Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders.

N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.

Association between alcohol consumption and sleep traits: observational and mendelian randomization studies in the UK biobank.

Previous studies have shown that excessive alcohol consumption is associated with poor sleep. However, the health risks of light-to-moderate alcohol consumption in relation to sleep traits (e.g., insomnia, snoring, sleep duration and chronotype) remain undefined, and their causality is still unclear in the general population. To identify the association between alcohol consumption and multiple sleep traits using an observational and Mendelian randomization (MR) design. Observational analyses and one-sample MR (linear and nonlinear) were performed using clinical and individual-level genetic data from the UK Biobank (UKB). Two-sample MR was assessed using summary data from genome-wide association studies from the UKB and other external consortia. Phenotype analyses were externally validated using data from the National Health and Nutrition Examination Survey (2017-2018). Data analysis was conducted from January 2022 to October 2022. The association between alcohol consumption and six self-reported sleep traits (short sleep duration, long sleep duration, chronotype, snoring, waking up in the morning, and insomnia) were analysed. This study included 383,357 UKB participants (mean [SD] age, 57.0 [8.0] years; 46% male) who consumed a mean (SD) of 9.0 (10.0) standard drinks (one standard drink equivalent to 14 g of alcohol) per week. In the observational analyses, alcohol consumption was significantly associated with all sleep traits. Light-moderate-heavy alcohol consumption was linearly linked to snoring and the evening chronotype but nonlinearly associated with insomnia, sleep duration, and napping. In linear MR analyses, a 1-SD (14 g) increase in genetically predicted alcohol consumption was associated with a 1.14-fold (95% CI, 1.07-1.22) higher risk of snoring (P < 0.001), a 1.28-fold (95% CI, 1.20-1.37) higher risk of evening chronotype (P < 0.001) and a 1.24-fold (95% CI, 1.13-1.36) higher risk of difficulty waking up in the morning (P < 0.001). Nonlinear MR analyses did not reveal significant results after Bonferroni adjustment. The results of the two-sample MR analyses were consistent with those of the one-sample MR analyses, but with a slightly attenuated overall estimate. Our findings suggest that even low levels of alcohol consumption may affect sleep health, particularly by increasing the risk of snoring and evening chronotypes. The negative effects of alcohol consumption on sleep should be made clear to the public in order to promote public health.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data.

BACKGROUND: Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes. METHODS: In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored. RESULTS: Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes. CONCLUSIONS: The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

[Nursing Experience Caring for a Premature Infant With Bronchopulmonary Dysplasia].

A nursing experience supporting parents experiencing anxiety related to their 26+2 weeks preterm infant on continued ventilator assistance at home due to bronchopulmonary dysplasia is described in this article. Data were collected from March 21st to June 1st, 2021 via observation, interviews, clinical care, medical record reviews, and discharge preparation services. A holistic nursing assessment identified the main health issues as: gas exchange disorder, inefficient infant feeding patterns, and caregiver role stress. The parents were encouraged to participate in care activities, create individualized discharge plans, and view health education videos and caregiving skill demonstrations. Kangaroo care, comfortable positioning and soothing techniques were used to stabilize the emotions of the infant and to strengthen the parent-child bond. Psychological support was provided to alleviate parental anxiety and to enhance parenting ability and confidence. This experience supports the importance of tailoring clinical care to individual needs, adopting a family-centered approach, assessing family interactions, and making early preparations to obtain appropriate continuous care after discharge to ensure proper continuum of care.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Ocular and neural genes jointly regulate the visuospatial working memory in ADHD children.

