Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint

Association of prescription data with clinical manifestations and polygenic risk scores in patients with bipolar I disorder: An exploratory study

BACKGROUND

We assessed the association of prescription data with clinical manifestations and polygenic risk scores (PRS) in patients with bipolar I disorder.

METHODS

We enrolled 1471 individuals with BID and divided them into several groups according to treatment options and clinical manifestations. BD-PRS of each patient was calculated using the Psychiatric Genomics Consortium data. Data on single nucleotide polymorphisms, clinical manifestations, and prescriptions were extracted from BiGS.

RESULTS

Chronicity, suicidality, substance misuse, mixed symptoms, and deterioration of life functioning were significantly more severe in the group that was not prescribed any mood stabilizers (MS). Chronicity, psychotic symptoms, suicidality, and deterioration of life functioning were significantly severe in the group that received two or more antipsychotics (APs). BD-PRS between the group with AP(s) only and that with other treatment options significantly differed. BD-PRS of the group with AP(s) only was significantly lower than that with other treatment options. Our linear regression results showed that high severity of particular clinical aspects, lower BD-PRS, and prescriptions with fewer MSs or more APs were independently associated with poor life functioning.

LIMITATIONS

This study had a cross-sectional design, without differentiating the bipolar phase, which could influence our results.

CONCLUSIONS

Poor life functioning in patients with BID was associated with a high severity of particular clinical aspects, BD-PRS, and prescriptions including fewer MSs or more APs. BD-PRS was significantly higher in the group receiving only AP(s) than that in the groups receiving other drugs.

Genetic underpinnings of YMRS and MADRS scores variations in a bipolar sample

Bipolar disorder (BPD) affects approximately 2% of the global population. Its clinical course is highly variable and current treatments are not always effective for all patients. Genetic factors play a significant role in BPD and its treatment, although the genetic background appear to be highly heterogeneous. Polygenic risk scores (PRS) are a powerful tool for risk assessment, yet using all genomic data may introduce confounding factors. Focusing on specific genetic clusters PRS (gcPRS) may mitigate this issue. This study aims to assess a neural network model's efficacy in predicting response to treatment (RtT) in BPD individuals using PRS calculated from specific gcPRS and other variables. 1538 individuals from STEP-BD (age 41.39 ± 12.66, 59.17% female) were analyzed. gcPRS were calculated from a Genome-wide association study (GWAS) with clinical covariates and a molecular pathway analysis (MPA) based on drugs interaction networks. A neural network was trained using gcPRS and clinical variables to predict RtT. Ten biological networks were identified through MPA, with gcPRS derived from risk variants within corresponding gene groups. However, the model did not show significant accuracy in predicting RtT in BPD individuals. RtT in BPD is influenced by multiple factors. This study attempted a comprehensive approach integrating clinical and biological data to predict RtT. However, the model did not achieve significant accuracy, possibly due to limitations such as sample size, disorder complexity, and population heterogeneity. This data highlights the challenge of developing personalized treatments for BPD and the necessity for further research in this area.

Intron retention as an excellent marker for diagnosing depression and for discovering new potential pathways for drug intervention

Background

Peripheral inflammation is often associated with depressive disorders, and immunological biomarkers of depression remain a focus of investigation.

Methods

We performed RNA-seq analysis of RNA transcripts of human peripheral blood mononuclear cells from a case-control study including subjects with self-reported depression in the pre-symptomatic state of major depressive disorder and analyzed differentially expressed genes (DEGs) and the frequency of intron retention (IR) using rMATS.

Results

Among the statistically significant DEGs identified, the 651 upregulated DEGs were particularly enriched in the term "bacterial infection and phagocytosis", whereas the 820 downregulated DEGs were enriched in the terms "antigen presentation" and "T-cell proliferation and maturation". We also analyzed 158 genes for which the IR was increased (IncIR) and 211 genes for which the IR was decreased (DecIR) in the depressed subjects. Although the Gene Ontology terms associated with IncIR and DecIR were very similar to those of the up- and downregulated genes, respectively, IR genes appeared to be particularly enriched in genes with sensor functions, with a preponderance of the term "ciliary assembly and function". The observation that IR genes specifically interact with innate immunity genes suggests that immune-related genes, as well as cilia-related genes, may be excellent markers of depression. Re-analysis of previously published RNA-seq data from patients with MDD showed that common IR genes, particularly our predicted immune- and cilia-related genes, are commonly detected in populations with different levels of depression, providing validity for using IR to detect depression.

