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Schizophrenia is a chronic mental illness with a substantial genetic component. To unfold the complex etiology of schizophrenia, it is important to understand the interplay between genetic and nongenetic factors. Genetic factors involve variation in the DNA sequences of protein-coding genes, which directly contribute to phenotypic traits, and variation in noncoding sequences, which comprise 98% of the genome and contain DNA elements known to play a role in regulating gene expression. The epigenome refers to the chemical modifications on both DNA and the structural proteins that package DNA into the nucleus, which together regulate gene expression in specific cell types, conditions, and developmental stages. The dynamic nature of the epigenome makes it an ideal tool to investigate the relationship between inherited genetic mutations associated with schizophrenia and altered gene regulation throughout the course of brain development. In this review, we focus on the current understanding of the role of epigenetic marks and their three-dimensional nuclear organization in the developmental trajectory of distinct brain cell types to decipher the complex gene regulatory mechanisms that are disrupted in schizophrenia.

Remarkable advances have been made in schizophrenia (SCZ) GWAS, but gleaning biological insight from these loci is challenging. Genetic influences on gene expression (e.g., eQTLs) are cell type-specific, but most studies that attempt to clarify GWAS loci's influence on gene expression have employed tissues with mixed cell compositions that can obscure cell-specific effects. Furthermore, enriched SCZ heritability in the fetal brain underscores the need to study the impact of SCZ risk loci in specific developing neurons. MGE-derived cortical interneurons (cINs) are consistently affected in SCZ brains and show enriched SCZ heritability in human fetal brains. We identified SCZ GWAS risk genes that are dysregulated in iPSC-derived homogeneous populations of developing SCZ cINs. These SCZ GWAS loci differential expression (DE) genes converge on the PKC pathway. Their disruption results in PKC hyperactivity in developing cINs, leading to arborization deficits. We show that the fine-mapped GWAS locus in the ATP2A2 gene of the PKC pathway harbors enhancer marks by ATACseq and ChIPseq, and regulates ATP2A2 expression. We also generated developing glutamatergic neurons (GNs), another population with enriched SCZ heritability, and confirmed their functionality after transplantation into the mouse brain. Then, we identified SCZ GWAS risk genes that are dysregulated in developing SCZ GNs. GN-specific SCZ GWAS loci DE genes converge on the ion transporter pathway, distinct from those for cINs. Disruption of the pathway gene CACNA1D resulted in deficits of Ca

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.

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are major risk factors of human disease. We measured chromatin accessibility in sorted neurons and glia from 1,932 samples of human postmortem brain and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTL). Only 10.4% of caQTL are shared between neurons and glia, supporting the cell type specificity of genetic regulation of the brain regulome. Incorporating allele specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms underlying disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs, identifying the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides novel insights into brain disease etiology.One sentence summaryCell-type specific chromatin accessibility QTL reveals regulatory mechanisms underlying brain diseases.

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.

Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell "signatures," which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.

The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements. We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across 6 broad developmental time-points from fetus to adult. We identified cell-type specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at cis-regulatory elements precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell-type and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination, serve as a developmental blueprint of the human brain and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.One-Sentence SummarySimultaneous profiling of gene expression and chromatin accessibility in single nuclei from 6 developmental time-points sheds light on cell fate determination in the human cerebral cortex and on the molecular basis of neuropsychiatric disease.

To characterize the dysregulation of chromatin accessibility in Alzheimer’s disease (AD), we generated 636 ATAC-seq libraries from neuronal and nonneuronal nuclei isolated from the superior temporal gyrus and entorhinal cortex of 153 AD cases and 56 controls. By analyzing a total of ~20 billion read pairs, we expanded the repertoire of known open chromatin regions (OCRs) in the human brain and identified cell-type-specific enhancer–promoter interactions. We show that interindividual variability in OCRs can be leveraged to identify cis-regulatory domains (CRDs) that capture the three-dimensional structure of the genome (3D genome). We identified AD-associated effects on chromatin accessibility, the 3D genome and transcription factor (TF) regulatory networks. For one of the most AD-perturbed TFs, USF2, we validated its regulatory effect on lysosomal genes. Overall, we applied a systematic approach to understanding the role of the 3D genome in AD. We provide all data as an online resource for widespread community-based analysis.

