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Background: Children of parents with a severe mental illness have an increased risk of developing a lifetime mental illness. We aimed to compare the effects of a preventive family‐based intervention, VIA Family, with treatment as usual (TAU) on these children's global functioning. Methods: Between 2017 and 2021, we conducted a pragmatic, rater‐blinded, two‐arm parallel‐group superiority trial in Denmark. Families with at least one child aged 6–12 years and at least one biological parent with schizophrenia spectrum disorder, bipolar disorder, or recurrent major or moderate depression were included. We randomly allocated 95 families with their 113 children to VIA Family or TAU (ratio 1:1). VIA Family was individually tailored and based on case management. The intervention included options for psychoeducation, parental support, and treatment for emerging child psychiatric symptoms. Blinded raters assessed children and their families at baseline and after 18 months. The primary outcome was the difference in change between groups at end‐of‐treatment in daily global functioning measured with the Children's Global Assessment Scale. Secondary outcomes were emotional and behavioral problems and days absent from school. We analyzed data blinded to allocation. Results: At post‐intervention, differences in mean change from baseline between VIA Family and TAU were non‐significant (CGAS: −1.20, 95% CI = −6.61; 4.21, p = 0.66), as were the differences on the secondary and exploratory outcomes. Conclusion: Contrary to our hypothesis, we did not find a superior effect of VIA Family compared with TAU. The short follow‐up period and large sample heterogeneity might explain the null findings. Therefore, a possible long‐term, preventive treatment effect has yet to be explored. [ABSTRACT FROM AUTHOR]

Background: Williams syndrome (WS), a rare neurodevelopmental disorder caused by hemizygous deletion of ~ 25 genes from chromosomal band 7q11.23, affords an exceptional opportunity to study associations between a well-delineated genetic abnormality and a well-characterized neurobehavioral profile. Clinically, WS is typified by increased social drive (often termed "hypersociability") and severe visuospatial construction deficits. Previous studies have linked visuospatial problems in WS with alterations in the dorsal visual processing stream. We investigated the impacts of hemideletion and haplotype variation of LIMK1, a gene hemideleted in WS and linked to neuronal maturation and migration, on the structure and function of the dorsal stream, specifically the intraparietal sulcus (IPS), a region known to be altered in adults with WS. Methods: We tested for IPS structural and functional changes using longitudinal MRI in a developing cohort of children with WS (76 visits from 33 participants, compared to 280 visits from 94 typically developing age- and sex-matched participants) over the age range of 5–22. We also performed MRI studies of 12 individuals with rare, shorter hemideletions at 7q11.23, all of which included LIMK1. Finally, we tested for effects of LIMK1 variation on IPS structure and imputed LIMK1 expression in two independent cohorts of healthy individuals from the general population. Results: IPS structural (p < 10−4 FDR corrected) and functional (p <.05 FDR corrected) anomalies previously reported in adults were confirmed in children with WS, and, consistent with an enduring genetic mechanism, were stable from early childhood into adulthood. In the short hemideletion cohort, IPS deficits similar to those in WS were found, although effect sizes were smaller than those found in WS for both structural and functional findings. Finally, in each of the two general population cohorts stratified by LIMK1 haplotype, IPS gray matter volume (pdiscovery < 0.05 SVC, preplication = 0.0015) and imputed LIMK1 expression (pdiscovery = 10−15, preplication = 10−23) varied according to LIMK1 haplotype. Conclusions: This work offers insight into neurobiological and genetic mechanisms responsible for the WS phenotype and also more generally provides a striking example of the mechanisms by which genetic variation, acting by means of molecular effects on a neural intermediary, can influence human cognition and, in some cases, lead to neurocognitive disorders. [ABSTRACT FROM AUTHOR]