Conclusion

Depression was found to be associated with activation of the innate immune response and relative inactivation of T-cell signaling. The DEGs we identified reflect physiological demands that are controlled at the transcriptional level, whereas the IR results reflect a more direct mechanism for monitoring protein homeostasis. Accordingly, an alteration in IR, namely IncIR or DecIR, is a stress response, and intron-retained transcripts are sensors of the physiological state of the cytoplasm. The results demonstrate the potential of relative IR as a biomarker for the immunological stratification of depressed patients and the utility of IR for the discovery of novel pathways involved in recovery from depression.

An externally validated resting-state brain connectivity signature of pain-related learning

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Shared genetic mechanisms underlying association between sleep disturbances and depressive symptoms

OBJECTIVES

Polygenic scores (PGS) for sleep disturbances and depressive symptoms in an epidemiological cohort were contrasted. The overlap between genes assigned to variants that compose the PGS predictions was tested to explore the shared genetic bases of sleep problems and depressive symptoms.

METHODS

PGS analysis was performed on the São Paulo Epidemiologic Sleep Study (EPISONO, N = 1042), an adult epidemiological sample. A genome wide association study (GWAS) for depression grounded the PGS calculations for Beck Depression Index (BDI), while insomnia GWAS based the PGS for Insomnia Severity Index (ISI) and Pittsburg Sleep Quality Index (PSQI). Pearson's correlation was applied to contrast PGS and clinical scores. Fisher's Exact and Benjamin-Hochberg tests were used to verify the overlaps between PGS-associated genes and the pathways enriched among their intersections.

RESULTS

All PGS models were significant when individuals were divided as cases or controls according to BDI (R2 = 1.2%, p = 0.00026), PSQI (R2 = 3.3%, p = 0.007) and ISI (R2 = 3.4%, p = 0.021) scales. When clinical scales were used as continuous variables, the PGS models for BDI (R2 = 1.5%, p = 0.0004) and PSQI scores (R2 = 3.3%, p = 0.0057) reached statistical significance. PSQI and BDI scores were correlated, and the same observation was applied to their PGS. Genes assigned to variants that compose the best-fit PGS predictions for sleep quality and depressive symptoms were significantly overlapped. Pathways enriched among the intersect genes are related to synapse function.

CONCLUSIONS

The genetic bases of sleep quality and depressive symptoms are correlated; their implicated genes are significantly overlapped and converge on neural pathways. This data suggests that sleep complaints accompanying depressive symptoms are not secondary issues, but part of the core mental illness.

Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores

Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.

Pharmacogenomics and non-genetic factors affecting drug response in autism spectrum disorder in Thai and other populations: current evidence and future implications

Autism spectrum disorder (ASD) may affect family and social life profoundly. Although there is no selective pharmacotherapy for ASD, the Food and Drug Administration (FDA) has recommended risperidone/aripiprazole to treat the associated symptoms of ASD, such as agitation/irritability. Strong associations of some pharmacokinetic/pharmacodynamic gene variants, e.g., CYP2D6 and DRD2, with risperidone-induced hyperprolactinemia have been found in children with ASD, but such strong genetic associations have not been found directly for aripiprazole in ASD. In addition to pharmacogenomic (PGx) factors, drug-drug interactions (DDIs) and possibly cumulative effects of DDIs and PGx may affect the safety or effectiveness of risperidone/aripiprazole, which should be assessed in future clinical studies in children with ASD. Reimbursement, knowledge, and education of healthcare professionals are the key obstacles preventing the successful implementation of ASD pharmacogenomics into routine clinical practice. The preparation of national and international PGx-based dosing guidelines for risperidone/aripiprazole based on robust evidence may advance precision medicine for ASD.

