Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice

Multimorbidity in Severe Mental Illness as Part of the Neurodevelopmental Continuum: Physical Health-Related Endophenotypes of Schizophrenia-A Narrative Review

BACKGROUND

The majority of deaths in patients with schizophrenia and other severe mental illnesses (SMIs) are caused by natural causes, such as cardiovascular diseases (CVDs). The increased risk of CVD and other somatic diseases in SMIs cannot be fully explained by the contribution of traditional risk factors, behavioral risk factors, patients' lifestyle peculiarities, and the influence of antipsychotics. The present review has the following main objectives: (1) to aggregate evidence that neurodevelopmental disorders are the basis of SMIs; (2) to provide a review of studies that have addressed the shared genetic architecture of SMI and cardiovascular disease; and (3) to propose and substantiate the consideration of somatic diseases as independent endophenotypes of SMIs, which will make it possible to place the research of somatic diseases in SMIs within the framework of the concepts of the "neurodevelopmental continuum and gradient" and "endophenotype".

METHODS

A comprehensive literature search was performed on 1 July 2024. The search was performed using PubMed and Google Scholar databases up to June 2024.

RESULTS

The current literature reveals considerable overlap between the genetic susceptibility loci for SMIs and CVDs. We propose that somatic diseases observed in SMIs that have a shared genetic architecture with SMIs can be considered distinct physical health-related endophenotypes.

CONCLUSIONS

In this narrative review, the results of recent studies of CVDs in SMIs are summarized. Reframing schizophrenia as a multisystem disease should contribute to the activation of new research on somatic diseases in SMIs.

Gut Microbiota and Clinical Manifestations in Thai Pediatric Patients with Attention-Deficit Hyperactivity Disorder

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder potentially linked to gut dysbiosis. This comparative cross-sectional study profiled the gut microbiota in 24 treatment-naïve Thai children diagnosed with ADHD and 24 healthy ones matched by age and gender (median age: 7 years). Fecal microbial compositions were genetically analyzed using 16s rRNA gene amplicon sequencing. The study findings indicated no statistically significant differences in microbial diversity between groups, although Firmicutes and Actinobacteria appeared dominant in both groups. Moreover, ADHD patients exhibited enrichment in Alloprevotella, CAG-352, Succinivibrio, and Acidaminococcus genera, while healthy controls had higher levels of Megamonas, Enterobacter, Eubacterium hallii, and Negativibacillus genera. Spearman correlation analysis demonstrated a significant positive association between CAG-352 and inattention and hyperactivity/impulsivity scores, whereas the Eubacterium hallii group and Megamonas exhibited negative correlations with these symptomatology domains. Beta-carotene intake was associated with the Eubacterium hallii group and Succinivibrio: likewise, vitamin B2 intake was associated with Alloprevotella. Additional research should aim to elucidate the underlying mechanisms influencing clinical biomarkers that signify alterations in specific gut microbiome profiles linked to ADHD.

Understanding copy number variations through their genes: a molecular view on 16p11.2 deletion and duplication syndromes

Neurodevelopmental disorders (NDDs) include a broad spectrum of pathological conditions that affect >4% of children worldwide, share common features and present a variegated genetic origin. They include clinically defined diseases, such as autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD), motor disorders such as Tics and Tourette's syndromes, but also much more heterogeneous conditions like intellectual disability (ID) and epilepsy. Schizophrenia (SCZ) has also recently been proposed to belong to NDDs. Relatively common causes of NDDs are copy number variations (CNVs), characterised by the gain or the loss of a portion of a chromosome. In this review, we focus on deletions and duplications at the 16p11.2 chromosomal region, associated with NDDs, ID, ASD but also epilepsy and SCZ. Some of the core phenotypes presented by human carriers could be recapitulated in animal and cellular models, which also highlighted prominent neurophysiological and signalling alterations underpinning 16p11.2 CNVs-associated phenotypes. In this review, we also provide an overview of the genes within the 16p11.2 locus, including those with partially known or unknown function as well as non-coding RNAs. A particularly interesting interplay was observed between MVP and MAPK3 in modulating some of the pathological phenotypes associated with the 16p11.2 deletion. Elucidating their role in intracellular signalling and their functional links will be a key step to devise novel therapeutic strategies for 16p11.2 CNVs-related syndromes.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

The structure of psychiatric comorbidity without selection and assortative mating

The widespread comorbidity observed across psychiatric disorders may be the result of processes such as assortative mating, gene-environment correlation, or selection into population studies. Between-family analyses of comorbidity are subject to these sources of bias, whereas within-family analyses are not. Because of Mendelian inheritance, alleles are randomly assigned within families, conditional on parental alleles. We exploit this variation to compare the structure of comorbidity across broad psychiatric polygenic scores when calculated either between-family (child polygenic scores) or within-family (child polygenic scores regressed on parental polygenic scores) in over 25,000 genotyped parent-offspring trios from the Norwegian Mother Father and Child Cohort study (MoBa). We fitted a series of factor models to the between- and within-family data, which consisted of a single genetic p-factor and a varying number of uncorrelated subfactors. The best-fitting model was identical for between- and within-family analyses and included three subfactors capturing variants associated with neurodevelopment, psychosis, and constraint, in addition to the genetic p-factor. Partner genetic correlations, indicating assortative mating, were not present for the genetic p-factor, but were substantial for the psychosis (b = 0.081;95% CI [0.038,0.124]) and constraint (b = 0.257;95% CI [0.075,0.439]) subfactors. When average factor levels for MoBa mothers and fathers were compared to a population mean of zero we found evidence of sex-specific participation bias, which has implications for the generalizability of findings from cohort studies. Our results demonstrate the power of the within-family design for better understanding the mechanisms driving psychiatric comorbidity and their consequences on population health.

nArgBP2 together with GKAP and SHANK3 forms a dynamic layered structure

nArgBP2, a protein whose disruption is implicated in intellectual disability, concentrates in excitatory spine-synapses. By forming a triad with GKAP and SHANK, it regulates spine structural rearrangement. We here find that GKAP and SHANK3 concentrate close to the synaptic contact, whereas nArgBP2 concentrates more centrally in the spine. The three proteins collaboratively form biomolecular condensates in living fibroblasts, exhibiting distinctive layered localizations. nArgBP2 concentrates in the inner phase, SHANK3 in the outer phase, and GKAP partially in both. Upon co-expression of GKAP and nArgBP2, they evenly distribute within condensates, with a notable peripheral localization of SHANK3 persisting when co-expressed with either GKAP or nArgBP2. Co-expression of SHANK3 and GKAP with CaMKIIα results in phase-in-phase condensates, with CaMKIIα at the central locus and SHANK3 and GKAP exhibiting peripheral localization. Additional co-expression of nArgBP2 maintains the layered organizational structure within condensates. Subsequent CaMKIIα activation disperses a majority of the condensates, with an even distribution of all proteins within the extant deformed condensates. Our findings suggest that protein segregation via phase separation may contribute to establishing layered organization in dendritic spines.

Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls

OBJECTIVE

There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls.

METHOD

Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components.

RESULTS

The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD.

CONCLUSION

BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Neuropsychiatric disorders, chronotype and sleep: A narrative review of GWAS findings and the application of Mendelian randomization to investigate causal relationships

Genome-wide association studies (GWAS) have been important for characterizing the genetic component and enhancing our understanding of the biological aetiology of both neuropsychiatric disorders and sleep-related phenotypes such as chronotype, which is our preference for morning or evening time. Mendelian randomization (MR) is a post-GWAS analysis that is used to infer causal relationships between potential risk factors and outcomes. MR uses genetic variants as instrumental variants for exposures to study the effect on outcomes. This review details the main results from GWAS of neuropsychiatric disorders and sleep-related phenotypes, and the application of MR to investigate their bidirectional relationship. The main results from MR studies of neuropsychiatric disorders and sleep-related phenotypes are summarized. These MR studies have identified 37 causal relationships between neuropsychiatric disorders and sleep-related phenotypes. MR studies identified evidence of a causal role for five neuropsychiatric disorders and symptoms (attention deficit hyperactivity disorder, bipolar disorder, depressive symptoms, major depressive disorder and schizophrenia) on sleep-related phenotypes and evidence of a causal role for five sleep-related phenotypes (daytime napping, insomnia, morning person, long sleep duration and sleep duration) on risk for neuropsychiatric disorders. These MR results show a bidirectional relationship between neuropsychiatric disorders and sleep-related phenotypes and identify potential risk factors for follow-up studies.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Job satisfaction and job tenure of people with mental health disorders: a UK Biobank cohort study

AIMS

Job satisfaction plays an important role for the life quality and health of working individuals. While studies have shown that self-reported mental health conditions such as stress, anxiety and depression are associated with job satisfaction, a large population-based study exploring and comparing self-reported physician posed diagnosed conditions and their association with job satisfaction and job tenure is missing. This study addresses the gap along with exploring the impact of the neurotic personality trait and other possible contributing factors.

METHODS

Sixteen mental health disorders diagnosed by physicians, categorised into four major groups were investigated in relation to employment status (108,711 participants) and in relation to job satisfaction and job tenure (34,808 participants). Analyses were performed using linear regression adjusted for age, sex, townsend deprivation index, body mass index, education, physical activity, work hours and neuroticism.

RESULTS

Neurotic and stress disorders, eating disorders and other mental health disorders were strongly associated with lower job satisfaction and shorter job tenure in both unadjusted and adjusted analyses. Neuroticism was strongly linked to job satisfaction but was not associated with job tenure.

CONCLUSIONS

Study findings clarify the complex relationship of mental health with job satisfaction and job tenure, which is very important to understand in designing measures to improve working life participation of individuals with mental health issues.

Cross-disorder GWAS meta-analysis of endocannabinoid DNA variations in major depressive disorder, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, and schizophrenia

The endocannabinoid system (ECS) is implicated in multiple mental disorders. In this study, we explored DNA variations in the ECS across major depressive disorder (MDD), bipolar disorder, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia by performing a cross-disorder genome-wide association study (GWAS) meta-analysis. We obtained six datasets from the Psychiatric Genomics Consortium containing GWAS summary statistics from European cohorts (284,023 cases and 508,515 controls). Effective sample size weighted meta-analysis was performed for 2241 single nucleotide polymorphisms (SNPs) pertaining to gene bodies of 33 endocannabinoid genes using METAL, where an overall z-statistic is calculated for each marker based on a weighted sum of individual statistics. Heterogeneity was examined with I2 and X2 tests. MAGMA gene-based analysis was also performed. We identified nine SNPs significantly associated with a change in risk of having a mental disorder. The lead SNP was rs12805732 (Gene: Diacylglycerol Lipase Alpha; DAGLA). Four SNPs had substantial heterogeneity (I2>60 %). DAGLA had the strongest association with disease risk in gene-based analysis. Our findings suggest that the ECS may be a shared pathway in mental disorders. Future studies validating these findings would contribute to the identification of biomarkers of disease risk across multiple mental disorders.

Blood miRNA levels associated with ADHD traits in children across six European birth cohorts

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent and highly heritable neurodevelopmental disorder of major societal concern. Diagnosis can be challenging and there are large knowledge gaps regarding its etiology, though studies suggest an interplay of genetic and environmental factors involving epigenetic mechanisms. MicroRNAs (miRNAs) show promise as biomarkers of human pathology and novel therapies, and here we aimed to identify blood miRNAs associated with traits of ADHD as possible biomarker candidates and further explore their biological relevance.

METHODS

Our study population consisted of 1126 children (aged 5-12 years, 46% female) from the Human Early Life Exposome study, a study spanning six ongoing population-based European birth cohorts. Expression profiles of miRNAs in whole blood samples were quantified by microarray and tested for association with ADHD-related measures of behavior and neuropsychological functions from questionnaires (Conner's Rating Scale and Child Behavior Checklist) and computer-based tests (the N-back task and Attention Network Test).

RESULTS

We identified 29 miRNAs significantly associated (false discovery rate < .05) with the Conner's questionnaire-rated trait hyperactivity, 15 of which have been linked to ADHD in previous studies. Investigation into their biological relevance revealed involvement in several pathways related to neurodevelopment and function, as well as being linked with other neurodevelopmental or psychiatric disorders known to overlap with ADHD both in symptomology, genetic risk, and co-occurrence, such as autism spectrum disorder or schizophrenia. An additional three miRNAs were significantly associated with Conner's-rated inattention. No associations were found with questionnaire-rated total ADHD index or with computer-based tests.

