Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

Loss of normal facial asymmetry in schizophrenia and bipolar disorder: Implications for development of brain asymmetry in psychotic illness

Development of the craniofacies occurs in embryological intimacy with development of the brain and both show normal left-right asymmetries. While facial dysmorphology occurs to excess in psychotic illness, facial asymmetry has yet to be investigated as a putative index of brain asymmetry. Ninety-three subjects (49 controls, 22 schizophrenia, 22 bipolar disorder) received 3D laser surface imaging of the face. On geometric morphometric analysis with (x, y, z) visualisations of statistical models for facial asymmetries, in controls the upper face and periorbital region, which share embryological intimacy with the forebrain, showed marked asymmetries. Their geometry included: along the x-axis, rightward asymmetry in its dorsal-medial aspects and leftward asymmetry in its ventral-lateral aspects; along the z-axis, anterior protrusion in its right ventral-lateral aspect. In both schizophrenia and bipolar disorder these normal facial asymmetries were diminished, with residual retention of asymmetries in bipolar disorder. This geometry of normal facial asymmetries shows commonalities with that of normal frontal lobe asymmetries. These findings indicate a trans-diagnostic process that involves loss of facial asymmetries in both schizophrenia and bipolar disorder. Embryologically, they implicate loss of face-brain asymmetries across gestational weeks 7-14 in processes that involve genes previously associated with risk for schizophrenia.

Genomic study of taste perception genes in African Americans reveals SNPs linked to Alzheimer's disease

While previous research has shown the potential links between taste perception pathways and brain-related conditions, the area involving Alzheimer's disease remains incompletely understood. Taste perception involves neurotransmitter signaling, including serotonin, glutamate, and dopamine. Disruptions in these pathways are implicated in neurodegenerative diseases. The integration of olfactory and taste signals in flavor perception may impact brain health, evident in olfactory dysfunction as an early symptom in neurodegenerative conditions. Shared immune response and inflammatory pathways may contribute to the association between altered taste perception and conditions like neurodegeneration, present in Alzheimer's disease. This study consists of an exploration of expression-quantitative trait loci (eQTL), utilizing whole-blood transcriptome profiles, of 28 taste perception genes, from a combined cohort of 475 African American subjects. This comprehensive dataset was subsequently intersected with single-nucleotide polymorphisms (SNPs) identified in Genome-Wide Association Studies (GWAS) of Alzheimer's Disease (AD). Finally, the investigation delved into assessing the association between eQTLs reported in GWAS of AD and the profiles of 741 proteins from the Olink Neurological Panel. The eQTL analysis unveiled 3,547 statistically significant SNP-Gene associations, involving 412 distinct SNPs that spanned all 28 taste genes. In 17 GWAS studies encompassing various traits, a total of 14 SNPs associated with 12 genes were identified, with three SNPs consistently linked to Alzheimer's disease across four GWAS studies. All three SNPs demonstrated significant associations with the down-regulation of TAS2R41, and two of them were additionally associated with the down-regulation of TAS2R60. In the subsequent pQTL analysis, two of the SNPs linked to TAS2R41 and TAS2R60 genes (rs117771145 and rs10228407) were correlated with the upregulation of two proteins, namely EPHB6 and ADGRB3. Our investigation introduces a new perspective to the understanding of Alzheimer's disease, emphasizing the significance of bitter taste receptor genes in its pathogenesis. These discoveries set the stage for subsequent research to delve into these receptors as promising avenues for both intervention and diagnosis. Nevertheless, the translation of these genetic insights into clinical practice requires a more profound understanding of the implicated pathways and their pertinence to the disease's progression across diverse populations.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population

BACKGROUND

Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data.

METHODS

The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions.

RESULTS

The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes.

CONCLUSIONS

Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

The Common Structure of the Major Psychoses: More Similarities Than Differences in the Network Structures of Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder

BACKGROUND AND HYPOTHESIS

There has been a century-long debate about whether the major psychoses (eg, bipolar disorder, schizophrenia, and schizoaffective disorder) are one disorder with various manifestations or different disease entities. Traditional approaches using dimensional models have not provided decisive findings. Here, we address this question by examining the network constellation of affective and psychotic syndromes.

DESIGN

Comparable symptom data of 1882 patients with psychotic bipolar disorder, schizoaffective disorders, and schizophrenia were extracted from three datasets: B-SNIP 1, B-SNIP2, and PARDIP. Twenty-six items from the Positive and Negative Syndrome Scale, YMRS, and the Montgomery-Asberg Depression Rating Scale were selected for the analysis using a principled approach to eliminate overlapping/redundant items. Gaussian graphical models were estimated and assessed for stability, and their communities were identified using bootstrapped exploratory graph analysis. The structures and global densities of the networks were compared with network comparison tests.

RESULTS

The network structures were highly similar (r >. 80) across diagnostic groups. For all diagnoses, manic symptoms were more connected with positive symptoms while depressive symptoms were more linked with negative symptoms. The depressive and negative symptoms were the strongest indicators of depressive and psychotic communities. Theoretically interesting variability in network edge weights between symptoms was found relating to thought disorder and pessimistic thinking.

CONCLUSIONS

The same broad structure of psychopathology underlies the symptom expressions of bipolar disorder, schizoaffective disorder, and schizophrenia. Future studies should build on the present finding by comparing specific inter-relations between symptoms in the different diagnostic groups using methods capable of detecting causality.