Genetic signatures of suicide attempt behavior: insights and applications

INTRODUCTION

Every year about 800,000 complete suicide events occur. The identification of biologic markers to identify subjects at risk would be helpful in targeting specific support treatments.

AREA COVERED

A narrative review defines the meta-analytic level of current evidence about the biologic markers of suicide behavior (SB). The meta-analytic evidence gathered so far indicates that the hypothesis-driven research largely failed to identify the biologic markers of suicide. The most consistent and replicated result was reported for: 1) 5-HTR2A T102C, associated with SB in patients with schizophrenia (OR = 1.73 (1.11-2.69)) and 2) BDNF Val66Met (rs6265), with the Met-Val + Val-Val carriers found to be at risk for suicide in the Caucasian population (OR: 1.96 (1.58-2.43)), while Val-Val vs. Val-Met + Met carriers found to be at risk for suicide in the Asian populations (OR: 1.36 (1.04-1.78)). GWAS-based meta-analyses indicate some positive replicated findings regarding the DRD2, Neuroligin gene, estrogen-related genes, and genes involved in gene expression.

EXPERT OPINION

Most consistent results were obtained when analyzing sub-samples of patients. Some promising results come from the implementation of the polygenic risk score. There is no current consensus about an implementable biomarker for SB.

Stakeholder Perception of the Implementation of Genetic Risk Testing for Twelve Multifactorial Diseases

Genome-wide association studies have been employed to develop numerous risk prediction models using polygenic risk scores (PRSs) for multifactorial diseases. However, healthcare providers lack confidence in their understanding of PRS risk stratification for multifactorial diseases, which underscores the need to assess the readiness of PRSs for clinical use. To address this issue, we surveyed the perceptions of healthcare providers as stakeholders in the clinical implementation of genetic-based risk prediction for multifactorial diseases. We conducted a web-based study on the need for risk prediction based on genetic information and the appropriate timing of testing for 12 multifactorial diseases. Responses were obtained from 506 stakeholders. Positive perceptions of genetic risk testing were found for adult-onset chronic diseases. As per participant opinion, testing for adult-onset diseases should be performed after the age of 20 years, whereas testing for psychiatric and allergic disorders that manifest during childhood should be performed from birth to 19 years of age. The stakeholders recognized the need for genetic risk testing for diseases that develop in adulthood, believing that the appropriate testing time is after maturity. This study contributes to the discussion on the clinical implementation of the PRS for genetic risk prediction of multifactorial diseases.

Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores

Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.

A Polygenic Risk Score Enhances Risk Prediction for Adolescents' Antisocial Behavior over the Combined Effect of 22 Extra-familial, Familial, and Individual Risk Factors in the Context of the Family Check-Up

Possessing informative tools to predict who is most at risk for antisocial behavior in adolescence is important to help identify families most in need of early intervention. Polygenic risk scores (PRSs) have been shown to predict antisocial behavior, but it remains unclear whether PRSs provide additional benefit above more conventional tools to early risk detection for antisocial behavior. This study examined the utility of a PRS in predicting adolescents' antisocial behavior after accounting for a broad index of children's contextual and individual risk factors for antisocial behavior. Participants were drawn from a longitudinal family-based prevention study (N = 463; Ncontrol = 224; 48.8% girls; 45.1% White; 30.2% Black; 12.7% Hispanic/Latino, 10.4% biracial; 0.2% Native American). Participants were recruited from US-based Women, Infants, and Children Nutritional Supplement programs. A risk tolerance PRS was created from a genome-wide association study. We created a robust measure capturing additive effects of 22 conventional measures of a risk of antisocial behavior assessed at child age 2 (before intervention). A latent variable capturing antisocial behavior (ages 10.5-16) was created. After accounting for intervention status and the conventional risk index, the risk tolerance PRS predicted independent variance in antisocial behavior. A PRS-by-conventional risk interaction showed that the conventional risk measure only predicted antisocial behavior at high levels of the PRS. Thus, the risk tolerance PRS provides unique predictive information above conventional screening tools and, when combined with them, identified a higher-risk subgroup of children. Integrating PRSs could facilitate risk identification and, ultimately, prevention screening, particularly in settings unable to serve all individuals in need.

