Myelin-Associated Glycoprotein Gene and Brain Morphometry in Schizophrenia

Myelin and oligodendrocyte disruption may be a core feature of schizophrenia pathophysiology. The purpose of the present study was to localize the effects of previously identified risk variants in the myelin-associated glycoprotein (MAG) gene on brain morphometry in schizophrenia patients and healthy controls. Forty-five schizophrenia patients and 47 matched healthy controls underwent clinical, structural magnetic resonance imaging, and genetics procedures. Gray and white matter cortical lobe volumes along with hippocampal volumes were calculated from T1-weighted MRI scans. Each subject was also genotyped for the two disease-associated MAG single nucleotide polymorphisms (rs720308 and rs720309). Repeated measures general linear model (GLM) analysis found significant region by genotype and region by genotype by diagnosis interactions for the effects of MAG risk variants on lobar gray matter volumes. No significant associations were found with lobar white matter volumes or hippocampal volumes. Follow-up univariate GLMs found the AA genotype of rs720308 predisposed schizophrenia patients to left temporal and parietal gray matter volume deficits. These results suggest that the effects of the MAG gene on cortical gray matter volume in schizophrenia patients can be localized to temporal and parietal cortices. Our results support a role for MAG gene variation in brain morphometry in schizophrenia, align with other lines of evidence implicating MAG in schizophrenia, and provide genetically based insight into the heterogeneity of brain imaging findings in this disorder

Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses : A cross-sectional study of 89,205 participants from the UK Biobank

Funding Information: The authors acknowledge Milos Milic for data curation assistance. MW and SJT acknowledge support from the Kavli Foundation, Krembil Foundation, CAMH Discovery Fund, the McLaughlin Foundation, NSERC (RGPIN-2020-05834 and DGECR-2020-00048) and CIHR (NGN-171423). DF is supported by the Michael and Sonja Koerner Foundation New Scientist Program, Krembil Foundation, CAMH Discovery Fund, and the McLaughlin Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes?. The authors acknowledge Milos Milic for data curation assistance. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes.? Publisher Copyright: Copyright: © 2021 Wainberg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.Peer reviewe

Genetic epistasis regulates amyloid deposition in resilient aging

AbstractIntroduction The brain-derived neurotrophic factor (BDNF) interacts with important genetic Alzheimer's disease (AD) risk factors. Specifically, variants within the SORL1 gene determine BDNF's ability to reduce amyloid β (Aβ) in vitro. We sought to test whether functional BDNF variation interacts with SORL1 genotypes to influence expression and downstream AD-related processes in humans. Methods We analyzed postmortem brain RNA sequencing and neuropathological data for 441 subjects from the Religious Orders Study/Memory and Aging Project and molecular and structural neuroimaging data for 1285 subjects from the Alzheimer's Disease Neuroimaging Initiative. Results We found one SORL1 RNA transcript strongly regulated by SORL1-BDNF interactions in elderly without pathological AD and showing stronger associations with diffuse than neuritic Aβ plaques. The same SORL1-BDNF interactions also significantly influenced Aβ load as measured with [18F]Florbetapir positron emission tomography. Discussion Our results bridge the gap between risk and resilience factors for AD, demonstrating interdependent roles of established SORL1 and BDNF functional genotypes

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data

Introduction: The molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation. Methods and findings: “Residual cognition” was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10−5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = −0.40, 95% CI −0.69 to −0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10−5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies. Conclusions: Through a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy

Catechol-O-Methyltransferase Val158Met Polymorphism and Clinical Response to Antipsychotic Treatment in Schizophrenia and Schizo-Affective Disorder Patients: a Meta-Analysis

