Examining the independent and joint effects of molecular genetic liability and environmental exposures in schizophrenia: results from the EUGEI study

Do Current Measures of Polygenic Risk for Mental Disorders Contribute to Population Variance in Mental Health?

The polygenic risk score (PRS) allows for quantification of the relative contributions of genes and environment in population-based studies of mental health. We analyzed the impact of transdiagnostic schizophrenia PRS and measures of familial and environmental risk on the level of and change in general mental health (Short-Form-36 mental health) in the Netherlands Mental Health Survey and Incidence Study-2 general population sample, interviewed 4 times over a period of 9 years, yielding 8901 observations in 2380 individuals. Schizophrenia PRS, family history, somatic pain, and a range of environmental risks and social circumstances were included in the regression model of level of and change in mental health. We calculated the relative contribution of each (group of) risk factor(s) to the variance in (change in) mental health. In the combined model, familial and environmental factors explained around 17% of the variance in mental health, of which around 5% was explained by age and sex, 30% by social circumstances, 16% by pain, 22% by environmental risk factors, 24% by family history, and 3% by PRS for schizophrenia (PRS-SZ). Results were similar, but attenuated, for the model of mental health change over time. Childhood trauma and gap between actual and desired social status explained most of the variance. PRS for bipolar disorder, cross-disorder, and depression explained less variance in mental health than PRS-SZ. Polygenic risk for mental suffering, derived from significance-testing in massive samples, lacks impact in analyses focusing on prediction in a general population epidemiological setting. Social-environmental circumstances, particularly childhood trauma and perceived status gap, drive most of the attributable variation in population mental health.

Predictive Performance of Exposome Score for Schizophrenia in the General Population

Previously, we established an estimated exposome score for schizophrenia (ES-SCZ) as a cumulative measure of environmental liability for schizophrenia to use in gene-environment interaction studies and for risk stratification in population cohorts. Hereby, we examined the discriminative function of ES-SCZ for identifying individuals diagnosed with schizophrenia spectrum disorder in the general population by measuring the area under the receiver operating characteristic curve (AUC). Furthermore, we compared this ES-SCZ method to an environmental sum score (Esum-SCZ) and an aggregate environmental score weighted by the meta-analytical estimates (Emet-SCZ). We also estimated ORs and Nagelkerke's R2 for ES-SCZ in association with psychiatric diagnoses and other medical outcomes. ES-SCZ showed a good discriminative function (AUC = 0.84) and statistically significantly performed better than both Esum-SCZ (AUC = 0.80) and Emet-SCZ (AUC = 0.80). At optimal cut point, ES-SCZ showed similar performance in ruling out (LR- = 0.20) and ruling in (LR+ = 3.86) schizophrenia. ES-SCZ at optimal cut point showed also a progressively greater magnitude of association with increasing psychosis risk strata. Among all clinical outcomes, ES-SCZ was associated with schizophrenia diagnosis with the highest OR (2.76, P < .001) and greatest explained variance (R2 = 14.03%), followed by bipolar disorder (OR = 2.61, P < .001, R2 = 13.01%) and suicide plan (OR = 2.44, P < .001, R2 = 12.44%). Our findings from an epidemiologically representative general population cohort demonstrate that an aggregate environmental exposure score for schizophrenia constructed using a predictive modeling approach-ES-SCZ-has the potential to improve risk prediction and stratification for research purposes and may help gain insight into the multicausal etiology of psychopathology.

Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum

AIMS

Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum.

METHODS

The sample consisted of 1699 patients, 1753 unaffected siblings, and 1542 healthy comparison participants. The Structured Interview for Schizotypy-Revised (SIS-R) was administered to analyse scores of total, positive, and negative schizotypy in siblings and healthy comparison participants. The PRS-SCZ was trained using the Psychiatric Genomics Consortiums results and the ES-SCZ was calculated guided by the approach validated in a previous report in the current data set. Regression models were applied to test the independent and joint effects of PRS-SCZ and ES-SCZ (adjusted for age, sex, and ancestry using 10 principal components).