Brain region- and cell-specific transcriptomic and epigenomic molecular features are associated with heritability for neuropsychiatric traits, but a systematic view, considering cortical and subcortical regions, is lacking. Here, we provide an atlas of chromatin accessibility and gene expression in neuronal and non-neuronal nuclei across 25 distinct human cortical and subcortical brain regions from 6 neurotypical controls. We identified extensive gene expression and chromatin accessibility differences across brain regions, including variation in alternative promoter-isoform usage and enhancer-promoter interactions. Genes with distinct promoter-isoform usage across brain regions are strongly enriched for neuropsychiatric disease risk variants. Using an integrative approach, we characterized the function of the brain region-specific chromatin co-accessibility and gene-coexpression modules that are robustly associated with genetic risk for neuropsychiatric disorders. In addition, we identified a novel set of genes that is enriched for disease risk variants but is independent of cell-type specific gene expression and known susceptibility pathways. Our results provide a valuable resource for studying molecular regulation across multiple regions of the human brain and suggest a unique contribution of epigenetic modifications from subcortical areas to neuropsychiatric disorders.

Genotype-phenotype association is found in many biological systems, such as brain-related diseases and behavioral traits. Despite the recent improvement in the prediction of phenotypes from genotypes, they can be further improved and explainability of these predictions remains challenging, primarily due to complex underlying molecular and cellular mechanisms. Emerging multimodal data enables studying such mechanisms at different scales from genotype to phenotypes involving intermediate phenotypes like gene expression. However, due to the black-box nature of many machine learning techniques, it is challenging to integrate these multi-modalities and interpret the biological insights in prediction, especially when some modality is missing. Biological knowledge has recently been incorporated into machine learning modeling to help understand the reasoning behind the choices made by these models.To this end, we developed DeepGAMI, an interpretable deep learning model to improve genotype-phenotype prediction from multimodal data. DeepGAMI uses prior biological knowledge to define the neural network architecture. Notably, it embeds an auxiliary-learning layer for cross-modal imputation while training the model from multimodal data. Using this pre-trained layer, we can impute latent features of additional modalities and thus enable predicting phenotypes from a single modality only. Finally, the model uses integrated gradient to prioritize multimodal features and links for phenotypes. We applied DeepGAMI to multiple emerging multimodal datasets: (1) population-level genotype and bulk-tissue gene expression data for predicting schizophrenia, (2) population-level genotype and gene expression data for predicting clinical phenotypes in Alzheimer’s Disease, (3) gene expression and electrophysiological data of single neuronal cells in the mouse visual cortex, and (4) cell-type gene expression and genotype data for predicting schizophrenia. We found that DeepGAMI outperforms existing state-of-the-art methods and provides a profound understanding of gene regulatory mechanisms from genotype to phenotype, especially at cellular resolution. DeepGAMI is an open-source tool and is available at https://github.com/daifengwanglab/DeepGAMI.

The brain consists of thousands of neuronal types that are generated by stem cells producing different neuronal types as they age. In Drosophila, this temporal patterning is driven by the successive expression of temporal transcription factors (tTFs)(1-3) We used single-cell mRNA sequencing to identify the complete series of tTFs that specify most Drosophila optic lobe neurons. We verify that tTFs regulate the progression of the series by activating the next tTF(s) and repressing the previous one(s), and also identify more complex regulations. Moreover, we establish the temporal window of origin and birth order of each neuronal type in the medulla and provide evidence that these tTFs are sufficient to explain the generation of all the neuronal diversity in this brain region. Finally, we describe the first steps of neuronal differentiation. We find that terminal differentiation genes, such as neurotransmitter-related genes, are present as transcripts, but not as proteins, in immature larval neurons; we show that these steps are conserved in humans. This comprehensive analysis of a temporal series of tTFs in the optic lobe offers mechanistic insights into how tTF series are regulated, and how they can lead to the generation of a complete set of neurons.

