Peripheral Blood Leukocyte RNA-Seq Identifies a Set of Genes Related to Abnormal Psychomotor Behavior Characteristics in Patients with Schizophrenia

Sex-related differences and associated transcriptional signatures in the brain ventricular system and cerebrospinal fluid development in full-term neonates

BACKGROUND

The cerebrospinal fluid (CSF) is known to serve as a unique environment for neurodevelopment, with specific proteins secreted by epithelial cells of the choroid plexus (CP) playing crucial roles in cortical development and cell differentiation. Sex-related differences in the brain in early life have been widely identified, but few studies have investigated the neonatal CSF system and associated transcriptional signatures.

METHODS

This study included 75 full-term neonates [44 males and 31 females; gestational age (GA) = 37-42 weeks] without significant MRI abnormalities from the dHCP (developing Human Connectome Project) database. Deep-learning automated segmentation was used to measure various metrics of the brain ventricular system and CSF. Sex-related differences and relationships with postnatal age were analyzed by linear regression. Correlations between the CP and CSF space metrics were also examined. LASSO regression was further applied to identify the key genes contributing to the sex-related CSF system differences by using regional gene expression data from the Allen Human Brain Atlas.

RESULTS

Right lateral ventricles [2.42 ± 0.98 vs. 2.04 ± 0.45 cm3 (mean ± standard deviation), p = 0.036] and right CP (0.16 ± 0.07 vs. 0.13 ± 0.04 cm3, p = 0.024) were larger in males, with a stronger volume correlation (male/female correlation coefficients r: 0.798 vs. 0.649, p < 1 × 10- 4). No difference was found in total CSF volume, while peripheral CSF (male/female β: 1.218 vs. 1.064) and CP (male/female β: 0.008 vs. 0.005) exhibited relatively faster growth in males. Additionally, the volumes of the lateral ventricular system, third ventricle, peripheral CSF, and total CSF were significantly correlated with their corresponding CP volume (r: 0.362 to 0.799, p < 0.05). DERL2 (Degradation in Endoplasmic Reticulum Protein 2) (r = 0.1319) and MRPL48 (Mitochondrial Large Ribosomal Subunit Protein) (r=-0.0370) were identified as potential key genes associated with sex-related differences in CSF system.

CONCLUSION

Male neonates present larger volumes and faster growth of the right lateral ventricle, likely linked to corresponding CP volume and growth pattern. The downregulation of DERL2 and upregulation of MRPL48 may contribute to these sex-related variations in the CSF system, suggesting a molecular basis for sex-specific brain development.

Care burden among Iranian family caregivers of patients with schizophrenia: the predictive role of quality of life and life satisfaction

Introduction

Caring for patients with schizophrenia poses significant challenges for families. This study examined the relationships between caregiving burden, quality of life, and life satisfaction among family caregivers.

Methods

This cross-sectional study was conducted in 2023-24 in Mashhad, Iran. Family caregivers who provided care to a patient with schizophrenia for at least six months completed the Zarit Burden Inventory to evaluate caregiver burden, the 12-item Short Form Health Survey to assess quality of life, and the Satisfaction with Life Scale to measure life satisfaction. The data were analyzed using multiple linear regression.

Results

A total of 211 family caregivers participated, with a mean age of 48.17 ± 14.98 years, of whom 130 (61.61%) were female. Caregiver burden was associated with caregiver-related factors, such as lower life satisfaction (β = -1.27, p < 0.001, 95% CI = -1.48, -1.06), employment status, where housewives experienced lower caregiving burden than unemployed individuals (p = 0.039, β = -4.4, 95% CI = -8.65, -0.21), and marital status, where singles experienced lower caregiving burden than married individuals (p = 0.001, β = -7.89, 95% CI = -11.88, -3.90). In addition, patient-related factors such as longer duration of illness (p < 0.001, β = 0.42, 95% CI = 0.20, 0.64) and lack of health insurance coverage (compared to having coverage) (p = 0.023, β = 5.10, 95% CI = 0.71, 9.49) were associated with higher caregiver burden. Together, these variables explained 62.9% of the variance in the total care burden score.

Conclusion

The findings of this study showed that the majority of family caregivers of patients with schizophrenia experienced moderate to severe levels of caregiving burden. This burden was associated with lower levels of life satisfaction, employment status, and marital status of the caregiver, as well as longer duration of illness and lack of health insurance coverage for the patient.

