The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: Systematic review

Evaluating and improving health equity and fairness of polygenic scores

Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.

Psychosis superspectrum I: Nosology, etiology, and lifespan development

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Polygenic risk scores mediating functioning outcomes through cognitive and clinical features in youth at family risk and controls

Schizophrenia and bipolar disorder exhibit substantial clinical overlap, particularly in individuals at familial high risk, who frequently present sub-threshold symptoms before the onset of illness. Severe mental disorders are highly polygenic traits, but their impact on the stages preceding the manifestation of mental disorders remains relatively unexplored. Our study aimed to examine the influence of polygenic risk scores (PRS) on sub-clinical outcomes over a 2-year period in youth at familial high risk for schizophrenia and bipolar disorder and controls. The sample included 222 children and adolescents, comprising offspring of parents with schizophrenia (n = 38), bipolar disorder (n = 80), and community controls (n = 104). We calculated PRS for psychiatric disorders, neuroticism and cognition using the PRS-CS method. Linear mixed-effects models were employed to investigate the association between PRS and cognition, symptom severity and functioning. Mediation analyses were conducted to explore whether clinical features acted as intermediaries in the impact of PRS on functioning outcomes. SZoff exhibited elevated PRS for schizophrenia. In the entire sample, PRS for depression, neuroticism, and cognitive traits showed associations with sub-clinical features. The effect of PRS for neuroticism and general intelligence on functioning outcomes were mediated by cognition and symptoms severity, respectively. This study delves into the interplay among genetics, the emergence of sub-clinical symptoms and functioning outcomes, providing novel evidence on mechanisms underpinning the continuum from sub-threshold features to the onset of mental disorders. The findings underscore the interplay of genetics, cognition, and clinical features, providing insights for personalized early interventions.

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

Exploring the relationship of cognitive function with and without COVID-19 recovered schizophrenic patients

Background

The Coronavirus disease 2019 (COVID-19) is linked to the deterioration of cognitive function among individuals suffering from schizophrenia. The purpose of this study was to compare the cognitive performance of schizophrenic patients before and after COVID-19.

Methods

A longitudinal cohort study involving a sample of 219 individuals diagnosed with schizophrenia was enrolled between June 2022 and May 2023. The participants were split into two groups infected with COVID-19 (n = 165) and not infected with COVID-19 (n = 54). The data were gathered via a questionnaire on demographic characteristics, the Brief Assessment of Cognition in Schizophrenia (BACS), the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Activities of Daily Living (ADL) scale, and the Insomnia Severity Index (ISI).

Results

The repeated-measures ANOVA showed that Among patients diagnosed with COVID-19, there was a deterioration in global cognitive function (before COVID-19 = -2.45 vs. after COVID-19 = -3.02, p = 0.007), working memory (before COVID-19 = -2.76 vs. after COVID-19 = -3.34, p < 0.00 1), motor speed (before COVID-19 = -1.64 vs. after COVID-19 = -2.12, p < 0.001), attention and speed of information processing (before COVID-19 = -1.93 vs. after COVID-19 = -1.16, p = 0.008). multi-variable analysis showed that several factors as having a secondary grade of education (β = 0.434), experiencing insomnia (β = 0.411)and the interaction between COVID-19 diagnosis and cognition at baseline (β = 0.796) were significantly associated with cognitive deficits. At the same time, no significant associations were found between global cognition and clinical symptoms, autonomy, or depression (p > 0.05).

Conclusion

The COVID-19 pandemic has significantly impacted various cognitive functions, such as verbal memory, working memory, and global cognition. Insomnia has been identified as the predominant determinant of cognitive impairment, alongside the confirmation of a COVID-19 diagnosis. Additional research is imperative to elucidate the diversification of cognitive functionality observed in individuals diagnosed with schizophrenia who have acquired COVID-19.

Schizophrenia polygenic risk score in psychosis proneness

Schizophrenia (SZ) is a complex disorder with a highly polygenic inheritance. It can be conceived as the extreme expression of a continuum of traits that are present in the general population often broadly referred to as schizotypy. However, it is still poorly understood how these traits overlap genetically with the disorder. We investigated whether polygenic risk for SZ is associated with these disorder-related phenotypes (schizotypy, psychotic-like experiences, and subclinical psychopathology) in a sample of 253 non-clinically identified participants. Polygenic risk scores (PRSs) were constructed based on the latest SZ genome-wide association study using the PRS-CS method. Their association with self-report and interview measures of SZ-related traits was tested. No association with either schizotypy or psychotic-like experiences was found. However, we identified a significant association with the Motor Change subscale of the Comprehensive Assessment of At-Risk Mental States (CAARMS) interview. Our results indicate that the genetic overlap of SZ with schizotypy and psychotic-like experiences is less robust than previously hypothesized. The relationship between high PRS for SZ and motor abnormalities could reflect neurodevelopmental processes associated with psychosis proneness and SZ.

Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors

OBJECTIVE

Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge.

METHODS

Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci.

RESULTS

Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells.

CONCLUSIONS

These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.

The polygenic basis of relapse after a first episode of schizophrenia

Little is known about genetic predisposition to relapse. Previous studies have linked cognitive and psychopathological (mainly schizophrenia and bipolar disorder) polygenic risk scores (PRS) with clinical manifestations of the disease. This study aims to explore the potential role of PRS from major mental disorders and cognition on schizophrenia relapse. 114 patients recruited in the 2EPs Project were included (56 patients who had not experienced relapse after 3 years of enrollment and 58 patients who relapsed during the 3-year follow-up). PRS for schizophrenia (PRS-SZ), bipolar disorder (PRS-BD), education attainment (PRS-EA) and cognitive performance (PRS-CP) were used to assess the genetic risk of schizophrenia relapse.Patients with higher PRS-EA, showed both a lower risk (OR=0.29, 95% CI [0.11-0.73]) and a later onset of relapse (30.96± 1.74 vs. 23.12± 1.14 months, p=0.007. Our study provides evidence that the genetic burden of neurocognitive function is a potentially predictors of relapse that could be incorporated into future risk prediction models. Moreover, appropriate treatments for cognitive symptoms appear to be important for improving the long-term clinical outcome of relapse.

Evaluating and Improving Health Equity and Fairness of Polygenic Scores

Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.