RESULTS

Both genetic and environmental vulnerability were associated with case-control status. Furthermore, there was evidence for additive interaction between binary modes of PRS-SCZ and ES-SCZ (above 75% of the control distribution) increasing the odds for schizophrenia spectrum diagnosis (relative excess risk due to interaction = 6.79, [95% confidential interval (CI) 3.32, 10.26], p < 0.001). Sensitivity analyses using continuous PRS-SCZ and ES-SCZ confirmed gene-environment interaction (relative excess risk due to interaction = 1.80 [95% CI 1.01, 3.32], p = 0.004). In siblings and healthy comparison participants, PRS-SCZ and ES-SCZ were associated with all SIS-R dimensions and evidence was found for an interaction between PRS-SCZ and ES-SCZ on the total (B = 0.006 [95% CI 0.003, 0.009], p < 0.001), positive (B = 0.006 [95% CI, 0.002, 0.009], p = 0.002), and negative (B = 0.006, [95% CI 0.004, 0.009], p < 0.001) schizotypy dimensions.

CONCLUSIONS

The interplay between exposome load and schizophrenia genetic liability contributing to psychosis across the spectrum of expression provide further empirical support to the notion of aetiological continuity underlying an extended psychosis phenotype.

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

Update on the genetic architecture of schizophrenia

A long-established hypothesis is that schizophrenia has a strong genetic component. In the early 1990s, the first genetic variant that substantially increases risk for psychosis was identified. Since this initial reporting of deletions in the chromosomal region 22q11.2, nearly two decades passed until substantial insights into schizophrenia’s genetic architecture were gained. Schizophrenia is a polygenic disorder and genetic risk is conferred by both common and rare alleles distributed across the genome. A small number of rare, deleterious copy number variants (CNVs) are associated with moderate to substantial increases in individual risk to schizophrenia. These deletions and duplications are also associated with a range of neurodevelopmental disorders. The diagnostic investigation of CNVs in patients with schizophrenia is likely to represent one of the first examples of genetic testing in clinical psychiatry. The prerequisites for this are currently being defined.

Replicated evidence that endophenotypic expression of schizophrenia polygenic risk is greater in healthy siblings of patients compared to controls, suggesting gene-environment interaction. The EUGEI study

BACKGROUND

First-degree relatives of patients with psychotic disorder have higher levels of polygenic risk (PRS) for schizophrenia and higher levels of intermediate phenotypes.

METHODS

We conducted, using two different samples for discovery (n = 336 controls and 649 siblings of patients with psychotic disorder) and replication (n = 1208 controls and 1106 siblings), an analysis of association between PRS on the one hand and psychopathological and cognitive intermediate phenotypes of schizophrenia on the other in a sample at average genetic risk (healthy controls) and a sample at higher than average risk (healthy siblings of patients). Two subthreshold psychosis phenotypes, as well as a standardised measure of cognitive ability, based on a short version of the WAIS-III short form, were used. In addition, a measure of jumping to conclusion bias (replication sample only) was tested for association with PRS.

RESULTS

In both discovery and replication sample, evidence for an association between PRS and subthreshold psychosis phenotypes was observed in the relatives of patients, whereas in the controls no association was observed. Jumping to conclusion bias was similarly only associated with PRS in the sibling group. Cognitive ability was weakly negatively and non-significantly associated with PRS in both the sibling and the control group.

CONCLUSIONS

The degree of endophenotypic expression of schizophrenia polygenic risk depends on having a sibling with psychotic disorder, suggestive of underlying gene-environment interaction. Cognitive biases may better index genetic risk of disorder than traditional measures of neurocognition, which instead may reflect the population distribution of cognitive ability impacting the prognosis of psychotic disorder.