A Transcriptomic Signature of Depressive Symptoms in Late Life

Background

Depressive symptoms in late life can impair daily function and accompany cognitive decline. However, the molecular mechanisms that underlie these changes in the brain remain poorly understood.

Methods

Differential expression analysis was performed on bulk-tissue RNA sequencing data generated from dorsolateral prefrontal cortex samples of elderly participants in ROS/MAP (Religious Orders Study and Memory and Aging Project; N = 998, mean age at death = 89.7 years). Bulk tissue RNA sequencing was analyzed against depressive symptoms measured prior to death, controlling for Alzheimer's disease neuropathology, medication status, and lifestyle factors. Sex-stratified models were also tested.

Results

Increased abundance of the Prader-Willi syndrome-associated gene PWAR1 (corrected p = 5.47 × 10-3) and CTDSPL2 (corrected p = .03) were associated with a higher burden of depressive symptoms in the combined sample. An additional 14 genes showed suggestive associations, including several with known links to neuropsychiatric illness (e.g., ACVR2B-AS1, COL19A1). Functional enrichment analysis revealed downregulation of aerobic metabolism and upregulation of both amino acid catabolism and DNA modification processes. Differential expression signatures were poorly correlated between males and females (Pearson r = 0.12; 95% CI, 0.10 to 0.13), and only the male group showed independently significant differential expression. Little overlap was found with previously published analyses of major depressive disorder.

Conclusions

Building on recently published single-nucleus profiling, we present the largest-ever study of transcriptomic correlates of depressive symptoms in late life, revealing new insights into sex-specific regulators. PWAR1 and CTDSPL2 were identified as putative markers of late-life depression in the dorsolateral prefrontal cortex and warrant further study.

Methylation of SSTR4 promoter region in multiple mental health disorders

The existence of a shared genetic basis for mental disorders has long been documented, yet research on whether acquired epigenetic modifications exhibit common alterations across diseases is limited. Previous studies have found that abnormal methylation of cg14631053 at the SSTR4 promoter region mediates the onset of alcohol use disorder. However, whether aberrant methylation of the SSTR4 gene promoter is involved in other mental health disorders remains unclear. In this study, leveraging publicly available data, we identified that changes in methylation of cg14631053 from the SSTR4 promoter region are involved in the development of bipolar disorder and schizophrenia. Furthermore, the direction of methylation changes in the SSTR4 promoter region is disease-specific: hypomethylation is associated with the onset of bipolar disorder and schizophrenia, rather than major depressive disorder. Methylation levels of cg14631053 correlate with chronological age, a correlation that can be disrupted in patients with mental health disorders including schizophrenia and bipolar disorder. In conclusion, SSTR4 promoter methylation may serve as a marker for identifying bipolar disorder and schizophrenia, providing insights into a transdiagnostic mechanism for precision medicine in the future.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Lymphoblast transcriptome analysis in 22q11.2 deletion syndrome individuals with schizophrenia-spectrum disorder

OBJECTIVES

22q11.2 deletion is the most prominent risk factor for schizophrenia (SZ). The aim of the present study was to identify unique transcriptome profile for 22q11.2 deletion syndrome (DS)-related SZ-spectrum disorder (SZ-SD).

METHODS

We performed RNA-Seq screening in lymphoblasts collected from 20 individuals with 22q11.2DS (10 men and 10 women, four of each sex with SZ-SD and six with no psychotic disorders (Np)).

RESULTS

Sex effect in RNA-Seq descriptive analysis led to separating the analyses between men and women. In women, only one differentially expressed gene (DEG), HLA-DQA2, was associated with SZ-SD. In men, 48 DEGs (adjp < 0.05) were found to be associated with SZ-SD. Ingenuity pathway analysis of top 85 DEGs (p < 4.66E - 04) indicated significant enrichment for immune-inflammatory response (IIR) and neuro-inflammatory signalling pathways. Additionally, NFATC2, IFNG, IFN-alpha, STAT1 and IL-4 were identified as upstream regulators. Co-expression network analysis revealed the contribution of endoplasmic reticulum protein processing and N-Glycan biosynthesis. These findings indicate dysregulation of IIR and post-translational protein modification processes in individuals with 22q11.2DS-related SZ-SD.