Dissecting Schizotypy and Its Association With Cognition and Polygenic Risk for Schizophrenia in a Nonclinical Sample

Schizotypy is a multidimensional construct that captures a continuum of risk for developing schizophrenia-spectrum psychopathology. Existing 3-factor models of schizotypy, consisting of positive, negative, and disorganized dimensions have yielded mixed evidence of genetic continuity with schizophrenia using polygenic risk scores. Here, we propose an approach that involves splitting positive and negative schizotypy into more specific subdimensions that are phenotypically continuous with distinct positive symptoms and negative symptoms recognized in clinical schizophrenia. We used item response theory to derive high-precision estimates of psychometric schizotypy using 251 self-report items obtained from a non-clinical sample of 727 (424 females) adults. These subdimensions were organized hierarchically using structural equation modeling into 3 empirically independent higher-order dimensions enabling associations with polygenic risk for schizophrenia to be examined at different levels of phenotypic generality and specificity. Results revealed that polygenic risk for schizophrenia was associated with variance specific to delusional experiences (γ = 0.093, P = .001) and reduced social interest and engagement (γ = 0.076, P = .020), and these effects were not mediated via the higher-order general, positive, or negative schizotypy factors. We further fractionated general intellectual functioning into fluid and crystallized intelligence in 446 (246 females) participants that underwent onsite cognitive assessment. Polygenic risk scores explained 3.6% of the variance in crystallized intelligence. Our precision phenotyping approach could be used to enhance the etiologic signal in future genetic association studies and improve the detection and prevention of schizophrenia-spectrum psychopathology.

Clinical characteristics indexing genetic differences in bipolar disorder - a systematic review

Bipolar disorder is a heterogenous condition with a varied clinical presentation. While progress has been made in identifying genetic variants associated with bipolar disorder, most common genetic variants have not yet been identified. More detailed phenotyping (beyond diagnosis) may increase the chance of finding genetic variants. Our aim therefore was to identify clinical characteristics that index genetic differences in bipolar disorder.We performed a systematic review of all genome-wide molecular genetic, family, and twin studies investigating familial/genetic influences on the clinical characteristics of bipolar disorder. We performed an electronic database search of PubMed and PsycInfo until October 2022. We reviewed title/abstracts of 2693 unique records and full texts of 391 reports, identifying 445 relevant analyses from 142 different reports. These reports described 199 analyses from family studies, 183 analyses from molecular genetic studies and 63 analyses from other types of studies. We summarized the overall evidence per phenotype considering study quality, power, and number of studies.We found moderate to strong evidence for a positive association of age at onset, subtype (bipolar I versus bipolar II), psychotic symptoms and manic symptoms with familial/genetic risk of bipolar disorder. Sex was not associated with overall genetic risk but could indicate qualitative genetic differences. Assessment of genetically relevant clinical characteristics of patients with bipolar disorder can be used to increase the phenotypic and genetic homogeneity of the sample in future genetic studies, which may yield more power, increase specificity, and improve understanding of the genetic architecture of bipolar disorder.

Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study

Given the polygenic nature of antipsychotic-induced weight gain (AIWG), we investigated whether polygenic risk scores (PRS) for various psychiatric and metabolic traits were associated with AIWG. We included individuals with schizophrenia (SCZ) of European ancestry from two cohorts (N = 151, age = 40.3 ± 11.8 and N = 138, age = 36.5 ± 10.8). We investigated associations of AIWG defined as binary and continuous variables with PRS calculated from genome-wide association studies of body mass index (BMI), coronary artery disease (CAD), fasting glucose, fasting insulin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, type 1 and 2 diabetes mellitus, and SCZ, using regression models. We observed nominal associations (uncorrected p < 0.05) between PRSs for BMI, CAD, and LDL-C, type 1 diabetes, and SCZ with AIWG. While results became non-significant after correction for multiple testing, these preliminary results suggest that PRS analyses might contribute to identifying risk factors of AIWG and might help to elucidate mechanisms at play in AIWG.

Trauma Matters: Integrating Genetic and Environmental Components of PTSD

Trauma is ubiquitous, but only a subset of those who experience trauma will develop posttraumatic stress disorder (PTSD). In this review, it is argued that to determine who is at risk of developing PTSD, it is critical to examine the genetic etiology of the disorder and individual trauma profiles of those who are susceptible. First, the state of current PTSD genetic research is described, with a particular focus on studies that present evidence for trauma type specificity, or for differential genetic etiology according to gender or race. Next, approaches that leverage non-traditional phenotyping approaches are reviewed to identify PTSD-associated variants and biology, and the relative advantages and limitations inherent in these studies are reflected on. Finally, it is discussed how trauma might influence the heritability of PTSD, through type, risk factors, genetics, and associations with PTSD symptomology.

Link between cognitive polygenic risk scores and clinical progression after a first-psychotic episode

BACKGROUND

Clinical intervention in early stages of psychotic disorders is crucial for the prevention of severe symptomatology trajectories and poor outcomes. Genetic variability is studied as a promising modulator of prognosis, thus novel approaches considering the polygenic nature of these complex phenotypes are required to unravel the mechanisms underlying the early progression of the disorder.

METHODS

The sample comprised of 233 first-episode psychosis (FEP) subjects with clinical and cognitive data assessed periodically for a 2-year period and 150 matched controls. Polygenic risk scores (PRSs) for schizophrenia, bipolar disorder, depression, education attainment and cognitive performance were used to assess the genetic risk of FEP and to characterize their association with premorbid, baseline and progression of clinical and cognitive status.

RESULTS

Schizophrenia, bipolar disorder and cognitive performance PRSs were associated with an increased risk of FEP [false discovery rate (FDR) ⩽ 0.027]. In FEP patients, increased cognitive PRSs were found for FEP patients with more cognitive reserve (FDR ⩽ 0.037). PRSs reflecting a genetic liability for improved cognition were associated with a better course of symptoms, functionality and working memory (FDR ⩽ 0.039). Moreover, the PRS of depression was associated with a worse trajectory of the executive function and the general cognitive status (FDR ⩽ 0.001).

CONCLUSIONS

Our study provides novel evidence of the polygenic bases of psychosis and its clinical manifestation in its first stage. The consistent effect of cognitive PRSs on the early clinical progression suggests that the mechanisms underlying the psychotic episode and its severity could be partially independent.

