Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies, which are often updated at lengthy intervals. With genetic data and health outcomes continuously being generated, the current PRS training and deployment paradigm is suboptimal in maximizing prediction accuracy for incoming patients in healthcare settings. We introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and standardization of PRS as each new sample is collected. We perform extensive simulations to evaluate rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from two large-scale biobanks, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts across seven Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically capturing health status changes and predicting disease risk across diverse genetic ancestries.

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries. The cross-population Sum of Single Effects (SuSiEx) model is a robust and computationally efficient method for conducting multi-ancestry fine-mapping of genome-wide association signals, producing smaller credible sets and capturing population-specific causal variants.

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

Background Internet Gaming Disorder (IGD) is a psychiatric disorder that often occurs during adolescent development, but its genetic mechanism is unknown. This study aims to discover the risk genetic loci and genes that are associated with the degree and development of IGD in adolescents. Methods This genome-wide association study included the Adolescent Brain Cognitive Development Study (ABCD study) participants drawn from the United States. Cross-sectional analyses were conducted using data from the third year of follow-up at the age of 12.91±0.64 years. The degree of IGD was assessed using the Video Game Addiction Questionnaire. The quality control was performed by PLINK v1.90, removing SNPs with call rate < 95%, minor allele frequency < 0.1% and Hardy–Weinberg equilibrium P < 10−6 and removing samples with call rate < 95%, deviating ±3 sd from the samples' heterozygosity rate mean and proportion identity by descent PI_HAT > 0.2. GWAS was performed by controlling for age, sex, and genetic structure. A polygenic risk score (PRS) was used to explore the genetic association between substance use disorders and IGD. GWAS summary data was obtained from our laboratory's PGC, GWAS Catalog, and addiction cohort. Results A total of 5865 participants (3143 male; 4712 female) and 4032260 SNP were included in the analysis. The GWAS identified rs376069954 located in EIF3IP1 (P = 2.649e-9) was significantly associated with the degree of IGD. Genetic correlation between IGD and substance use disorders as well as other mental disorders were analyzed using PRS. Results showed IGD was positively correlated with alcohol use disorder (β = 3328.18, P = 0.057), cannabis use disorder (β = 1042.47, P = 0.0075), and smoking (β = 150047, P = 0.033), and negatively correlated with heroin addiction (β = -4329.48, P = 0.0011). Under the optimal model (P threshold = 0.0002) methamphetamine addiction (β = 180.782, P = 0.020) was positively correlated with Internet gaming disorder, but as the P-value thresholds increased methamphetamine addiction and IGD showed to be negatively correlated. Moreover, IGD was positively correlated with eating disorder (β = 2928.96, P = 0.00029), autistic disorder (β = 3623.36, P = 0.0013), insomnia (β = 46394.7, P = 0.0028), and attention deficit hyperactivity disorder (ADHD, β = 13796.10, P = 0.0055) and negatively correlated with obsessive-compulsive disorder (OCD, β = - 2928.96, P = 0.00029). Discussion EIF3IP1 loci which peaked in rs376069954 was significantly correlated with IGD. Carrying the genetic risk of alcohol use disorder, cannabis use disorder and smoking could increase the risk of IGD, while the genetic risk of heroin addiction and methamphetamine addiction could resist the risk of IGD. Among psychiatric disorders, the genetic risk of eating disorders, autistic disorders, insomnia, and ADHD would increase the risk of IGD, but the genetic risk of OCD might protect it.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet mostof these studies have been centered on European populations, leading to a constrained understanding of population diversities and dis-ease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA,n 1/4 158), Europeans (EUR,n 1/4 408), and East Asians (EAS,n 1/4 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patternsof genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737cis-eQTLs linked to 1,276 genes and198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be tracedback to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating braineQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched pop-ulations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2,VPS37B,DENR,FTCDNL1, andNT5DC2) and three potential regulatory variants in known risk genes (CNNM2,MTRFR, andMPHOSPH9) that were missed in theEUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merelyincreasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the bio-logical underpinnings of population structures but also pave the way for the identification of risk genes in SCZ

