Mapping genomic loci implicates genes and synaptic biology in schizophrenia.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Convergence of the dysregulated regulome in schizophrenia with polygenic risk and evolutionarily constrained enhancers.

Enhancers play an essential role in the etiology of schizophrenia; however, the dysregulation of enhancer activity and its impact on the regulome in schizophrenia remains understudied. To address this gap in our knowledge, we assessed enhancer and gene expression in 1,382 brain samples comprising cases with schizophrenia and unaffected controls. Dysregulation of enhancer expression was concordant with changes in gene expression, and was more closely associated with schizophrenia polygenic risk, suggesting that enhancer dysregulation is proximal to the genetic etiology of the disease. Modeling the shared variance of cis-coordinated genes and enhancers revealed a gene regulatory program that was highly associated with genetic vulnerability to schizophrenia. By integrating coordinated factors with evolutionary constraints, we found that enhancers acquired during human evolution are more likely to regulate genes that are implicated in neuropsychiatric disorders and, thus, hold potential as therapeutic targets. Our analysis provides a systematic view of regulome dysregulation in schizophrenia and highlights its convergence with schizophrenia polygenic risk and human-gained enhancers.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

PS1 FAD mutants decrease ephrinB2-regulated angiogenic functions, ischemia-induced brain neovascularization and neuronal survival.

Microvascular pathology and ischemic lesions contribute substantially to neuronal dysfunction and loss that lead to Alzheimer disease (AD). To facilitate recovery, the brain stimulates neovascularization of damaged tissue via sprouting angiogenesis, a process regulated by endothelial cell (EC) sprouting and the EphB4/ephrinB2 system. Here, we show that in cultures of brain ECs, EphB4 stimulates the VE-cadherin/Rok-α angiogenic complexes known to mediate sprouting angiogenesis. Importantly, brain EC cultures expressing PS1 FAD mutants decrease the EphB4-stimulated γ-secretase cleavage of ephrinB2 and reduce production of the angiogenic peptide ephrinB2/CTF2, the VE-cadherin angiogenic complexes and EC sprouting and tube formation. These data suggest that FAD mutants may attenuate ischemia-induced brain angiogenesis. Supporting this hypothesis, ischemia-induced VE-cadherin angiogenic complexes, levels of neoangiogenesis marker Endoglin, vascular density, and cerebral blood flow recovery, are all decreased in brains of mouse models expressing PS1 FAD mutants. Ischemia-induced brain neuronal death and cognitive deficits also increase in these mice. Furthermore, a small peptide comprising the C-terminal sequence of peptide ephrinB2/CTF2 rescues angiogenic functions of brain ECs expressing PS1 FAD mutants. Together, our data show that PS1 FAD mutations impede the EphB4/ephrinB2-mediated angiogenic functions of ECs and impair brain neovascularization, neuronal survival and cognitive recovery following ischemia. Furthermore, our data reveal a novel brain angiogenic mechanism targeted by PS1 FAD mutants and a potential therapeutic target for ischemia-induced neurodegeneration. Importantly, FAD mutant effects occur in absence of neuropathological hallmarks of AD, supporting that such hallmarks may form downstream of mutant effects on neoangiogenesis and neuronal survival.

Open chromatin profiling of human postmortem brain infers functional roles for non-coding schizophrenia loci.