Six-year trajectories and associated factors of positive and negative symptoms in schizophrenia patients, siblings, and controls: Genetic Risk and Outcome of Psychosis (GROUP) study

Positive and negative symptoms are prominent but heterogeneous characteristics of schizophrenia spectrum disorder (SSD). Within the framework of the Genetic Risk and Outcome of Psychosis (GROUP) longitudinal cohort study, we aimed to distinguish and identify the genetic and non-genetics predictors of homogenous subgroups of the long-term course of positive and negative symptoms in SSD patients (n = 1119) and their unaffected siblings (n = 1059) in comparison to controls (n = 586). Data were collected at baseline, and after 3- and 6-year follow-ups. Group-based trajectory modeling was applied to identify latent subgroups using positive and negative symptoms or schizotypy scores. A multinomial random-effects logistic regression model was used to identify predictors of latent subgroups. Patients had decreasing, increasing, and relapsing symptoms course. Unaffected siblings and healthy controls had three to four subgroups characterized by stable, decreasing, or increasing schizotypy. PRSSCZ did not predict the latent subgroups. Baseline symptoms severity in patients, premorbid adjustment, depressive symptoms, and quality of life in siblings predicted long-term trajectories while were nonsignificant in controls. In conclusion, up to four homogenous latent subgroups of symptom course can be distinguished within patients, siblings, and controls, while non-genetic factors are the main factors associated with the latent subgroups.

Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis

Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.

Polygenic risk, familial liability and stress reactivity in psychosis: an experience sampling study

BACKGROUND

There is evidence for a polygenic contribution to psychosis. One targetable mechanism through which polygenic variation may impact on individuals and interact with the social environment is stress sensitization, characterized by elevated reactivity to minor stressors in daily life. The current study aimed to investigate whether stress reactivity is modified by polygenic risk score for schizophrenia (PRS) in cases with enduring non-affective psychotic disorder, first-degree relatives of cases, and controls.

METHODS

We used the experience sampling method to assess minor stressors, negative affect, positive affect and psychotic experiences in 96 cases, 79 first-degree relatives, i.e. siblings, and 73 controls at wave 3 of the Dutch Genetic Risk and Outcome of Psychosis (GROUP) study. Genome-wide data were collected at baseline to calculate PRS.

RESULTS

We found that associations of momentary stress with psychotic experiences, but not with negative and positive affect, were modified by PRS and group (all pFWE<0.001). In contrast to our hypotheses, siblings with high PRS reported less intense psychotic experiences in response to momentary stress compared to siblings with low PRS. No differences in magnitude of these associations were observed in cases with high v. low level of PRS. By contrast, controls with high PRS showed more intense psychotic experiences in response to stress compared to those with low PRS.

CONCLUSIONS

This tentatively suggests that polygenic risk may operate in different ways than previously assumed and amplify reactivity to stress in unaffected individuals but operate as a resilience factor in relatives by attenuating their stress reactivity.

Polygenic Risk for Schizophrenia, Major Depression, and Post-traumatic Stress Disorder and Hippocampal Subregion Volumes in Middle Childhood

Studies demonstrate that individuals with diagnoses for Major Depressive Disorder (MDD), Post-traumatic Stress Disorder (PTSD), and Schizophrenia (SCZ) may exhibit smaller hippocampal gray matter relative to otherwise healthy controls, although the effect sizes vary in each disorder. Existing work suggests that hippocampal abnormalities in each disorder may be attributable to genetic liability and/or environmental variables. The following study uses baseline data from the Adolescent Brain and Cognitive Development[Formula: see text] Study (ABCD Study[Formula: see text]) to address three open questions regarding the relationship between genetic risk for each disorder and hippocampal volume reductions: (a) whether polygenic risk scores (PGRS) for MDD, PTSD, and SCZ are related to hippocampal volume; (b) whether PGRS for MDD, PTSD, and SCZ are differentially related to specific hippocampal subregions along the longitudinal axis; and (c) whether the association between PGRS for MDD, PTSD, and SCZ and hippocampal volume is moderated by sex and/or environmental adversity. In short, we did not find associations between PGRS for MDD, PTSD, and SCZ to be significantly related to any hippocampal subregion volumes. Furthermore, neither sex nor enviornmental adversity significantly moderated these associations. Our study provides an important null finding on the relationship genetic risk for MDD, PTSD, and SCZ to measures of hippocampal volume.

Evidence of the folate-mediated one-carbon metabolism pathway genes in controlling the non-syndromic oral clefts risks

The folate-mediated one-carbon metabolism pathway is thought to play an important role in the etiology of non-syndromic oral clefts (NSOFC), although none of the genes in this pathway has shown significant signals in genome-wide association studies (GWAS). Recent evidence indicated that enhanced understanding could be gained by aggregating multiple SNPs effect simultaneously into polygenic risk score (PRS) to assess its association with disease risks. This study is aimed to assess the association between the genetic effect of folate-mediated one-carbon metabolism pathway and NSOFC risks using PRS based on a case-parent trio design. A total of 297 SNPs mapped from 18 genes in the folate-mediated one-carbon metabolism pathway were aggregated from a GWAS of 2458 case-parent trios recruited from an international consortium. We found a PRS based on the folate-mediated one-carbon metabolism pathway was significant among all NSOFC trios (OR = 1.95, 95% CI: 1.66-2.28, p = 2.39 × 10^-16 ), as well as two major subtypes, non-syndromic cleft lip with or without cleft palate (NSCL/P) trios (OR = 1.71, 95% CI: 1.50-1.96, p = 7.66 × 10^-15 ) and non-syndromic cleft palate only (NSCPO) trios (OR = 1.51, 95% CI: 1.36-1.68, p = 2.1 × 10^-14 ). Similar results were also observed in further subgroup analyses stratified into Asian and European trios. The averaged PRS of the folate-mediated one-carbon metabolism pathway varied between the NSOFC case group and its comparison group (p < 0.05) with higher average PRS in the cases. Moreover, the top 5% pathway PRS group had 2.25 (95% CI: 1.85-2.73) times increased NSOFC risk, also 3.09 (95% CI: 2.50-3.81) and 2.06 (95% CI: 1.39-3.02) times increased risk of NSCL/P and NSCPO compared to the remainder of the distribution. The results of our study confirmed the folate-mediated one-carbon metabolism pathway was important in controlling risk to NSOFC and this study enhanced evidence towards understanding the genetic risks of NSOFC.

The relative and interactive impact of multiple risk factors in schizophrenia spectrum disorders: a combined register-based and clinical twin study

BACKGROUND

Research has yielded evidence for genetic and environmental factors influencing the risk of schizophrenia. Numerous environmental factors have been identified; however, the individual effects are small. The additive and interactive effects of multiple risk factors are not well elucidated. Twin pairs discordant for schizophrenia offer a unique opportunity to identify factors that differ between patients and unaffected co-twins, who are perfectly matched for age, sex and genetic background.