Causal Association Between Sedentary Behaviors and Health Outcomes: A Systematic Review and Meta-Analysis of Mendelian Randomization Studies

BACKGROUND

Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.

OBJECTIVES

To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.

METHODS

A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.

RESULTS

A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer's disease, and gastroesophageal reflux disease.

CONCLUSION

These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.

PROSPERO REGISTRATION

CRD42023453828.

Polygenic Risk Scores and Twin Concordance for Schizophrenia and Bipolar Disorder

Importance

Schizophrenia and bipolar disorder are highly heritable psychiatric disorders with strong genetic and phenotypic overlap. Twin and molecular methods can be leveraged to predict the shared genetic liability to these disorders.

Objective

To investigate whether twin concordance for psychosis depends on the level of polygenic risk score (PRS) for psychosis and zygosity and compare PRS from cases and controls from several large samples and estimate the twin heritability of psychosis.

Design, Setting, and Participants

In this case-control study, psychosis PRS were generated from a genome-wide association study (GWAS) combining schizophrenia and bipolar disorder into a single psychosis phenotype and compared between cases and controls from the Schizophrenia and Bipolar Twin Study in Sweden (STAR) project. Further tests were conducted to ascertain if twin concordance for psychosis depended on the mean PRS for psychosis. Structural equation modeling was used to estimate heritability. This study constituted an analysis of existing clinical and population datasets with genotype and/or twin data. Included were twins from the STAR cohort and from the Swedish Twin Registry. Data were collected during the 2006 to 2013 period and analyzed from March 2023 to June 2024.

Exposures

PRS for psychosis based on the most recent GWAS of combined schizophrenia/bipolar disorder.

Main Outcomes and Measures

Psychosis case status was assessed by clinical interviews and/or Swedish National Register data.

Results

The final cohort comprised 87 pairs of twins with 1 or both affected and 59 unaffected pairs from the STAR project (for a total of 292 twins) as well as 443 pairs with 1 or both affected and 20 913 unaffected pairs from the Swedish Twin Registry. Among the 292 twins (mean [SD] birth year, 1960 [10.8] years; 158 female [54.1%]; 134 male [45.9%]), 134 were monozygotic twins, and 158 were dyzygotic twins. PRS for psychosis was higher in cases than in controls and associated with twin concordance for psychosis (1-SD increase in PRS, odds ratio [OR], 2.12; 95% CI, 1.23-3.87 on case status in monozygotic twins and OR, 2.74; 95% CI, 1.56-5.30 in dizygotic twins). The association between PRS for psychosis and concordance was not modified by zygosity. The twin heritability was estimated at 0.73 (95% CI, 0.30-1.00), which overlapped with the estimate in the full Swedish Twin Registry (0.69; 95% CI, 0.43-0.85).

Conclusions and Relevance

In this case-control study, using the natural experiment of twins, results suggest that twins with greater inherited liability for psychosis were more likely to have an affected co-twin. Results from twin and molecular designs largely aligned. Even as illness vulnerability is not solely genetic, PRS carried predictive power for psychosis even in a modest sample size.

Using Computational Phenotyping to Identify Divergent Strategies for Effort Allocation Across the Psychosis Spectrum

BACKGROUND AND HYPOTHESIS

Disturbances in effort-cost decision-making have been highlighted as a potential transdiagnostic process underpinning negative symptoms in individuals with schizophrenia. However, recent studies using computational phenotyping show that individuals employ a range of strategies to allocate effort, and use of different strategies is associated with unique clinical and cognitive characteristics. Building on prior work in schizophrenia, this study evaluated whether effort allocation strategies differed in individuals with distinct psychotic disorders.

STUDY DESIGN

We applied computational modeling to effort-cost decision-making data obtained from individuals with psychotic disorders (n = 190) who performed the Effort Expenditure for Rewards Task. The sample included 91 individuals with schizophrenia/schizoaffective disorder, 90 individuals with psychotic bipolar disorder, and 52 controls.

STUDY RESULTS

Different effort allocation strategies were observed both across and within different disorders. Relative to individuals with psychotic bipolar disorder, a greater proportion of individuals with schizophrenia/schizoaffective disorder did not use reward value or probability information to guide effort allocation. Furthermore, across disorders, different effort allocation strategies were associated with specific clinical and cognitive features. Those who did not use reward value or probability information to guide effort allocation had more severe positive and negative symptoms, and poorer cognitive and community functioning. In contrast, those who only used reward value information showed a trend toward more severe positive symptoms.

CONCLUSIONS

These findings indicate that similar deficits in effort-cost decision-making may arise from different computational mechanisms across the psychosis spectrum.

Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach

BACKGROUND AND HYPOTHESIS

Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets.

STUDY DESIGN

To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined.

STUDY RESULTS

Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons.

CONCLUSIONS

These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

BACKGROUND

Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases.

RESULTS

We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.