CONCLUSIONS

The large overlap of our hyperactivity-associated miRNAs with previous studies on ADHD is intriguing and warrant further investigation. Though this study should be considered explorative and preliminary, these findings contribute towards identifying a set of miRNAs for use as blood-based biomarkers to aid in earlier and easier ADHD diagnosis.

Copy Number Variations in Neuropsychiatric Disorders

Neuropsychiatric disorders are complex conditions that represent a significant global health burden with complex and multifactorial etiologies. Technological advances in recent years have improved our understanding of the genetic architecture of the major neuropsychiatric disorders and the genetic loci involved. Previous studies mainly investigated genome-wide significant SNPs to elucidate the cross-disorder and disorder-specific genetic basis of neuropsychiatric disorders. Although copy number variations represent a major source of genetic variations, they are known risk factors in developing a variety of human disorders, including certain neuropsychiatric diseases. In this review, we demonstrate the current understanding of CNVs contributing to liability for schizophrenia, bipolar disorder, and major depressive disorder.

Disruptive mutations in the serotonin transporter associate serotonin dysfunction with treatment-resistant affective disorder

Affective or mood disorders are a leading cause of disability worldwide. The serotonergic system has been heavily implicated in the complex etiology and serves as a therapeutic target. The serotonin transporter (SERT) is a major regulator of serotonin neurotransmission, yet the disease-relevance of impaired SERT function remains unknown. Here, we present the first identification and functional characterization of disruptive coding SERT variants found in patients with psychiatric diseases. In a unique cohort of 144 patients characterized by treatment-resistant chronic affective disorders with a lifetime history of electroconvulsive therapy, we identified two previously uncharacterized coding SERT variants: SERT-N217S and SERT-A500T. Both variants were significantly enriched in the patient cohort compared to GnomAD (SERT-N217S: OR = 151, P = 0.0001 and SERT-A500T: OR = 1348, P = 0.0022) and ethnicity-matched healthy controls (SERT-N217S: OR ≥ 17.7, P ≤ 0.013 and SERT-A500T: OR = ∞, P = 0.029). Functional investigations revealed that the mutations exert distinct perturbations to SERT function, but their overall effects converge on a partial loss-of-function molecular phenotype. Thus, the SERT-A500T variant compromises the catalytic activity, while SERT-N217S disrupts proper glycosylation of SERT with a resulting dominant-negative trafficking deficiency. Moreover, we demonstrate that the trafficking deficiency of SERT-N217S is amenable to pharmacochaperoning by noribogaine. Collectively, our findings describe the first disease-associated loss-of-function SERT variants and implicate serotonergic disturbances arising from SERT dysfunction as a risk factor for chronic affective disorders.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.

Understanding the causal relationships of attention-deficit/hyperactivity disorder with mental disorders and suicide attempt: a network Mendelian randomisation study

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a lifespan neurodevelopmental condition resulting from complex interactions between genetic and environmental risk factors. There is evidence that ADHD is associated with other mental disorders, but it remains unclear whether and in what way a causal relationship exists.

OBJECTIVE

To investigate the direct and indirect causal paths between ADHD and seven common mental disorders.

METHODS

Two-sample network Mendelian randomisation analysis was performed to identify psychiatric disorders causally related to ADHD. Total and direct effects were estimated in an univariable and multivariable setting, respectively. Robustness of results was ensured in three ways: a range of pleiotropy-robust methods, an iterative approach identifying and excluding outliers, and use of up to two genome-wide association studies per outcome to replicate results and calculate subsequently pooled meta-estimates.

RESULTS

Genetic liability to ADHD was independently associated with the risk of anorexia nervosa (OR 1.28 (95% CI 1.11 to 1.47); p=0.001). A bidirectional association was found with major depressive disorder (OR 1.09 (95% CI 1.03 to 1.15); p=0.003 in the forward direction and OR 1.76 (95% CI 1.50 to 2.06); p=4×10-12 in the reverse direction). Moreover, after adjustment for major depression disorder, a direct association with both suicide attempt (OR 1.30 (95% CI 1.16 to 1.547); p=2×10-5) and post-traumatic stress disorder (OR 1.18 (95% CI 1.05 to 1.33); p=0.007) was observed. There was no evidence of a relationship with anxiety, bipolar disorder or schizophrenia.

CONCLUSIONS

This study suggests that ADHD is an independent risk factor for a number of common psychiatric disorders.

CLINICAL IMPLICATIONS

The risk of comorbid psychiatric disorders in individuals with ADHD needs to be considered both in diagnosis and treatment.

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Recent Updates on Corticosteroid-Induced Neuropsychiatric Disorders and Theranostic Advancements through Gene Editing Tools

The vast use of corticosteroids (CCSs) globally has led to an increase in CCS-induced neuropsychiatric disorders (NPDs), a very common manifestation in patients after CCS consumption. These neuropsychiatric disorders range from depression, insomnia, and bipolar disorders to panic attacks, overt psychosis, and many other cognitive changes in such subjects. Though their therapeutic importance in treating and improving many clinical symptoms overrides the complications that arise after their consumption, still, there has been an alarming rise in NPD cases in recent years, and they are seen as the greatest public health challenge globally; therefore, these potential side effects cannot be ignored. It has also been observed that many of the neuronal functional activities are regulated and controlled by genomic variants with epigenetic factors (DNA methylation, non-coding RNA, and histone modeling, etc.), and any alterations in these regulatory mechanisms affect normal cerebral development and functioning. This study explores a general overview of emerging concerns of CCS-induced NPDs, the effective molecular biology approaches that can revitalize NPD therapy in an extremely specialized, reliable, and effective manner, and the possible gene-editing-based therapeutic strategies to either prevent or cure NPDs in the future.

Contributing risk factors of common psychiatric disorders in the Pakistani population

With an increasing incidence of psychiatric disorders worldwide, there is a need for a better understanding of the population-specific contributing risk factors that are associated with common psychiatric conditions. This study aimed to assess the correlation between socioeconomic, environmental and clinical features associated with major depression (MDD n = 479), bipolar disorder (BD n = 222) and schizophrenia (SHZ n = 146), in the Pakistani population. Multinomial logistic regression and Pearson's correlation were applied to assess the association and correlation between demographic, socioeconomic, environmental, and clinical features of MDD, BD and SHZ. In the present study, MDD was found to be more prevalent than BD and SHZ. The average age at onset (AAO), was observed to be earlier in females with BD and SHZ, in addition, females with a positive family history of MDD, BD and SHZ also had an earlier AAO. The fitted multinomial logistic regression model indicated a significant association of; aggression, tobacco use, drugs abuse, history of head injuries and family history with BD as compared to MDD, while insomnia and suicidality were significantly associated with MDD. Strong positive correlations were observed mainly between age/AAO, AAO/tobacco use and aggression/insomnia in all three cohorts. In conclusion, the present study identifies possible contributing socio-demographic, biological and environmental factors that are correlated and associated with the psychiatric conditions in the Pakistani population.