Cannabinoids and psychotic symptoms: A potential role for a genetic variant in the P2X purinoceptor 7 (P2RX7) gene

To investigate the biological mechanisms underlying the higher risk for psychosis in those that use cannabis, we conducted a genome-wide environment-interaction study (GWEIS). In a sample of individuals without a psychiatric disorder (N = 1262), we analyzed the interactions between regular cannabis use and genotype with psychotic-like experiences (PLE) as outcome. PLE were measured using the Community Assessment of Psychic Experiences (CAPE). The sample was enriched for those at the extremes of both cannabis use and PLE to increase power. A single nucleotide polymorphism in the P2RX7 gene (rs7958311) was associated with risk for a high level of psychotic experiences in regular cannabis users (p = 1.10 x10^-7) and in those with high levels of lifetime cannabis use (p = 4.5 × 10^-6). This interaction was replicated in individuals with high levels of lifetime cannabis use in the IMAGEN cohort (N = 1217, p = 0.020). Functional relevance of P2RX7 in cannabis users was suggested by in vitro experiments on activated monocytes. Exposure of these cells to tetrahydrocannabinol (THC) or cannabidiol (CBD) reduced the immunological response of the P2X7 receptor, which was dependent on the identified genetic variant. P2RX7 variants have been implicated in psychiatric disorders before and the P2X7 receptor is involved in pathways relevant to psychosis, such as neurotransmission, synaptic plasticity and immune regulation. We conclude that P2RX7 plays a role in vulnerability to develop psychotic symptoms when using cannabis and point to a new pathway that can potentially be targeted by newly developed P2X7 antagonists.

Genetic risk scores and hallucinations in patients with Parkinson disease

Objective

We examine the hypothesized overlap of genetic architecture for Alzheimer disease (AD), schizophrenia (SZ), and Parkinson disease (PD) through the use of polygenic risk scores (PRSs) with the occurrence of hallucinations in PD.

Methods

We used 2 population-based studies (ParkWest, Norway, and Parkinson's Environment and Gene, USA) providing us with 399 patients with PD with European ancestry and a PD diagnosis after age 55 years to assess the associations between 4 PRSs and hallucinations after 5 years of mean disease duration. Based on the existing genome-wide association study of other large consortia, 4 PRSs were created: one each using AD, SZ, and PD cohorts and another PRS for height, which served as a negative control.

Results

A higher prevalence of hallucinations was observed with each SD increase of the AD-PRS (odds ratio [OR]: 1.37, 95% confidence interval [CI]: 1.03-1.83). This effect was mainly driven by APOE (OR: 1.92, 95% CI: 1.14-3.22). In addition, a suggestive decrease and increase, respectively, in hallucination prevalence were observed with the SZ-PRS and the PD-PRS (OR: 0.77, 95% CI: 0.59-1.01; and OR: 1.29, 95% CI: 0.95-1.76, respectively). No association was observed with the height PRS.

Conclusions

These results suggest that mechanisms for hallucinations in PD may in part be driven by the same genetic architecture that leads to cognitive decline in AD, especially by APOE.

Examining Gene-Environment Interactions Using Aggregate Scores in a First-Episode Psychosis Cohort

Gene-environment (GxE) interactions have been related to psychosis spectrum disorders, involving multiple common genetic variants in multiple genes with very small effect sizes, and several environmental factors that constitute a dense network of exposures named the exposome. Here, we aimed to analyze GxE in a cohort of 310 first-episode psychotic (FEP) and 236 healthy controls, by using aggregate scores estimated in large populations such as the polygenic risk score for schizophrenia and (PRS-SCZ) and the Maudsley environmental risk score (ERS). In contrast to previous findings, in our study, the PRS-SCZ did not discriminate cases from controls, but the ERS score explained a similar percentage of the variance as in other studies using similar approaches. Our study supports a positive additive interaction, indicating synergy between genetic susceptibility to schizophrenia (PRS-SCZ dichotomized according to the highest quartile distribution of the control population) and the exposome (ERS > 75% of the controls). This additive interaction showed genetic and environmental dose dependence. Our study shows that the use of aggregate scores derived from large and powered studies instead of statistics derived from specific sample characteristics is a powerful tool for the study of the effects of GxE on the risk of psychotic spectrum disorders. In conclusion, by using a genetic risk score and an ERS we have provided further evidence for the role of GxE in psychosis.