CONCLUSIONS

Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

Dysfunctional Parvalbumin Neurons in Schizophrenia and the Pathway to the Clinical Application of Kv3 Channel Modulators

Based on the pathophysiological changes observed in schizophrenia, the gamma-aminobutyric acid (GABA) hypothesis may facilitate the development of targeted treatments for this disease. This hypothesis, mainly derived from postmortem brain results, postulates dysfunctions in a subset of GABAergic neurons, particularly parvalbumin-containing interneurons. In the cerebral cortex, the fast spike firing of parvalbumin-positive GABAergic interneurons is regulated by the Kv3.1 and Kv3.2 channels, which belong to a potassium channel subfamily. Decreased Kv3.1 levels have been observed in the prefrontal cortex of patients with schizophrenia, prompting the investigation of Kv3 channel modulators for the treatment of schizophrenia. However, biomarkers that capture the dysfunction of parvalbumin neurons are required for these modulators to be effective in the pharmacotherapy of schizophrenia. Electroencephalography and magnetoencephalography studies have demonstrated impairments in evoked gamma oscillations in patients with schizophrenia, which may reflect the dysfunction of cortical parvalbumin neurons. This review summarizes these topics and provides an overview of how the development of therapeutics that incorporate biomarkers could innovate the treatment of schizophrenia and potentially change the targets of pharmacotherapy.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

Polygenic scores for psychiatric traits mediate the impact of multigenerational history for depression on offspring psychopathology

A family history of depression is a well-documented risk factor for offspring psychopathology. However, the genetic mechanisms underlying the intergenerational transmission of depression remain unclear. We used genetic, family history, and diagnostic data from 11,875 9-10 year-old children from the Adolescent Brain Cognitive Development study. We estimated and investigated the children's polygenic scores (PGSs) for 30 distinct traits and their association with a family history of depression (including grandparents and parents) and the children's overall psychopathology through logistic regression analyses. We assessed the role of polygenic risk for psychiatric disorders in mediating the transmission of depression from one generation to the next. Among 11,875 multi-ancestry children, 8,111 participants had matching phenotypic and genotypic data (3,832 female [47.2%]; mean (SD) age, 9.5 (0.5) years), including 6,151 [71.4%] of European ancestry). Greater PGSs for depression (estimate = 0.129, 95% CI = 0.070-0.187) and bipolar disorder (estimate = 0.109, 95% CI = 0.051-0.168) were significantly associated with higher family history of depression (Bonferroni-corrected P < .05). Depression PGS was the only PGS that significantly associated with both family risk and offspring's psychopathology, and robustly mediated the impact of family history of depression on several youth psychopathologies including anxiety disorders, suicidal ideation, and any psychiatric disorder (proportions mediated 1.39%-5.87% of the total effect on psychopathology; FDR-corrected P < .05). These findings suggest that increased polygenic risk for depression partially mediates the associations between family risk for depression and offspring psychopathology, showing a genetic basis for intergenerational transmission of depression. Future approaches that combine assessments of family risk with polygenic profiles may offer a more accurate method for identifying children at elevated risk.

Relationships between nine neuropsychiatric disorders and cervical cancer: insights from genetics, causality and shared gene expression patterns

BACKGROUND

Neuropsychiatric disorders and cervical cancer exert substantial influences on women's health. Furthermore, neuropsychiatric disorders frequently manifest as common symptoms in cancer patients, potentially increasing the risk of malignant neoplasms. This study aimed to identify neuropsychiatric disorders that are genetically and causally related to cervical cancer and to investigate the molecular mechanisms underlying these associations.

METHODS

GWAS data related to nine neuropsychiatric disorders, namely, schizophrenia, bipolar disorder, autism spectrum disorder, Parkinson's disease, anxiety, Alzheimer's disease, mood disorders, depression, and alcohol dependence, were obtained to calculate heritability (h2) and genetic correlation (rg) with cervical cancer using linkage disequilibrium score regression (LDSC). Mendelian randomization (MR) analysis of the two cohorts was employed to assess the causal effects. Shared gene expression pattern analysis was subsequently conducted to investigate the molecular mechanism underlying these significant associations.

RESULTS

Anxiety, mood disorders, depression, and alcohol dependence were genetically correlated with cervical cancer (all adjusted P < 0.05). Only depression was causally related to cervical cancer in both the discovery (ORIVW: 1.41, PIVW = 0.02) and replication cohorts (ORIVW: 1.80, PIVW = 0.03) in the MR analysis. Gene expression pattern analysis revealed that 270 genes related to depression and cervical cancer, including tumour necrosis factor (TNF), were significantly upregulated in cervical cancer patients, while vascular endothelial growth factor A (VEGFA), transcription factor AP-1 (JUN), and insulin-like growth factor I (IGF-I) were associated with prognosis in cervical cancer patients (all P < 0.05). These overlapping genes implicated the involvement of multiple biological mechanisms, such as neuron death, the PI3K-Akt signalling pathway, and human papillomavirus infection.

CONCLUSIONS

Genetic, causal and molecular evidence indicates that depression increases the risk of cervical cancer. The TNF, VEGFA, JUN, and IGF-1 genes and the neuron death, PI3K-Akt, and human papillomavirus infection signalling pathways may possibly explain this association.

The contribution of silencer variants to human diseases

BACKGROUND

Although disease-causal genetic variants have been found within silencer sequences, we still lack a comprehensive analysis of the association of silencers with diseases. Here, we profiled GWAS variants in 2.8 million candidate silencers across 97 human samples derived from a diverse panel of tissues and developmental time points, using deep learning models.

