Comparative genetic architectures of schizophrenia in East Asian and European populations

GenomicSEM Modelling of Diverse Executive Function GWAS Improves Gene Discovery

Previous research has supported the use of latent variables as the gold-standard in measuring executive function. However, for logistical reasons genome-wide association studies (GWAS) of executive function have largely eschewed latent variables in favour of singular task measures. As low correlations have traditionally been found between individual executive function (EF) tests, it is unclear whether these GWAS have truly been measuring the same construct. In this study, we addressed this question by performing a factor analysis on summary statistics from eleven GWAS of EF taken from five studies, using GenomicSEM. Models demonstrated a bifactor structure consistent with previous research, with factors capturing common EF and working memory- specific variance. Furthermore, the GWAS performed on this model identified 20 new genomic risk loci for common EF and 4 for working memory reaching genome-wide significance beyond what was found in the constituent GWAS, together resulting in 29 newly mapped EF genes. These results help to clarify the underlying genetic structure of EF and support the idea that EF GWAS are capable of measuring genetic variance related to latent EF constructs even when not using factor scores. Furthermore, they demonstrate that GenomicSEM can combine GWAS with divergent and non-ideal measures of the same phenotype to improve statistical power.

Genetic evidence of the causal relationships between psychiatric disorders and cardiovascular diseases

OBJECTIVE

Our primary objective is to investigate the causal relationships between 12 psychiatric disorders (PDs) and atrial fibrillation (AF), coronary artery disease (CAD), myocardial infarction (MI), and heart failure (HF).

METHODS

Firstly, we used linkage disequilibrium score regression to calculate the genetic correlations between 12 PDs and 4 cardiovascular diseases (CVDs). Subsequently, we performed two-sample and bidirectional Mendelian randomization (MR) analyses of phenotypes with significant genetic correlations to explore the causal relationships between PDs and CVDs. Inverse variance weighted with modified weights (MW-IVW), Robust Adjusted Profile Score, Inverse Variance Weighted, weighted median and weighted mode were used to evaluate causal effects, with MW-IVW being the main analysis method. And to validate the MR results, we conducted the replicate analyses using data from the FinnGen database.

RESULTS

Conducting MR analyses in phenotypes with significant genetic correlations, we identified bidirectional causal relationships between depression (DEP) and MI (DEP as exposure: OR = 1.1324, 95 % confidence interval (CI): 1.0984-1.1663, P < 0.0001; MI as exposure: OR = 1.0268, 95 % CI: 1.0160-1.0375, P < 0.0001). Similar relationships were observed in Attention Deficit/Hyperactivity Disorder (ADHD) and HF (ADHD as exposure: OR = 1.0270, 95 % CI: 1.0144-1.0395, P < 0.0001; HF as exposure: OR = 1.0980, 95 % CI: 1.0502-1.1458, P < 0.0001).

CONCLUSIONS

In our study, we conducted the comprehensive analyses between 12 PDs and CVDs. By bidirectional MR analysis, we observed significant causal relationships between MI and DEP, HF and ADHD. These findings suggest possible complex causal relationships between PDs and CVDs.

The X chromosome's influences on the human brain

Genes on the X chromosome are extensively expressed in the human brain. However, little is known for the X chromosome's impact on the brain anatomy, microstructure, and functional networks. We examined 1045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X chromosome interactions while proposing an atlas outlining dosage compensation for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we found unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.

Prevalence and correlates of common mental disorders among participants of the Uganda Genome Resource: Opportunities for psychiatric genetics research

Genetics research has potential to alleviate the burden of mental disorders in low- and middle-income-countries through identification of new mechanistic pathways which can lead to efficacious drugs or new drug targets. However, there is currently limited genetics data from Africa. The Uganda Genome Resource provides opportunity for psychiatric genetics research among underrepresented people from Africa. We aimed at determining the prevalence and correlates of major depressive disorder (MDD), suicidality, post-traumatic stress disorder (PTSD), alcohol abuse, generalised anxiety disorder (GAD) and probable attention-deficit hyperactivity disorder (ADHD) among participants of the Uganda Genome Resource. Standardised tools assessed for each mental disorder. Prevalence of each disorder was calculated with 95% confidence intervals. Multivariate logistic regression models evaluated the association between each mental disorder and associated demographic and clinical factors. Among 985 participants, prevalence of the disorders were: current MDD 19.3%, life-time MDD 23.3%, suicidality 10.6%, PTSD 3.1%, alcohol abuse 5.7%, GAD 12.9% and probable ADHD 9.2%. This is the first study to determine the prevalence of probable ADHD among adult Ugandans from a general population. We found significant association between sex and alcohol abuse (adjusted odds ratio [AOR] = 0.26 [0.14,0.45], p < 0.001) and GAD (AOR = 1.78 [1.09,2.49], p = 0.019) respectively. We also found significant association between body mass index and suicidality (AOR = 0.85 [0.73,0.99], p = 0.041), alcohol abuse (AOR = 0.86 [0.78,0.94], p = 0.003) and GAD (AOR = 0.93 [0.87,0.98], p = 0.008) respectively. We also found a significant association between high blood pressure and life-time MDD (AOR = 2.87 [1.08,7.66], p = 0.035) and probable ADHD (AOR = 1.99 [1.00,3.97], p = 0.050) respectively. We also found a statistically significant association between tobacco smoking and alcohol abuse (AOR = 3.2 [1.56,6.67], p = 0.002). We also found ever been married to be a risk factor for probable ADHD (AOR = 2.12 [0.88,5.14], p = 0.049). The Uganda Genome Resource presents opportunity for psychiatric genetics research among underrepresented people from Africa.