Testing a polygenic risk score for morphological microglial activation in Alzheimer's disease and aging

Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation. We sought to determine whether a PRS for microglial activation (PRS mic ) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment. First, PRS mic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n=450), with resampling. Second, predictive performance of optimal PRS mic was assessed in two independent, population-based cohorts (total n=212,237). Our PRS mic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance. Finally, we explored associations of PRS mic with a comprehensive set of imaging and fluid AD biomarkers in ADNI. This revealed some nominal associations, but with inconsistent effect directions. While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

The genetic architecture of schizophrenia: review of large-scale genetic studies

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

Maternal age at birth and child attention-deficit hyperactivity disorder: causal association or familial confounding?

BACKGROUND

Causal explanations for the association of young motherhood with increased risk for child attention-deficit hyperactivity disorder (ADHD) remain unclear.

METHODS

The ABCD Study recruited 11,878 youth from 22 sites across the United States between June 1, 2016 and October 15, 2018. This cross-sectional analysis of 8,514 children aged 8-11 years excluded 2,260 twins/triplets, 265 adopted children, and 839 younger siblings. We examined associations of maternal age with ADHD clinical range diagnoses based on the Child Behavior Checklist and NIH Toolbox Flanker Attention Scores using mixed logistic and linear regression models, respectively. We conducted confounding and causal mediation analyses using genotype array, demographic, socioeconomic, and prenatal environment data to investigate which genetic and environmental variables may explain the association between young maternal age and child ADHD.

RESULTS

In crude models, each 10-year increase in maternal age was associated with 32% decreased odds of ADHD clinical range diagnosis (OR = 0.68; 95% CI [0.59, 0.78]) and 1.09-points increased NIH Flanker Attention Scores (β = 1.09; 95% CI [0.76, 1.41]), indicating better child visual selective attention. However, adjustment for confounders weakened these associations. The strongest confounders were family income, caregiver education, and ADHD polygenic risk score for ADHD clinical range diagnoses, and family income, caregiver education, and race/ethnicity for NIH Flanker Attention Scores. Breastfeeding duration, prenatal alcohol exposure, and prenatal tobacco exposure were responsible for up to 18%, 6%, and 4% mediation, respectively.

CONCLUSIONS

Socioeconomic disadvantages were likely the primary explanation for the association of young maternal age with child ADHD, although genetics and modifiable environmental factors also played a role. Public policies aimed at reducing the burden of ADHD associated with young motherhood should target socioeconomic inequalities and support young pregnant women by advocating for reduced prenatal tobacco exposure and healthy breastfeeding practices after childbirth.

Testing a Polygenic Risk Score for Morphological Microglial Activation in Alzheimer's Disease and Aging

BACKGROUND

Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation.

OBJECTIVE

We sought to determine whether a PRS for microglial activation (PRSmic) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment.

METHODS

First, PRSmic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 450), with resampling. Second, predictive performance of optimal PRSmic was assessed in two independent, population-based cohorts (total n = 212,237). Finally, we explored associations of PRSmic with a comprehensive set of imaging and fluid AD biomarkers in ADNI.

RESULTS

Our PRSmic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance in either external cohort. Some nominal associations were found in ADNI, but with inconsistent effect directions.

CONCLUSION

While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

Novel functional genomics approaches bridging neuroscience and psychiatry

The possibility of establishing a metric of individual genetic risk for a particular disease or trait has sparked the interest of the clinical and research communities, with many groups developing and validating genomic profiling methodologies for their potential application in clinical care. Current approaches for calculating genetic risk to specific psychiatric conditions consist of aggregating genome-wide association studies-derived estimates into polygenic risk scores, which broadly represent the number of inherited risk alleles for an individual. While the traditional approach for polygenic risk score calculation aggregates estimates of gene-disease associations, novel alternative approaches have started to consider functional molecular phenotypes that are closer to genetic variation and are less penalized by the multiple testing required in genome-wide association studies. Moving the focus from genotype-disease to genotype-gene regulation frameworks, these novel approaches incorporate prior knowledge regarding biological processes involved in disease and aggregate estimates for the association of genotypes and phenotypes using multi-omics data modalities. In this review, we discuss and list different functional genomics tools that can be used and integrated to inform researchers and clinicians for a better understanding and diagnosis of psychopathology. We suggest that these novel approaches can help generate biologically driven hypotheses for polygenic signals that can ultimately serve the clinical community as potential biomarkers of psychiatric disease susceptibility.