RESULTS

We show that candidate silencers exhibit strong enrichment in disease-associated variants, and several diseases display a much stronger association with silencer variants than enhancer variants. Close to 52% of candidate silencers cluster, forming silencer-rich loci, and, in the loci of Parkinson's-disease-hallmark genes TRIM31 and MAL, the associated SNPs densely populate clustered candidate silencers rather than enhancers displaying an overall twofold enrichment in silencers versus enhancers. The disruption of apoptosis in neuronal cells is associated with both schizophrenia and bipolar disorder and can largely be attributed to variants within candidate silencers. Our model permits a mechanistic explanation of causative SNP effects by identifying altered binding of tissue-specific repressors and activators, validated with a 70% of directional concordance using SNP-SELEX. Narrowing the focus of the analysis to individual silencer variants, experimental data confirms the role of the rs62055708 SNP in Parkinson's disease, rs2535629 in schizophrenia, and rs6207121 in type 1 diabetes.

CONCLUSIONS

In summary, our results indicate that advances in deep learning models for the discovery of disease-causal variants within candidate silencers effectively "double" the number of functionally characterized GWAS variants. This provides a basis for explaining mechanisms of action and designing novel diagnostics and therapeutics.

Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort

Background

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood.

Objective

To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU).

Methods

Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD.

Results

In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30).

Conclusions

Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility

Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Multiomic profiling of transcription factor binding and function in human brain

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning

OBJECTIVE

Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques.

METHODS

Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated.

RESULTS

All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks.

CONCLUSIONS

These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

Association of Occupational Dysfunction and Hospital Admissions With Different Polygenic Profiles in Bipolar Disorder

OBJECTIVE

Many but not all persons with bipolar disorder require hospital care because of severe mood episodes. Likewise, some but not all patients experience long-term occupational dysfunction that extends beyond acute mood episodes. It is not known whether these dissimilar outcomes of bipolar disorder are driven by different polygenic profiles. Here, polygenic scores (PGSs) for major psychiatric disorders and educational attainment were assessed for associations with occupational functioning and psychiatric hospital admissions in bipolar disorder.

METHODS

A total of 4,782 patients with bipolar disorder and 2,963 control subjects were genotyped and linked to Swedish national registers. Longitudinal measures from at least 10 years of registry data were used to derive percentage of years without employment, percentage of years with long-term sick leave, and mean number of psychiatric hospital admissions per year. Ordinal regression was used to test associations between outcomes and PGSs for bipolar disorder, schizophrenia, major depressive disorder, attention deficit hyperactivity disorder (ADHD), and educational attainment. Replication analyses of hospital admissions were conducted with data from the Bipolar Disorder Research Network cohort (N=4,219).

RESULTS

Long-term sick leave and unemployment in bipolar disorder were significantly associated with PGSs for schizophrenia, ADHD, major depressive disorder, and educational attainment, but not with the PGS for bipolar disorder. By contrast, the number of hospital admissions per year was associated with higher PGSs for bipolar disorder and schizophrenia, but not with the other PGSs.

CONCLUSIONS

Bipolar disorder severity (indexed by hospital admissions) was associated with a different polygenic profile than long-term occupational dysfunction. These findings have clinical implications, suggesting that mitigating occupational dysfunction requires interventions other than those deployed to prevent mood episodes.

Key subphenotypes of bipolar disorder are differentially associated with polygenic liabilities for bipolar disorder, schizophrenia, and major depressive disorder

Bipolar disorder (BD) features heterogenous clinical presentation and course of illness. It remains unclear how subphenotypes associate with genetic loadings of BD and related psychiatric disorders. We investigated associations between the subphenotypes and polygenic risk scores (PRS) for BD, schizophrenia, and major depressive disorder (MDD) in two BD cohorts from Sweden (N = 5180) and the UK (N = 2577). Participants were assessed through interviews and medical records for inter-episode remission, psychotic features during mood episodes, global assessment of functioning (GAF, function and symptom burden dimensions), and comorbid anxiety disorders. Meta-analyses based on both cohorts showed that inter-episode remission and GAF-function were positively correlated with BD-PRS but negatively correlated with schizophrenia-PRS (SCZ-PRS) and MDD-PRS. Moreover, BD-PRS was negatively, and MDD-PRS positively, associated with the risk of comorbid anxiety disorders. Finally, SCZ-PRS was positively associated with psychotic symptoms during mood episodes. Assuming a higher PRS of certain psychiatric disorders in cases with a positive family history, we further tested the associations between subphenotypes in index BD people and occurrence of BD, schizophrenia, or MDD in their relatives using Swedish national registries. BD patients with a relative diagnosed with BD had: (1) higher GAF and lower risk of comorbid anxiety than those with a relative diagnosed with schizophrenia or MDD, (2) lower risk of psychotic symptoms than those with a relative diagnosed with schizophrenia. Our findings shed light on the genetic underpinnings of the heterogeneity in clinical manifestations and course of illness in BD, which ultimately provide insights for developing personalized approaches to the diagnosis and treatment.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder

AIM

We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK.

METHODS

We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used.

RESULTS

SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified.

LIMITATIONS

The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information.