OBJECTIVE: Working memory (WM) deficits have frequently been linked to attention deficit hyperactivity disorder (ADHD). Despite previous studies suggested its high heritability, its genetic basis, especially in ADHD, remains unclear. The current study aimed to comprehensively explore the genetic basis of visual-spatial working memory (VSWM) in ADHD using wide-ranging genetic analyses. METHODS: The current study recruited a cohort consisted of 802 ADHD individuals, all met DSM-IV ADHD diagnostic criteria. VSWM was assessed by Rey-Osterrieth complex figure test (RCFT), which is a widely used psychological test include four memory indexes: detail delayed (DD), structure delayed (SD), structure immediate (SI), detail immediate (DI). Genetic analyses were conducted at the single nucleotide polymorphism (SNP), gene, pathway, polygenic and protein network levels. Polygenic Risk Scores (PRS) were based on summary statistics of various psychiatric disorders, including ADHD, autism spectrum disorder (ASD), major depressive disorder (MDD), schizophrenia (SCZ), obsessive compulsive disorders (OCD), and substance use disorder (SUD). RESULTS: Analyses at the single-marker level did not yield significant results (5E-08). However, the potential signals with P values less than E-05 and their mapped genes suggested the regulation of VSWM involved both ocular and neural system related genes, moreover, ADHD-related genes were also involved. The gene-based analysis found RAB11FIP1, whose encoded protein modulates several neurodevelopment processes and visual system, as significantly associated with DD scores (P = 1.96E-06, Padj = 0.036). Candidate pathway enrichment analyses (N = 53) found that forebrain neuron fate commitment significantly enriched in DD (P = 4.78E-04, Padj = 0.025), and dopamine transport enriched in SD (P = 5.90E-04, Padj = 0.031). We also observed a significant negative relationship between DD scores and ADHD PRS scores (P = 0.0025, Empirical P = 0.048). CONCLUSIONS: Our results emphasized the joint contribution of ocular and neural genes in regulating VSWM. The study reveals a shared genetic basis between ADHD and VSWM, with GWAS indicating the involvement of ADHD-related genes in VSWM. Additionally, the PRS analysis identifies a significant relationship between ADHD-PRS and DD scores. Overall, our findings shed light on the genetic basis of VSWM deficits in ADHD, and may have important implications for future research and clinical practice.

Identification of novel risk loci with shared effects on alcoholism, heroin, and methamphetamine dependence.

Different substance dependences have common effects on reward pathway and molecular adaptations, however little is known regarding their shared genetic factors. We aimed to identify the risk genetic variants that are shared for substance dependence (SD). First, promising genome-wide significant loci were identified from 3296 patients (521 alcoholic/1026 heroin/1749 methamphetamine) vs 2859 healthy controls and independently replicated using 1954 patients vs 1904 controls. Second, the functional effects of promising variants on gene expression, addiction characteristics, brain structure (gray and white matter), and addiction behaviors in addiction animal models (chronic administration and self-administration) were assessed. In addition, we assessed the genetic correlation among the three SDs using LD score regression. We identified and replicated three novel loci that were associated with the common risk of heroin, methamphetamine addiction, and alcoholism: ANKS1B rs2133896 (Pmeta = 3.60 × 10-9), AGBL4 rs147247472 (Pmeta = 3.40 × 10-12), and CTNNA2 rs10196867 (Pmeta = 4.73 × 10-9). Rs2133896 in ANKS1B was associated with ANKS1B gene expression and had effects on gray matter of the left calcarine and white matter of the right superior longitudinal fasciculus in heroin dependence. Overexpression of anks1b gene in the ventral tegmental area decreased addiction vulnerability for heroin and methamphetamine in self-administration rat models. Our findings could shed light on the root cause for substance dependence and will be helpful for the development of cost-effective prevention strategies for general addiction disorders.

Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms.

BACKGROUND: Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect. METHODS: Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms. RESULTS: Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01). CONCLUSIONS: Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.

Peripheral cytokine levels and response to antidepressant treatment in depression: a systematic review and meta-analysis.