Genetic liability associated with schizophrenia most frequently arises from common genetic variation that confers small individual effects and contributes to phenotypes only when considered in aggregate. These risk variants are typically non-coding and act by genetically regulating the expression of one or more gene targets (eGenes), but the mechanisms by which unlinked eGenes interact to contribute to complex genetic risk remains unclear. Here we apply a pooled CRISPR approach to evaluate in parallel ten schizophrenia eGenes in human glutamatergic neurons. Querying the shared neuronal impacts across eGenes reveals shared downstream transcriptomic impacts (“convergence”) concentrated on pathways of brain development and synaptic signaling. The composition and strength of convergent networks is influenced by both the similarity of eGene functional annotation and the strength of eGene co-expression in human postmortem brain tissue. Convergent networks resolve distinct patterns of eGene up-regulation associated with individual-level risk in the post-mortem dorsolateral prefrontal cortex that are broadly enriched for neuropsychiatric disorder risk genes and may represent molecular subtypes of schizophrenia. Convergent gene targets are druggable as novel points of therapeutic intervention. Overall, convergence suggests a model to explain how non-additive interactions arise between risk genes and may explain cross-disorder pleiotropy of genetic risk for psychiatric disorders.

Microglia are brain resident myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer’s Disease (AD). Yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to disease is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically-driven variation and cell-specific enhancer-promoter interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 novel genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides novel insights into the etiology of neurodegenerative disease.One-Sentence SummaryCharacterizing the genetic regulation of chromatin accessibility and gene expression in human microglia refines molecular mechanisms of Alzheimer’s disease risk loci.

SARS-CoV-2 infects less than 1% of cells in the human body, yet it can cause severe damage in a variety of organs. Thus, deciphering the non-cell autonomous effects of SARS-CoV-2 infection is imperative for understanding the cellular and molecular disruption it elicits. Neurological and cognitive defects are among the least understood symptoms of COVID-19 patients, with olfactory dysfunction being their most common sensory deficit. Here, we show that both in humans and hamsters SARS-CoV-2 infection causes widespread downregulation of olfactory receptors (OR) and of their signaling components. This non-cell autonomous effect is preceded by a dramatic reorganization of the neuronal nuclear architecture, which results in dissipation of genomic compartments harboring OR genes. Our data provide a potential mechanism by which SARS-CoV-2 infection alters the cellular morphology and the transcriptome of cells it cannot infect, offering insight to its systemic effects in olfaction and beyond., Graphical Abstract, SARS-CoV-2 induces non-cell autonomous effects in olfactory epithelium that disrupts nuclear architecture and downregulates olfactory receptor expression in olfactory sensory neurons.

The brain consists of thousands of different neuronal types that are generated through multiple divisions of neuronal stem cells. These stem cells have the capacity to generate different neuronal types at different stages of their development. In Drosophila, this temporal patterning is driven by the successive expression of temporal transcription factors (tTFs). While a number of tTFs are known in different animals and across various parts of the nervous system, these have been mostly identified by informed guesses and antibody availability. We used single-cell mRNA sequencing to identify the complete series of tTFs that specify most Drosophila medulla neurons in the optic lobe. We tested the genetic interactions among these tTFs. While we verify the general principle that tTFs regulate the progression of the series by activating the next tTFs in the series and repressing the previous ones, we also identify more complex regulations. Two of the tTFs, Eyeless and Dichaete, act as hubs integrating the input of several upstream tTFs before allowing the series to progress and in turn regulating the expression of several downstream tTFs. Moreover, we show that tTFs not only specify neuronal identity by controlling the expression of cell type-specific genes. Finally, we describe the very first steps of neuronal differentiation and find that terminal differentiation genes, such as neurotransmitter-related genes, are present as transcripts, but not as proteins, in immature larval neurons days before they are being used in functioning neurons; we show that these mechanisms are conserved in humans. Our results offer a comprehensive description of a temporal series of tTFs in a neuronal system, offering mechanistic insights into the regulation of the progression of the series and the regulation of neuronal diversity. This represents a proof-of-principle for the use of single-cell mRNA sequencing for the comparison of temporal patterning across phyla that can lead to an understanding of how the human brain develops and how it has evolved.

BackgroundRecent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step.MethodsWe integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by in vitro perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the in silico drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence.ResultsWe identify IL10RB as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. In vitro IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19.ConclusionsWe establish an integrative data-driven approach for gene target prioritization. We identify and validate IL10RB as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.