CONCLUSION

Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

Early Developmental Origins of Cortical Disorders Modeled in Human Neural Stem Cells

The implications of the early phases of human telencephalic development, involving neural stem cells (NSCs), in the etiology of cortical disorders remain elusive. Here, we explored the expression dynamics of cortical and neuropsychiatric disorder-associated genes in datasets generated from human NSCs across telencephalic fate transitions in vitro and in vivo. We identified risk genes expressed in brain organizers and sequential gene regulatory networks across corticogenesis revealing disease-specific critical phases, when NSCs are more vulnerable to gene dysfunctions, and converging signaling across multiple diseases. Moreover, we simulated the impact of risk transcription factor (TF) depletions on different neural cell types spanning the developing human neocortex and observed a spatiotemporal-dependent effect for each perturbation. Finally, single-cell transcriptomics of newly generated autism-affected patient-derived NSCs in vitro revealed recurrent alterations of TFs orchestrating brain patterning and NSC lineage commitment. This work opens new perspectives to explore human brain dysfunctions at the early phases of development.

Polygenic Resilience Scores are Associated with Lower Penetrance of Schizophrenia Risk Genes, Protection Against Psychiatric and Medical Disorders, and Enhanced Mental Well-Being and Cognition

In the past decade, significant advances have been made in finding genomic risk loci for schizophrenia (SCZ). This, in turn, has enabled the search for SCZ resilience loci that mitigate the impact of SCZ risk genes. Recently, we discovered the first genomic resilience profile for SCZ, completely independent from the established risk loci for SCZ. We posited that these resilience loci protect against SCZ for those having a heighted genomic risk for SCZ. Nevertheless, our understanding of genetic resilience remains limited. It remains unclear whether resilience loci foster protection against adverse states associated with SCZ risk related to clinical, cognitive, and brain-structural phenotypes. To address this knowledge gap, we analyzed data from 487,409 participants from the UK Biobank, and found that resilience loci for SCZ afforded protection against lifetime psychiatric (schizophrenia, bipolar disorder, anxiety, and depression) and non-psychiatric medical disorders (such as asthma, cardiovascular disease, digestive disorders, metabolic disorders, and external causes of morbidity and mortality). Resilience loci also protected against self-harm behaviors, improved fluid intelligence, and larger whole-brain and brain-regional sizes. Overall, this study sheds light on the range of phenotypes that are significantly associated with resilience loci within the general population, revealing distinct patterns separate from those associated with SCZ risk loci. Our findings indicate that resilience loci may offer protection against serious psychiatric and medical outcomes, co-morbidities, and cognitive impairment. Therefore, it is conceivable that resilience loci facilitate adaptive processes linked to improved health and life expectancy.

Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

Independent Prediction of Child Psychiatric Symptoms by Maternal Mental Health and Child Polygenic Risk Scores

OBJECTIVE

Prenatal maternal symptoms of depression and anxiety are associated with an increased risk for child socioemotional and behavioral difficulties, supporting the fetal origins of mental health hypothesis. However, to date, studies have not considered specific genomic risk as a possible confound.

METHOD

The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 5,546) was used to test if child polygenic risk score for attention-deficit/hyperactivity disorder (ADHD), schizophrenia, or depression confounds or modifies the impact of prenatal maternal depression and anxiety on child internalizing, externalizing, and total emotional/behavioral symptoms from age 4 to 16 years. Longitudinal child and adolescent symptom data were analyzed in the ALSPAC cohort using generalized estimating equations. Replication analyses were done in an independent cohort (Prevention of Preeclampsia and Intrauterine Growth Restriction [PREDO] cohort; n = 514) from Finland, which provided complementary measures of maternal mental health and child psychiatric symptoms.

RESULTS

Maternal depression and anxiety and child polygenic risk scores independently and additively predicted behavioral and emotional symptoms from childhood through mid-adolescence. There was a robust prediction of child and adolescent symptoms from both prenatal maternal depression (generalized estimating equation estimate = 0.093, 95% CI 0.065-0.121, p = 2.66 × 10-10) and anxiety (generalized estimating equation estimate = 0.065, 95% CI 0.037-0.093, p = 1.62 × 10-5) after adjusting for child genomic risk for mental disorders. There was a similar independent effect of maternal depression (B = 0.156, 95% CI 0.066-0.246, p = .001) on child symptoms in the PREDO cohort. Genetically informed sensitivity analyses suggest that shared genetic risk only partially explains the reported association between prenatal maternal depression and offspring mental health.

CONCLUSION

These findings highlight the genomic contribution to the fetal origins of mental health hypothesis and further evidence that prenatal maternal depression and anxiety are robust in utero risks for child and adolescent psychiatric symptoms.

PLAIN LANGUAGE SUMMARY

Depression and anxiety affect approximately 15% of pregnant women, and children exposed to maternal depression or anxiety during pregnancy are at higher risk of developing mental health problems. However, the degree to which shared genetics explains the association between maternal and child mental health is unknown. In this study the authors generated polygenic risk scores (PRS), which provide a single measure of genetic risk for complex traits, to investigate the impact of shared genetic risk on the development of childhood mental health problems. Utilizing two longitudinal studies (n = 6,060), the authors found that PRS only partially explained the association between prenatal maternal depression and childhood mental health problems. These analyses show prenatal maternal depression remained a significant predictor of childhood mental health problems after accounting for shared genetic risk, further highlighting that prenatal maternal mental health is a robust predictor of child and adolescent mental health problems.

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

Polygenic risk of social isolation behavior and its influence on psychopathology and personality

Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

Background

Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases.

Results

We collected epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we constructed networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks served as the base for a rich series of analyses, through which we demonstrated their temporal dynamics and enrichment for various disease-associated variants. We applied the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrated methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.