METHODS

Register data were combined with clinical data for 216 twins including monozygotic (MZ) and dizygotic (DZ) proband pairs (one or both twins having a schizophrenia spectrum diagnosis) and MZ/DZ healthy control (HC) pairs. Logistic regression models were applied to predict (1) illness vulnerability (being a proband v. HC pair) and (2) illness status (being the patient v. unaffected co-twin). Risk factors included: A polygenic risk score (PRS) for schizophrenia, birth complications, birth weight, Apgar scores, paternal age, maternal smoking, season of birth, parental socioeconomic status, urbanicity, childhood trauma, estimated premorbid intelligence and cannabis.

RESULTS

The PRS [odds ratio (OR) 1.6 (1.1-2.3)], childhood trauma [OR 4.5 (2.3-8.8)], and regular cannabis use [OR 8.3 (2.1-32.7)] independently predicted illness vulnerability as did an interaction between childhood trauma and cannabis use [OR 0.17 (0.03-0.9)]. Only regular cannabis use predicted having a schizophrenia spectrum diagnosis between patients and unaffected co-twins [OR 3.3 (1.1-10.4)].

CONCLUSION

The findings suggest that several risk factors contribute to increasing schizophrenia spectrum vulnerability. Moreover, cannabis, a potentially completely avoidable environmental risk factor, seems to play a substantial role in schizophrenia pathology.

Mapping, clustering, and analysis of research in psychiatric genomics

INTRODUCTION

The distribution pattern and knowledge structure of psychiatric genomics were surveyed based on literature dealing with both psychiatry and genomics/genetics. Coword analysis and bibliographic coupling of the records retrieved from Scopus and PubMed for 2016-2020 revealed the subsurface research aspects.

METHOD

The data were analyzed using coword analysis and clustering methods using Sci2 and VOSviewer.

RESULT

Analysis of ~3800 records showed that psychiatric genomics is, as expectedly, covered largely under biomedical subjects with a visible interest in other disciplines such as humanities and ethics. A coword analysis was done for all the years, followed by a year-wise analysis based on the keywords, and then a bibliographic coupling based on the cited references. This led to the generation of different clusters of prevalent research areas. The centrality values described the position of each component.

DISCUSSION

'Schizophrenia', 'depression', 'pharmacogenomics', and 'immunopathogenesis' were the research topics of overarching interest. 'Gut-brain axis' and 'gene-environment interaction' were the emerging topics, whereas certain topics such as 'child and adolescent psychiatry' remained priorities when compared to earlier studies. The keywords and research focus were diverse. They ranged from genetics to transcriptomics and epigenetics to proteomics of psychiatric disorders. We found a stagnation of science communication in the field with only 0.2% of the articles from the entire corpus relevant to it. The research categories identified in this study reflect the current publication and research trends in psychiatric genomics.

Comparative serum proteomic analysis of a selected protein panel in individuals with schizophrenia and bipolar disorder and the impact of genetic risk burden on serum proteomic profiles

The diagnostic criteria for schizophrenia (SCZ) and bipolar disorder (BD) are based on clinical assessments of symptoms. In this pilot study, we applied high-throughput antibody-based protein profiling to serum samples of healthy controls and individuals with SCZ and BD with the aim of identifying differentially expressed proteins in these disorders. Moreover, we explored the influence of polygenic burden for SCZ and BD on the serum levels of these proteins. Serum samples from 113 individuals with SCZ and 125 with BD from the PsyCourse Study and from 44 healthy controls were analyzed by using a set of 155 antibodies in an antibody-based assay targeting a selected panel of 95 proteins. For the cases, genotyping and imputation were conducted for DNA samples and SCZ and BD polygenic risk scores (PRS) were calculated. Univariate linear and logistic models were used for association analyses. The comparison between SCZ and BD revealed two serum proteins that were significantly elevated in BD after multiple testing adjustment: "complement C9" and "Interleukin 1 Receptor Accessory Protein". Moreover, the first principal component of variance in the proteomics dataset differed significantly between SCZ and BD. After multiple testing correction, SCZ-PRS, BD-PRS, and SCZ-vs-BD-PRS were not significantly associated with the levels of the individual proteins or the values of the proteome principal components indicating no detectable genetic effects. Overall, our findings contribute to the evidence suggesting that the analysis of circulating proteins could lead to the identification of distinctive biomarkers for SCZ and BD. Our investigation warrants replication in large-scale studies to confirm these findings.

Genetic and psychosocial stressors have independent effects on the level of subclinical psychosis: findings from the multinational EU-GEI study

AIMS

Gene x environment (G×E) interactions, i.e. genetic modulation of the sensitivity to environmental factors and/or environmental control of the gene expression, have not been reliably established regarding aetiology of psychotic disorders. Moreover, recent studies have shown associations between the polygenic risk scores for schizophrenia (PRS-SZ) and some risk factors of psychotic disorders, challenging the traditional gene v. environment dichotomy. In the present article, we studied the role of GxE interaction between psychosocial stressors (childhood trauma, stressful life-events, self-reported discrimination experiences and low social capital) and the PRS-SZ on subclinical psychosis in a population-based sample.

METHODS

Data were drawn from the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study, in which subjects without psychotic disorders were included in six countries. The sample was restricted to European descendant subjects (n = 706). Subclinical dimensions of psychosis (positive, negative, and depressive) were measured by the Community Assessment of Psychic Experiences (CAPE) scale. Associations between the PRS-SZ and the psychosocial stressors were tested. For each dimension, the interactions between genes and environment were assessed using linear models and comparing explained variances of 'Genetic' models (solely fitted with PRS-SZ), 'Environmental' models (solely fitted with each environmental stressor), 'Independent' models (with PRS-SZ and each environmental factor), and 'Interaction' models (Independent models plus an interaction term between the PRS-SZ and each environmental factor). Likelihood ration tests (LRT) compared the fit of the different models.

RESULTS

There were no genes-environment associations. PRS-SZ was associated with positive dimensions (β = 0.092, R² = 7.50%), and most psychosocial stressors were associated with all three subclinical psychotic dimensions (except social capital and positive dimension). Concerning the positive dimension, Independent models fitted better than Environmental and Genetic models. No significant GxE interaction was observed for any dimension.

CONCLUSIONS

This study in subjects without psychotic disorders suggests that (i) the aetiological continuum hypothesis could concern particularly the positive dimension of subclinical psychosis, (ii) genetic and environmental factors have independent effects on the level of this positive dimension, (iii) and that interactions between genetic and individual environmental factors could not be identified in this sample.