Association of preceding psychosis risk states and non-psychotic mental disorders with incidence of clinical psychosis in the general population: a prospective study in the NEMESIS-2 cohort

The validity and clinical utility of the concept of "clinical high risk" (CHR) for psychosis have so far been investigated only in risk-enriched samples in clinical settings. In this population-based prospective study, we aimed - for the first time - to assess the incidence rate of clinical psychosis and es-timate the population attributable fraction (PAF) of that incidence for preceding psychosis risk states and DSM-IV diagnoses of non-psychotic mental disorders (mood disorders, anxiety disorders, alcohol use disorders, and drug use disorders). All analyses were adjusted for age, gender and education. The incidence rate of clinical psychosis was 63.0 per 100,000 person-years. The mutually-adjusted Cox proportional hazards model indicated that preceding diagnoses of mood disorders (hazard ratio, HR=10.67, 95% CI: 3.12-36.49), psychosis high-risk state (HR=7.86, 95% CI: 2.76-22.42) and drug use disorders (HR=5.33, 95% CI: 1.61-17.64) were associated with an increased risk for clinical psychosis incidence. Of the clinical psychosis incidence in the population, 85.5% (95% CI: 64.6-94.1) was attributable to prior psychopathology, with mood disorders (PAF=66.2, 95% CI: 33.4-82.9), psychosis high-risk state (PAF=36.9, 95% CI: 11.3-55.1), and drug use disorders (PAF=18.7, 95% CI: -0.9 to 34.6) as the most important factors. Although the psychosis high-risk state displayed a high relative risk for clinical psychosis outcome even after adjusting for other psychopathology, the PAF was comparatively low, given the low prevalence of psychosis high-risk states in the population. These findings provide empirical evidence for the "prevention paradox" of targeted CHR early intervention. A comprehensive prevention strategy with a focus on broader psychopathology may be more effective than the current psychosis-focused approach for achieving population-based improvements in prevention of psychotic disorders.

Analysis of GWAS-Derived Schizophrenia Genes for Links to Ischemia-Hypoxia Response of the Brain

Obstetric complications (OCs) can induce major adverse conditions for early brain development and predispose to mental disorders, including schizophrenia (SCZ). We previously hypothesized that SCZ candidate genes respond to ischemia-hypoxia as part of OCs which impacts neurodevelopment. We here tested for an overlap between SCZ genes from genome-wide association study (GWAS) (n=458 genes from 145 loci of the most recent GWAS dataset in SCZ) and gene sets for ischemia-hypoxia response. Subsets of SCZ genes were related to (a) mutation-intolerant genes (LoF database), (b) role in monogenic disorders of the nervous system (OMIM, manual annotations), and (c) synaptic function (SynGO). Ischemia-hypoxia response genes of the brain (IHR genes, n=1,629), a gene set from RNAseq in focal brain ischemia (BH, n=2,449) and genes from HypoxiaDB (HDB, n=2,289) were overlapped with the subset of SCZ genes and tested for enrichment with Chi-square tests (p < 0.017). The SCZ GWAS dataset was enriched for LoF (n=112; p=0.0001), and the LoF subset was enriched for IHR genes (n=25; p=0.0002), BH genes (n=35; p=0.0001), and HDB genes (n=23; p=0.0005). N=96 genes of the SCZ GWAS dataset (21%) could be linked to a monogenic disorder of the nervous system whereby IHR genes (n=19, p=0.008) and BH genes (n=23; p=0.002) were found enriched. N=46 synaptic genes were found in the SCZ GWAS gene set (p=0.0095) whereby enrichments for IHR genes (n=20; p=0.0001) and BH genes (n=13; p=0.0064) were found. In parallel, detailed annotations of SCZ genes for a role of the hypoxia-inducible factors (HIFs) identified n=33 genes of high interest. Genes from SCZ GWAS were enriched for mutation-intolerant genes which in turn were strongly enriched for three sets of genes for the ischemia-hypoxia response that may be invoked by OCs. A subset of one fifth of SCZ genes has established roles in monogenic disorders of the nervous system which was enriched for two gene sets related to ischemia-hypoxia. SCZ genes related to synaptic functions were also related to ischemia-hypoxia. Variants of SCZ genes interacting with ischemia-hypoxia provide a specific starting point for functional and genomic studies related to OCs.