Predicting antidepressant treatment response has been a clinical challenge for major depressive disorder (MDD). The inflammation hypothesis of depression suggests that cytokines play a key role in the pathophysiology of MDD and alterations in peripheral cytokine levels are associated with antidepressant treatment outcome. Present meta-analysis aimed to examine the association between baseline peripheral cytokine levels and the response to antidepressant treatment and to evaluate whether changes of cytokine levels were associated with the response to antidepressant treatment in patients with MDD. Human-based studies published in any language in peer-reviewed journals were systematically searched from the PubMed, Embase and Web of Science databases, from inception up to October 2018. The search terms included cytokine, depressive disorder and antidepressant and their synonyms. Case-control or case-case studies reporting on levels of IL-1ß, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, CRP, TNF-α, IFN-γ, GM-CSF, MIP-1α, and Eotaxin-1 in patients with MDD based on validated depression scales both before and after antidepressant treatment were included. Of 7408 identified records, 44 studies met inclusion. Standardized mean differences in each cytokine were evaluated, and random-effects meta-analyses were performed. MDD patients who responded to antidepressant treatment had lower baseline IL-8 levels than the nonresponders (Hedge's g = -0.28; 95%CI, -0.43 to -0.13; P = 0.0003; FDR = 0.004). Antidepressant treatment significantly decreased levels of TNF-α (Hedge's g = 0.60; 95%CI, 0.26-0.94; P = 0.0006; FDR = 0.004) only in responders, and responders showed significantly more decreased TNF-α levels compared with nonresponders (P = 0.046). These findings suggested that alterations in peripheral cytokine levels were associated with antidepressant treatment outcomes in MDD. Further investigations are warranted to elucidate sources of heterogeneity and examine the potentiality of using inflammatory cytokines as novel predictive markers for the pharmacological treatment of MDD.

Career choice and influential factors among medical students majoring in psychiatry in China.

BACKGROUND: The undergraduate program of psychiatry has been widely established in recent years to improve the education and recruitment of psychiatrists in China. We aim to investigate the career choice of medical students majoring in psychiatry in China and the influential factors. METHOD: This multicenter study was conducted in 26 medical schools in China from May to October of 2019. Participants included 4610 medical students majoring in psychiatry and 3857 medical students majoring in clinical medicine. Multivariable logistic regression was used to investigate the influential factors of students' choices of psychiatry at matriculation and as a career. RESULTS: 44.08% of psychiatry majored students gave psychiatry as a first choice at matriculation, and 56.67% of them would choose psychiatry as a career, which was in sharp contrast to the proportion of clinical medicine majored students who would choose psychiatry as a career (0.69%). Personal interest (59.61%), suggestions from family members (27.96%), and experiencing mental problems (23.19%) were main reasons for choosing psychiatry major at matriculation. Personal interest (odds ratio [OR] = 2.12, 95% confidence interval [CI] = 1.87-2.40), experiencing a psychiatry clerkship (OR = 1.99, 95% CI = 1.28-3.08), being female (OR = 1.50, 95% CI = 1.30-1.68), experiencing mental problems (OR = 1.33, 95% CI = 1.28-1.56), and suggestions from family members (OR = 1.25, 95% CI = 1.08-1.46) correlated positively with students' choice of psychiatry as career. Students who lacked psychiatry knowledge (OR = 0.49, 95% CI = 0.29-0.85) or chose psychiatry because of lower admission scores (OR = 0.80, 95% CI = 0.63-0.97) were less likely to choose psychiatry as a career. CONCLUSION: More than half of psychiatry majored medical school students planned to choose psychiatry as their career, whereas very few students in the clinic medicine major would make this choice. Increasing students' interest in psychiatry, strengthening psychiatry clerkships, and popularizing psychiatric knowledge are modifiable factors to increase the psychiatry career intention. The extent to which medical students' attitudes toward psychiatry can be changed through medical school education and greater exposure to psychiatry will need further investigation.

Risk factors and an early prediction model for persistent methamphetamine-related psychiatric symptoms.

Methamphetamine (MA)-related psychiatric symptoms (MAP) are serious comorbidities of MA use and result in many social problems such as violence and suicide. We investigated the sociodemographic and genetic risk factors for persistent MAP of MA users (MUs) and constructed an early MAP prediction model. Derivation and replication samples had 1734 and 905 MUs, respectively. Symptom Checklist 90, Childhood Trauma Questionnaire, Attention-Deficit Hyperactivity Disorder (ADHD) Rating Scale-IV, and Social Support Rating Scale were used to assess the past-year prevalence of general MAP and life events retrospectively. Genome-wide association study (GWAS) was used to analyze MAP-related genetic factors. The prediction model was constructed by integrating the risk life events and clinical and genetic features using logistic regression. Of the 2639 MUs, 1293 (48.83%) had past-year MAP. The severity of MA addiction (SMA), childhood trauma, childhood ADHD symptoms, and social support were reliable risk factors for persistent MAP. By integrating these risk factors and the polygenic risk score from GWAS from derivation samples, the area under the curve (AUC) of the predictive model for MAP was 0.754 (95% CI 0.717~0.771). The risk factors and prediction model were also verified in replication samples. In addition, SMA, ADHD, and social support were mediators for the effect of the risk genetic factor on persistent MAP. Our study identified several risk factors for persistent MAP and will be helpful for developing scalable tools for the prevention of persistent and general MAP.