DNA Methylation Profiles of the DRD2 and NR3C1 Genes in Patients with Recent-Onset Psychosis

Objectives

Dopamine receptor D2 gene (DRD2) and glucocorticoid receptor gene (NR3C1) are implicated in the development of psychosis. We investigated methylation levels of DRD2 and NR3C1 in peripheral blood of patients with recent-onset (RO) psychosis using bisulfite pyrosequencing as well as its association with childhood trauma and rumination.

Methods

In all, 51 individuals with RO psychosis and 47 healthy controls were recruited. DNA methylation levels in the targeted regions of two genes were analyzed and compared. Childhood trauma and rumination were evaluated using the Early Trauma Inventory Self-Report Short Form (ETI-SF) and Brooding Scale (BS), respectively. Correlations between the scores of the ETI-SF and BS and methylation levels were explored.

Results

For DRD2, we found no significant differences between groups in terms of methylation level or association with childhood trauma or rumination. For NR3C1, we found a trend level significance for average value of all CpG sites and significant hypermethylation or hypomethylation at specific sites. There was also a significant positive correlation between the methylation level at the CpG8 site of NR3C1 exon 1F and negative symptom subscale score of the PANSS (PANSS-N).

Conclusion

Epigenetic alterations of NR3C1 are associated with the pathophysiology of psychosis. Further epigenetic studies will elucidate the molecular mechanisms underpinning the pathophysiology of psychosis.

Metabolic polygenic risk scores effect on antipsychotic-induced metabolic dysregulation: A longitudinal study in a first episode psychosis cohort

OBJECTIVE

Metabolic syndrome is a health-threatening condition suffered by approximately one third of schizophrenia patients and largely attributed to antipsychotic medication. Previous evidence reports a common genetic background of psychotic and metabolic disorders. In this study, we aimed to assess the role of polygenic risk scores (PRSs) on the progression of the metabolic profile in a first-episode psychosis (FEP) cohort.

METHOD

Of the 231 FEP individuals included in the study, 192-220 participants were included in basal analysis and 118-179 in longitudinal 6-month models. Eleven psychopathologic and metabolic PRSs were constructed. Basal and longitudinal PRSs association with metabolic measurements was assessed by statistical analyses.

RESULTS

No major association of psychopathological PRSs with the metabolic progression was found. However, high risk individuals for depression and cholesterol-related PRSs reported a higher increase of cholesterol levels during the follow-up (FDR ≤ 0.023 for all analyses). Their effect was comparable to other well-established pharmacological and environmental risk factors (explaining at least 1.2% of total variance).

CONCLUSION

Our findings provide new evidence of the effects of metabolic genetic risk on the development of metabolic dysregulation. The future establishment of genetic profiling tools in clinical procedures could enable practitioners to better personalize antipsychotic treatment selection and dosage.

Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes

Background

Polygenic risk scores (PRSs) are indices of genetic liability for illness, but their clinical utility for predicting risk for a specific psychiatric disorder is limited. Genetic overlap among disorders and their effects on allied phenotypes may be a possible explanation, but this has been difficult to quantify given focus on singular disorders and/or allied phenotypes.

Methods

We constructed PRSs for 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder) and 3 nonpsychiatric control traits (height, type II diabetes, irritable bowel disease) in the UK Biobank (N = 31,616) and quantified associations between PRSs and phenotypes allied with mental illness: behavioral (symptoms, cognition, trauma) and brain measures from magnetic resonance imaging. We then evaluated the extent of specificity among PRSs and their effects on these allied phenotypes.

Results

Correlations among psychiatric PRSs replicated previous work, with overlap between schizophrenia and bipolar disorder, which was distinct from overlap between autism spectrum disorder and attention-deficit/hyperactivity disorder; overlap between psychiatric and control PRSs was minimal. There was, however, substantial overlap of PRS effects on allied phenotypes among psychiatric disorders and among psychiatric disorders and control traits, where the extent and pattern of overlap was phenotype specific.

Conclusions

Results show that genetic distinctions between psychiatric disorders and between psychiatric disorders and control traits exist, but this does not extend to their effects on allied phenotypes. Although overlap can be informative, work is needed to construct PRSs that will function at the level of specificity needed for clinical application.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects

BACKGROUND

Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood.

METHODS

We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors.

RESULTS

We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy.

CONCLUSIONS

This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).

Impact of polygenic risk for coronary artery disease and cardiovascular medication burden on cognitive impairment in psychotic disorders

BACKGROUND

Cognitive impairment is a core deficit across psychotic disorders, the causes and therapeutics of which remain unclear. Epidemiological observations have suggested associations between cognitive dysfunction in psychotic disorders and cardiovascular risk factors, but an underlying etiology has not been established.

METHODS

Neuropsychological performance using the Brief Assessment of Cognition in Schizophrenia (BACS) was assessed in 616 individuals of European ancestry (403 psychosis, 213 controls). Polygenic risk scores for coronary artery disease (PRSCAD) were quantified for each participant across 13 p-value thresholds (PT 0.5-5e-8). Cardiovascular and psychotropic medications were categorized for association analyses. Each PRSCAD was examined in relation to the BACS and the optimized PT was confirmed with five-fold cross-validation and independent validation. Functional enrichment analyses were used to identify biological mechanisms linked to PRSCAD-cognition associations. Multiple regression analyses examined PRSCAD under the optimal PT and medication burden in relation to the BACS composite and subtest scores.

RESULTS

Higher PRSCAD was associated with lower BACS composite scores (p = 0.001) in the psychosis group, primarily driven by the Verbal Memory subtest (p < 0.001). Genes linked to multiple nervous system related processes and pathways were significantly enriched in PRSCAD. After controlling for PRSCAD, a greater number of cardiovascular medications was also correlated with worse BACS performance in patients with psychotic disorders (p = 0.029).

CONCLUSIONS

Higher PRSCAD and taking more cardiovascular medications were both significantly associated with cognitive impairment in psychosis. These findings indicate that cardiovascular factors may increase the risk for cognitive dysfunction and related functional outcomes among individuals with psychotic disorders.