Gene-Environment Interactions in Major Mental Disorders in the Czech Republic

BACKGROUND

Mental disorders affect about one-third of the human population, are typically chronic and significantly decrease the quality of life. Presently, the treatment of mental illnesses is far from adequate with a substantial proportion of the patients being pharmacoresistant and suffering from relapses. One of the reasons for this complicated situation is that we do not precisely know about the causes of mental disorders, so their treatment cannot be causal. The etiology of a mental disorder is typically based on a combination of molecular (genetic) and environmental factors.

AIM

The aim of the project is to discover the gene-environment interactions (GxE) in a wide spectrum of mental disorders.

METHODS

The design of our study is innovative in the sense that we intend to study large groups of associated mental disorders as a whole instead of in isolation. This would enable us to map out the possible environmental causal factors in detail in relation to their character, magnitude and timing. The project also allows a study of genetics (including epigenetics and microbiomes) as well as the environment simultaneously. We plan on involving three study groups: the first group are patients suffering from schizophrenia or a mood disorder such as major depression, recurrent depressive disorder and bipolar affective disorder; the second group of patients have anxiety disorders; and the third group are healthy volunteers from the general population who are genetically unrelated. All of the study subjects will undergo the following assessments: a psychiatric examination, the identification of stressful life events with the aid of a questionnaire, the examination of their reaction to stress, genetic and epigenetic (microRNA) assessments and the analysis of oral and gut microbiome.

CONCLUSION

We expect that some of the genetic as well as environmental factors in the studied mental disorders are shared, while some others are specific. We also expect that the GxE (gene-environment interaction) in schizophrenic and affective disorders will be different from the GxE in anxiety disorders and that the GxE in the studied mental disorders will differ generally from the GxE in healthy volunteers. Our results can help in the prevention and individualized treatment of a range of mental disorders.

Polygenic liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness

OBJECTIVE

To test whether polygenic risk score for schizophrenia (PRS-S) interacts with childhood adversity and daily-life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress-sensitivity measures.

METHODS

The data were retrieved from a general population twin cohort including 593 adolescents and young adults. Childhood adversity was assessed using the Childhood Trauma Questionnaire. Daily-life stressors and momentary mental state domains were measured using ecological momentary assessment. PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta-analysis. The analyses were conducted using multilevel mixed-effects tobit regression models.

RESULTS

Both childhood adversity and daily-life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS-S was only associated with increased positive affect. No gene-environment correlation was detected. There is novel evidence for interaction effects between PRS-S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, P = 0.013), positive affect (b = -0.05, P = 0.043), and subtle psychosis expression (b = 0.11, P = 0.007)] and stress-sensitivity measures.

CONCLUSION

Exposure to childhood adversities, particularly in individuals with high PRS-S, is pleiotropically associated with emotion dysregulation and psychosis proneness.

Familial and socioeconomic contributions to premorbid functioning in psychosis: Impact on age at onset and treatment response

Background.

Premorbid adjustment (PA) abnormalities in psychotic disorders are associated with an earlier age at onset (AAO) and unfavorable clinical outcomes, including treatment resistance. Prior family studies suggest that familial liability, likely reflecting increased genetic risk, and socioeconomic status (SES) contribute to premorbid maladjustment. However, their joint effect possibly indicating gene–environment interaction has not been evaluated.

Methods.