Whole genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole genome sequencing of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls. To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of BD association with single-variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD GWAS loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB). However, this approach may miss genetic associations where one single nucleotide polymorphism (SNP) is moderately associated with multiple UDIP dimensions. Here, we present Joint Analysis of multi-phenotype GWAS (JAGWAS), a new tool that can efficiently calculate multivariate association statistics using single-phenotype summary statistics for hundreds of phenotypes. When applied to UDIPs of T1 and T2 brain magnetic resonance imaging (MRI) on discovery and replication cohorts from the UKB, JAGWAS identified 195/168 independently replicated genomic loci for T1/T2, 6 times more than those from the single-phenotype GWAS. The replicated loci were mapped into 555/494 genes, and 217/188 genes overlapped with the expression quantitative trait loci (eQTL) of brain tissues. Gene enrichment analysis indicated that the genes mapped are closely related to neurobiological functions. Our results suggested that multi-phenotype GWAS is a powerful approach for genetic discovery using high-dimensional UDIPs.

Genetic overlap between schizophrenia spectrum disorders and Alzheimer's disease: Current evidence and future directions - An integrative review

Schizophrenia and Alzheimer's disease (AD) are distinct neurodegenerative disorders characterized by progressive cognitive deficits and structural alterations in the brain. Schizophrenia typically emerges in adolescence or early adulthood with symptoms such as hallucinations, delusions, and cognitive impairments, whereas AD primarily affects elderly individuals, causing progressive memory loss, cognitive decline, and behavioral changes. Delusional disorder, which often emerges later in life, shares some features with schizophrenia and is considered a schizophrenia spectrum disorder. Patients with schizophrenia or delusional disorder, particularly women and those aged 65 years or older, have an increased risk of developing AD later in life. In contrast, approximately 30 % of AD patients exhibit psychotic symptoms, which accelerate cognitive decline and worsen health outcomes. This integrative review explored the genetic overlap between schizophrenia spectrum disorders and AD to identify potential shared genetic factors. The genetic correlations between schizophrenia and AD were weak but positive (rg=0.03-0.10). Polygenic risk scores (PRSs) for schizophrenia and AD indicate some genetic predisposition, although findings are inconsistent among studies; e.g., PRS-schizophrenia or PRS-AD were associated with the risk of developing psychosis in patients with AD. A higher PRS for various developmental and psychiatric disorders was correlated with an earlier age at onset of schizophrenia. Research gaps include the need for studies on the impacts of PRS-AD on the risk of schizophrenia, genetic correlations between later-onset delusional disorder and AD, and genetic relationships between AD and late-onset schizophrenia (LOS) with a greater risk of progressing to AD. Further investigation into these genetic overlaps is crucial to enhance prevention, treatment, and prognosis for affected patients.

Genome-wide association study and polygenic risk score analysis for schizophrenia in a Korean population

Although large-scale genome-wide association studies (GWASs) have revealed the genetic architecture of schizophrenia, these studies have mainly focused on populations of European ancestry. This study aimed to identify common genetic variants associated with schizophrenia in the Korean population and evaluate the performance of polygenic risk scores (PRSs) derived from large-scale GWASs across ancestries. In the Korean psychiatric GWAS project (KPGP), seven academic institutes and their affiliated hospitals across South Korea recruited a cohort of 1670 patients with DSM-IV-defined schizophrenia and 2271 healthy controls. A total of 6690,822 SNPs were tested for association with schizophrenia. We identified one previously unreported genome-wide significant locus rs2423464 (P = 2.83 × 10-11; odds ratio = 1.65; 95 % confidence interval = 1.43-1.91, minor allele frequency = 0.126). This variant was also associated with increased lysosomal-associated membrane protein family member 5 (LAMP5) gene expression. The PRS derived from the meta-analysis results of East Asian and European GWASs explained a larger proportion of the phenotypic variance in the Korean schizophrenia sample than the PRS of an East Asian or European GWAS. (R2 = 0.073 for meta-analysis; 0.028 for East Asian GWAS; 0.037 for European GWAS). GWASs involving diverse ethnic groups will expand our understanding of the genetic architecture of schizophrenia.

Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia

The comorbidity between cardiovascular diseases (CVD) and schizophrenia (SCZ) has attracted widespread attention from researchers, with shared genetic causes potentially providing important insights into their association. This study conducted a comprehensive analysis of genetic data from 17 types of CVD and SCZ using genome-wide multi-trait association studies (GWAS), employing statistical methods such as LDSC, MTAG, LAVA, and bidirectional Mendelian randomization to explore global and local genetic correlations and identify pleiotropic single nucleotide variants (SNVs). The analysis revealed a significant genetic correlation between CVD and SCZ, identifying 842 potential pleiotropic single nucleotide variants (SNVs) and multiple associated biological pathways. Notably, genes such as TRIM27, CENPM, and MYH7B played critical roles in the shared genetic variations of both types of diseases. This study reveals the complex genetic relationship between CVD and SCZ, highlighting potential shared biological mechanisms involving immune responses, metabolic factors, and neurodevelopmental processes, thereby providing new directions for future interventions and treatments.