Novel disease associations with schizophrenia genetic risk revealed in ~400,000 UK Biobank participants

Schizophrenia is a serious mental disorder with considerable somatic and psychiatric morbidity. It is unclear whether comorbid health conditions predominantly arise due to shared genetic risk or consequent to having schizophrenia. To explore the contribution of genetic risk for schizophrenia, we analysed the effect of schizophrenia polygenic risk scores (PRS) on a broad range of health problems in 406 929 individuals with no schizophrenia diagnosis from the UK Biobank. Diagnoses were derived from linked health data including primary care, hospital inpatient records, and registers with information on cancer and deaths. Schizophrenia PRS were generated and tested for associations with general health conditions, 16 ICD10 main chapters, and 603 diseases using linear and logistic regressions. Higher schizophrenia PRS was significantly associated with poorer overall health ratings, more hospital inpatient diagnoses, and more unique illnesses. It was also significantly positively associated with 4 ICD10 chapters: mental disorders; respiratory diseases; digestive diseases; and pregnancy, childbirth and the puerperium, but negatively associated with musculoskeletal disorders. Thirty-one specific phenotypes were significantly associated with schizophrenia PRS, and the 19 novel findings include several musculoskeletal diseases, respiratory diseases, digestive diseases, varicose veins, pituitary hyperfunction, and other peripheral nerve disorders. These findings extend knowledge of the pleiotropic effect of genetic risk for schizophrenia and offer insight into how some conditions often comorbid with schizophrenia arise. Additional studies incorporating the genetic basis of hormone regulation and involvement of immune mechanisms in the pathophysiology of schizophrenia may further elucidate the biological mechanisms underlying schizophrenia and its comorbid conditions.

Development of the Korea-Polyenvironmental Risk Score for Psychosis

OBJECTIVE

Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs).

METHODS

We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the "population proportion" (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We first constructed the "K-PERS-I," comprising five factors based on the PPS, and then the "K-PERS-II" comprising six factors based on the ERS. The instruments accurately predicted participants' status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS.

CONCLUSION

The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.

A systematic review and analysis of the use of polygenic scores in pharmacogenomics

Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

Effects of selected inherited factors on susceptibility to SARS-CoV-2 infection and COVID-19 progression

Genetic predispositions may influence geographical and interethnic differences in COVID-19 prevalence and mortality in affected populations. Of the many genes implicated in COVID-19 progression, a substantial number have no direct functional link on virus transfer/viability or on the host immune system. To address this knowledge deficit, a large number of in silico studies have recently been published. However, the results of these studies often contradict the findings of studies involving real patients. For example, the ACE2 has been shown to play an important role in regulating coronavirus entry into cells, but none of its variations have been directly associated with COVID-19 susceptibility or severity. Consistently was reported that increased risk of COVID-19 is associated with blood group A and with the APOE4 allele. Among other genes with potential impacts are the genes for CCR5, IL-10, CD14, TMPRSS2 and angiotensin-converting enzyme. Variants within the protein-coding genes OAS1 and LZTFL1 (transferred to the human genome from Neanderthals) are understood to be among the strongest predictors of disease severity. The intensive research efforts have helped to identify the genes and polymorphisms that contribute to SARS-CoV-2 infection and COVID-19 severity.

A genetic risk score using human chromosomal-scale length variation can predict schizophrenia

Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539-0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.

Detection of Morphological Abnormalities in Schizophrenia: An Important Step to Identify Associated Genetic Disorders or Etiologic Subtypes

Current research suggests that alterations in neurodevelopmental processes, involving gene X environment interactions during key stages of brain development (prenatal period and adolescence), are a major risk for schizophrenia. First, epidemiological studies supporting a genetic contribution to schizophrenia are presented in this article, including family, twin, and adoption studies. Then, an extensive literature review on genetic disorders associated with schizophrenia is reviewed. These epidemiological findings and clinical observations led researchers to conduct studies on genetic associations in schizophrenia, and more specifically on genomics (CNV: copy-number variant, and SNP: single nucleotide polymorphism). The main structural (CNV) and sequence (SNP) variants found in individuals with schizophrenia are reported here. Evidence of genetic contributions to schizophrenia and current knowledge on genetic syndromes associated with this psychiatric disorder highlight the importance of a clinical genetic examination to detect minor physical anomalies in individuals with ultra-high risk of schizophrenia. Several dysmorphic features have been described in schizophrenia, especially in early onset schizophrenia, and can be viewed as neurodevelopmental markers of vulnerability. Early detection of individuals with neurodevelopmental abnormalities is a fundamental issue to develop prevention and diagnostic strategies, therapeutic intervention and follow-up, and to ascertain better the underlying mechanisms involved in the pathophysiology of schizophrenia.

Comparison of mouse models reveals a molecular distinction between psychotic illness in PWS and schizophrenia

Prader-Willi Syndrome (PWS) is a neurodevelopmental disorder caused by mutations affecting paternal chromosome 15q11-q13, and characterized by hypotonia, hyperphagia, impaired cognition, and behavioural problems. Psychotic illness is a challenging problem for individuals with PWS and has different rates of prevalence in distinct PWS genotypes. Previously, we demonstrated behavioural and cognitive endophenotypes of relevance to psychiatric illness in a mouse model for one of the associated PWS genotypes, namely PWS-IC, in which deletion of the imprinting centre leads to loss of paternally imprinted gene expression and over-expression of Ube3a. Here we examine the broader gene expression changes that are specific to the psychiatric endophenotypes seen in this model. To do this we compared the brain transcriptomic profile of the PWS-IC mouse to the PWS-cr model that carries a deletion of the PWS minimal critical interval spanning the snoRNA Snord116 and Ipw. Firstly, we examined the same behavioural and cognitive endophenotypes of relevance to psychiatric illness in the PWS-cr mice. Unlike the PWS-IC mice, PWS-cr exhibit no differences in locomotor activity, sensory-motor gating, and attention. RNA-seq analysis of neonatal whole brain tissue revealed a greater number of transcriptional changes between PWS-IC and wild-type littermates than between PWS-cr and wild-type littermates. Moreover, the differentially expressed genes in the PWS-IC brain were enriched for GWAS variants of episodes of psychotic illness but, interestingly, not schizophrenia. These data illustrate the molecular pathways that may underpin psychotic illness in PWS and have implications for potential therapeutic interventions.

The continuity of effect of schizophrenia polygenic risk score and patterns of cannabis use on transdiagnostic symptom dimensions at first-episode psychosis: findings from the EU-GEI study

Diagnostic categories do not completely reflect the heterogeneous expression of psychosis. Using data from the EU-GEI study, we evaluated the impact of schizophrenia polygenic risk score (SZ-PRS) and patterns of cannabis use on the transdiagnostic expression of psychosis. We analysed first-episode psychosis patients (FEP) and controls, generating transdiagnostic dimensions of psychotic symptoms and experiences using item response bi-factor modelling. Linear regression was used to test the associations between these dimensions and SZ-PRS, as well as the combined effect of SZ-PRS and cannabis use on the dimensions of positive psychotic symptoms and experiences. We found associations between SZ-PRS and (1) both negative (B = 0.18; 95%CI 0.03-0.33) and positive (B = 0.19; 95%CI 0.03-0.35) symptom dimensions in 617 FEP patients, regardless of their categorical diagnosis; and (2) all the psychotic experience dimensions in 979 controls. We did not observe associations between SZ-PRS and the general and affective dimensions in FEP. Daily and current cannabis use were associated with the positive dimensions in FEP (B = 0.31; 95%CI 0.11-0.52) and in controls (B = 0.26; 95%CI 0.06-0.46), over and above SZ-PRS. We provide evidence that genetic liability to schizophrenia and cannabis use map onto transdiagnostic symptom dimensions, supporting the validity and utility of the dimensional representation of psychosis. In our sample, genetic liability to schizophrenia correlated with more severe psychosis presentation, and cannabis use conferred risk to positive symptomatology beyond the genetic risk. Our findings support the hypothesis that psychotic experiences in the general population have similar genetic substrates as clinical disorders.