We examined whether family history of psychosis (FHP) and parental SES may predict PA and AAO in unrelated cases with first-episode psychosis (n = 108) and schizophrenia (n = 104). Premorbid academic and social functioning domains during childhood and early adolescence were retrospectively assessed. Regression analyses were performed to investigate main effects of FHP and parental SES, as well as their interaction. The relationships between PA, AAO, and response to antipsychotic medication were also explored.

Results.

Positive FHP associated with academic PA difficulties and importantly interacted with parental SES to moderate social PA during childhood (interaction p = 0.024). Positive FHP and parental SES did not predict differences in AAO. Nevertheless, an earlier AAO was observed among cases with worse social PA in childhood (β = −0.20; p = 0.005) and early adolescence (β = −0.19; p = 0.007). Further, confirming evidence emerged for an association between deficient childhood social PA and poor treatment response (p = 0.04).

Conclusions.

Familial risk for psychosis may interact with parental socioeconomic position influencing social PA in childhood. In addition, this study supports the link between social PA deviations, early psychosis onset, and treatment resistance, which highlights premorbid social functioning as a promising clinical indicator.

Mitochondrial Dysfunction in Schizophrenia

Schizophrenia (SCZ) is a severe neurodevelopmental disorder affecting 1% of populations worldwide with a grave disability and socioeconomic burden. Current antipsychotic medications are effective treatments for positive symptoms, but poorly address negative symptoms and cognitive symptoms, warranting the development of better treatment options. Further understanding of SCZ pathogenesis is critical in these endeavors. Accumulating evidence has pointed to the role of mitochondria and metabolic dysregulation in SCZ pathogenesis. This review critically summarizes recent studies associating a compromised mitochondrial function with people with SCZ, including postmortem studies, imaging studies, genetic studies, and induced pluripotent stem cell studies. This review also discusses animal models with mitochondrial dysfunction resulting in SCZ-relevant neurobehavioral abnormalities, as well as restoration of mitochondrial function as potential therapeutic targets. Further understanding of mitochondrial dysfunction in SCZ may open the door to develop novel therapeutic strategies that can address the symptoms that cannot be adequately addressed by current antipsychotics alone.

Magnetoencephalography for Schizophrenia

Schizophrenia (Sz) is a chronic mental disorder characterized by disturbances in thought (such as delusions and confused thinking), perception (hearing voices), and behavior (lack of motivation). The lifetime prevalence of Sz is between 0.3% and 0.7%, with late adolescence and early adulthood, the peak period for the onset of psychotic symptoms. Causal factors in Sz include environmental and genetic factors and especially their interaction. About 50% of individuals with a diagnosis of Sz have lifelong impairment.

Validation of the Portuguese version of the Community Assessment of Psychic Experiences and characterization of psychotic experiences in a Brazilian sample

Objective:

We investigated: i) the reliability and validity of a Brazilian version of the Community Assessment of Psychic Experiences (CAPE), developed to detect and characterize psychotic experiences in the general population; and ii) the association between psychotic experiences, childhood adversity, and cannabis use in a population-based sample.

Methods:

We performed factorial analyses and generalized linear models with CAPE scores as the dependent variable in a sample composed of 217 first-episode psychosis patients, 104 unaffected biological siblings, and 319 non-psychotic population-based participants.

Results:

After removing seven items from its positive dimension and two items from its negative dimension, a 33-item Brazilian version of the CAPE showed acceptable adjustment indices (confirmatory fit index = 0.895; goodness of fit index = 0.822; parsimony goodness of fit index = 0.761; root mean square error of approximation [RMSEA] = 0.055, p [RMSEA ≤ 0.05] = 0.04) and internal consistency in all its dimensions (> 0.70). Childhood adversity was associated with higher scores in all three dimensions, as well as with total score. Lifetime cannabis use was associated with higher scores only in the positive dimension.

Conclusion:

The proposed Brazilian version of the CAPE corroborates the tridimensional approach for assessing psychosis-proneness, and the frequency and severity of psychotic manifestations are distributed as a spectrum in the general population.