Cortical assembloids support the development of fast-spiking human PVALB+ cortical interneurons and uncover schizophrenia-associated defects

Disruption of parvalbumin positive (PVALB+) cortical interneurons is implicated in the pathogenesis of schizophrenia. However, how these defects emerge during brain development remains poorly understood. The protracted maturation of these cells during postnatal life has made their derivation from human pluripotent stem cells (hPSCs) extremely difficult, precluding hPSC-based disease modeling of their role in neuropsychiatric disease. Here we present a cortical assembloid system that supports the development of PVALB+ cortical interneurons which match the molecular profiles of primary PVALB+ interneurons and display their distinctive electrophysiological features. Further, we characterized cortical interneuron development in a series of CRISPR-generated isogenic structural variants associated with schizophrenia and identified variant-specific phenotypes affecting cortical interneuron migration and the molecular profile of PVALB+ cortical interneurons. These findings offer plausible mechanisms on how the disruption of cortical interneuron development may impact schizophrenia risk and provide the first human experimental platform to study of PVALB+ cortical interneurons.

Association of polygenic liabilities for schizophrenia and bipolar disorder with educational attainment and cognitive aging

To elucidate the specific and shared genetic background of schizophrenia (SCZ) and bipolar disorder (BPD), this study explored the association of polygenic liabilities for SCZ and BPD with educational attainment and cognitive aging. Among 106,806 unrelated community participants from the Taiwan Biobank, we calculated the polygenic risk score (PRS) for SCZ (PRSSCZ) and BPD (PRSBPD), shared PRS between SCZ and BPD (PRSSCZ+BPD), and SCZ-specific PRS (PRSSCZvsBPD). Based on the sign-concordance of the susceptibility variants with SCZ/BPD, PRSSCZ was split into PRSSCZ_concordant/PRSSCZ_discordant, and PRSBPD was split into PRSBPD_concordant/PRSBPD_discordant. Ordinal logistic regression models were used to estimate the association with educational attainment. Linear regression models were used to estimate the associations with cognitive aging (n = 27,005), measured by the Mini-Mental State Examination (MMSE), and with MMSE change (n = 6194 with mean follow-up duration of 3.9 y) in individuals aged≥ 60 years. PRSSCZ, PRSBPD, and PRSSCZ+BPD were positively associated with educational attainment, whereas PRSSCZvsBPD was negatively associated with educational attainment. PRSSCZ was negatively associated with MMSE, while PRSBPD was positively associated with MMSE. The concordant and discordant parts of polygenic liabilities have contrasting association, PRSSCZ_concordant and PRSBPD_concordant mainly determined these effects mentioned above. PRSSCZvsBPD predicted decreases in the MMSE scores. Using a large collection of community samples, this study provided evidence for the contrasting effects of polygenic architecture in SCZ and BPD on educational attainment and cognitive aging and suggested that SCZ and BPD were not genetically homogeneous.

Schizophrenia risk-associated SNPs affect expression of microRNA 137 host gene: a postmortem study

Common variants in the MicroRNA 137 host gene MIR137HG and its adjacent gene DPYD have been associated with schizophrenia risk and the latest Psychiatric Genomics Consortium (PGC). Genome-Wide Association Study on schizophrenia has confirmed and extended these findings. To elucidate the association of schizophrenia risk-associated SNPs in this genomic region, we examined the expression of both mature and immature transcripts of the miR-137 host gene (MIR137HG) in the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex (sgACC) of postmortem brain samples of donors with schizophrenia and psychiatrically-unaffected controls using qPCR and RNA-Seq approaches. No differential expression of miR-137, MIR137HG, or its transcripts was observed. Two schizophrenia risk-associated SNPs identified in the PGC study, rs11165917 (DLPFC: P = 2.0e-16; sgACC: P = 6.4e-10) and rs4274102 (DLPFC: P = 0.036; sgACC: P = 0.002), were associated with expression of the MIR137HG long non-coding RNA transcript MIR137HG-203 (ENST00000602672.2) in individuals of European ancestry. Carriers of the minor (risk) allele of rs11165917 had significantly lower expression of MIR137HG-203 compared with those carrying the major allele. However, we were unable to validate this result by short-read sequencing of RNA extracted from DLPFC or sgACC tissue. This finding suggests that immature transcripts of MIR137HG may contribute to genetic risk for schizophrenia.

Shared Genetic Determinants of Schizophrenia and Autism Spectrum Disorder Implicate Opposite Risk Patterns: A Genome-Wide Analysis of Common Variants

BACKGROUND AND HYPOTHESIS

The synaptic pruning hypothesis posits that schizophrenia (SCZ) and autism spectrum disorder (ASD) may represent opposite ends of neurodevelopmental disorders: individuals with ASD exhibit an overabundance of synapses and connections while SCZ was characterized by excessive pruning of synapses and a reduction. Given the strong genetic predisposition of both disorders, we propose a shared genetic component, with certain loci having differential regulatory impacts.