As an important part in international disease, mental disorders seriously damage human health and social stability, which show the complex pathogenesis and increasing incidence year by year. In order to analyze the pathogenesis of mental disorders as soon as possible and to look for the targeted drug treatment for psychiatric diseases, a more reasonable animal model is imperious demands. Benefiting from its high homology with the human genome, its brain tissue is highly similar to that of humans, and it is easy to realize whole-body optical visualization and high-throughput screening; zebrafish stands out among many animal models of mental disorders. Here, valuable qualified zebrafish mental disorders models could be established through behavioral test and sociological analysis, which are simulated to humans, and combined with molecular analyses and other detection methods. This review focuses on the advances in the zebrafish model to simulate the human mental disorders; summarizes the various behavioral characterization means, the use of equipment, and operation principle; sums up the various mental disorder zebrafish model modeling methods; puts forward the current challenges and future development trend, which is to contribute the theoretical supports for the exploration of the mechanisms and treatment strategies of mental disorders. [ABSTRACT FROM AUTHOR]

Objective: The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants., Methods: This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used., Results: Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD., Conclusions: By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined., (© 2023 University College London (UCL) and John Wiley & Sons Ltd.)

The pathogenesis of autism spectrum disorder (ASD) is not well understood, especially in terms of immunity and inflammation, and there are currently no early diagnostic or treatment methods. In this study, we obtained six existing Gene Expression Omnibus transcriptome datasets from the blood of ASD patients. We performed functional enrichment analysis, PPI analysis, CIBERSORT algorithm, and Spearman correlation analysis, with a focus on expression profiling in hub genes and immune cells. We validated that monocytes and nonclassical monocytes were upregulated in the ASD group using peripheral blood (30 children with ASD and 30 age and sex-matched typically developing children) using flow cytometry. The receiver operating characteristic curves (PSMC4 and ALAS2) and analysis stratified by ASD severity (LIlRB1 and CD69) showed that they had predictive value using the “training” and verification groups. Three immune cell types – monocytes, M2 macrophages, and activated dendritic cells – had different degrees of correlation with 15 identified hub genes. In addition, we analyzed the miRNA-mRNA network and agents-gene interactions using miRNA databases (starBase and miRDB) and the DSigDB database. Two miRNAs (miR-342-3p and miR-1321) and 23 agents were linked with ASD. These findings suggest that dysregulation of the immune system may contribute to ASD development, especially dysregulation of monocytes and monocyte-derived cells. ASD-related hub genes may serve as potential predictors for ASD, and the potential ASD-related miRNAs and agents identified here may open up new strategies for the prevention and treatment of ASD. [ABSTRACT FROM AUTHOR]

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain‐wide similarity to the expected SMI patterns derived from meta‐analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome‐wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI‐MDD (Cohen's d = 0.20, p = 1 × 10−23) and PRS‐MDD (d = 0.17, p = 1 × 10−15) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10−5 and.009, respectively) and PRS (d = 0.57 and 1.34; p =.002 and.002, respectively). Elevated RVI‐SSD were replicated in an independent sample (d = 0.53, p = 5 × 10−5). RVI‐MDD and RVI‐SSD but not RVI‐BD were associated with childhood adversity (p <.01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10−5) in six out of seven domains and showed specificity with disorder‐associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI. [ABSTRACT FROM AUTHOR]

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions. [ABSTRACT FROM AUTHOR]

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent. We integrated and analyzed datasets related to major depressive disorder (MDD), bipolar disorder (BIP), and schizophrenia (SCZ) from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis (MTAG). MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD, from 909,061 to 1,450,972 for BIP, and from 856,677 to 940,613 for SCZ. We discovered 7, 32, and 43 novel lead single nucleotide polymorphisms (SNPs) and 1, 6, and 3 novel causal SNPs for MDD, BIP, and SCZ, respectively, after fine-mapping. We identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three disorders. We performed marker analysis of genomic annotation (MAGMA) and Hi-C-coupled MAGMA (H-MAGMA) based gene-set analysis and identified 101 genes associated with the three disorders, which were enriched in the regulation of postsynaptic membranes, postsynaptic membrane dopaminergic synapses, and Notch signaling pathway. Next, we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on SCZ. Overall, we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders, providing an alternative approach to integrate publicly available summary data. [ABSTRACT FROM AUTHOR]