Psychometric evaluation of the Swedish Multidimensional Psychological Flexibility Inventory (MPFI)

Psychiatric disorders are common, and reliable measures are crucial for research and clinical practice. A cross-diagnostic construct that can be used to index treatment outcomes as well as prevalence of psychological ill health is psychological flexibility. The aim of this study was to validate a Swedish version of the Multidimensional Psychological Flexibility Inventory (MPFI). The MPFI has 12 subscales, six of which measure flexibility, and six that measure inflexibility. Using confirmatory factor analysis in a community sample of 670 participants, we found that a model with two higher order factors had satisfactory fit (CFI = .933) and a 12-factor model had the best fit to the data (CFI = .955). All 12 subscales showed adequate reliability (CRs = .803-.933) and the factor structure was similar across age groups and gender. Findings suggest that the Swedish version of the MPFI is a reliable instrument that can be used to index psychological flexibility. Potential areas for improvement of the instrument are discussed.

Affinity scores: An individual-centric fingerprinting framework for neuropsychiatric disorders

Population-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific 'fingerprints' across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level 'fingerprints', capable of capturing intra-illness heterogeneity and inter-illness overlap. A multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined each participant's "neighbourhood" across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset, which included data from individuals with chronic and treatment resistant schizophrenia and healthy controls. Individualised affinity scores provided a 'fingerprint' of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset. Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. Affinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

Familial coaggregation of major psychiatric disorders in first-degree relatives of individuals with autism spectrum disorder: a nationwide population-based study

BACKGROUND

Family coaggregation of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD) and schizophrenia have been presented in previous studies. The shared genetic and environmental factors among psychiatric disorders remain elusive.

METHODS

This nationwide population-based study examined familial coaggregation of major psychiatric disorders in first-degree relatives (FDRs) of individuals with ASD. Taiwan's National Health Insurance Research Database was used to identify 26 667 individuals with ASD and 67 998 FDRs of individuals with ASD. The cohort was matched in 1:4 ratio to 271 992 controls. The relative risks (RRs) and 95% confidence intervals (CI) of ADHD, ASD, BD, MDD and schizophrenia were assessed among FDRs of individuals with ASD and ASD with intellectual disability (ASD-ID).

RESULTS

FDRs of individuals with ASD have higher RRs of major psychiatric disorders compared with controls: ASD 17.46 (CI 15.50-19.67), ADHD 3.94 (CI 3.72-4.17), schizophrenia 3.05 (CI 2.74-3.40), BD 2.22 (CI 1.98-2.48) and MDD 1.88 (CI 1.76-2.00). Higher RRs of schizophrenia (4.47, CI 3.95-5.06) and ASD (18.54, CI 16.18-21.23) were observed in FDRs of individuals with both ASD-ID, compared with ASD only.

CONCLUSIONS

The risk for major psychiatric disorders was consistently elevated across all types of FDRs of individuals with ASD. FDRs of individuals with ASD-ID are at further higher risk for ASD and schizophrenia. Our results provide leads for future investigation of shared etiologic pathways of ASD, ID and major psychiatric disorders and highlight the importance of mental health care delivered to at-risk families for early diagnoses and interventions.

Selective behavioural impairments in mice heterozygous for the cross disorder psychiatric risk gene DLG2

Mutations affecting DLG2 are emerging as a genetic risk factor associated with neurodevelopmental psychiatric disorders including schizophrenia, autism spectrum disorder, and bipolar disorder. Discs large homolog 2 (DLG2) is a member of the membrane-associated guanylate kinase protein superfamily of scaffold proteins, a component of the post-synaptic density in excitatory neurons and regulator of synaptic function and plasticity. It remains an important question whether and how haploinsuffiency of DLG2 contributes to impairments in basic behavioural and cognitive functions that may underlie symptomatic domains in patients that cross diagnostic boundaries. Using a heterozygous Dlg2 mouse model we examined the impact of reduced Dlg2 expression on functions commonly impaired in neurodevelopmental psychiatric disorders including motor co-ordination and learning, pre-pulse inhibition and habituation to novel stimuli. The heterozygous Dlg2 mice exhibited behavioural impairments in long-term motor learning and long-term habituation to a novel context, but not motor co-ordination, initial responses to a novel context, PPI of acoustic startle or anxiety. We additionally showed evidence for the reduced regulation of the synaptic plasticity-associated protein cFos in the motor cortex during motor learning. The sensitivity of selective behavioural and cognitive functions, particularly those dependent on synaptic plasticity, to reduced expression of DLG2 give further credence for DLG2 playing a critical role in specific brain functions but also a mechanistic understanding of symptom expression shared across psychiatric disorders.

Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations

Mental illness, a serious problem across the globe, requires multi-pronged solutions including effective computational models to predict illness. Mental illness diagnosis is complicated by the pronounced sharing of symptoms and mutual pre-dispositions. Set in this context we offer a systematic comparison of seven deep learning and two conventional machine learning models for predicting mental illness from the history of present illness free-text descriptions in patient records. The models tested include a new architecture CB-MH which ranks best for F1 (0.62) while another attention model is best for F2 (0.71). We also explore model decisions using Integrated Gradients interpretability method which we use to identify key influential features. Overall, the majority of true positives have key features appearing in meaningful contexts. False negatives are most challenging with most key features appearing in unclear contexts. False positives are mostly true positives in actuality as supported by a small-scale clinician-based user judgement study.