STUDY DESIGN

Genome-Wide single nucleotide polymorphism (SNP) data of European descent from SCZ (N cases = 53 386, N controls = 77 258) and ASD (N cases = 18 381, N controls = 27 969) were analyzed. We used genetic correlation, bivariate causal mixture model, conditional false discovery rate method, colocalization, Transcriptome-Wide Association Study (TWAS), and Phenome-Wide Association Study (PheWAS) to investigate the genetic overlap and gene expression pattern.

STUDY RESULTS

We found a positive genetic correlation between SCZ and ASD (rg = .26, SE = 0.01, P = 7.87e-14), with 11 genomic loci jointly influencing both conditions (conjFDR <0.05). Functional analysis highlights a significant enrichment of shared genes during early to mid-fetal developmental stages. A notable genetic region on chromosome 17q21.31 (lead SNP rs2696609) showed strong evidence of colocalization (PP.H4.abf = 0.85). This SNP rs2696609 is linked to many imaging-derived brain phenotypes. TWAS indicated opposing gene expression patterns (primarily pseudogenes and long noncoding RNAs [lncRNAs]) for ASD and SCZ in the 17q21.31 region and some genes (LRRC37A4P, LINC02210, and DND1P1) exhibit considerable variation in the cerebellum across the lifespan.

CONCLUSIONS

Our findings support a shared genetic basis for SCZ and ASD. A common genetic variant, rs2696609, located in the Chr17q21.31 locus, may exert differential risk regulation on SCZ and ASD by altering brain structure. Future studies should focus on the role of pseudogenes, lncRNAs, and cerebellum in synaptic pruning and neurodevelopmental disorders.

Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies

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.

Current progress in understanding schizophrenia using genomics and pluripotent stem cells: A meta-analytical overview

Schizophrenia (SCZ) is a complex, heritable and polygenic neuropsychiatric disease, which disables the patients as well as decreases their life expectancy and quality of life. Common and rare variants studies on SCZ subjects have provided >100 genomic loci that hold importance in the context of SCZ pathophysiology. Transcriptomic studies from clinical samples have informed about the differentially expressed genes (DEGs) and non-coding RNAs in SCZ patients. Despite these advancements, no causative genes for SCZ were found and hence SCZ is difficult to recapitulate in animal models. In the last decade, induced Pluripotent Stem Cells (iPSCs)-based models have helped in understanding the neural phenotypes of SCZ by studying patient iPSC-derived 2D neuronal cultures and 3D brain organoids. Here, we have aimed to provide a simplistic overview of the current progress and advancements after synthesizing the enormous literature on SCZ genetics and SCZ iPSC-based models. Although further understanding of SCZ genetics and pathophysiological mechanisms using these technological advancements is required, the recent approaches have allowed to delineate important cellular mechanisms and biological pathways affected in SCZ.

Investigating the Shared Genetic Architecture Between Psychiatric Disorders and Executive Function

Background

Evidence for widespread comorbidity of executive dysfunctions with psychiatric disorders suggests common mechanisms underlying their pathophysiology. However, the shared genetic architectures between psychiatric disorders and executive function (EF) remain poorly understood.

Methods

Leveraging large genome-wide association study datasets of European ancestry on bipolar disorder (N = 353,899), major depressive disorder (N = 674,452), and schizophrenia (N = 130,644) from the Psychiatric Genomics Consortium and iPSYCH and a common factor of EF (N = 427,037) from UK Biobank, we systematically investigated the shared genomic architectures between psychiatric disorders and EF with a set of statistical genetic, functional genomic, and gene-level analyses.

Results

Our study demonstrated substantial genetic overlaps and significant genetic correlations between psychiatric disorders and EF. EF showed an estimated 95.9%, 98.1%, and 99.2% of phenotype-influencing variants, as well as 50, 23, and 130 genomic loci shared with bipolar disorder, major depressive disorder, and schizophrenia, respectively. Single nucleotide polymorphism heritability enrichment suggests that the genetic architecture of psychiatric disorders and EF involves the brain's frontal cortex and prefrontal glutamatergic neurons 1 and 2. Functional genomic analysis of shared variants identified 12 functional regulatory variants that regulate gene expression by affecting the binding affinities of 5 transcription factors. In addition, functional characterization analyses of shared genes revealed potential common biological mechanisms related to synaptic processes and fetal brain development.

Conclusions

Our findings provide evidence for extensive shared genetic architectures between psychiatric disorders and EF and have valuable implications for future mechanistic investigations and drug development efforts.