Here, we present a study on rare copy number variants (rCNVs) in schizophrenia, emphasizing the shift from traditional European (EUR) populations to a large East Asian (EAS) cohort, the largest to date, with 20,903 cases and 23,258 controls. The study confirms previous findings about the heightened genome-wide rCNV burden in schizophrenia patients within this EAS cohort. A combined meta-analysis of EAS and EUR cohorts, totaling 38,409 cases and 40,009 controls, identified 15 significant rCNV loci. Of these, five were novel, found at locations 1q21.2, 8p21.3, 11q13.1, 19p13.3, and 19q13.42. The comparison between EAS and EUR data suggested that differences in rCNV frequencies contribute to variability in discovery power across these populations rather than differences in genetic effect sizes.Among the eight rCNV loci implicated in the PGC EUR study with genome-wide significance, seven had rCNVs captured in the EAS dataset, among which three achieved genome-wide significance (P<6.88e-5, 22q11.21 deletion, 3q29 deletion, and 16p11.2 duplication) and an additional three reached nominal significance (P < 0.05, 1q21.1 deletion, 16p11.2 deletion, and 7q11.23 duplication). None of these loci showed a significant difference in effect size between the two populations.rCNVs, particularly, are significant as they have a pronounced potential to disrupt neuronal development and synaptic connectivity. The discovery of novel rCNV loci in the EAS population enriches our understanding of the genetic architecture of schizophrenia and underscores the potential influence of rCNVs on neurodevelopmental processes. Comparing rCNV profiles between EAS and EUR cohorts illuminates how population-specific genomic structures can influence the prevalence and impact of these genetic variations.The current findings underscore the variability in genetic factors influencing schizophrenia across different populations and highlight the necessity of expanding genetic studies to include diverse populations beyond those of European descent. Identifying novel loci in the EAS population not only enriches our understanding of the genetic architecture of schizophrenia but also suggests that population-specific genetic variations could be crucial for tailoring more effective diagnostics and treatments.Furthermore, this study enhances our understanding of the genetic diversity and complexity of schizophrenia, contributing valuable insights into how different populations may exhibit unique genetic profiles that influence the disease. By exploring these distinctions, the research advocates for a more inclusive approach to genetic research, which is essential for developing global health strategies and interventions sensitive to genetic diversity. This aligns with the conference theme by emphasizing the importance of including diverse genetic backgrounds to achieve a more comprehensive understanding of psychiatric disorders.

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

Depression is one of the most common mental disorders. Perceived stress is a significant trigger and has adverse effects on depression. The complex longitudinal relationship between perceived stress and depression at the symptom level has significant implications for clinical intervention but is understudied. In our study, 823 students (67% female, median age 20.38, IQR 19.42-21.43) from a university in Tianjin were randomly sampled and completed measures of PHQ-9 and PSS-10, while 393 (65% female, median age 20.42, IQR 19.46-21.45) were followed up at three points, six months apart. The longitudinal relationships were estimated using cross-lagged modelling and cross-lagged panel network modelling. Among them, 49 students (59% female, median age 19.48, IQR 18.76-20.12) participated in resting-state functional magnetic resonance imaging (fMRI) scans. Cross-lagged analyses showed that depression and perceived stress predicted each other at the global level. At the dimensional level, depression and perceived helplessness were mutually predictive, while depression and perceived coping did not. In the cross-lagged panel network analyses, we identified symptoms in the top 20% of Bridge Expected Influence as bridging symptoms, specifically 'Guilt' (PHQ6) and 'Felt nervous and stressed' (PSS3). Notably, 'guilt' consistently demonstrated the highest Bridge Expected Influence across all time points and showed the strongest predictive power for perceived stress. We found that fALFF in the left superior frontal gyrus (SFG) mediated the association between "guilt" and perceived stress. Our findings elucidate the bidirectional relationship between symptoms of depression and perceived stress, identifying guilt is the most critical symptom of depression for the followed perceived stress, with SFG activity mediating this association.

Based on the clinical overlap between schizophrenia (SCZ) and obsessive-compulsive disorder (OCD), both disorders may share neurobiological substrates. In this study, we first analyzed recent large genome-wide associations studies (GWAS) on SCZ ( n = 53,386, Psychiatric Genomics Consortium Wave 3) and OCD ( n = 2688, the International Obsessive-Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and the OCD Collaborative Genetics Association Study (OCGAS)) using a conjunctional false discovery rate (FDR) approach to evaluate overlap in common genetic variants of European descent. Using a variety of biological resources, we functionally characterized the identified genomic loci. Then we used two-sample Mendelian randomization (MR) to estimate the bidirectional causal association between SCZ and OCD. Results showed that there is a positive genetic correlation between SCZ and OCD (r g = 0.36, P = 0.02). We identified that one genetic locus (lead SNP rs5757717 in an intergenic region at CACNA1I) was jointly associated with SCZ and OCD (conjFDR = 2.12 × 10 −2 ). Mendelian randomization results showed that variants associated with increased risk for SCZ also increased the risk of OCD. This study broadens our understanding of the genetic architectures underpinning SCZ and OCD and suggests that the same molecular genetic processes may be responsible for shared pathophysiological and clinical characteristics between the two disorders.