BACKGROUND: The catechol-O-methyltransferase (COMT) enzyme plays a crucial role in dopamine degradation, and the COMT Val158Met polymorphism (rs4680) is associated with significant differences in enzymatic activity and consequently dopamine concentrations in the prefrontal cortex. Multiple studies have analyzed the COMT Val158Met variant in relation to antipsychotic response. Here, we conducted a meta-analysis examining the relationship between COMT Val158Met and antipsychotic response. METHODS: Searches using PubMed, Web of Science, and PsycInfo databases (03/01/2015) yielded 23 studies investigating COMT Val158Met variation and antipsychotic response in schizophrenia and schizo-affective disorder. Responders/nonresponders were defined using each study's original criteria. If no binary response definition was used, authors were asked to define response according to at least 30% Positive and Negative Syndrome Scale score reduction (or equivalent in other scales). Analysis was conducted under a fixed-effects model. RESULTS: Ten studies met inclusion criteria for the meta-analysis. Five additional antipsychotic-treated samples were analyzed for Val158Met and response and included in the meta-analysis (ntotal=1416). Met/Met individuals were significantly more likely to respond than Val-carriers (P=.039, ORMet/Met=1.37, 95% CI: 1.02-1.85). Met/Met patients also experienced significantly greater improvement in positive symptoms relative to Val-carriers (P=.030, SMD=0.24, 95% CI: 0.024-0.46). Posthoc analyses on patients treated with atypical antipsychotics (n=1207) showed that Met/Met patients were significantly more likely to respond relative to Val-carriers (P=.0098, ORMet/Met=1.54, 95% CI: 1.11-2.14), while no difference was observed for typical-antipsychotic-treated patients (n=155) (P=.65). CONCLUSIONS: Our findings suggest that the COMT Val158Met polymorphism is associated with response to antipsychotics in schizophrenia and schizo-affective disorder patients. This effect may be more pronounced for atypical antipsychotics.C.C.Z. is supported by the Brain and Behavior Research Foundation, American Foundation for Suicide Prevention and Eli Lilly. D.F. is supported by the Vanier Canada Graduate Scholarship. D.J.M. has been or is supported by the Canadian Institute of Health Research (CIHR) Operating Grant: “Genetics of antipsychotic-induced metabolic syndrome,” Michael Smith New Investigator Salary Prize for Research in Schizophrenia, NARSAD Independent Investigator Award by the Brain & Behavior Research Foundation, and Early Researcher Award from Ministry of Research and Innovation of Ontario. E.H. is supported by the Canada Graduate Scholarship. H.Y.M. has grant support from Sumitomo Dainippon, Sunovion, Boehringer Ingelheim, Eli Lilly, Janssen, Reviva, Alkermes, Auspex, and FORUM. J.A.L. has received research funding from Alkermes, Biomarin, EnVivo/Forum, Genentech, and Novartis. J.L.K. is supported the CIHR grant “Strategies for gene discovery in schizophrenia: subphenotypes, deep sequencing and interaction.” J.R.B. is supported by NIH grant MH083888. A.K.T. is supported by a NARSAD Young Investigator Award. J.S. is supported by a Pfizer independent grant. P.M. receives salary from Clinica Universidad de Navarra and has received research grants from the Ministry of Education (Spain), the Government of Navarra (Spain), the Spanish Foundation of Psychiatry and Mental Health, and Astrazeneca. S.G. is supported by the Ningbo Medical Technology Project Fund (No. 2004050), the Natural Science Foundation of Ningbo (No. 2009A610186, No. 2013A610249), and the Zhejiang Provincial Medical and Health Project Fund (No. 2015127713). S.G.P. has received research support from Otsuka, Lundbeck, FORUM, and Alkermes