Approaches to enable equitable psychiatric genetic research in Africa

Kalungi et al. highlight the underrepresentation of African populations in psychiatric genetic research. They advocate for strategic investments, capacity building, and collaboration to empower African scientists and institutions, emphasizing community engagement and ethical considerations for robust and culturally sensitive research in Africa.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

The neuroscience of mental illness: Building toward the future

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission

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

Genetic determinants of coping, resilience and self-esteem in schizophrenia suggest a primary role for social factors and hippocampal neurogenesis

Schizophrenia is a severe psychiatric disorder, associated with a reduction in life expectancy of 15-20 years. Available treatments are at least partially effective in most affected individuals, and personal resources such as resilience (successful adaptation despite adversity) and coping abilities (strategies used to deal with stressful or threatening situations), are important determinants of disease outcomes and long-term sustained recovery. Published findings support the existence of a genetic background underlying resilience and coping, with variable heritability estimates. However, genome-wide analyses concerning the genetic determinants of these personal resources, especially in the context of schizophrenia, are lacking. Here, we performed a genome-wide association study coupled with accessory analyses to investigate potential genetic determinants of resilience, coping and self-esteem in 490 schizophrenia patients. Results revealed a complex genetic background partly overlapping with that of neuroticism, worry and schizophrenia itself and support the importance of social aspects in shapingthese psychological constructs. Hippocampal neurogenesis and lipid metabolism appear to be potentially relevant biological underpinnings, and specific miRNAs such as miR-124 and miR-137 may warrant further studies as potential biomarkers. In conclusion, this study represents an important first step in the identification of genetic and biological correlates shaping resilience, coping resources and self-esteem in schizophrenia.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2

BACKGROUND

As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.

RESULTS

The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.

CONCLUSIONS

Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Association study reveals a susceptibility locus with male pattern baldness in the Han Chinese population

Introduction

Male pattern baldness (MPB), also known as androgenetic alopecia, represents the most prevalent form of progressive hair loss in humans. It is characterized by a distinctive pattern of hair loss progression from the scalp; however, its underlying mechanism remains elusive and is influenced by hereditary, immune, and environmental factors. Genome-wide association studies (GWASs) have uncovered numerous risk genes/loci among European individuals with MPB. However, the validation of these susceptibility genes/loci within Han Chinese men remains largely unexplored. The aim of this study was to investigate whether the 71 susceptibility loci identified in a recent GWAS among European men also confer risk for MPB in Chinese men.

Methods

Forty-seven single nucleotide polymorphisms (SNPs) previously reported in GWASs of MPB were selected and genotyped in independent individuals comprising 499 Han Chinese cases and 1,489 controls using the Sequenom MassArray system. After stringent quality control measures, 25 SNPs were subjected to statistical analyses. Cochran-Armitage trend test was used to evaluate the association between SNPs and disease susceptibility. To address multiple tests, Bonferroni correction was conducted, setting the threshold for statistical significance at a p-value <2 × 10-3 (0.05/25).

Results

The rs13405699 SNP located at 2q31.1 exhibited a significant association with MPB in Han Chinese men (p = 4.84 × 10-5, OR = 1.37, 95% CI: 1.18-1.59). Moreover, the difference in rs13405699 genotype distribution between MPB cases and controls was statistically significant (p = 7.00 × 10-5). Genotype-based association analysis suggested that the recessive model provided the best fit for the rs13405699 polymorphism.

Conclusion

This study represents the first confirmation of the association between the rs13405699 SNP at 2q31.1 and MPB within the Han Chinese population, thereby enhancing our understanding of the genetic underpinnings of MPB.

Schizophrenia genomics: genetic complexity and functional insights

Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.

Genome-wide meta-analysis identifies ancestry-specific loci for Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is a devastating neurological disease with complex genetic etiology. Yet most known loci have only identified from the late-onset type AD in populations of European ancestry.

METHODS

We performed a two-stage genome-wide association study (GWAS) of AD totaling 6878 Chinese and 63,926 European individuals.

RESULTS

In addition to the apolipoprotein E (APOE) locus, our GWAS of two independent Chinese samples uncovered three novel AD susceptibility loci (KIAA2013, SLC52A3, and TCN2) and a novel ancestry-specific variant within EGFR (rs1815157). More replicated variants were observed in the Chinese (31%) than in the European samples (15%). In combining genome-wide associations and functional annotations, EGFR and TCN2 were prioritized as two of the most biologically significant genes. Phenome-wide Mendelian randomization suggests that high mean corpuscular hemoglobin concentration might protect against AD.

DISCUSSION

The current study reveals novel AD susceptibility loci, emphasizes the importance of diverse populations in AD genetic research, and advances our understanding of disease etiology.

HIGHLIGHTS

Loci KIAA2013, SLC52A3, and TCN2 were associated with Alzheimer's disease (AD) in Chinese populations. rs1815157 within the EGFR locus was associated with AD in Chinese populations. The genetic architecture of AD varied between Chinese and European populations. EGFR and TCN2 were prioritized as two of the most biologically significant genes. High mean corpuscular hemoglobin concentrations might have protective effects against AD.

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review

BACKGROUND

Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders.

OBJECTIVE

As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care.

METHODS

A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care.

RESULTS

A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption.