Antipsychotic-induced hyperprolactinemia (AP-induced HPRL) occurs overall in up to 70% of patients with schizophrenia, which is associated with hypogonadism and sexual dysfunction. We summarized the latest evidence for the benefits of prolactin-lowering drugs. We performed network meta-analyses to summarize the evidence and applied Grading of Recommendations Assessment, Development, and Evaluation frameworks (GRADE) to rate the certainty of evidence, categorize interventions, and present the findings. The search identified 3,022 citations, 31 studies of which with 1999 participants were included in network meta-analysis. All options were not significantly better than placebo among patients with prolactin (PRL) less than 50 ng/ml. However, adjunctive aripiprazole (ARI) (5 mg: MD = -64.26, 95% CI = -87.00 to -41.37; 10 mg: MD = -59.81, 95% CI = -90.10 to -29.76; more than 10 mg: MD = -68.01, 95% CI = -97.12 to -39.72), switching to ARI in titration (MD = -74.80, 95% CI = -134.22 to -15.99) and adjunctive vitamin B6 (MD = -91.84, 95% CI = -165.31 to -17.74) were associated with significant decrease in AP-induced PRL among patients with PRL more than 50 ng/ml with moderated (adjunctive vitamin B6) to high (adjunctive ARI) certainty of evidence. Pharmacological treatment strategies for AP-induced HPRL depends on initial PRL level. No effective strategy was found for patients with AP-induced HPRL less than 50 ng/ml, while adjunctive ARI, switching to ARI in titration and adjunctive high-dose vitamin B6 showed better PRL decrease effect on AP-induced HPRL more than 50 ng/ml.

Structured AbstractImportancePsychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy.ObjectiveTo identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes.DesignThis genomic study applied a multivariate transcriptomic method, Transcriptome-wide Structural Equation Modeling (T-SEM), to investigate gene expression patterns associated with four genomic factors indexing shared risk across 11 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. Public databases describing drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk.Main Outcomes and MeasuresGene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes.ResultsIn total, T-SEM identified 451 genes whose expression was associated with the genomic factors and 41 genes with disorder-specific effects. We find the most hits for a Thought Disorders factor defined by bipolar disorder and schizophrenia. We identify 39 existing pharmacological interventions that could be repurposed to target gene expression hits for this same factor.Conclusions and RelevanceThe findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

Fear extinction is easy to achieve but difficult to maintain, as evidenced by the relapse of fear after extinction. Counterconditioning and novelty-facilitated extinction have been shown to interfere with fear expression without erasing it. Because of the similarity between the two extinction paradigms, we extended the standard extinction, which merely omitted the expected threat outcomes after exposure to original threat cues. The modified paradigm provided a stimulus (neutral picture or positive picture) to replace the omitted threat outcomes during extinction. Sixty-four healthy volunteers were randomized into three groups for a three-day procedure: fear acquisition (day 1), fear extinction (day 2), and fear recall and generalization test (day 3). Our results showed the modified extinction paradigm failed to prevent fear expression in spontaneous recovery and reinstatement tests. However, novelty-facilitated extinction showed powerful effects in preventing fear generalization. Besides, there was a negative correlation between spontaneous recovery index and emotion regulation scores. We speculated that emotion and prediction error may be important factors influencing fear extinction and affect fear recall and generalization. Overall, this study suggests that novelty-facilitated extinction had a superior effect in preventing fear generalization, providing new perspectives for enhancing the effect of exposure therapy.