Circulating hsa-let-7e-5p and hsa-miR-125a-5p as Possible Biomarkers in the Diagnosis of Major Depression and Bipolar Disorders

Background. Evidence shows that microRNAs (miRNAs) could play a key role in the homeostasis and development of major depressive disorder and bipolar disorder. The present study is aimed at investigating the changes in circulating miRNA expression profiles in a plasma of patients suffering from major depressive disorder (MDD) and bipolar disorder (BD) to distinguish and evaluate these molecules as biomarkers for mood disorders. Methods. A study enrolled a total of 184 subjects: 74 controls, 84 MDD patients, and 26 BD patients. Small RNA sequencing revealed 11 deregulated circulating miRNAs in MDD and BD plasma, of which expression of 5, hsa-miR-139-3p, miRNAs hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-125a-5p, and hsa-miR-483-5p, were further verified using qPCR. miRNA gene expression data was evaluated alongside the data from clinical assessment questionnaires. Results. hsa-let-7e-5p and hsa-miR-125a-5p were both confirmed upregulated: 0.75-fold and 0.25-fold, respectively, in the MDD group as well as 1.36-fold and 0.68-fold in the BD group. Receiver operating curve (ROC) analysis showed mediocre diagnostic sensitivity and specificity of both hsa-let-7e-5p and hsa-miR-125a-5p with approximate area under the curve (AOC) of 0.66. ROC analysis of combined miRNA and clinical assessment data showed that hsa-let-7e-5p and hsa-miR-125a-5p testing could improve MDD and BD diagnostic accuracy by approximately 10%. Conclusions. Circulating hsa-let-7e-5 and hsa-miR-125a-5p could serve as additional peripheral biomarkers for mood disorders; however, suicidal ideation remains the major diagnostic factor for MDD and BD.

What Do We Know About the Genetic Architecture of Psychopathology?

In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Trends in big data analyses by multicenter collaborative translational research in psychiatry

The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.

Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization

Autism spectrum disorder (ASD) and schizophrenia have been conceived as partly opposing disorders in terms of systemizing vs. empathizing cognitive styles, with resemblances to male vs. female average sex differences. Left-right asymmetry of the brain is an important aspect of its organization that shows average differences between the sexes and can be altered in both ASD and schizophrenia. Here we mapped multivariate associations of polygenic risk scores for ASD and schizophrenia with asymmetries of regional cerebral cortical surface area, thickness, and subcortical volume measures in 32,256 participants from the UK Biobank. Polygenic risks for the two disorders were positively correlated (r = 0.08, p = 7.13 × 10-50) and both were higher in females compared to males, consistent with biased participation against higher-risk males. Each polygenic risk score was associated with multivariate brain asymmetry after adjusting for sex, ASD r = 0.03, p = 2.17 × 10-9, and schizophrenia r = 0.04, p = 2.61 × 10-11, but the multivariate patterns were mostly distinct for the two polygenic risks and neither resembled average sex differences. Annotation based on meta-analyzed functional imaging data showed that both polygenic risks were associated with asymmetries of regions important for language and executive functions, consistent with behavioral associations that arose in phenome-wide association analysis. Overall, the results indicate that distinct patterns of subtly altered brain asymmetry may be functionally relevant manifestations of polygenic risks for ASD and schizophrenia, but do not support brain masculinization or feminization in their etiologies.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Neuroplastin in Neuropsychiatric Diseases

Molecular mechanisms underlying neuropsychiatric and neurodegenerative diseases are insufficiently elucidated. A detailed understanding of these mechanisms may help to further improve medical intervention. Recently, intellectual abilities, creativity, and amnesia have been associated with neuroplastin, a cell recognition glycoprotein of the immunoglobulin superfamily that participates in synapse formation and function and calcium signaling. Data from animal models suggest a role for neuroplastin in pathways affected in neuropsychiatric and neurodegenerative diseases. Neuroplastin loss or disruption of molecular pathways related to neuronal processes has been linked to various neurological diseases, including dementia, schizophrenia, and Alzheimer's disease. Here, we review the molecular features of the cell recognition molecule neuroplastin, and its binding partners, which are related to neurological processes and involved in learning and memory. The emerging functions of neuroplastin may have implications for the treatment of diseases, particularly those of the nervous system.

Developmental Profile of Psychiatric Risk Associated With Voltage-Gated Cation Channel Activity

BACKGROUND

Recent breakthroughs in psychiatric genetics have implicated biological pathways onto which genetic risk for psychiatric disorders converges. However, these studies do not reveal the developmental time point(s) at which these pathways are relevant.

METHODS

We aimed to determine the relationship between psychiatric risk and developmental gene expression relating to discrete biological pathways. We used postmortem RNA sequencing data (BrainSeq and BrainSpan) from brain tissue at multiple prenatal and postnatal time points, with summary statistics from recent genome-wide association studies of schizophrenia, bipolar disorder, and major depressive disorder. We prioritized gene sets for overall enrichment of association with each disorder and then tested the relationship between the association of their constituent genes with their relative expression at each developmental stage.

RESULTS

We observed relationships between the expression of genes involved in voltage-gated cation channel activity during early midfetal, adolescence, and early adulthood time points and association with schizophrenia and bipolar disorder, such that genes more strongly associated with these disorders had relatively low expression during early midfetal development and higher expression during adolescence and early adulthood. The relationship with schizophrenia was strongest for the subset of genes related to calcium channel activity, while for bipolar disorder, the relationship was distributed between calcium and potassium channel activity genes.

CONCLUSIONS

Our results indicate periods during development when biological pathways related to the activity of calcium and potassium channels may be most vulnerable to the effects of genetic variants conferring risk for psychiatric disorders. Furthermore, they indicate key time points and potential targets for disorder-specific therapeutic interventions.

Sex differences in parent-offspring recurrence of attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder sharing genetic risk factors with other common psychiatric disorders. However, intergenerational recurrence patterns of ADHD from parents to sons and daughters are not known. We aimed to examine the risk of ADHD in offspring of parents with ADHD and parents with other psychiatric disorders by parental and offspring sex, using parents without the specific disorders as comparison.

METHODS

In a generation study linking data from several population-based registries, all Norwegians born 1967-2011 (n = 2,486,088; Medical Birth Registry of Norway) and their parents were followed to 2015. To estimate intergenerational recurrence risk, we calculated prevalence differences (PD) and the relative risk (RR) of ADHD in offspring by parental ADHD, bipolar disorder (BD), schizophrenia spectrum disorder (SCZ), major depression (MDD), all by parental and offspring sex.