CONCLUSIONS

The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

Biological Insights from Schizophrenia-associated Loci in Ancestral Populations

Large-scale genome-wide association studies of schizophrenia have uncovered hundreds of associated loci but with extremely limited representation of African diaspora populations. We surveyed electronic health records of 200,000 individuals of African ancestry in the Million Veteran and All of Us Research Programs, and, coupled with genotype-level data from four case-control studies, realized a combined sample size of 13,012 affected and 54,266 unaffected persons. Three genome-wide significant signals - near PLXNA4, PMAIP1, and TRPA1 - are the first to be independently identified in populations of predominantly African ancestry. Joint analyses of African, European, and East Asian ancestries across 86,981 cases and 303,771 controls, yielded 376 distinct autosomal loci, which were refined to 708 putatively causal variants via multi-ancestry fine-mapping. Utilizing single-cell functional genomic data from human brain tissue and two complementary approaches, transcriptome-wide association studies and enhancer-promoter contact mapping, we identified a consensus set of 94 genes across ancestries and pinpointed the specific cell types in which they act. We identified reproducible associations of schizophrenia polygenic risk scores with schizophrenia diagnoses and a range of other mental and physical health problems. Our study addresses a longstanding gap in the generalizability of research findings for schizophrenia across ancestral populations, underlining shared biological underpinnings of schizophrenia across global populations in the presence of broadly divergent risk allele frequencies.

Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians

BACKGROUND AND HYPOTHESIS

While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited.

STUDY DESIGN

To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG).

STUDY RESULTS

Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ.

CONCLUSIONS

Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.

Identification of a Predictive Model for Schizophrenia Based on SNPs in a Chinese Population

Background

Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia.

Methods

A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy.

Results

5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, p = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively.

Conclusion

The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.

Identification of Schizophrenia Susceptibility Loci in the Urban Taiwanese Population

Background and Objectives: Genomic studies have identified several SNP loci associated with schizophrenia in East Asian populations. Environmental factors, particularly urbanization, play a significant role in schizophrenia development. This study aimed to identify schizophrenia susceptibility loci and characterize their biological functions and molecular pathways in Taiwanese urban Han individuals. Materials and Methods: Participants with schizophrenia were recruited from the Taiwan Precision Medicine Initiative at Tri-Service General Hospital. Genotype-phenotype association analysis was performed, with significant variants annotated and analyzed for functional relevance. Results: A total of 137 schizophrenia patients and 26,129 controls were enrolled. Ten significant variants (p < 1 × 10-5) and 15 expressed genes were identified, including rs1010840 (SOWAHC and RGPD6), rs11083963 (TRPM4), rs11619878 (LINC00355 and LINC01052), rs117010638 (AGBL1 and MIR548AP), rs1170702 (LINC01680 and LINC01720), rs12028521 (KAZN and PRDM2), rs12859097 (DMD), rs1556812 (ATP11A), rs78144262 (LINC00977), and rs9997349 (ENPEP). These variants and associated genes are involved in immune response, blood pressure regulation, muscle function, and the cytoskeleton. Conclusions: Identified variants and associated genes suggest a potential genetic predisposition to schizophrenia in the Taiwanese urban Han population, highlighting the importance of potential comorbidities, considering population-specific genetic and environmental interactions.

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

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

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

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

Air pollutants, genetic susceptibility, and incident schizophrenia in later life: A prospective study in the UK Biobank

OBJECTIVE

Air pollution has been linked to multiple psychiatric disorders, but little is known on its long-term association with schizophrenia. The interaction between air pollution and genetic susceptibility on incident schizophrenia has never been reported. We aimed to explore the associations between long-term air pollution exposure and late-onset schizophrenia and evaluate whether genetic susceptibility could modify the association.

METHODS

This population-based prospective cohort study included 437,802 middle-aged and elderly individuals free of schizophrenia at baseline in the UK Biobank. Land use regression models were applied in the estimation of the annual average concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) at residence. The associations between air pollutants and schizophrenia were evaluated by using Cox proportional hazard models. A polygenic risk score of schizophrenia was constructed for exploring potential interaction of air pollutants with genetic susceptibility.

RESULTS

An interquartile range increase in PM2.5, PM10, NO2, and NOx was associated with the hazard ratios (HR) for incident schizophrenia at 1.19, 1.16, 1.22, and 1.09, respectively. The exposure-response curves for the association of air pollution with incident schizophrenia were approximately linear. There are additive interactions of air pollution score (APS), PM10, NO2, and NOx with genetic risk. Specifically, compared with participants with low genetic susceptibility and low APS, the HR was 3.23 for individuals with high genetic risk and high APS, among which 0.49 excess risk could be attributed to the additive interaction, accounting for 15 % of the schizophrenia risk.

CONCLUSION

This large-scale, prospective cohort study conveys the first-hand evidence that long-term air pollution exposure could elevate schizophrenia incidence in later life, especially for individuals with higher genetic risks. The findings highlight the importance of improving air quality for preventing the late-onset schizophrenia in an aging era, especially among those with high genetic risks.

Real-time dynamic polygenic prediction for streaming data

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 (GWASs), which are often updated at lengthy intervals. As genetic data and health outcomes are continuously being generated at an ever-increasing pace, the current PRS training and deployment paradigm is suboptimal in maximizing the prediction accuracy of PRSs for incoming patients in healthcare settings. Here, we introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and calibration of PRS as each new sample is collected, without the need to perform intermediate GWASs. Through extensive simulation studies, we evaluate the performance of rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from the Mass General Brigham Biobank and UK Biobank, 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 in 7 Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically predicting and stratifying disease risk across diverse genetic ancestries.

