CCL5 is essential for axonogenesis and neuronal restoration after brain injury.

BACKGROUND: Traumatic brain injury (TBI) causes axon tearing and synapse degradation, resulting in multiple neurological dysfunctions and exacerbation of early neurodegeneration; the repair of axonal and synaptic structures is critical for restoring neuronal function. C-C Motif Chemokine Ligand 5 (CCL5) shows many neuroprotective activities. METHOD: A close-head weight-drop system was used to induce mild brain trauma in C57BL/6 (wild-type, WT) and CCL5 knockout (CCL5-KO) mice. The mNSS score, rotarod, beam walking, and sticker removal tests were used to assay neurological function after mTBI in different groups of mice. The restoration of motor and sensory functions was impaired in CCL5-KO mice after one month of injury, with swelling of axons and synapses from Golgi staining and reduced synaptic proteins-synaptophysin and PSD95. Administration of recombinant CCL5 (Pre-treatment: 300 pg/g once before injury; or post-treatment: 30 pg/g every 2 days, since 3 days after injury for 1 month) through intranasal delivery into mouse brain improved the motor and sensory neurological dysfunctions in CCL5-KO TBI mice. RESULTS: Proteomic analysis using LC-MS/MS identified that the "Nervous system development and function"-related proteins, including axonogenesis, synaptogenesis, and myelination signaling pathways, were reduced in injured cortex of CCL5-KO mice; both pre-treatment and post-treatment with CCL5 augmented those pathways. Immunostaining and western blot analysis confirmed axonogenesis and synaptogenesis related Semaphorin, Ephrin, p70S6/mTOR signaling, and myelination-related Neuregulin/ErbB and FGF/FAK signaling pathways were up-regulated in the cortical tissue by CCL5 after brain injury. We also noticed cortex redevelopment after long-term administration of CCL5 after brain injury with increased Reelin positive Cajal-Rerzius Cells and CXCR4 expression. CCL5 enhanced the growth of cone filopodia in a primary neuron culture system; blocking CCL5's receptor CCR5 by Maraviroc reduced the intensity of filopodia in growth cone and also CCL5 mediated mTOR and Rho signalling activation. Inhibiting mTOR and Rho signaling abolished CCL5 induced growth cone formation. CONCLUSIONS: CCL5 plays a critical role in starting the intrinsic neuronal regeneration system following TBI, which includes growth cone formation, axonogenesis and synaptogensis, remyelination, and the subsequent proper wiring of cortical circuits. Our study underscores the potential of CCL5 as a robust therapeutic stratagem in treating axonal injury and degeneration during the chronic phase after mild brain injury.

Associations of Polygenic Risk for Depression, Traditional Chinese Medicine Constitution, and Depression: A Population-Based Study in Taiwan.

To comprehensively investigate the risk factors associated with depression, traditional Chinese medicine constitution (TCMC) has been found to be related to depression. However, the underlying mechanism remains unclear. This study examined the association between the concept of unbalanced TCMCs and major depressive disorder (MDD), investigated the overlapping polygenic risks between unbalanced TCMC and MDD, and performed a mediation test to establish potential pathways. In total, 11,030 individuals were recruited from the Taiwan Biobank, and the polygenic risk score (PRS) for MDD for each participant was calculated using the data from the Psychiatric Genomics Consortium. Unbalanced TCMC were classified as yang-deficiency, yin-deficiency, and stasis. The MDD PRS was associated with yang-deficiency odds ratio [OR] per standard deviation increase in standardized (PRS = 1.07, p = 0.0080), yin-deficiency (OR = 1.07, p = 0.0030), and stasis constitution (OR = 1.06, p = 0.0331). Yang-deficiency (OR = 2.07, p < 0.0001) and stasis constitutions (OR = 1.65, p = 0.0015) were associated with an increased risk of MDD. A higher number of unbalanced constitutions was associated with MDD (p < 0.0001). The effect of MDD PRS on MDD was partly mediated by yang-deficiency (10.21%) and stasis (8.41%) constitutions. This study provides evidence for the shared polygenic risk mechanism underlying depression and TCMC and the potential mediating role of TCMC in the polygenic liability for MDD.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases.

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.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood.

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

Background: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. Conclusion: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression.

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Shared genetic architectures of educational attainment in East Asian and European populations.

Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance.

Background: Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance. Methods: We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes. Results: MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance. Conclusions: Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits.

Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders.

Posttraumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 novel). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (e.g., GRIA1, GRM8, CACNA1E ), developmental, axon guidance, and transcription factors (e.g., FOXP2, EFNA5, DCC ), synaptic structure and function genes (e.g., PCLO, NCAM1, PDE4B ), and endocrine or immune regulators (e.g., ESR1, TRAF3, TANK ). Additional top genes influence stress, immune, fear, and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Causal influence of sleeping phenotypes on the risk of coronary artery disease and sudden cardiac arrest: A Mendelian randomization analysis.

OBJECTIVES: To assess the causal influence of sleep and circadian traits on coronary artery disease and sudden cardiac arrest with adjustment for obesity through a two-sample Mendelian randomization study. METHODS: We used summary statistics of 5 sleep and circadian traits for genome-wide association studies, including chronotype, sleep duration, long sleep (≥9 h a day), short sleep (<7 h a day), and insomnia (sample size range: 237,622-651,295). Coronary artery disease genome-wide association studies with 60,801 cases and 123,504 controls, sudden cardiac arrest genome-wide association studies with 3939 cases and 25,989 controls, and obesity genome-wide association studies with 806,834 individuals were also used. Multivariable Mendelian randomization was performed to estimate the causality. RESULTS: After adjusting for obesity, genetically predicted short sleep (odds ratio = 1.87 and p = .02), and genetically predicted insomnia (odds ratio = 1.17 and p = .001) were causally associated with increased odds of coronary artery disease. Genetically predicted long sleep (odds ratio = 0.06 and p = .02) and genetically predicted longer sleep duration (odds ratio = 0.36 for per-hour increase in sleep duration and p = .0006) were causally associated with decreased odds of sudden cardiac arrest. CONCLUSIONS: The findings of this Mendelian randomization study indicate that insomnia and short sleep contribute to the development of coronary artery disease, whereas a longer sleep duration protects from sudden cardiac arrest, independent of the influence of obesity. The mechanisms underlying these associations warrant further investigation.

Rare genetic variants impact muscle strength.

Muscle strength is highly heritable and predictive for multiple adverse health outcomes including mortality. Here, we present a rare protein-coding variant association study in 340,319 individuals for hand grip strength, a proxy measure of muscle strength. We show that the exome-wide burden of rare protein-truncating and damaging missense variants is associated with a reduction in hand grip strength. We identify six significant hand grip strength genes, KDM5B, OBSCN, GIGYF1, TTN, RB1CC1, and EIF3J. In the example of the titin (TTN) locus we demonstrate a convergence of rare with common variant association signals and uncover genetic relationships between reduced hand grip strength and disease. Finally, we identify shared mechanisms between brain and muscle function and uncover additive effects between rare and common genetic variation on muscle strength.

The impact of rare protein coding genetic variation on adult cognitive function.

Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.