Conclusions

Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes. This includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

Genetic variants for head size share genes and pathways with cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

A randomized controlled trial to compare the effects of time-restricted eating versus Mediterranean diet on symptoms and quality of life in bipolar disorder

BACKGROUND

The primary objective of this randomized controlled trial (RCT) is to establish the effectiveness of time-restricted eating (TRE) compared with the Mediterranean diet for people with bipolar disorder (BD) who have symptoms of sleep disorders or circadian rhythm sleep-wake disruption. This work builds on the growing evidence that TRE has benefits for improving circadian rhythms. TRE and Mediterranean diet guidance will be offered remotely using self-help materials and an app, with coaching support.

METHODS

This study is an international RCT to compare the effectiveness of TRE and the Mediterranean diet. Three hundred participants will be recruited primarily via social media. Main inclusion criteria are: receiving treatment for a diagnosis of BD I or II (confirmed via DIAMOND structured diagnostic interview), endorsement of sleep or circadian problems, self-reported eating window of ≥ 12 h, and no current mood episode, acute suicidality, eating disorder, psychosis, alcohol or substance use disorder, or other health conditions that would interfere with or limit the safety of following the dietary guidance. Participants will be asked to complete baseline daily food logging for two weeks and then will be randomly allocated to follow TRE or the Mediterranean diet for 8 weeks, during which time, they will continue to complete daily food logging. Intervention content will be delivered via an app. Symptom severity interviews will be conducted at baseline; mid-intervention (4 weeks after the intervention begins); end of intervention; and at 6, 9, and 15 months post-baseline by phone or videoconference. Self-rated symptom severity and quality of life data will be gathered at those timepoints, as well as at 16 weeks post baseline. To provide a more refined index of whether TRE successfully decreases emotional lability and improves sleep, participants will be asked to complete a sleep diary (core CSD) each morning and complete six mood assessments per day for eight days at baseline and again at mid-intervention.

DISCUSSION

The planned research will provide novel and important information on whether TRE is more beneficial than the Mediterranean diet for reducing mood symptoms and improving quality of life in individuals with BD who also experience sleep or circadian problems.

TRIAL REGISTRATION

ClinicalTrials.gov ID NCT06188754.

Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission

Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. To address the question of how and when concordance in monozygotic twins occur, we have obtained fibroblasts from two pairs of schizophrenia discordant twins (one sibling with schizophrenia while the second one is unaffected by schizophrenia) and three pairs of healthy twins (both of the siblings are healthy). We have prepared iPSC models for these 3 groups of patients with schizophrenia, unaffected co-twins, and the healthy twins. When the study started the co-twins were considered healthy and unaffected but both the co-twins were later diagnosed with a depressive disorder. The reprogrammed iPSCs were differentiated into hippocampal neurons to measure the neurophysiological abnormalities in the patients. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. Importantly, their synaptic activity was not affected. Our measurements that were obtained from schizophrenia patients and their monozygotic twin and compared also to control healthy twins point to hippocampal synaptic deficits as a central mechanism in schizophrenia.

Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable

Background

Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations.

Methods

Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks.

Results

Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes.

Conclusions

This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

Heterogeneity in the longitudinal courses of global functioning in children at familial risk of major psychiatric disorders: Association with trauma and familial characteristics

OBJECTIVES

The extent to which heterogeneity in childhood risk trajectories may underlie later heterogeneity in schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder (MDD) remains a chief question. Answers may optimally be found by studying the longitudinal trajectories of children born to an affected parent. We aimed to differentiate trajectories of global functioning and their sensitive periods from the age of 6 to 17 years in children at familial risk (FHRs).

METHODS

First, a latent class mixed model analysis (LCMM) was applied to yearly ratings of the Children's Global Assessment Scale (CGAS) from the age of 6 to 17 years in 170 FHRs born to a parent affected by DSM-IV SZ (N = 37), BP (N = 82) or MDD (N = 51). Then, we compared the obtained Classes or trajectories of FHRs in terms of sex, parental diagnosis, IQ, child clinical status, childhood trauma, polygenic risk score (PRS), and outcome in transition to illness.

RESULTS

The LCMM on yearly CGAS trajectories identified a 4-class solution showing markedly different childhood and adolescence dynamic courses and temporal vulnerability windows marked by a functioning decline and a degree of specificity in parental diagnosis. Moreover, IQ, trauma exposure, PRS level, and timing of later transition to illness differentiated the trajectories. Almost half (46%) of the FHRs exhibited a good and stable global functioning trajectory.

CONCLUSIONS

FHRs of major psychiatric disorders show heterogeneous functional decline during development associated with parental diagnosis, polygenic risk loading, and childhood trauma.

Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets

Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate method to detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.

Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder

Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data, identify a more phenotypically homogeneous set of subjects, and perform a genome-wide association-study (GWAS) and subsequent secondary analyses restricted to this homogeneous subset. Because of the variability in how phenotypic data was collected by the various BD studies over the years, homogenizing the phenotypic data is a challenging task, and so is replication. As members of the Psychiatric Genomics Consortium (PGC), we have access to the raw genotypes of 18,711 BD cases and 29,738 controls. This amount of data makes it possible for us to set aside the intricacies of phenotype and allow the genetic data itself to determine which subjects define a homogeneous genetic subgroup. In this paper, we leverage recent advances in heterogeneity analysis to look for distinct homogeneous genetic BD subgroups (or biclusters) that manifest the broad phenotype we think of as Bipolar Disorder. As our data was generated by 27 studies and genotyped on a variety of platforms (OMEX, Affymetrix, Illumina), we use a biclustering algorithm capable of covariate-correction. Covariate-correction is critical if we wish to distinguish disease-related signals from those which are a byproduct of ancestry, study or genotyping platform. We rely on the raw genotyped data and do not include any data generated through imputation. We first apply this covariate-corrected biclustering algorithm to a cohort of 2524 BD cases and 4106 controls from the Bipolar Disease Research Network (BDRN: OMEX). We find evidence of genetic heterogeneity delineating a statistically significant bicluster comprising a subset of BD cases which exhibits a disease-specific pattern of differential-expression across a subset of SNPs. This pattern replicates across the remaining data-sets collected by the PGC containing 5781/8289 (OMEX), 3581/7591 (Illumina), and 6825/9752(Affymetrix) cases/controls, respectively. This bicluster includes subjects diagnosed with bipolar type-I, as well as subjects diagnosed with bipolar type-II. However, the bicluster is enriched for bipolar type-I over type-II and may represent a collection of correlated genetic risk-factors. By investigating the bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluster can be leveraged to eliminate noise from our GWAS analyses and improve not only risk prediction, particularly when using only a relatively small subset (e.g., ~ 1%) of the available SNPs, but also SNP replication. Though our primary focus is only the analysis of disease-related signal, we also identify replicable control-related heterogeneity. Covariate-corrected biclustering of raw genetic data appears to be a promising route for untangling heterogeneity and identifying replicable homogeneous genetic subtypes of complex disease. It may also prove useful in identifying protective effects within the control group. This approach circumvents some of the difficulties presented by subphenotype data collected by meta-analyses or 23 andMe, e.g., missingness, assessment variation, and reliance on self-report.

Bipolar disorders and schizophrenia: discrete disorders?

Background

With similarities in heritability, neurobiology and symptomatology, the question has been raised whether schizophrenia and bipolar disorder are truly distinctive disorders or belong to a continuum. This narrative review summarizes common and distinctive findings from genetics, neuroimaging, cognition and clinical course that may help to solve this ethiopathogenetic puzzle.

Methods

The authors conducted a literature search for papers listed in PubMed and Google Scholar, using the search terms "schizophrenia" and "bipolar disorder" combined with different terms such as "genes", "neuroimaging studies", "phenomenology differences", "cognition", "epidemiology". Articles were considered for inclusion if they were written in English or Spanish, published as full articles, if they compared subjects with schizophrenia and bipolar disorder, or subjects with either disorder with healthy controls, addressing differences between groups.

Results

Several findings support the hypothesis that schizophrenia and bipolar disorder are discrete disorders, yet some overlapping of findings exists. The evidence for heritability of both SZ and BD is obvious, as well as the environmental impact on individual manifestations of both disorders. Neuroimaging studies support subtle differences between disorders, it appears to be rather a pattern of irregularities than an unequivocally unique finding distinguishing schizophrenia from bipolar disorder. The cognitive profile displays differences between disorders in certain domains, such as premorbid intellectual functioning and executive functions. Finally, the timing and trajectory of cognitive impairment in both disorders also differs.

Conclusion

The question whether SZ and BD belong to a continuum or are separate disorders remains a challenge for further research. Currently, our research tools may be not precise enough to carve out distinctive, unique and undisputable differences between SZ and BD, but current evidence favors separate disorders. Given that differences are subtle, a way to overcome diagnostic uncertainties in the future could be the application of artificial intelligence based on BigData.

Limitations

Despite the detailed search, this article is not a full and complete review of all available studies on the topic. The search and selection of papers was also limited to articles in English and Spanish. Selection of papers and conclusions may be biased by the personal view and clinical experience of the authors.

Altered neuroepithelial morphogenesis and migration defects in iPSC-derived cerebral organoids and 2D neural stem cells in familial bipolar disorder

Bipolar disorder (BD) is a severe mental illness that can result from neurodevelopmental aberrations, particularly in familial BD, which may include causative genetic variants. In the present study, we derived cortical organoids from BD patients and healthy (control) individuals from a clinically dense family in the Indian population. Our data reveal that the patient organoids show neurodevelopmental anomalies, including organisational, proliferation and migration defects. The BD organoids show a reduction in both the number of neuroepithelial buds/cortical rosettes and the ventricular zone size. Additionally, patient organoids show a lower number of SOX2-positive and EdU-positive cycling progenitors, suggesting a progenitor proliferation defect. Further, the patient neurons show abnormal positioning in the ventricular/intermediate zone of the neuroepithelial bud. Transcriptomic analysis of control and patient organoids supports our cellular topology data and reveals dysregulation of genes crucial for progenitor proliferation and neuronal migration. Lastly, time-lapse imaging of neural stem cells in 2D in vitro cultures reveals abnormal cellular migration in BD samples. Overall, our study pinpoints a cellular and molecular deficit in BD patient-derived organoids and neural stem cell cultures.

Mechanism of electroconvulsive therapy in schizophrenia: a bioinformatics analysis study of RNA-seq data

OBJECTIVE

The molecular mechanism of electroconvulsive therapy (ECT) for schizophrenia remains unclear. The aim of this study was to uncover the underlying biological mechanisms of ECT in the treatment of schizophrenia using a transcriptional dataset.