Family-based genetic analysis in schizophrenia by whole-exome sequence to identify rare pathogenic variants

Schizophrenia (SCZ) is influenced by a combination of genetic and environmental factors. Although several studies have been conducted to identify the causative loci and genes, few of these loci or genes can be repeated due to the high phenotypic and genetic heterogeneity of disease, and their mechanisms are not fully understood. There may be some "missing heritability" that has not yet been found. In order to investigate the deleterious heritable mutations, whole-exome sequencing (WES) in pedigrees with SCZ was used in the current work. Two unrelated pedigrees with SCZ were recruited to perform WES. Genetic analysis was next performed to find potential variants in accordance with the prioritized strategy. Followed by genetic analysis to detect candidate variants according to the prioritized strategy. Next, a series of algorithms was used to predict the pathogenicity of variants. Sanger sequencing was finally conducted to verify the co-segregation. Recessive mutations in six genes (TFEB, SNAI2, TFAP2B, PRKDC, ST18 in Pedigree 1 and PKHD1L1 in Pedigree 2) that co-segregated with SCZ in two families were discovered through genetic analysis by WES. Sanger sequencing verified that all of the mutations in the affected siblings were homozygous. These results corroborated the hypothesis that SCZ exhibits strong heterogeneity and complex inheritance patterns. The newly discovered homozygous variations deepen our understanding of the mutation spectrum and offer more proof for the involvement of TFEB, SNAI2, TFAP2B, PRKDC, ST18, and PKHD1L1 in the development of SCZ.

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers

BACKGROUND

Suicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation exome-wide in a large, ancestrally diverse sample.

METHODS

We sequenced whole genomes of 13,584 soldiers from the Army STARRS (Army Study to Assess Risk and Resilience in Servicemembers), including 979 individuals with a history of suicide attempt. Uncommon, nonsilent protein-coding variants were analyzed exome-wide for association with suicide attempt using gene-collapsed and single-variant analyses.

RESULTS

We identified 19 genes with variants enriched in individuals with history of suicide attempt, either through gene-collapsed or single-variant analysis (Bonferroni padjusted < .05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS2, and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with 9 genes expressed at the highest levels in the brain versus other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (padjusted = .035) in addition to nominally significant genes including YWHAE and ACTR6, all of which have reported associations with other mental disorders.

CONCLUSIONS

These results advance the molecular characterization of suicide attempt behavior and support the utility of whole-genome sequencing for complementing the findings of genome-wide association studies in suicide research.

Identification of differentially expressed genes of blood leukocytes for Schizophrenia

Background

Schizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ.

Methods

The study collected peripheral blood leukocyte samples of 9 SCZ patients and 20 healthy controls for RNA sequencing analysis. Bioinformatic analyses included differentially expressed genes (DEGs) analysis, pathway enrichment analysis, and weighted gene co-expression network analysis (WGCNA).

Results

This study identified 1,205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and five modules were significantly correlated with SCZ (p < 0.05). There were 106 upregulated genes and 90 downregulated genes in the five modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other nine genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding.

Conclusion

This study finds that the gene functions in key modules and related enrichment pathways may help to elucidate the molecular pathogenesis of SCZ, and the potential of key genes to become blood biomarkers for SCZ warrants further validation.

Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals

Cannabis is one of the most widely used drugs globally. Decriminalization of cannabis is further increasing cannabis consumption. We performed genome-wide association studies (GWASs) of lifetime (N=131,895) and frequency (N=73,374) of cannabis use. Lifetime cannabis use GWAS identified two loci, one near CADM2 (rs11922956, p=2.40E-11) and another near GRM3 (rs12673181, p=6.90E-09). Frequency of use GWAS identified one locus near CADM2 (rs4856591, p=8.10E-09; r2 =0.76 with rs11922956). Both traits were heritable and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use associated cannabis use phenotypes in AllofUs participants. Phenome-wide association study of lifetime cannabis use PGS in a hospital cohort replicated associations with substance use and mood disorders, and uncovered associations with celiac and infectious diseases. This work demonstrates the value of GWASs of CUD transition risk factors.

Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps

It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.

Navigating centriolar satellites: the role of PCM1 in cellular and organismal processes

Centriolar satellites are ubiquitous membrane-less organelles that play critical roles in numerous cellular and organismal processes. They were initially discovered through electron microscopy as cytoplasmic granules surrounding centrosomes in vertebrate cells. These structures remained enigmatic until the identification of pericentriolar material 1 protein (PCM1) as their molecular marker, which has enabled their in-depth characterization. Recently, centriolar satellites have come into the spotlight due to their links to developmental and neurodegenerative disorders. This review presents a comprehensive summary of the major advances in centriolar satellite biology, with a focus on studies that investigated their biology associated with the essential scaffolding protein PCM1. We begin by exploring the molecular, cellular, and biochemical properties of centriolar satellites, laying the groundwork for a deeper understanding of their functions and mechanisms at both cellular and organismal levels. We then examine the implications of their dysregulation in various diseases, particularly highlighting their emerging roles in neurodegenerative and developmental disorders, as revealed by organismal models of PCM1. We conclude by discussing the current state of knowledge and posing questions about the adaptable nature of these organelles, thereby setting the stage for future research.