A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations.

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.

Controlled synthesis of metallic iron nanoparticles and their magnetic hyperthermia performance in polyaniline composite nanofibers.

Electrospun magnetic iron/polyaniline nanofibers with applicable heating performance in an AC magnetic field were developed. A new and low-cost method was introduced to synthesize metallic iron (Fe0) nanoparticles with uniform size distribution. The Fe0 nanoparticles were synthesized in an aqueous environment at room temperature with the assistance of polyvinylpyrrolidone and sodium citrate to tailor their particle sizes ranging from 10 to 20 nm. The experimental results showed that regulating the free iron ions present in the solution is critical for obtaining Fe0 nanoparticles with narrow size distribution. The Fe0 nanoparticles were subsequently incorporated with conductive polyaniline (PANI) to fabricate Fe0/PANI/polycaprolactone nanofibers using an electrospinning technique. The resultant composite nanofibers have controlled fiber diameters and also show electrochemical redox properties originating from the PANI polymer. The heating performance test concluded that both eddy current loss from PANI and Neel relaxation loss of magnetic Fe0 nanoparticles can contribute to the power dissipation of the prepared composite nanofibers. The optimal heating performance can be obtained by adjusting the composition of Fe0 nanoparticles and PANI in nanofibers.

Mutations in apoptosis-inducing factor cause X-linked recessive auditory neuropathy spectrum disorder.

BACKGROUND: Auditory neuropathy spectrum disorder (ANSD) is a form of hearing loss in which auditory signal transmission from the inner ear to the auditory nerve and brain stem is distorted, giving rise to speech perception difficulties beyond that expected for the observed degree of hearing loss. For many cases of ANSD, the underlying molecular pathology and the site of lesion remain unclear. The X-linked form of the condition, AUNX1, has been mapped to Xq23-q27.3, although the causative gene has yet to be identified. METHODS: We performed whole-exome sequencing on DNA samples from the AUNX1 family and another small phenotypically similar but unrelated ANSD family. RESULTS: We identified two missense mutations in AIFM1 in these families: c.1352G>A (p.R451Q) in the AUNX1 family and c.1030C>T (p.L344F) in the second ANSD family. Mutation screening in a large cohort of 3 additional unrelated families and 93 sporadic cases with ANSD identified 9 more missense mutations in AIFM1. Bioinformatics analysis and expression studies support this gene as being causative of ANSD. CONCLUSIONS: Variants in AIFM1 gene are a common cause of familial and sporadic ANSD and provide insight into the expanded spectrum of AIFM1-associated diseases. The finding of cochlear nerve hypoplasia in some patients was AIFM1-related ANSD implies that MRI may be of value in localising the site of lesion and suggests that cochlea implantation in these patients may have limited success.

rSNPBase: a database for curated regulatory SNPs.

In recent years, human regulatory SNPs (rSNPs) have been widely studied. Here, we present database rSNPBase, freely available at http://rsnp.psych.ac.cn/, to provide curated rSNPs that analyses the regulatory features of all SNPs in the human genome with reference to experimentally supported regulatory elements. In contrast with previous SNP functional annotation databases, rSNPBase is characterized by several unique features. (i) To improve reliability, all SNPs in rSNPBase are annotated with reference to experimentally supported regulatory elements. (ii) rSNPBase focuses on rSNPs involved in a wide range of regulation types, including proximal and distal transcriptional regulation and post-transcriptional regulation, and identifies their potentially regulated genes. (iii) Linkage disequilibrium (LD) correlations between SNPs were analysed so that the regulatory feature is annotated to SNP-set rather than a single SNP. (iv) rSNPBase provides the spatio-temporal labels and experimental eQTL labels for SNPs. In summary, rSNPBase provides more reliable, comprehensive and user-friendly regulatory annotations on rSNPs and will assist researchers in selecting candidate SNPs for further genetic studies and in exploring causal SNPs for in-depth molecular mechanisms of complex phenotypes.