Background: Copy number variants in coding and noncoding genomic regions have been implicated as risk factor for schizophrenia (SCZ). Rare duplications of the RB1CC1 gene were found enriched in SCZ patients. Considering that the effect of such duplications on RB1CC1 expression has never been evaluated and partial gene duplications of RB1CC1 have also been reported in SCZ patients, it is unclear whether the pathogenesis is mediated by haploinsufficiency rather than genuine overexpression of the gene. Methods and Results: We studied a patient with schizophrenia, suicidality, and obesity, who carried a de novo RB1CC1 complete duplication, as assessed by high‐resolution array‐CGH. Molecular breakpoint cloning allowed to identify nonhomologous end joining (NHEJ) as driving mechanism in this rearrangement. On the contrary, trio‐based whole‐exome sequencing excluded other potential causative variants related to the phenotype. Functional assays showed significant overexpression of RB1CC1 in the peripheral blood lymphocytes of the proband compared to control subjects, suggesting overdosage as leading mechanism in SCZ pathophysiology. Conclusion: We hypothesized a pathogenetic model that might explain the correlation between RB1CC1 overexpression and schizophrenia by altering different cell signaling pathways, including autophagy, a promising therapeutic target for schizophrenic patients. [ABSTRACT FROM AUTHOR]

Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported. [ABSTRACT FROM AUTHOR]

Williams syndrome (WS) is a clinical condition, involving cognitive deficits and an uneven language profile, which has been the object of intense inquiry over the last decades. Although WS results from the hemideletion of around two dozen genes in chromosome 7, no gene has yet been probed to account for, or contribute significantly to, the language problems exhibited by the affected people. In this paper we have relied on gene expression profiles in the peripheral blood of WS patients obtained by microarray analysis and show that several robust candidates for language disorders and/or for language evolution in the species, all of them located outside the hemideleted region, are up- or downregulated in the blood of subjects with WS. Most of these genes play a role in the development and function of brain areas involved in language processing, which exhibit structural and functional anomalies in people with this condition. Overall, these genes emerge as robust candidates for language dysfunction in WS. [ABSTRACT FROM AUTHOR]

The precise expression of genes in time and space during embryogenesis is largely influenced by communication between enhancers and promoters, which is propagated and governed by the physical proximity of these elements in the nucleus. Here, we review how chromatin domains organize the genome by guiding enhancers to their target genes thereby preventing non-specific interactions with other neighboring regions. We also discuss the dynamics of chromatin interactions between enhancers and promoters, as well as the consequent changes in gene expression, that occur in pluripotent cells and during development. Finally, we evaluate how genomic changes such as deletions, inversions and duplications affect 3D chromatin configuration overall and lead to ectopic enhancer-promoter contacts, and thus gene misexpression, which can contribute to abnormal development and disease. [ABSTRACT FROM AUTHOR]

Williams syndrome (WS) is a multisystem neurodevelopmental disorder caused by a de novo hemizygous deletion of ~26 genes from chromosome 7q11.23, among them the general transcription factor II-I (GTF2I). By studying a novel murine model for the hypersociability phenotype associated with WS, we previously revealed surprising aberrations in myelination and cell differentiation properties in the cortices of mutant mice compared to controls. These mutant mice had selective deletion of Gtf2i in the excitatory neurons of the forebrain. Here, we applied diffusion magnetic resonance imaging and fiber tracking, which showed a reduction in the number of streamlines in limbic outputs such as the fimbria/fornix fibers and the stria terminalis, as well as the corpus callosum of these mutant mice compared to controls. Furthermore, we utilized next-generation sequencing (NGS) analysis of cortical small RNAs' expression (RNA-Seq) levels to identify altered expression of microRNAs (miRNAs), including two from the miR-34 cluster, known to be involved in prominent processes in the developing nervous system. Luciferase reporter assay confirmed the direct binding of miR-34c-5p to the 3'UTR of PTPRU—a gene involved in neural development that was elevated in the cortices of mutant mice relative to controls. Moreover, we found an age-dependent variation in the expression levels of doublecortin (Dcx)—a verified miR-34 target. Thus, we demonstrate the substantial effect a single gene deletion can exert on miRNA regulation and brain structure, and advance our understanding and, hopefully, treatment of WS. [ABSTRACT FROM AUTHOR]