The Role of 3' Regulatory Region Flanking Kinectin 1 Gene in Schizophrenia

Objective

Schizophrenia is often associated with volumetric reductions in cortices and expansions in basal ganglia, particularly the putamen. Recent genome-wide association studies have highlighted the significance of variants in the 3' regulatory region adjacent to the kinectin 1 gene (KTN1) in regulating gray matter volume (GMV) of the putamen. This study aimed to comprehensively investigate the involvement of this region in schizophrenia.

Methods

We analyzed 1136 single-nucleotide polymorphisms (SNPs) covering the entire 3' regulatory region in 4 independent dbGaP samples (4604 schizophrenia patients vs. 4884 healthy subjects) and 3 independent Psychiatric Genomics Consortium samples (107 240 cases vs. 210 203 controls) to identify consistent associations. Additionally, we examined the regulatory effects of schizophrenia-associated alleles on KTN1 mRNA expression in 16 brain areas among 348 subjects, as well as GMVs of 7 subcortical nuclei in 38 258 subjects, and surface areas (SA) and thickness (TH) of the entire cortex and 34 cortical areas in 36 936 subjects.

Results

The major alleles (f > 0.5) of 25 variants increased (β > 0) the risk of schizophrenia across 2 to 5 independent samples (8.4 × 10-4 ≤ P ≤ .049). These schizophrenia-associated alleles significantly elevated (β > 0) GMVs of basal ganglia, including the putamen (6.0 × 10-11 ≤ P ≤ 1.1 × 10-4), caudate (8.7 × 10-4 ≤ P ≤ 9.4 × 10-3), pallidum (P = 6.0 × 10-4), and nucleus accumbens (P = 2.7 × 10-5). Moreover, they potentially augmented (β > 0) the SA of posterior cingulate and insular cortices, as well as the TH of frontal (pars triangularis and medial orbitofrontal), parietal (superior, precuneus, and inferior), and temporal (transverse) cortices, but potentially reduced (β < 0) the SA of the whole, frontal (medial orbitofrontal), and temporal (pole, superior, middle, and entorhinal) cortices, as well as the TH of rostral middle frontal and superior frontal cortices (8.9 × 10-4 ≤ P ≤ .050).

Conclusion

Our findings identify significant and functionally relevant risk alleles in the 3' regulatory region adjacent to KTN1, implicating their crucial roles in the development of schizophrenia.

Latin American Trans-ancestry INitiative for OCD genomics (LATINO): Study protocol

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder. Worldwide, its prevalence is ~2% and its etiology is mostly unknown. Identifying biological factors contributing to OCD will elucidate underlying mechanisms and might contribute to improved treatment outcomes. Genomic studies of OCD are beginning to reveal long-sought risk loci, but >95% of the cases currently in analysis are of homogenous European ancestry. If not addressed, this Eurocentric bias will result in OCD genomic findings being more accurate for individuals of European ancestry than other ancestries, thereby contributing to health disparities in potential future applications of genomics. In this study protocol paper, we describe the Latin American Trans-ancestry INitiative for OCD genomics (LATINO, https://www.latinostudy.org). LATINO is a new network of investigators from across Latin America, the United States, and Canada who have begun to collect DNA and clinical data from 5000 richly phenotyped OCD cases of Latin American ancestry in a culturally sensitive and ethical manner. In this project, we will utilize trans-ancestry genomic analyses to accelerate the identification of OCD risk loci, fine-map putative causal variants, and improve the performance of polygenic risk scores in diverse populations. We will also capitalize on rich clinical data to examine the genetics of treatment response, biologically plausible OCD subtypes, and symptom dimensions. Additionally, LATINO will help elucidate the diversity of the clinical presentations of OCD across cultures through various trainings developed and offered in collaboration with Latin American investigators. We believe this study will advance the important goal of global mental health discovery and equity.

STON2 variations are involved in synaptic dysfunction and schizophrenia-like behaviors by regulating Syt1 trafficking

Synaptic dysfunction is a core component of the pathophysiology of schizophrenia. However, the genetic risk factors and molecular mechanisms related to synaptic dysfunction are still not fully understood. The Stonin 2 (STON2) gene encodes a major adaptor for clathrin-mediated endocytosis (CME) of synaptic vesicles. In this study, we showed that the C-C (307Pro-851Ala) haplotype of STON2 increases the susceptibility to schizophrenia and examined whether STON2 variations cause schizophrenia-like behaviors through the regulation of CME. We found that schizophrenia-related STON2 variations led to protein dephosphorylation, which affected its interaction with synaptotagmin 1 (Syt1), a calcium sensor protein located in the presynaptic membrane that is critical for CME. STON2307Pro851Ala knockin mice exhibited deficits in synaptic transmission, short-term plasticity, and schizophrenia-like behaviors. Moreover, among seven antipsychotic drugs, patients with the C-C (307Pro-851Ala) haplotype responded better to haloperidol than did the T-A (307Ser-851Ser) carriers. The recovery of deficits in Syt1 sorting and synaptic transmission by acute administration of haloperidol effectively improved schizophrenia-like behaviors in STON2307Pro851Ala knockin mice. Our findings demonstrated the effect of schizophrenia-related STON2 variations on synaptic dysfunction through the regulation of CME, which might be attractive therapeutic targets for treating schizophrenia-like phenotypes.

Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders

A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient's immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.

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

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