RESULTS

The absolute prevalence of ADHD in offspring of parents with ADHD was very high, especially in sons of two affected parents (41.5% and 25.1% in sons and daughters, respectively), and far higher than in offspring of parents with BD, SCZ or MDD. Intergenerational recurrence risks were higher for maternal than paternal ADHD (RR_maternal 8.4, 95% confidence interval (CI) 8.2-8.6 vs. RR_paternal 6.2, 6.0-6.4) and this was also true on the absolute scale (PD_maternal 21.1% (20.5-21.7) vs. PD_paternal 14.8% (14.3-15.4)). RRs were higher in daughters, while PDs higher in sons. Parental SCZ, BD and MDD were associated with an approximately doubled risk of offspring ADHD compared to parents without the respective disorders, and estimates did not differ significantly between daughters and sons.

CONCLUSIONS

The intergenerational recurrence risks of ADHD were high and higher from mothers with ADHD than fathers with ADHD. Other parental psychiatric disorders also conferred increased risk of offspring ADHD, but far lower, indicating a sex- and diagnosis-specific intergenerational recurrence risk in parents with ADHD.

Exploring real-world evidence to uncover unknown drug benefits and support the discovery of new treatment targets for depressive and bipolar disorders

BACKGROUND

Major depressive and bipolar disorders are associated with impaired quality of life and high economic burden. Although progress has been made in our understanding of the underlying pathophysiology and the development of novel pharmacological treatments, a large unmet need remains for finding effective treatment options. The purpose of this study was to identify potential new mechanisms of actions or treatment targets that could inform future research and development opportunities for major depressive and bipolar disorders.

METHODS

A self-controlled cohort study was conducted to examine associations between 1933 medications and incidence of major depressive and bipolar disorders across four US insurance claims databases. Presence of incident depressive or bipolar disorders were captured for each patient prior to or after drug exposure and incident rate ratios were calculated. Medications that demonstrated ≥50% reduction in risk for both depressive and bipolar disorders within two or more databases were evaluated as potential treatment targets.

RESULTS

Eight medications met our inclusion criteria, which fell into three treatment groups: drugs used in substance use disorders; drugs that affect the cholinergic system; and drugs used for the management of cardiovascular-related conditions.

LIMITATIONS

This study was not designed to confirm a causal association nor inform current clinical practice. Instead, this research and the methods employed intended to be hypothesis generating and help uncover potential treatment pathways that could warrant further investigation.

CONCLUSIONS

Several potential drug targets that could aid further research and discovery into novel treatments for depressive and bipolar disorders were identified.

Genetic association of FMRP targets with psychiatric disorders

Genes encoding the mRNA targets of fragile X mental retardation protein (FMRP) are enriched for genetic association with psychiatric disorders. However, many FMRP targets possess functions that are themselves genetically associated with psychiatric disorders, including synaptic transmission and plasticity, making it unclear whether the genetic risk is truly related to binding by FMRP or is alternatively mediated by the sampling of genes better characterised by another trait or functional annotation. Using published common variant, rare coding variant and copy number variant data, we examined the relationship between FMRP binding and genetic association with schizophrenia, major depressive disorder and bipolar disorder. High-confidence targets of FMRP, derived from studies of multiple tissue types, were enriched for common schizophrenia risk alleles, as well as rare loss-of-function and de novo nonsynonymous variants in schizophrenia cases. Similarly, through common variation, FMRP targets were associated with major depressive disorder, and we present novel evidence of association with bipolar disorder. These relationships could not be explained by other functional annotations known to be associated with psychiatric disorders, including those related to synaptic structure and function. This study reinforces the evidence that targeting by FMRP captures a subpopulation of genes enriched for genetic association with a range of psychiatric disorders.

Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts

BACKGROUND

Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed, and the relationships between androgen exposure and behavior remain unclear.

METHODS

Here, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N = 763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores.

RESULTS

We found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all [Formula: see text]), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ([Formula: see text], [Formula: see text]; [Formula: see text], [Formula: see text], respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels ([Formula: see text], [Formula: see text]), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels ([Formula: see text], [Formula: see text]). Finally, using the SPARK cohort (N = 9419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning ([Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text] for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: [Formula: see text]; testosterone: [Formula: see text] from 0.1%-ile to 99.9%-ile; SHBG: [Formula: see text] from 0.1%-ile to 99.9%-ile).

LIMITATIONS

In the devGenes and SPARK cohorts, our analyses rely on indirect, rather than direct measurement of androgens and related molecules.

CONCLUSIONS

These findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females.

Clinical evaluation of patients with a neuropsychiatric risk copy number variant

Several copy number variants (CNVs) have been identified to confer high risk for a range of neuropsychiatric conditions. Because of advances in genetic testing within clinical settings, patients are increasingly receiving diagnoses of copy number variant genomic disorders. However, clinical guidelines surrounding assessment and management are limited. This review synthesises recent research and makes preliminary recommendations regarding the clinical evaluation of patients with neuropsychiatric risk CNVs. We recommend multi-system assessment beyond the initial referral reason, recognition of the potential need for co-ordinated multidisciplinary care, and that interventions take account of relevant multimorbidity. The frequently complex needs of patients with CNVs across the life-course pose challenges for many health care systems and may be best provided for by the establishment of specialist clinics.

Transcriptomic networks implicate neuronal energetic abnormalities in three mouse models harboring autism and schizophrenia-associated mutations

Genetic risk for psychiatric illness is complex, so identification of shared molecular pathways where distinct forms of genetic risk might coincide is of substantial interest. A growing body of genetic and genomic studies suggest that such shared molecular pathways exist across disorders with different clinical presentations, such as schizophrenia and autism spectrum disorder (ASD). But how this relates to specific genetic risk factors is unknown. Further, whether some of the molecular changes identified in brain relate to potentially confounding antemortem or postmortem factors are difficult to prove. We analyzed the transcriptome from the cortex and hippocampus of three mouse lines modeling human copy number variants (CNVs) associated with schizophrenia and ASD: Df(h15q13)/+, Df(h22q11)/+, and Df(h1q21)/+ which carry the 15q13.3 deletion, 22q11.2 deletion, and 1q21.1 deletion, respectively. Although we found very little overlap of differential expression at the level of individual genes, gene network analysis identified two cortical and two hippocampal modules of co-expressed genes that were dysregulated across all three mouse models. One cortical module was associated with neuronal energetics and firing rate, and overlapped with changes identified in postmortem human brain from SCZ and ASD patients. These data highlight aspects of convergent gene expression in mouse models harboring major risk alleles, and strengthen the connection between changes in neuronal energetics and neuropsychiatric disorders in humans.