Phenotypic Impact of Genetic Risk Pathways for Alzheimer's Disease

The contribution of genetic variation to the risk for late-onset Alzheimer’s disease is well-accepted; however, the roles of specific mutations within established risk genes are not clear. Comprehensive datasets with informative in vivo and postmortem biomarkers now offer the opportunity to understand when, where, and how mutations within these genes individually exert their effects on the brain. Moreover, it is known that many of these genes interact at the pathway level, and therefore genetic effects should also be considered in context using gene-gene interaction approaches. I hypothesized that common functional variants modifying established Alzheimer’s risk pathways would demonstrate a) independent effects and b) synergistic effects on human brain structure and other Alzheimer’s biomarkers. First, the Apolipoprotein E (APOE) gene ε4 mutation was found to be associated with white matter integrity in an age-dependent manner. Second, mutations within the sortilin-like receptor (SORL1) gene were associated with differences in white matter integrity, SORL1 gene mRNA expression, and amyloid neuropathology that suggested an early genetic risk mechanism beginning as early as childhood. Third, a translocator protein (TSPO) gene variant known to alter TSPO binding characteristics was found to have no direct effects on inflammatory and cerebrovascular brain changes in over 2 300 elderly subjects. Finally, based on evidence from recent human stem cell experiments, RNA sequencing was used to identify a novel interaction of gene variants across the SORL1 gene with the brain derived neurotrophic factor (BDNF) Val66Met polymorphism regulating isoform-specific SORL1 expression related to amyloid pathology and brain structural alterations. Altogether, these experiments demonstrate that some genetic modifiers of AD risk pathways are linked either directly via biochemical function or indirectly via the convergence of pathways they influence. These studies have begun to parse the immense heterogeneity of the Alzheimer’s disease diagnosis as well as uncover distinct genetically-defined molecular subtypes of at-risk individuals who should be targeted in future therapeutic trials. Novel interventions designed to engage specific neural circuits or molecular pathways would be of most benefit to the molecular subtypes in which they are most greatly altered.Ph.D

Table_1_Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.DOCX

BackgroundTraditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual’s fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.MethodsWe conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide.ResultsOut of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance.ConclusionStudies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.</p

An overview of systematic reviews on predictors of smoking cessation among young people.

Understanding the factors that influence smoking cessation among young people is crucial for planning targeted cessation approaches. The objective of this review was to comprehensively summarize evidence for predictors of different smoking cessation related behaviors among young people from currently available systematic reviews. We searched six databases and reference lists of the included articles for studies published up to October 20, 2023. All systematic reviews summarizing predictors of intention to quit smoking, quit attempts, or smoking abstinence among people aged 10-35 years were included. We excluded reviews on effectiveness of smoking cessation intervention; smoking prevention and other smoking behaviors; cessation of other tobacco products use, dual use, and polysubstance use. We categorized the identified predictors into 5 different categories for 3 overlapping age groups. JBI critical appraisal tool and GRADE-CERqual approach were used for quality and certainty assessment respectively. A total of 11 systematic reviews were included in this study; all summarized predictors of smoking abstinence/quit attempts and two also identified predictors of intention to quit smoking. Seven reviews had satisfactory critical appraisal score and there was minimal overlapping between the reviews. We found 4 'possible' predictors of intention to quit smoking and 119 predictors of smoking abstinence/quit attempts. Most of these 119 predictors were applicable for ~10-29 years age group. We had moderate confidence on the 'probable', 'possible', 'insufficient evidence', and 'inconsistent direction' predictors and low confidence on the 'probably unrelated' factors. The 'probable' predictors include a wide variety of socio-demographic factors, nicotine dependence, mental health, attitudes, behavioral and psychological factors, peer and family related factors, and jurisdictional policies. These predictors can guide improvement of existing smoking cessation interventions or planning of new targeted intervention programs. Other predictors as well as predictors of intention to quit smoking need to be further investigated among adolescents and young adults separately

Investigating microglia-neuron crosstalk by characterizing microglial contamination in human and mouse patch-seq datasets

Summary: Microglia are cells with diverse roles, including the regulation of neuronal excitability. We leveraged Patch-seq to assess the presence and effects of microglia in the local microenvironment of recorded neurons. We first quantified the amounts of microglial transcripts in three Patch-seq datasets of human and mouse neocortical neurons, observing extensive contamination. Variation in microglial contamination was explained foremost by donor identity, particularly in human samples, and additionally by neuronal cell type identity in mice. Gene set enrichment analysis suggests that microglial contamination is reflective of activated microglia, and that these transcriptional signatures are distinct from those captured via single-nucleus RNA-seq. Finally, neurons with greater microglial contamination differed markedly in their electrophysiological characteristics, including lowered input resistances and more depolarized action potential thresholds. Our results generalize beyond Patch-seq to suggest that activated microglia may be widely present across brain slice preparations and contribute to neuron- and donor-related electrophysiological variability in vitro