To explore modular organization of chromosomes in schizophrenia (SCZ) and bipolar disorder (BD), we applied ‘population-scale’ correlational structuring of 739 histone H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from the prefrontal cortex (PFC) of 568 cases and controls. Neuronal histone acetylomes and methylomes assembled as thousands of cis-regulatory domains (CRDs), revealing fine-grained, kilo-to megabase scale chromatin organization at higher resolution but firmly integrated into Hi-C chromosomal conformations. Large clusters of domains that were hyperacetylated in disease shared spatial positioning within the nucleus, predominantly regulating PFC projection neuron function and excitatory neurotransmission. Hypoacetylated domains were linked to inhibitory interneuron- and myelination-relevant genes. Chromosomal modular architecture is affected in SCZ and BD, with hyperacetylated domains showing unexpectedly strong convergences defined by cell type, nuclear topography, genetic risk, and active chromatin state across a wide developmental window.

Brain regions communicate with each other via tracts of myelinated axons, commonly referred to as white matter. White matter microstructure can be measured in the living human brain using diffusion based magnetic resonance imaging (dMRI), and has been found to be altered in patients with neuropsychiatric disorders. Although under strong genetic control, few genetic variants influencing white matter microstructure have ever been identified. Here we identified common genetic variants influencing white matter microstructure using dMRI in 42,919 individuals (35,741 in the UK Biobank). The dMRIs were summarized into 215 white matter microstructure traits, including 105 measures from tract-specific functional principal component analysis. Genome-wide association analysis identified many novel white matter microstructure associated loci (P< 2.3 × 10−10). We identified shared genetic influences through genetic correlations between white matter tracts and 62 other complex traits, including stroke, neuropsychiatric disorders (e.g., ADHD, bipolar disorder, major depressive disorder, schizophrenia), cognition, neuroticism, chronotype, as well as non-brain traits. Common variants associated with white matter microstructure alter the function of regulatory elements in glial cells, particularly oligodendrocytes. White matter associated genes were enriched in pathways involved in brain disease pathogenesis, neurodevelopment process, and repair of white matter damage (P< 1.5 × 10−8). In summary, this large-scale tract-specific study provides a big step forward in understanding the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding the population-level variation of enhancers in the human brain. Besides regulating tissue- and cell-type-specific transcription of target genes, enhancers themselves can be transcribed. We expanded the catalog of known human brain transcribed enhancers by an order of magnitude by generating and jointly analyzing large-scale cell-type-specific transcriptome and regulome data. Examination of the transcriptome in 1,382 brain samples in two independent cohorts identified robust expression of transcribed enhancers. We explored gene-enhancer coordination and found that enhancer-linked genes are strongly implicated in neuropsychiatric disease. We identified significant expression quantitative trait loci (eQTL) for 25,958 enhancers which mediate 6.8% of schizophrenia heritability, mostly independent from standard gene eQTL. Inclusion of enhancer eQTL in transcriptome-wide association studies enhanced functional interpretation of disease loci. Overall, our study characterizes the enhancer-gene regulome and genetic mechanisms in the human cortex in both healthy and disease states.

Background While schizophrenia differs between males and females in the age of onset, symptomatology, and disease course, the molecular mechanisms underlying these differences remain uncharacterized. Methods To address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA sequencing data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium. Results Analysis across the cohorts identified a reproducible gene expression signature of schizophrenia that was highly concordant with previous work. Differential expression across sex was reproducible across cohorts and identified X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature was also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality from thousands of genes to dozens of modules and elucidate interactions among genes. We found enrichment of coexpression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involved a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation. Conclusions Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.

Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer’s disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown, impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS with myeloid epigenomic and transcriptomic datasets using analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We identify AD risk enhancers and nominate candidate causal genes among their likely targets (including AP4E1, AP4M1, APBB3, BIN1, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, TP53INP1, and ZYX) in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify AD risk by regulating gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Taken together, this study integrates AD GWAS with multiple myeloid genomic datasets to investigate the mechanisms of AD risk alleles and nominates candidate functional variants, regulatory elements and genes that likely modulate disease susceptibility., This study integrates Alzheimer’s disease (AD) GWAS data with myeloid cell genomics, and reports that myeloid active enhancers are most burdened by AD risk alleles. The authors also nominate candidate causal regulatory elements, variants and genes that likely modulate the risk for AD.