Applying differentially expressed genes from rodent models of chronic stress to research of stress-related disease: an online database.

OBJECTIVE: To systematically collect differentially expressed genes (DEGs) from rodent models of chronic stress (CS) and apply them to research of stress-related disease. CS is an important environmental factor that may affect numerous complex diseases. Its relevant DEGs identified from rodent models provide valuable information for understanding the mechanisms underlying stress-related diseases. Currently, no suitable data tool have been developed to use such data. METHODS: We systematically searched and reviewed publications in PubMed. CS-DEGs were collected from original studies that reported gene expression statuses in rodent models of CS. CS disease overlapping genes, CS pathways and CS pathway clusters, and CS regulatory elements were analyzed on the basis of CS-DEGs. An online database was developed to store and manage curated CS-DEGs and analyzed data. RESULTS: A total of 2956 CS-DEGs were collected from 195 articles, among which 815 genes are shared among CS and seven stress-related diseases. Nine hundred twenty-seven CS pathway clusters were identified. Three types of CS regulatory elements are predicted for all CS genes. An online database (CS-DEGs), freely available at http://cs.psych.ac.cn, includes and presents CS-DEGs and all analyzed data. CONCLUSIONS: CS-DEGs is the first gene database on CS research. It enables researchers to apply rodent expression data in candidate gene and pathway identification for stress-related disease study.

ADHDgene: a genetic database for attention deficit hyperactivity disorder.

With a worldwide prevalence of ~5%, attention deficit hyperactivity disorder (ADHD) has become one of the most common psychiatric disorders. The polygenetic nature of ADHD indicates that multiple genes jointly contribute to the development of this complex disease. Studies aiming to explore genetic susceptibility of ADHD have been increasing in recent years. There is a growing need to integrate the genetic data from various genetic studies to provide a comprehensive data set and uniform access for convenience of in-depth data mining. So far, there has been no such effort for ADHD. To address the genetic complexity of ADHD, we developed the ADHDgene database by integrating ADHD-related genetic factors by profound literature reading. Based on the data from the literature, extended functional analysis, including linkage disequilibrium analysis, pathway-based analysis and gene mapping were performed to provide new insights into genetic causes of ADHD. Moreover, powerful search tools and a graphical browser were developed to facilitate the navigation of the data and data connections. As the first genetic database for ADHD, ADHDgene aims to provide researchers with a central genetic resource and analysis platform for ADHD and is freely available at http://adhd.psych.ac.cn/.

Novel genetic loci of inhibitory control in ADHD and healthy children and genetic correlations with ADHD.

Cumulative evidence has showed the deficits of inhibitory control in patients with attention deficit hyperactivity disorder (ADHD), which is considered as an endophenotype of ADHD. Genetic study of inhibitory control could advance gene discovery and further facilitate the understanding of ADHD genetic basis, but the studies were limited in both the general population and ADHD patients. To reveal genetic risk variants of inhibitory control and its potential genetic relationship with ADHD, we conducted genome-wide association studies (GWAS) on inhibitory control using three datasets, which included 783 and 957 ADHD patients and 1350 healthy children. Subsequently, we employed polygenic risk scores (PRS) to explore the association of inhibitory control with ADHD and related psychiatric disorders. Firstly, we identified three significant loci for inhibitory control in the healthy dataset, two loci in the case dataset, and one locus in the meta-analysis of three datasets. Besides, we found more risk genes and variants by applying transcriptome-wide association study (TWAS) and conditional FDR method. Then, we constructed a network by connecting the genes identified in our study, leading to the identification of several vital genes. Lastly, we identified a potential relationship between inhibitory control and ADHD and autism by PRS analysis and found the direct and mediated contribution of the identified genetic loci on ADHD symptoms by mediation analysis. In conclusion, we revealed some genetic risk variants associated with inhibitory control and elucidated the benefit of inhibitory control as an endophenotype, providing valuable insights into the mechanisms underlying ADHD.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.