CONCLUSIONS

Candidate pathways and upstream regulators may serve as novel biomarkers and treatment targets for SZ. Future transcriptome studies, including larger samples and proteomic analysis, are needed to substantiate our findings.

Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia

Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Knowledge about schizophrenia test: the Chinese Mandarin version and its sociodemographic and clinical factors

BACKGROUND

Schizophrenia is a chronic, complex, and severe mental disorder and caregivers having knowledge about it can help improve patient adherence to treatment. This study aims to translate the Knowledge About Schizophrenia Test (KAST) into a Chinese Mandarin version and test it among caregivers to validate its reliability and reproducibility, as well as to determine its associated sociodemographic factors and clinical factors.

METHODS

The project surveyed 160 patients with schizophrenia and their caregivers at four community health facilities in Beijing, China, from January 2022 to February 2022. All patients and caregivers completed the sociodemographic questionnaire, and caregivers also completed the Chinese-version KAST, and 143 of these caregivers completed the Chinese-version KAST again 2-4 weeks later.

RESULTS

The mean (SD) of the caregiver score was 11.49 (± 3.13). After item analysis, there was acceptable internal consistency among the 17 items in the Chinese version (KR-20 coefficient 0.702). The intraclass correlation coefficient in the retest (0.686) was statistically significant. Gender, educational attainment, marital status, relationship with the patient, and occupational status were associated with the KAST score.

CONCLUSION

The findings demonstrate that the Chinese-version KAST is a reliable and reproducible instrument that can measure knowledge about schizophrenia and is valid to be applied in schizophrenia research.

Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis

Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.

Effects of Glycogen Synthase Kinase-3 Beta Gene Polymorphisms on the Plasma Concentration of Aripiprazole in Chinese Patients with Schizophrenia: A Preliminary Study

This study explored the differences in glycogen synthase kinase-3 beta (GSK3β) gene polymorphisms between patients with schizophrenia and healthy controls and investigated the association between gene polymorphisms and plasma concentration of aripiprazole. We enrolled 127 patients with schizophrenia and 125 healthy controls from southern Fujian. The genotypes of the rs6438552, rs12630592, and rs3732361 loci of GSK3β were evaluated by sequencing with amplified polymerase chain reaction, and the plasma concentration of aripiprazole was determined by high-performance liquid chromatography-tandem mass spectrometry. All three loci of GSK3β had three genotypes each. The genotype distribution in each locus was not significantly different, but there was a significant difference in the allele frequency between the schizophrenia and control groups within each locus. Linkage disequilibrium analyses of the three single-nucleotide polymorphisms (SNPs) revealed strong linkage. The haplotype analysis results showed two haplotypes in the three SNPs of GSK3β. The plasma concentrations, dose-corrected concentrations, and normalized concentrations of aripiprazole were significantly different among the different genotypes of the three SNPs. In conclusion, the rs6438552, rs12630592, and rs3732361 loci of GSK3β may be involved in schizophrenia, and GSK3β gene polymorphism may be correlated with the plasma concentration of aripiprazole.

Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study

Recent studies show that multi-modal data fusion techniques combine information from diverse sources for comprehensive diagnosis and prognosis of complex brain disorder, often resulting in improved accuracy compared to single-modality approaches. However, many existing data fusion methods extract features from homogeneous networs, ignoring heterogeneous structural information among multiple modalities. To this end, we propose a Hypergraph-based Multi-modal data Fusion algorithm, namely HMF. Specifically, we first generate a hypergraph similarity matrix to represent the high-order relationships among subjects, and then enforce the regularization term based upon both the inter- and intra-modality relationships of the subjects. Finally, we apply HMF to integrate imaging and genetics datasets. Validation of the proposed method is performed on both synthetic data and real samples from schizophrenia study. Results show that our algorithm outperforms several competing methods, and reveals significant interactions among risk genes, environmental factors and abnormal brain regions.