Open chromatin provides access to DNA-binding proteins for the correct spatiotemporal regulation of gene expression. Mapping chromatin accessibility has been widely used to identify the location of cis regulatory elements (CREs) including promoters and enhancers. CREs show tissue- and cell-type specificity and disease-associated variants are often enriched for CREs in the tissues and cells that pertain to a given disease. To better understand the role of CREs in neuropsychiatric disorders we applied the Assay for Transposase Accessible Chromatin followed by sequencing (ATAC-seq) to neuronal and non-neuronal nuclei isolated from frozen postmortem human brain by fluorescence-activated nuclear sorting (FANS). Most of the identified open chromatin regions (OCRs) are differentially accessible between neurons and non-neurons, and show enrichment with known cell type markers, promoters and enhancers. Relative to those of non-neurons, neuronal OCRs are more evolutionarily conserved and are enriched in distal regulatory elements. Transcription factor (TF) footprinting analysis identifies differences in the regulome between neuronal and non-neuronal cells and ascribes putative functional roles to a number of non-coding schizophrenia (SCZ) risk variants. Among the identified variants is a Single Nucleotide Polymorphism (SNP) proximal to the gene encoding SNX19. In vitro experiments reveal that this SNP leads to an increase in transcriptional activity. As elevated expression of SNX19 has been associated with SCZ, our data provide evidence that the identified SNP contributes to disease. These results represent the first analysis of OCRs and TF-binding sites in distinct populations of postmortem human brain cells and further our understanding of the regulome and the impact of neuropsychiatric disease-associated genetic risk variants.

Presenilin1/γ-secretase protects neurons from glucose deprivation-induced death by regulating miR-212 and PEA15.

Reduced cerebral glucose utilization is found in aged individuals and often is an early sign of neurodegeneration. Here, we show that under glucose deprivation (GD) conditions, decreased expression of presenilin 1 (PS1) results in decreased neuronal survival, whereas increased PS1 increases neuronal survival. Inhibition of γ-secretase also decreases neuronal survival under GD conditions, which suggests the PS1/γ-secretase system protects neurons from GD-induced death. We also show that neuronal levels of the survival protein, phosphoprotein enriched in astrocytes at ∼15 kDa (PEA15), and its mRNA are regulated by PS1/γ-secretase. Furthermore, down-regulation of PEA15 decreases neuronal survival under reduced glucose conditions, whereas exogenous PEA15 increases neuronal survival even in the absence of PS1, which indicates that PEA15 promotes neuronal survival under GD conditions. The absence or reduction of PS1, as well as γ-secretase inhibitors, increases neuronal miR-212, which targets PEA15 mRNA. PS1/γ-secretase activates the transcription factor, cAMP response element-binding protein, regulating miR-212, which targets PEA15 mRNA. Taken together, our data show that under conditions of reduced glucose, the PS1/γ-secretase system decreases neuronal losses by suppressing miR-212 and increasing its target survival factor, PEA15. These observations have implications for mechanisms of neuronal death under conditions of reduced glucose and may provide targets for intervention in neurodegenerative disorders.-Huang, Q., Voloudakis, G., Ren, Y., Yoon, Y., Zhang, E., Kajiwara, Y., Shao, Z., Xuan, Z., Lebedev, D., Georgakopoulos, A., Robakis, N. K. Presenilin1/γ-secretase protects neurons from glucose deprivation-induced death by regulating miR-212 and PEA15.

The product of the γ-secretase processing of ephrinB2 regulates VE-cadherin complexes and angiogenesis.

Presenilin-1 (PS1) gene encodes the catalytic component of γ-secretase, which proteolytically processes several type I transmembrane proteins. We here present evidence that the cytosolic peptide efnB2/CTF2 produced by the PS1/γ-secretase cleavage of efnB2 ligand promotes EphB4 receptor-dependent angiogenesis in vitro. EfnB2/CTF2 increases endothelial cell sprouting and tube formation, stimulates the formation of angiogenic complexes that include VE-cadherin, Raf-1 and Rok-α, and increases MLC2 phosphorylation. These functions are mediated by the PDZ-binding domain of efnB2. Acute downregulation of PS1 or inhibition of γ-secretase inhibits the angiogenic functions of EphB4 while absence of PS1 decreases the VE-cadherin angiogenic complexes of mouse brain. Our data reveal a mechanism by which PS1/γ-secretase regulates efnB2/EphB4 mediated angiogenesis.

Presenilin 1 is necessary for neuronal, but not glial, EGFR expression and neuroprotection via γ-secretase-independent transcriptional mechanisms.