A Novel Schizophrenia Diagnostic Model Based on Statistically Significant Changes in Gene Methylation in Specific Brain Regions

Objective

The present study identified methylation patterns of schizophrenia- (SCZ-) related genes in different brain regions and used them to construct a novel DNA methylation-based SCZ diagnostic model.

Methods

Four DNA methylation datasets representing different brain regions were downloaded from the Gene Expression Omnibus. The common differentially methylated genes (CDMGs) in all datasets were identified to perform functional enrichment analysis. The differential methylation sites of 10 CDMGs involved in the largest numbers of neurological or psychiatric-related biological processes were used to construct a DNA methylation-based diagnostic model for SCZ in the respective datasets.

Results

A total of 849 CDMGs were identified in the four datasets, but the methylation sites as well as degree of methylation differed across the brain regions. Functional enrichment analysis showed CDMGs were significantly involved in biological processes associated with neuronal axon development, intercellular adhesion, and cell morphology changes and, specifically, in PI3K-Akt, AMPK, and MAPK signaling pathways. Four DNA methylation-based classifiers for diagnosing SCZ were constructed in the four datasets, respectively. The sample recognition efficiency of the classifiers showed an area under the receiver operating characteristic curve of 1.00 in three datasets and >0.9 in one dataset.

Conclusion

DNA methylation patterns in SCZ vary across different brain regions, which may be a useful epigenetic characteristic for diagnosing SCZ. Our novel model based on SCZ-gene methylation shows promising diagnostic power.

Developmental Genes and Regulatory Proteins, Domains of Cognitive Impairment in Schizophrenia Spectrum Psychosis and Implications for Antipsychotic Drug Discovery: The Example of Dysbindin-1 Isoforms and Beyond

Alongside positive and negative symptomatology, deficits in working memory, attention, selective learning processes, and executive function have been widely documented in schizophrenia spectrum psychosis. These cognitive abnormalities are strongly associated with impairment across multiple function domains and are generally treatment-resistant. The DTNBP1 (dystrobrevin-binding protein-1) gene, encoding dysbindin, is considered a risk factor for schizophrenia and is associated with variation in cognitive function in both clinical and nonclinical samples. Downregulation of DTNBP1 expression in dorsolateral prefrontal cortex and hippocampal formation of patients with schizophrenia has been suggested to serve as a primary pathophysiological process. Described as a "hub," dysbindin is an important regulatory protein that is linked with multiple complexes in the brain and is involved in a wide variety of functions implicated in neurodevelopment and neuroplasticity. The expression pattern of the various dysbindin isoforms (-1A, -1B, -1C) changes depending upon stage of brain development, tissue areas and subcellular localizations, and can involve interaction with different protein partners. We review evidence describing how sequence variation in DTNBP1 isoforms has been differentially associated with schizophrenia-associated symptoms. We discuss results linking these isoform proteins, and their interacting molecular partners, with cognitive dysfunction in schizophrenia, including evidence from drosophila through to genetic mouse models of dysbindin function. Finally, we discuss preclinical evidence investigating the antipsychotic potential of molecules that influence dysbindin expression and functionality. These studies, and other recent work that has extended this approach to other developmental regulators, may facilitate identification of novel molecular pathways leading to improved antipsychotic treatments.

Looking at Intergenerational Risk Factors in Schizophrenia Spectrum Disorders: New Frontiers for Early Vulnerability Identification?

Offspring of individuals with serious mental illness (SMI) constitute a special population with a higher risk of developing psychiatric disorders, which is also highly prevalent among referrals to child and adolescent mental health services (CAMHS). They often exhibit more or less subclinical conditions of vulnerability, fueled by mutually potentiating combinations of risk factors, such as presumed genetic risk, poor or inadequate affective and cognitive parenting, and low socio-economic status. Despite this evidence, neither specific preventive programs for offspring of parents with SMI are usually implemented in CAMHS, nor dedicated supportive programs for parenting are generally available in adult mental health services (AMHS). Needless to say, while both service systems tend to focus on individual recovery and clinical management (rather than on the whole family system), these blind spots add up to frequent gaps in communication and continuity of care between CAMHS and AMHS. This is particularly problematic in an age-range in which an offspring's vulnerabilities encounter the highest epidemiological peak of incident risk of SMI. This paper offers a clinical-conceptual perspective aimed to disentangle the complex intertwine of intergenerational risk factors that contribute to the risk of developing SMI in offspring, taking schizophrenia spectrum disorders as a paradigmatic example.