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.

Data sharing in the age of predictive psychiatry: an adolescent perspective

BACKGROUND

Advances in genetics and digital phenotyping in psychiatry have given rise to testing services targeting young people, which claim to predict psychiatric outcomes before difficulties emerge. These services raise several ethical challenges surrounding data sharing and information privacy.

OBJECTIVES

This study aimed to investigate young people's interest in predictive testing for mental health challenges and their attitudes towards sharing biological, psychosocial and digital data for such purpose.

METHODS

Eighty UK adolescents aged 16-18 years took part in a digital role-play where they played the role of clients of a fictional predictive psychiatry company and chose what sources of personal data they wished to provide for a risk assessment. After the role-play, participants reflected on their choices during a peer-led interview.

FINDINGS

Participants saw multiple benefits in predictive testing services, but were highly selective with regard to the type of data they were willing to share. Largely due to privacy concerns, digital data sources such as social media or Google search history were less likely to be shared than psychosocial and biological data, including school grades and one's DNA. Participants were particularly reluctant to share social media data with schools (but less so with health systems).

CONCLUSIONS

Emerging predictive psychiatric services are valued by young people; however, these services must consider privacy versus utility trade-offs from the perspective of different stakeholders, including adolescents.

CLINICAL IMPLICATIONS

Respecting adolescents' need for transparency, privacy and choice in the age of digital phenotyping is critical to the responsible implementation of predictive psychiatric services.

Precision medicine in mood disorders

The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

Dissecting the genetic architecture of suicide attempt and repeated attempts in Korean patients with bipolar disorder using polygenic risk scores

BACKGROUND

Bipolar disorder (BD) has the greatest suicide risk among mental and physical disorders. A recent genome-wide association study (GWAS) of European ancestry (EUR) samples revealed that the genetic etiology of suicide attempt (SA) was not only polygenic but also, in part, diagnosis-specific. The authors aimed to examine whether the polygenic risk score (PRS) for SA derived from that study is associated with SA or repeated attempts in Korean patients with BD. This study also investigated the shared heritability of SA and mental disorders which showed an increased risk of SA and a high genetic correlation with BD.

METHODS

The study participants were 383 patients with BD. The history of SA was assessed on a lifetime basis. PRSs for reference disorders were calculated using the aforementioned GWAS data for SA and the Psychiatric Genomics Consortium data of BD, schizophrenia, major depressive disorder (MDD), and obsessive-compulsive disorder (OCD).

RESULTS

The PRS for SA was significantly associated with lifetime SA in the current subjects (Nagelkerke's R2 = 2.73%, odds ratio [OR] = 1.36, p = 0.007). Among other PRSs, only the PRS for OCD was significantly associated with lifetime SA (Nagelkerke's R2 = 2.72%, OR = 1.36, p = 0.007). The PRS for OCD was higher in multiple attempters than in single attempters (Nagelkerke's R2 = 4.91%, OR = 1.53, p = 0.043).

CONCLUSION

The PRS for SA derived from EUR data was generalized to SA in Korean patients with BD. The PRS for OCD seemed to affect repeated attempts. Genetic studies on suicide could benefit from focusing on specific psychiatric diagnoses and refined sub-phenotypes, as well as from utilizing multiple PRSs for related disorders.

Perturbations in Gut Microbiota Composition in Psychiatric Disorders: A Review and Meta-analysis

Importance

Evidence of gut microbiota perturbations has accumulated for multiple psychiatric disorders, with microbiota signatures proposed as potential biomarkers. However, no attempts have been made to evaluate the specificity of these across the range of psychiatric conditions.