Olfaction relies on a coordinated partnership between odorant flow and neuronal communication. Disruption in our ability to detect odors, or anosmia, has emerged as a hallmark symptom of infection with SARS-CoV-2, yet the mechanism behind this abrupt sensory deficit remains elusive. Here, using molecular evaluation of human olfactory epithelium (OE) from subjects succumbing to COVID-19 and a hamster model of SARS-CoV-2 infection, we discovered widespread downregulation of olfactory receptors (ORs) as well as key components of their signaling pathway. OR downregulation likely represents a non-cell autonomous effect, since SARS-CoV-2 detection in OSNs is extremely rare both in human and hamster OEs. A likely explanation for the reduction of OR transcription is the striking reorganization of nuclear architecture observed in the OSN lineage, which disrupts multi-chromosomal compartments regulating OR expression in humans and hamsters. Our experiments uncover a novel molecular mechanism by which a virus with a very selective tropism can elicit persistent transcriptional changes in cells that evade it, contributing to the severity of COVID-19.

While large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL’s that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.

In coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the relationship between brain tropism, neuroinflammation and host immune response has not been well characterized. We analyzed 68,557 single-nucleus transcriptomes from three brain regions (dorsolateral prefrontal cortex, medulla oblongata and choroid plexus) and identified an increased proportion of stromal cells and monocytes in the choroid plexus of COVID-19 patients. Differential gene expression, pseudo-temporal trajectory and gene regulatory network analyses revealed microglial transcriptome perturbations, mediating a range of biological processes, including cellular activation, mobility and phagocytosis. Quantification of viral spike S1 protein and SARS-CoV-2 transcripts did not support the notion of brain tropism. Overall, our findings suggest extensive neuroinflammation in patients with acute COVID-19.One Sentence SummarySingle-nucleus transcriptome analysis suggests extensive neuroinflammation in human brain tissue of patients with acute coronavirus disease 2019.

SARS-CoV-2 infects less than 1% of cells in the human body, yet it can cause severe damage in a variety of organs. Thus, deciphering the non-cell autonomous effects of SARS-CoV-2 infection is imperative for understanding the cellular and molecular disruption it elicits. Neurological and cognitive defects are among the least understood symptoms of COVID-19 patients, with olfactory dysfunction being their most common sensory deficit. Here, we show that both in humans and hamsters SARS-CoV-2 infection causes widespread downregulation of olfactory receptors (OR) and of their signaling components. This non-cell autonomous effect coincides with a dramatic reorganization of the neuronal nuclear architecture, which results in dissipation of genomic OR compartments and elimination of genomic contact domains genomewide. Our data provide a novel mechanism by which SARS-CoV-2 infection alters the cellular morphology and the transcriptome of cells it cannot infect, providing insight to its systemic effects in the nervous system and beyond. Funding Information: NIDCD 3R01DC018744-01S1 (SL, JO) National Institutes of Health grant, 4D Nucleome Consortium U01DA052783 (SL) Howard Hughes Medical Institute Faculty Scholar Award (SL), Zegar Family Foundation (SL). Ethics Approval Statement: The study was approved by the ethics and Institutional Review Board of Columbia University Medical Center (IRB AAAT0689, AAAS7370). LVG Golden Syrian hamsters (Mesocricetus auratus) were treated in compliance with the rules and regulations of IACUC under protocol number PROTO202000113-20-0743.

SummaryPrior to identifying clusters in single cell gene expression experiments, selecting the top principal components is a critical step for filtering out noise in the data set. Identifying these top principal components typically focuses on the total variance explained, and principal components that explain small clusters from rare populations will not necessarily capture a large percentage of variance in the data. We present a computationally efficient alternative for identifying the optimal principal components based on the tails of the distribution of variance explained for each observation. We then evaluate the efficacy of our approach in three different single cell RNA-sequencing data sets and find that our method matches, or outperforms, other selection criteria that are typically employed in the literature.Availability and implementationpcqc is written in Python and available at github.com/RoussosLab/pcqc