The advent of next-generation sequencing (NGS) is heavily changing both the diagnosis of human conditions and basic biological research. It is now possible to dig deep inside the genome of hundreds of thousands or even millions of people and find both common and rare genomic variants and to perform detailed phenotypic characterizations of both physiological organs and experimental models. Recent years have seen the introduction of multiple techniques using NGS to profile transcription, DNA and chromatin modifications, protein binding, etc., that are now allowing us to profile cells in bulk or even at a single-cell level. Although rare and ultra-rare diseases only affect a few people, each of these diseases represent scholarly cases from which a great deal can be learned about the pathological and physiological function of genes, pathways, and mechanisms. Therefore, for rare diseases, state-of-the-art investigations using NGS have double valence: their genomic cause (new variants) and the characterize the underlining the mechanisms associated with them (discovery of gene function) can be found. In a non-exhaustive manner, this review will outline the main usage of NGS-based techniques for the diagnosis and characterization of neurodevelopmental disorders (NDDs), under whose umbrella many rare and ultra-rare diseases fall. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent functional psychosis characterized by clinical behavioural symptoms and underlying abnormalities in brain function. Genome-wide association studies (GWAS) of schizophrenia have revealed many loci that do not directly identify processes disturbed in the disease. For this reason, the development of cellular models containing SZ-associated variations has become a focus in the post-GWAS research era. The application of revolutionary clustered regularly interspaced palindromic repeats CRISPR/Cas9 gene-editing tools, along with recently developed technologies for cultivating brain organoids in vitro, have opened new perspectives for the construction of these models. In general, cellular models are intended to unravel particular biological phenomena. They can provide the missing link between schizophrenia-related phenotypic features (such as transcriptional dysregulation, oxidative stress and synaptic dysregulation) and data from pathomorphological, electrophysiological and behavioural studies. The objectives of this review are the systematization and classification of cellular models of schizophrenia, based on their complexity and validity for understanding schizophrenia-related phenotypes. [ABSTRACT FROM AUTHOR]

The genetic architecture of complex traits is multifactorial. Genome-wide association studies (GWASs) have identified risk loci for complex traits and diseases that are disproportionately located at the non-coding regions of the genome. On the other hand, we have just begun to understand the regulatory roles of the non-coding genome, making it challenging to precisely interpret the functions of non-coding variants associated with complex diseases. Additionally, the epigenome plays an active role in mediating cellular responses to fluctuations of sensory or environmental stimuli. However, it remains unclear how exactly non-coding elements associate with epigenetic modifications to regulate gene expression changes and mediate phenotypic outcomes. Therefore, finer interrogations of the human epigenomic landscape in associating with non-coding variants are warranted. Recently, chromatin-profiling techniques have vastly improved our understanding of the numerous functions mediated by the epigenome and DNA structure. Here, we review various chromatin-profiling techniques, such as assays of chromatin accessibility, nucleosome distribution, histone modifications, and chromatin topology, and discuss their applications in unraveling the brain epigenome and etiology of complex traits at tissue homogenate and single-cell resolution. These techniques have elucidated compositional and structural organizing principles of the chromatin environment. Taken together, we believe that high-resolution epigenomic and DNA structure profiling will be one of the best ways to elucidate how non-coding genetic variations impact complex diseases, ultimately allowing us to pinpoint cell-type targets with therapeutic potential. [ABSTRACT FROM AUTHOR]