All for one and one for all: heterogeneity of genetic etiologies in neurodevelopmental psychiatric disorders

Alexandre Dumas' famous phrase All for One and One for All recapitulates our current understanding of the genomic architecture of neurodevelopmental psychiatric disorders (NPD), like autism Spectrum disorder, bipolar disorder, and schizophrenia. Many rare genomic variants of large effect size have been identified; all of them together can explain a significant proportion of NPD. In parallel, one rare genomic variant can cause all of the above NPD. Finally, common genomic variants of individually small effect size can be combined to further explain risk for NPD. How do we reconcile different genomic variants accounting for one clinical diagnosis, and different clinical diagnoses arising from a single genomic variant? Here, we discuss a framework to understand genetic and clinical heterogeneity in NPD.

Cross-Disorder Analysis of De Novo Variants Increases the Power of Prioritising Candidate Genes

De novo variants (DNVs) are critical to the treatment of neurodevelopmental disorders (NDDs). However, effectively identifying candidate genes in small cohorts is challenging in most NDDs because of high genetic heterogeneity. We hypothesised that integrating DNVs from multiple NDDs with genetic similarity can significantly increase the possibility of prioritising the candidate gene. We catalogued 66,186 coding DNVs in 50,028 individuals with nine types of NDDs in cohorts with sizes spanning from 118 to 31,260 from Gene4Denovo database to validate this hypothesis. Interestingly, we found that integrated DNVs can effectively increase the number of prioritised candidate genes for each disorder. We identified 654 candidate genes including 481 shared candidate genes carrying putative functional variants in at least two disorders. Notably, 13.51% (65/481) of shared candidate genes were prioritised only via integrated analysis including 44.62% (29/65) genes validated in recent large cohort studies. Moreover, we estimated that more novel candidate genes will be prioritised with the increase in cohort size, in particular for some disorders with high putative functional DNVs per individual. In conclusion, integrated DNVs may increase the power of prioritising candidate genes, which is important for NDDs with small cohort size.

A review of resting-state fMRI and its use to examine psychiatric disorders

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.

Neurodevelopmental Trajectories and Psychiatric Morbidity: Lessons Learned From the 22q11.2 Deletion Syndrome

PURPOSE OF REVIEW

The 22q11.2 deletion syndrome (22q11DS) is associated with a broad spectrum of neurodevelopmental phenotypes and is the strongest known single genetic risk factor for schizophrenia. Compared to other rare structural pathogenic genetic variants, 22q11DS is relatively common and one of the most extensively studied. This review provides a state-of-the-art overview of current insights regarding associated neurodevelopmental phenotypes and potential implications for 22q11DS and beyond.

RECENT FINDINGS

We will first discuss recent findings with respect to neurodevelopmental phenotypic expression associated with 22q11DS, including psychotic disorders, intellectual functioning, autism spectrum disorders, as well as their interactions. Second, we will address considerations that are important in interpreting these data and propose potential implications for both the clinical care for and the empirical study of individuals with 22q11DS. Third, we will highlight variable penetrance and pleiotropy with respect to neurodevelopmental phenotypes in 22q11DS. We will discuss how these phenomena are consistently observed in the context of virtually all rare pathogenic variants and that they pose substantial challenges from both a clinical and a research perspective. We outline how 22q11DS could be viewed as a genetic model for studying neurodevelopmental phenotypes. In addition, we propose that 22q11DS research can help elucidate mechanisms underlying variable expression and pleiotropy of neurodevelopmental phenotypes, insights that are likely relevant for 22q11DS and beyond, including for individuals with other rare pathogenic genetic variants and for individuals with idiopathic neurodevelopmental conditions.

Experiences of falling and depression: Results from the Korean Longitudinal Study of Ageing

OBJECTIVE

This study estimates the association between fall experiences during the last two years and risk of depression.

METHODS

Data from 9,355 subjects of the Korean Longitudinal Study of Ageing from 2006 to 2016 were included with baseline at 2006. To analyze the association between fall experiences during the last two years and depression, a generalized estimating equation (GEE) model and chi-square tests were used.

RESULTS

At baseline 2006, the odds ratio (OR) of depressive symptoms in those with fall experiences was 1.36 times higher (p < .0001) than those with non-fall experience. In those 64 years or younger and 65 years or older, the OR of depressive symptoms in fall experience was 1.45 times higher (p =0.003) and 1.34 times higher (p =0.000) than those with non-fall experience, respectively. In males and females, the OR of depressive symptoms in those with fall experience was 1.47 times higher (p 0.008) and 1.34 times higher (p =0.000) than those with non-fall experience, respectively.

CONCLUSION

Fall experiences during the last two years are associated with depressive symptoms. Therefore, self-reported screening for fall experiences might aid in population-based prevention strategies for depressive symptoms.

Mice carrying a schizophrenia-associated mutation of the Arhgap10 gene are vulnerable to the effects of methamphetamine treatment on cognitive function: association with morphological abnormalities in striatal neurons

We recently found a significant association between exonic copy-number variations in the Rho GTPase activating protein 10 (Arhgap10) gene and schizophrenia in Japanese patients. Special attention was paid to one patient carrying a missense variant (p.S490P) in exon 17, which overlapped with an exonic deletion in the other allele. Accordingly, we generated a mouse model (Arhgap10 S490P/NHEJ mice) carrying a missense variant and a coexisting frameshift mutation. We examined the spatiotemporal expression of Arhgap10 mRNA in the brain and found the highest expression levels in the cerebellum, striatum, and nucleus accumbens (NAc), followed by the frontal cortex in adolescent mice. The expression levels of phosphorylated myosin phosphatase-targeting subunit 1 and phosphorylated p21-activated kinases in the striatum and NAc were significantly increased in Arhgap10 S490P/NHEJ mice compared with wild-type littermates. Arhgap10 S490P/NHEJ mice exhibited a significant increase in neuronal complexity and spine density in the striatum and NAc. There was no difference in touchscreen-based visual discrimination learning between Arhgap10 S490P/NHEJ and wild-type mice, but a significant impairment of visual discrimination was evident in Arhgap10 S490P/NHEJ mice but not wild-type mice when they were treated with methamphetamine. The number of c-Fos-positive cells was significantly increased after methamphetamine treatment in the dorsomedial striatum and NAc core of Arhgap10 S490P/NHEJ mice. Taken together, these results suggested that schizophrenia-associated Arhgap10 gene mutations result in morphological abnormality of neurons in the striatum and NAc, which may be associated with vulnerability of cognition to methamphetamine treatment.