The neurodevelopmental gene MSANTD2 belongs to a gene family formed by recurrent molecular domestication of Harbinger transposons at the base of vertebrates

The formation of new genes is a major source of organism evolutionary innovation. Beyond their mutational effects, transposable elements can be co-opted by host genomes to form different types of sequences including novel genes, through a mechanism named molecular domestication.We report the formation of four genes through molecular domestication of Harbinger transposons, three in a common ancestor of jawed vertebrates about 500 million years ago and one in sarcopterygians approx. 430 million years ago. Additionally, one processed pseudogene arose approx. 60 million years ago in simians. In zebrafish, Harbinger-derived genes are expressed during early development but also in adult tissues, and predominantly co-expressed in male brain. In human, expression was detected in multiple organs, with major expression in the brain particularly during fetal development. We used CRISPR/Cas9 with direct gene knock-out in the F0 generation and the morpholino antisense oligonucleotide knock-down technique to study in zebrafish the function of one of these genes called MSANTD2, which has been suggested to be associated to neuro-developmental diseases such as autism spectrum disorders and schizophrenia in human. MSANTD2 inactivation led to developmental delays including tail and nervous system malformation at one day post fertilization. Affected embryos showed dead cell accumulation, major anatomical defects characterized by impaired brain ventricle formation and alterations in expression of some characteristic genes involved in vertebrate nervous system development. Hence, the characterization of MSANTD2 and other Harbinger-derived genes might contribute to a better understanding of the genetic innovations having driven the early evolution of the vertebrate nervous system.

MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review

Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.

Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression

Several studies have reported association between leukocyte telomere length (LTL) and neuropsychiatric disorders. Although telomere length is affected by environmental factors, genetic variants in certain loci are strongly associated with LTL. Thus, we aimed to identify the genomic relationship between genetic variants of LTL with brain-based regulatory changes and brain volume. We tested genetic colocalization of seven and nine LTL loci in two ancestry groups, European (EUR) and East-Asian (EAS), respectively, with brain morphology measures for 101 T1-magnetic resonance imaging-based region of interests (n = 21 821). The posterior probability (>90%) was observed for 'fourth ventricle', 'gray matter' and 'cerebellar vermal lobules I-IV' volumes. We then tested causal relationship using LTL loci for gene and methylation expression. We found causal pleiotropy for gene (EAS = four genes; EUR = five genes) and methylation expression (EUR = 17 probes; EAS = 4 probes) of brain tissues (P ≤ 2.47 × 10-6). Integrating chromatin profiles with LTL-single nucleotide polymorphisms identified 45 genes (EUR) and 79 genes (EAS) (P ≤ 9.78×10-7). We found additional 38 LTL-genes using chromatin-based gene mapping for EUR ancestry population. Gene variants in three LTL-genes-GPR37, OBFC1 and RTEL1/RTEL1-TNFRSF6B-show convergent evidence of pleiotropy with brain morphology, gene and methylation expression and chromatin association. Mapping gene functions to drug-gene interactions, we identified process 'transmission across chemical synapses' (P < 2.78 × 10-4). This study provides evidence that genetic variants of LTL have pleiotropic roles with brain-based effects that could explain the phenotypic association of LTL with several neuropsychiatric traits.

Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.

Associations of Caregiving Knowledge and Skills With Caregiver Burden, Psychological Well-Being, and Coping Styles Among Primary Family Caregivers of People Living With Schizophrenia in China

Background: There is a lack of clarity regarding the correlation of caregiving knowledge and skills with caregiving experiences of people living with schizophrenia (PLSs). To address this gap, this comprehensive study examines the relationships of caregiving knowledge and skills to the primary family caregiver's experiences of burden, psychological well-being (stress, anxiety, depression, caregiving rewarding feelings), and coping styles in China. Methods: A total of 395 primary family caregivers of PLSs were enrolled in a cross-sectional study between May 2019 and September 2019. Each family caregiver was independently assessed on caregiving knowledge and skills, caregiver burden, and psychological well-being, as well as coping styles. Results: A higher level of caregiving knowledge and skills was positively correlated with less stress (b = -0.48, P < 0.001), anxiety (b = -0.23, P = 0.029), depression (b = -0.29, P = 0.013), and more caregiving rewarding feelings (b = 0.54, P < 0.001). Also, caregivers with more knowledge and skills were more inclined to adopt positive coping strategies (b = 0.44, P < 0.001). Despite these differences, caregivers with different levels of caregiving knowledge and skills reported comparable caregiver burden (b = 0.11, P = 0.705) and the use of a passive coping style (b = 0.10, P = 0.169). Conclusion: Caregiving knowledge and skills are a reliable predictor of psychological well-being and active coping among the primary family caregivers of PLSs. These findings inform the development of psychoeducational interventions to support family caregivers of PLSs.