Schizophrenia is a common neuropsychiatric disorder with complex pathophysiology. Recent reports suggested that complement system alterations contributed to pathological synapse elimination that was associated with psychiatric symptoms in schizophrenia. Complement component 3 (C3) and complement component 4 (C4) play central roles in complement cascades. In this study, we compared peripheral C3 and C4 protein levels between first-episode psychosis (FEP) and healthy control (HC). Then we explored whether single nucleotide polymorphisms (SNPs) at C3 or C4 genes affect peripheral C3 or C4 protein levels. In total, 181 FEPs and 204 HCs were recruited after providing written informed consent. We measured serum C3 and C4 protein levels using turbidimetric inhibition immunoassay and genotyped C3 and C4 polymorphisms using the Sequenom MassArray genotyping. Our results showed that three SNPs were nominally associated with schizophrenia (rs11569562/C3: A > G, p = 0.048; rs2277983/C3: A > G, p = 0.040; rs149898426/C4: G > A, p = 0.012); one haplotype was nominally associated with schizophrenia, constructed by rs11569562–rs2277983–rs1389623 (GGG, p = 0.048); FEP had higher serum C3 and C4 (both p < 0.001) levels than HC; rs1389623 polymorphisms were associated with elevated C3 levels in our meta-analysis (standard mean difference, 0.50; 95% confidence interval, 0.30 to 0.71); the FEP with CG genotype of rs149898426 had higher C4 levels than that with GG genotypes (p = 0.005). Overall, these findings indicated that complement system altered in FEP and rs149898426 of C4 gene represented a genetic risk marker for schizophrenia likely through mediating complement system. Further studies with larger sample sizes needs to be validated.

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide. To decode the molecular framework of these diseases, many studies use human postmortem brain samples. These studies reveal brain-specific genetic and epigenetic patterns via high-throughput sequencing technologies. Identifying best practices for the collection of postmortem brain samples, analyzing such large amounts of sequencing data, and interpreting these results are critical to advance neuropsychiatry. We provide an overview of human brain banks worldwide, including progress in China, highlighting some well-known projects using human postmortem brain samples to understand molecular regulation in both normal brains and those with neuropsychiatric disorders. Finally, we discuss future research strategies, as well as state-of-the-art statistical and experimental methods that are drawn upon brain bank resources to improve our understanding of the agents of neuropsychiatric disorders.

The Chinese National Twin Registry (CNTR) currently includes data from 61 566 twin pair from 11 provinces or cities in China. Of these, 31 705, 15 060 and 13 531 pairs are monozygotic, same‐sex dizygotic and opposite‐sex dizygotic pairs, respectively, determined by opposite sex or intrapair similarity. Since its establishment in 2001, the CNTR has provided an important resource for analysing genetic and environmental influences on chronic diseases especially cardiovascular diseases. Recently, the CNTR has focused on collecting biologic specimens from disease‐concordant or disease‐discordant twin pairs or from twin pairs reared apart. More than 8000 pairs of these twins have been registered, and blood samples have been collected from more than 1500 pairs. In this review, we summarize the main findings from univariate and multivariate genetic effects analyses, gene–environment interaction studies, omics studies exploring DNA methylation and metabolomic markers associated with phenotypes. There remains further scope for CNTR research and data mining. The plan for future development of the CNTR is described. The CNTR welcomes worldwide collaboration.

Sleep is known to benefit consolidation of memories, especially those of motivational relevance. Yet, it remains largely unknown the extent to which sleep influences reward-associated behavior, in particular, whether and how sleep modulates reward evaluation that critically underlies value-based decisions. Here, we show that neural processing during sleep can selectively bias preferences in simple economic choices when the sleeper is stimulated by covert, reward-associated cues. Specifically, presenting the spoken name of a familiar, valued snack item during midday nap significantly improves the preference for that item relative to items not externally cued. The cueing-specific preference enhancement is sleep-dependent and can be predicted by cue-induced neurophysiological signals at the subject and item level. Computational modeling further suggests that sleep cueing accelerates evidence accumulation for cued options during the post-sleep choice process in a manner consistent with the preference shift. These findings suggest that neurocognitive processing during sleep contributes to the fine-tuning of subjective preferences in a flexible, selective manner.

Addiction is marked by repeating a certain behavior while ignoring the potential physical or mental consequences. Non-substance addiction provides an ideal model for researching the emergence and development of addiction's basic mechanism. Comparative studies of substance and non-substance addiction are helpful to reveal the common basis of addiction development. This article explores this topic from a psychological angle, touching upon sensation seeking, inhibitory control, attentional bias, intertemporal choice and environment. A review of previous literature urges future research to propose a biopsychosocial model of addiction and consider addiction's effect on basic cognitive function alongside cognitive neuroscience technology.