Integrative analysis of genome-wide association studies identifies novel loci associated with neuropsychiatric disorders

Neuropsychiatric disorders, such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) share common clinical presentations, suggesting etiologic overlap. A substantial proportion of SNP-based heritability for neuropsychiatric disorders is attributable to genetic components, and genome-wide association studies (GWASs) focusing on individual diseases have identified multiple genetic loci shared between these diseases. Here, we aimed at identifying novel genetic loci associated with individual neuropsychiatric diseases and genetic loci shared by neuropsychiatric diseases. We performed multi-trait joint analyses and meta-analysis across five neuropsychiatric disorders based on their summary statistics from the Psychiatric Genomics Consortium (PGC), and further carried out a replication study of ADHD among 2726 cases and 16299 controls in an independent pediatric cohort. In the multi-trait joint analyses, we found five novel genome-wide significant loci for ADHD, one novel locus for BIP, and ten novel loci for MDD. We further achieved modest replication in our independent pediatric dataset. We conducted fine-mapping and functional annotation through an integrative multi-omics approach and identified causal variants and potential target genes at each novel locus. Gene expression profile and gene-set enrichment analysis further suggested early developmental stage expression pattern and postsynaptic membrane compartment enrichment of candidate genes at the genome-wide significant loci of these neuropsychiatric disorders. Therefore, through a multi-omics approach, we identified novel genetic loci associated with the five neuropsychiatric disorders which may help to better understand the underlying molecular mechanism of neuropsychiatric diseases.

Genomic perspectives on the circadian clock hypothesis of psychiatric disorders

Circadian rhythm disturbances are frequently described in psychiatric disorders such as major depressive disorder, bipolar disorder, and schizophrenia. Growing evidence suggests a biological connection between mental health and circadian rhythmicity, including the circadian influence on brain function and mood and the requirement for circadian entrainment by external factors, which is often impaired in mental illness. Mental (as well as physical) health is also adversely affected by circadian misalignment. The marked interindividual differences in this combined susceptibility, in addition to the phenotypic spectrum in traits related both to circadian rhythms and mental health, suggested the possibility of a shared genetic background and that circadian clock genes may also be candidate genes for psychiatric disorders. This hypothesis was further strengthened by observations in animal models where clock genes had been knocked out or mutated. The introduction of genome-wide association studies (GWAS) enabled hypothesis-free testing. GWAS analysis of chronotype confirmed the prominent role of circadian genes in these phenotypes and their extensive polygenicity. However, in GWAS on psychiatric traits, only one clock gene, ARNTL (BMAL1) was identified as one of the few loci differentiating bipolar disorder from schizophrenia, and macaque monkeys where the ARNTL gene has been knocked out display symptoms similar to schizophrenia. Another lesson from genomic analyses is that chronotype has an important genetic correlation with several psychiatric disorders and that this effect is unidirectional. We conclude that the effect of circadian disturbances on psychiatric disorders probably relates to modulation of rhythm parameters and extend beyond the core clock genes themselves.

High Rates of Genetic Diagnosis in Psychiatric Patients with and without Neurodevelopmental Disorders: Toward Improved Genetic Diagnosis in Psychiatric Populations

OBJECTIVE

There is a paucity of literature on genetic diagnosis in psychiatric populations, particularly the vulnerable population of patients with concomitant neurodevelopmental disorder (NDD). In this cross-sectional study, we investigated the genetic diagnostic rate in 151 adult psychiatric patients from two centers in Ontario, Canada, including a large subset (73.5%) with concurrent NDD, and performed phenotypic analysis to determine the strongest predictors for the presence of a genetic diagnosis.

METHOD

Patients 16 years of age or older and affected with a psychiatric disorder plus at least one of NDD, neurological disorder, congenital anomaly, dysmorphic features, or family history of NDD were recruited through the genetics clinics between 2012 and 2016. Patients underwent genetic assessment and testing according to clinical standards. Chi-squared test was used for phenotypic comparisons. Multivariate logistic regression analysis was performed to determine which phenotypic features were predictive of genetic diagnosis types.

RESULTS

Overall, 45.7% of patients in the total cohort were diagnosed with genetic disorders with the vast majority of diagnoses (89.9%) comprising single gene and chromosomal disorders. There were management and treatment implications for almost two-thirds (63.8%) of diagnosed patients. Presence of a single gene disorder or chromosomal diagnosis was predicted by differing combinations of neurological, NDD, and psychiatric phenotypes.

CONCLUSION

The results of this study highlight the frequency and impact of genetic diagnosis in psychiatric populations, particularly those with concomitant NDD. Genetic assessment should be considered in psychiatric patients, particularly those with multiple brain phenotypes (psychiatric, neurodevelopmental, neurological).

Pan-european landscape of research into neurodevelopmental copy number variants: A survey by the MINDDS consortium

BACKGROUND

Several rare copy number variants have been identified to confer risk for neurodevelopmental disorders (NDD-CNVs), and increasingly NDD-CNVs are being identified in patients. There is a clinical need to understand the phenotypes of NDD-CNVs. However due to rarity of NDD-CNVs in the population, within individual countries there is a limited number of NDD-CNV carriers who can participate in research. The pan-european MINDDS (Maximizing Impact of Research in Neurodevelopmental Disorders) consortium was established in part to address this issue.

METHODOLOGY

A survey was developed to scope out the current landscape of NDD-CNV research across member countries of the MINDDS consortium, and to identify clinical cohorts with potential for future research.

RESULTS

36 centres from across 16 countries completed the survey. We provide a list of centres who can be contacted for future collaborations. 3844 NDD-CNV carriers were identified across clinical and research centres spanning a range of medical specialties, including psychiatry, paediatrics, medical genetics. A broad range of phenotypic data was available; including medical history, developmental history, family history and anthropometric data. In 12/16 countries, over 75% of NDD-CNV carriers could be recontacted for future studies.

CONCLUSION

This survey has highlighted the potential within Europe for large multi-centre studies of NDD-CNV carriers, to improve knowledge of the complex relationship between NDD-CNV and clinical phenotype. The MINNDS consortium is in a position to facilitate collaboration, data-sharing and knowledge exchange on NDD-CNV phenotypes across Europe.