METHODS

The peripheral blood mRNA sequencing data of eight patients (before and after ECT) and eight healthy controls were analyzed by integrated co-expression network analysis and the differentially expressed genes were analyzed by cluster analysis. Gene set overlap analysis was performed using the hypergeometric distribution of phypfunction in R. Associations of these gene sets with psychiatric disorders were explored. Tissue-specific enrichment analysis, gene ontology enrichment analysis, and protein-protein interaction enrichment analysis were used for gene set organization localization and pathway analysis.

RESULTS

We found the genes of the green-yellow module were significantly associated with the effect of ECT treatment and the common gene variants of schizophrenia ( P  = 0.0061; family-wise error correction). The genes of the green-yellow module are mainly enriched in brain tissue and mainly involved in the pathways of neurotrophin, mitogen-activated protein kinase and long-term potentiation.

CONCLUSION

Genes associated with the efficacy of ECT were predominantly enriched in neurotrophin, mitogen-activated protein kinase and long-term potentiation signaling pathways.

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.

Unravelling the complex causal effects of substance use behaviours on common diseases

BACKGROUND

Substance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake.

METHODS

In this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects.

RESULTS

We show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake ( b ̂ G W A S = - 0.091 , s . e . = 0.007 , P = 4.90 × 10 - 35 ) but positive on moderate tea intake ( b ̂ G W A S = 0.034 , s . e . = 0.006 , P = 3.40 × 10 - 8 ) , compared to the non-tea-drinkers.

CONCLUSION

Our study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.

Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Genetic risk for hospitalization of African American patients with severe mental illness reveals HLA loci

Background

Mood disorders such as major depressive and bipolar disorders, along with posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and other psychotic disorders, constitute serious mental illnesses (SMI) and often lead to inpatient psychiatric care for adults. Risk factors associated with increased hospitalization rate in SMI (H-SMI) are largely unknown but likely involve a combination of genetic, environmental, and socio-behavioral factors. We performed a genome-wide association study in an African American cohort to identify possible genes associated with hospitalization due to SMI (H-SMI).

Methods

Patients hospitalized for psychiatric disorders (H-SMI; n=690) were compared with demographically matched controls (n=4467). Quality control and imputation of genome-wide data were performed following the Psychiatric Genetic Consortium (PGC)-PTSD guidelines. Imputation of the Human Leukocyte Antigen (HLA) locus was performed using the HIBAG package.

Results

Genome-wide association analysis revealed a genome-wide significant association at 6p22.1 locus in the ubiquitin D (UBD/FAT10) gene (rs362514, p=9.43x10-9) and around the HLA locus. Heritability of H-SMI (14.6%) was comparable to other psychiatric disorders (4% to 45%). We observed a nominally significant association with 2 HLA alleles: HLA-A*23:01 (OR=1.04, p=2.3x10-3) and HLA-C*06:02 (OR=1.04, p=1.5x10-3). Two other genes (VSP13D and TSPAN9), possibly associated with immune response, were found to be associated with H-SMI using gene-based analyses.

Conclusion

We observed a strong association between H-SMI and a locus that has been consistently and strongly associated with SCZ in multiple studies (6p21.32-p22.1), possibly indicating an involvement of the immune system and the immune response in the development of severe transdiagnostic SMI.

Brain eQTLs of European, African American, and Asian ancestry improve interpretation of schizophrenia GWAS

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet, the majority of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n=158), Europeans (EUR, n=408), and East Asians (EAS, n=217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs (representing ∼17% of all eQTLs pairs) linked to 1,276 genes (about 10% of all eGenes) and 198,769 SNPs (approximately 16% of all eSNPs) were identified only in the non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare (MAF < 0.05) in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified seven new risk genes ( SFXN2 , RP11-282018.3 , CYP17A1 , VPS37B , DENR , FTCDNL1 , and NT5DC2 ), and three potential novel regulatory variants in known risk genes ( CNNM2 , C12orf65 , and MPHOSPH9 ) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of novel risk genes in SCZ.

BDNF, DRD4, and HTR2A Gene Allele Frequency Distribution and Association with Mental Illnesses in the European Part of Russia

The prevalence of mental disorders and how they are diagnosed represent some of the major problems in psychiatry. Modern genetic tools offer the potential to reduce the complications concerning diagnosis. However, the vast genetic diversity in the world population requires a closer investigation of any selected populations. In the current research, four polymorphisms, namely rs6265 in BDNF, rs10835210 in BDNF, rs6313 in HTR2A, and rs1800955 in DRD4, were analyzed in a case-control study of 2393 individuals (1639 patients with mental disorders (F20-F29, F30-F48) and 754 controls) from the European part of Russia using the TaqMan SNP genotyping method. Significant associations between rs6265 BDNF and rs1800955 DRD4 and mental impairments were detected when comparing the general group of patients with mental disorders (without separation into diagnoses) to the control group. Associations of rs6265 in BDNF, rs1800955 in DRD4, and rs6313 in HTR2A with schizophrenia in patients from the schizophrenia group separately compared to the control group were also found. The obtained results can extend the concept of a genetic basis for mental disorders in the Russian population and provide a basis for the future improvement in psychiatric diagnostics.

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.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.