Epidermal growth factor receptor (EGFR) plays pivotal roles in cell proliferation, differentiation, and tissue development, while EGFs protect neurons from toxic insults by binding EGFR and stimulating survival signaling. Furthermore, recent evidence implicates this receptor in neurometabolic disorders like Alzheimer disease and aging. Here we show that absence of presenilin 1 (PS1) results in dramatic decrease (>95%) of neuronal EGFR and that PS1-null (PS1(-/-)) brains have reduced amounts of this receptor. PS1(-/-) cortical neurons contain little EGFR and show no epidermal growth factor-induced survival signaling or protection against excitotoxicity, but exogenous EGFR rescues both functions even in absence of PS1. EGFR mRNA is greatly reduced (>95%) in PS1(-/-) neurons, and PS1(-/-) brains contain decreased amounts of this mRNA, although PS1 affects the stability of neither EGFR nor its mRNA. Exogenous PS1 increases neuronal EGFR mRNA, while down-regulation of PS1 decreases this mRNA. These effects are neuron specific, as PS1 affects the EGFR of neither glial nor fibroblast cells. In addition, PS1 controls EGFR through novel mechanisms shared with neither γ-secretase nor PS2. Our data reveal that PS1 functions as a positive transcriptional regulator of neuronal EGFR controlling its expression in a cell-specific manner. Severe downregulation of EGFR may contribute to developmental abnormalities and lethal phenotype found in PS1, but not PS2, null mice. Furthermore, PS1 may affect neuroprotection and Alzheimer disease by controlling survival signaling of neuronal EGFR.

A critical role for human caspase-4 in endotoxin sensitivity.

Response to endotoxins is an important part of the organismal reaction to Gram-negative bacteria and plays a critical role in sepsis and septic shock, as well as other conditions such as metabolic endotoxemia. Humans are generally more sensitive to endotoxins when compared with experimental animals such as mice. Inflammatory caspases mediate endotoxin-induced IL-1ß secretion and lethality in mice, and caspase-4 is an inflammatory caspase that is found in the human, and not mouse, genome. To test whether caspase-4 is involved in endotoxin sensitivity, we developed a transgenic mouse expressing human caspase-4 in its genomic context. Caspase-4 transgenic mice exhibited significantly higher endotoxin sensitivity, as measured by enhanced cytokine secretion and lethality following LPS challenge. Using bone marrow-derived macrophages, we then observed that caspase-4 can support activation of caspase-1 and secretion of IL-1ß and IL-18 in response to priming signals (LPS or Pam3CSK4) alone, without the need for second signals to stimulate the assembly of the inflammasome. These findings indicate that the regulation of caspase-1 activity by human caspase-4 could represent a unique mechanism in humans, as compared with laboratory rodents, and may partially explain the higher sensitivity to endotoxins observed in humans. Regulation of the expression, activation, or activity of caspase-4 therefore represents targets for systemic inflammatory response syndrome, sepsis, septic shock, and related disorders.

Single-cell multi-cohort dissection of the schizophrenia transcriptome.

The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

Alzheimer's disease transcriptional landscape in ex-vivo human microglia.

Microglia are resident immune cells of the brain and are implicated in the etiology of Alzheimer's Disease (AD) and other diseases. Yet the cellular and molecular processes regulating their function throughout the course of the disease are poorly understood. Here, we present the transcriptional landscape of primary microglia from 189 human postmortem brains, including 58 healthy aging individuals and 131 with a range of disease phenotypes, including 63 patients representing the full spectrum of clinical and pathological severity of AD. We identified transcriptional changes associated with multiple AD phenotypes, capturing the severity of dementia and neuropathological lesions. Transcript-level analyses identified additional genes with heterogeneous isoform usage and AD phenotypes. We identified changes in gene-gene coordination in AD, dysregulation of co-expression modules, and disease subtypes with distinct gene expression. Taken together, these data further our understanding of the key role of microglia in AD biology and nominate candidates for therapeutic intervention.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies.

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity.

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.