Objective

To conduct an umbrella and updated meta-analysis of gut microbiota alterations in general adult psychiatric populations and perform a within- and between-diagnostic comparison.

Data Sources

Cochrane Library, PubMed, PsycINFO, and Embase were searched up to February 2, 2021, for systematic reviews, meta-analyses, and original evidence.

Study Selection

A total of 59 case-control studies evaluating diversity or abundance of gut microbes in adult populations with major depressive disorder, bipolar disorder, psychosis and schizophrenia, anorexia nervosa, anxiety, obsessive compulsive disorder, posttraumatic stress disorder, or attention-deficit/hyperactivity disorder were included.

Data Extraction and Synthesis

Between-group comparisons of relative abundance of gut microbes and beta diversity indices were extracted and summarized qualitatively. Random-effects meta-analyses on standardized mean difference (SMD) were performed for alpha diversity indices.

Main Outcomes and Measures

Alpha and beta diversity and relative abundance of gut microbes.

Results

A total of 34 studies provided data and were included in alpha diversity meta-analyses (n = 1519 patients, n = 1429 control participants). Significant decrease in microbial richness in patients compared with control participants were found (observed species SMD = -0.26; 95% CI, -0.47 to -0.06; Chao1 SMD = -0.5; 95% CI, -0.79 to -0.21); however, this was consistently decreased only in bipolar disorder when individual diagnoses were examined. There was a small decrease in phylogenetic diversity (SMD = -0.24; 95% CI, -0.47 to -0.001) and no significant differences in Shannon and Simpson indices. Differences in beta diversity were consistently observed only for major depressive disorder and psychosis and schizophrenia. Regarding relative abundance, little evidence of disorder specificity was found. Instead, a transdiagnostic pattern of microbiota signatures was found. Depleted levels of Faecalibacterium and Coprococcus and enriched levels of Eggerthella were consistently shared between major depressive disorder, bipolar disorder, psychosis and schizophrenia, and anxiety, suggesting these disorders are characterized by a reduction of anti-inflammatory butyrate-producing bacteria, while pro-inflammatory genera are enriched. The confounding associations of region and medication were also evaluated.

Conclusions and Relevance

This systematic review and meta-analysis found that gut microbiota perturbations were associated with a transdiagnostic pattern with a depletion of certain anti-inflammatory butyrate-producing bacteria and an enrichment of pro-inflammatory bacteria in patients with depression, bipolar disorder, schizophrenia, and anxiety.

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke's pseudo-R2: 1.3-7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

Additional Evidence for Neuropsychiatric Manifestations in Mosaic Trisomy 20: A Case Report and Brief Review

Mosaic trisomy 20 is a genetic condition in which three chromosomes 20 are found in some cells. Its clinical phenotype seems to be highly variable, with most features not reported across all individuals and not considered pathognomonic of the condition. Limited and recent evidence indicates that neuropsychiatric manifestations may be more present in the context of trisomy 20 than was once thought. Here, we present a case of a 14-year-old female adolescent of White/Caucasian ethnicity with mosaic trisomy 20, who was admitted twice to an inpatient Child and Adolescent Neuropsychiatry Unit for persisting self-injury and suicidal ideation. A severe and complex neuropsychiatric presentation emerged at the cognitive, emotional, and behavioral levels, including mild neurodevelopmental issues, isolation, socio-relational difficulties, depressed mood, temper outbursts, irritability, low self-esteem, lack of interest, social anxiety, panic attacks, self-cutting, and low-average-range and heterogeneous intelligence quotient profile. Particularly, the patient was considered at high risk of causing harm, mainly to self, and appeared to be only partially responsive to medication, even when polypharmacy was attempted to improve clinical response. Except for school bullying, no other severe environmental risk factors were present in the patient’s history. The patient received a diagnosis of disruptive mood dysregulation disorder.

From genetics to systems biology of stress-related mental disorders

Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.

Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data

Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.

Dissecting polygenic signals from genome-wide association studies on human behaviour

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.