Genetic risks of schizophrenia identified in a matched case-control study

It has been suggested that dopaminergic neurotransmission plays important roles for the psychotic symptoms and probably etiology of schizophrenia. In our recent preliminary study, we demonstrated that the specific allele combinations of dopamine-related functional single nucleotide polymorphisms (SNPs), rs10770141, rs4680, and rs1800497 could indicate risks for schizophrenia. The present validation study involved a total of 2542 individuals who were age- and sex-matched in a propensity score matching analysis, and the results supported the statistical significances of the proposed genetic risks described in our previous reports. The estimated odds ratios were 1.24 (95% CI 1.06-1.45, p < 0.001) for rs4680, 1.73 (95% CI 1.47-2.02, p < 0.0001) for rs1800497, and 1.79 (95% CI 1.35-2.36, p < 0.0001) for rs10770141. A significant relationship was also revealed among these three polymorphisms and schizophrenia, with corresponding coefficients (p < 0.0001). In this study, we also present a new scoring model for the identification of individuals with the disease risks. Using the cut-off value of 2, our model exhibited sensitivity for almost two-thirds of all of the schizophrenia patients: odds ratio 1.87, 95% CI 1.59-2.19, p < 0.0001. In conclusion, we identified significant associations of dopamine-related genetic combinations with schizophrenia. These findings suggest that some types of dopaminergic neurotransmission play important roles for development of schizophrenia, and this type of approach may also be applicable for other multifactorial diseases, providing a potent new risk predictor.

Associations of Genetic Polymorphisms and Neuroimmune Markers With Some Parameters of Frontal Lobe Dysfunction in Schizophrenia

We investigated the associations of DRD3 rs6280, HTR1A rs6295, BDNF rs6265, SCL6A4 rs16965628, and 5HT2A rs7322347 with schizophrenia in a case-control study, and associations of these genetic variants with several clinical features. We also investigated markers of inflammatory response (C-reactive protein, IL-2, IL-6, IL-10), the activity of leukocytic elastase (LE) and α1-proteinase inhibitor (a1-PI), antibodies to S100B and myelin basic protein (MBP) in schizophrenia. Clinical symptoms were assessed on three scales: Positive and Negative Syndrome Scale, The Bush - Francis Catatonia Rating Scale and Frontal Assessment Battery. All SNPs were typed using predesigned TaqMan SNP genotyping assays. The biomarkers related to the immune system were routinely tested using ELISA kits. The association with schizophrenia was found for DRD3 rs6280 (p = 0.05) and HTR2A rs7322347 (p = 0.0013). We found differences between groups by parameters of LE and a1-PI and LE/a1-PI (p < 0.001). And IL-6 was evaluated in the schizophrenia group (p < 0.001). We showed that patients with the TT allele (BDNF rs6265) had more severe impairments in frontal lobe function. a1-PI can serve as a marker for assessing the severity of frontal lobe damage in patients with frontal dementia. We found some biological parameters reflecting the severity of frontal dysfunction in schizophrenia.

Risk Stratification and Clinical Utility of Polygenic Risk Scores in Ophthalmology

Translational Relevance

Common genetic variants can be used to effectively stratify the risk of disease development and progression and may be used to guide screening, triaging, monitoring, or treatment thresholds.

The "missing heritability"-Problem in psychiatry: Is the interaction of genetics, epigenetics and transposable elements a potential solution?

Psychiatric disorders exhibit an enormous burden on the health care systems worldwide accounting for around one-third of years lost due to disability among adults. Their etiology is largely unknown and diagnostic classification is based on symptomatology and course of illness and not on objective biomarkers. Most psychiatric disorders are moderately to highly heritable. However, it is still unknown what mechanisms may explain the discrepancy between heritability estimates and the present data from genetic analysis. In addition to genetic differences also epigenetic modifications are considered as potentially relevant in the transfer of susceptibility to psychiatric diseases. Though, whether or not epigenetic alterations can be inherited for many generations is highly controversial. In the present article, we will critically summarize both the genetic findings and the results from epigenetic analyses, including also those of noncoding RNAs. We will argue that one possible solution to the "missing heritability" problem in psychiatry is a potential role of retrotransposons, the exploration of which is presently only in its beginnings.

Jumping to conclusions, general intelligence, and psychosis liability: findings from the multi-centre EU-GEI case-control study

BACKGROUND

The 'jumping to conclusions' (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ.

METHODS

A total of 817 first episode psychosis patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC, and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.

RESULTS

The estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia polygenic risk score was non-significantly associated with a higher number of beads drawn (B = 0.47, 95% CI -0.21 to 1.16, p = 0.17); whereas IQ PRS (B = 0.51, 95% CI 0.25-0.76, p < 0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with the higher level of psychotic-like experiences (PLEs) in controls, including after controlling for IQ (B = -1.7, 95% CI -2.8 to -0.5, p = 0.006), but did not relate to delusions in patients.

CONCLUSIONS

Our findings suggest that the JTC reasoning bias in psychosis might not be a specific cognitive deficit but rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to PLEs, independent of IQ. The work has the potential to inform interventions targeting cognitive biases in early psychosis.

