel cellobiose 2-epimerase from Clostridium disporicum.

Lactulose is a semisynthetic nondigestive sugar derived from lactose, with wide applications in the food and pharmaceutical industries. Its biological production routes which use cellobiose 2-epimerase (C2E) as the key enzyme have attracted widespread attention. In this study, a set of C2Es from different sources were overexpressed in Escherichia coli to produce lactulose.

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report.

Background: A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD.

Methods: We followed 97 patients with remitted MDD for an average of 394 days.

Predicting recurrence of major depressive episodes using the Depression Implicit Association Test: A Canadian biomarker integration network in depression (CAN-BIND) report.

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g.

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study.

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants.

Genome-wide association study of abnormal elevation of ALT in patients exposed to atabecestat.

Background: Atabecestat, a potent brain penetrable BACE1 inhibitor that reduces CSF amyloid beta (Aβ), was developed as an oral treatment for Alzheimer's disease (AD). Elevated liver enzyme adverse events were reported in three studies although only one case met Hy's law criteria to predict serious hepatotoxicity.

Method: We performed a case-control genome-wide association study (GWAS) to identify genetic risk variants associated with liver enzyme elevation using 42 cases with alanine transaminase (ALT) above three times the upper limit of normal (ULN) and 141 controls below ULN.