Cannabis Use and the Risk for Psychosis and Affective Disorders

Objective: This review discusses the relationship between cannabis use and psychotic, bipolar, depressive, and anxiety disorders, as well as suicide. It summarizes epidemiological evidence from cross-sectional and long-term prospective studies and considers possible etiological mechanisms. Methods: Systematic reviews and methodologically robust studies in the field (from inception to February 2019) were identified using a comprehensive search of Medline, PsychINFO, and Embase and summarized using a narrative synthesis. Results: Consistent evidence, both from observational and experimental studies, has confirmed the important role of cannabis use in the initiation and persistence of psychotic disorders. The size of the effect is related to the extent of cannabis use, with greater risk for early cannabis use and use of high-potency varieties and synthetic cannabinoids. Accumulating evidence suggests that frequent cannabis use also increases the risk for mania as well as for suicide. However, the effect on depression is less clear and findings on anxiety are contradictory with only a few methodologically robust studies. Furthermore, the relationship with common mental disorders may involve reverse causality, as depression and anxiety are reported to lead to greater cannabis consumption in some studies. Pathogenetic mechanisms focus on the effect of tetrahydrocannabinol (THC, the main psychoactive ingredient of cannabis) interacting with genetic predisposition and perhaps other environmental risk factors. Cannabidiol (CBD), the other important ingredient of traditional cannabis, ameliorates the psychotogenic effects of THC but is absent from the high-potency varieties that are increasingly available. Conclusions: The evidence that heavy use of high-THC/low-CBD types of cannabis increases the risk of psychosis is sufficiently strong to merit public health education. Evidence of similar but smaller effects in mania and suicide is growing, but is not convincing for depression and anxiety. There is much current interest in the possibility that CBD may be therapeutically useful.

TwinssCan — Gene-Environment Interaction in Psychotic and Depressive Intermediate Phenotypes: Risk and Protective Factors in a General Population Twin Sample

Meta-analyses suggest that clinical psychopathology is preceded by dimensional behavioral and cognitive phenotypes such as psychotic experiences, executive functioning, working memory and affective dysregulation that are determined by the interplay between genetic and nongenetic factors contributing to the severity of psychopathology. The liability to mental ill health can be psychometrically measured using experimental paradigms that assess neurocognitive processes such as salience attribution, sensitivity to social defeat and reward sensitivity. Here, we describe the TwinssCan, a longitudinal general population twin cohort, which comprises 1202 individuals (796 adolescent/young adult twins, 43 siblings and 363 parents) at baseline. The TwinssCan is part of the European Network of National Networks studying Gene-Environment Interactions in Schizophrenia project and recruited from the East Flanders Prospective Twin Survey. The main objective of this project is to understand psychopathology by evaluating the contribution of genetic and nongenetic factors on subclinical expressions of dimensional phenotypes at a young age before the onset of disorder and their association with neurocognitive processes, such as salience attribution, sensitivity to social defeat and reward sensitivity.

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.

Non-Genetic Factors in Schizophrenia

PURPOSE OF REVIEW

We review recent developments on risk factors in schizophrenia.

RECENT FINDINGS

The way we think about schizophrenia today is profoundly different from the way this illness was seen in the twentieth century. We now know that the etiology of schizophrenia is multifactorial and reflects an interaction between genetic vulnerability and environmental contributors. Environmental risk factors such as pregnancy and birth complications, childhood trauma, migration, social isolation, urbanicity, and substance abuse, alone and in combination, acting at a number of levels over time, influence the individual's likelihood to develop the disorder. Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene-environment interaction and environment-environment interaction have hugely increased our knowledge of the disorder.