Genetic Liability for Schizophrenia and Childhood Psychopathology in the General Population

Genetic liability for schizophrenia is associated with psychopathology in early life. It is not clear if these associations are time dependent during childhood, nor if they are specific across different forms of psychopathology. Using genotype and questionnaire data on children (N = 15 105) from the Norwegian Mother, Father and Child Cohort Study, we used schizophrenia polygenic risk scores to test developmental stability in associations with measures of emotional and behavioral problems between 18 months and 5 years, and domain specificity in associations with symptoms of depression, anxiety, conduct problems, oppositionality, inattention, and hyperactivity at 8 years. We then sought to identify symptom profiles-across development and domains-associated with schizophrenia polygenic liability. We found evidence for developmental stability in associations between schizophrenia polygenic risk scores and emotional and behavioral problems, with the latter being mediated specifically via the rate of change in symptoms (β slope = 0.032; 95% CI: 0.007-0.057). At age 8, associations were better explained by a model of symptom-specific polygenic effects rather than effects mediated via a general psychopathology factor or by domain-specific factors. Overall, individuals with higher schizophrenia polygenic risk scores were more likely (OR = 1.310 [95% CIs: 1.122-1.528]) to have a profile of increasing behavioral and emotional symptoms in early childhood, followed by elevated symptoms of conduct disorder, oppositionality, hyperactivity, and inattention by age 8. Schizophrenia-associated alleles are linked to specific patterns of early-life psychopathology. The associations are small, but findings of this nature can help us better understand the developmental emergence of schizophrenia.

Polygenic Scores for ADHD: A Meta-Analysis

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder that is known to have a polygenic (i.e., many genes of individually small effects) architecture. Polygenic scores (PGS), which characterize this polygenicity as a single score for a given individual, are considered the state-of-the-art in psychiatric genetics research. Despite the proliferation of ADHD studies adopting this approach and its clinical implications, remarkably little is known about the predictive utility of PGS in ADHD research to date, given that there have not yet been any systematic or meta-analytic reviews of this rapidly developing literature. We meta-analyzed 12 unique effect sizes from ADHD PGS studies, yielding an N = 40,088. These studies, which included a mixture of large population-based cohorts and case-control samples of predominantly European ancestry, yielded a pooled ADHD PGS effect size of r_random = 0.201 (95% CI = [0.144, 0.288]) and an r_fixed = 0.190 (95% CI = [0.180, 0.199]) in predicting ADHD. In other words, ADHD PGS reliably account for between 3.6% (in the fixed effects model) to 4.0% (in the random effects model) of the variance in broadly defined phenotypic ADHD. Findings provide important insights into the genetics of psychiatric outcomes and raise several key questions about the impact of PGS on psychiatric research moving forward. Our review concludes by providing recommendations for future research directions in the use of PGS, including new methods to account for comorbidities, integrating bioinformatics to elucidate biological pathways, and leveraging PGS to test mechanistic models of ADHD.

Association of Recent Stressful Life Events With Mental and Physical Health in the Context of Genomic and Exposomic Liability for Schizophrenia

Importance

Both adulthood stressful life events (SLEs) and liability for schizophrenia have been associated with poor mental and physical health in the general population, but their interaction remains to be elucidated to improve population-based health outcomes.

Objective

To test whether recent SLEs interact with genetic and environmental liability for schizophrenia in models of mental and physical health.

Design, Setting, and Participants

The Netherlands Mental Health Survey and Incidence Study-2 is a population-based prospective cohort study designed to investigate the prevalence, incidence, course, and consequences of mental disorders in the Dutch general population. Participants were enrolled from November 5, 2007, to July 31, 2009, and followed up with 3 assessments during 9 years. Follow-up was completed on June 19, 2018, and data were analyzed from September 1 to November 1, 2019.

Exposures

Recent SLEs assessed at each wave and aggregate scores of genetic and environmental liability for schizophrenia: polygenic risk score for schizophrenia (PRS-SCZ) trained using the Psychiatric Genomics Consortium analysis results and exposome score for schizophrenia (ES-SCZ) trained using an independent data set.

Main Outcomes and Measures

Independent and interacting associations of SLEs with ES-SCZ and PRS-SCZ on mental and physical health assessed at each wave using regression coefficients.

Results

Of the 6646 participants included at baseline, the mean (SD) age was 44.26 (12.54) years, and 3672 (55.25%) were female. The SLEs were associated with poorer physical health (B = -3.22 [95% CI, -3.66 to -2.79]) and mental health (B = -3.68 [95% CI, -4.05 to -3.32]). Genetic and environmental liability for schizophrenia was associated with poorer mental health (ES-SCZ: B = -3.07 [95% CI, -3.35 to -2.79]; PRS-SCZ: B = -0.93 [95% CI, -1.31 to -0.54]). Environmental liability was also associated with poorer physical health (B = -3.19 [95% CI, -3.56 to -2.82]). The interaction model showed that ES-SCZ moderated the association of SLEs with mental (B = -1.08 [95% CI, -1.47 to -0.69]) and physical health (B = -0.64 [95% CI, -1.11 to -0.17]), whereas PRS-SCZ did not. Several sensitivity analyses confirmed these results.

Conclusions and Relevance

In this study, schizophrenia liability was associated with broad mental health outcomes at the population level. Consistent with the diathesis-stress model, exposure to SLEs, particularly in individuals with high environmental liability for schizophrenia, was associated with poorer health. These findings underline the importance of modifiable environmental factors during the life span for population-based mental health outcomes.

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.

The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders: Major depression summary

OBJECTIVES

To provide a succinct, clinically useful summary of the management of major depression, based on the 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders (MDcpg²⁰²⁰ ).

METHODS

To develop the MDcpg²⁰²⁰ , the mood disorders committee conducted an extensive review of the available literature to develop evidence-based recommendations (EBR) based on National Health and Medical Research Council (NHMRC) guidelines. In the MDcpg²⁰²⁰ , these recommendations sit alongside consensus-based recommendations (CBR) that were derived from extensive deliberations of the mood disorders committee, drawing on their expertise and clinical experience. This guideline summary is an abridged version that focuses on major depression. In collaboration with international experts in the field, it synthesises the key recommendations made in relation to the diagnosis and management of major depression.

RESULTS

The depression summary provides a systematic approach to diagnosis, and a logical clinical framework for management. The latter begins with Actions, which include important strategies that should be implemented from the outset. These include lifestyle changes, psychoeducation and psychological interventions. The summary advocates the use of antidepressants in the management of depression as Choices and nominates seven medications that can be trialled as clinically indicated before moving to Alternatives for managing depression. Subsequent strategies regarding Medication include Increasing Dose, Augmenting and Switching (MIDAS). The summary also recommends the use of electroconvulsive therapy (ECT), and discusses how to approach non-response.

CONCLUSIONS

The major depression summary provides up to date guidance regarding the management of major depressive disorder, as set out in the MDcpg²⁰²⁰ . The recommendations are informed by research evidence in conjunction with clinical expertise and experience. The summary is intended for use by psychiatrists, psychologists and primary care physicians, but will be of interest to all clinicians and carers involved in the management of patients with depressive disorders.

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.