Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

Silencer variants are key drivers of gene upregulation in Alzheimer's disease

Alzheimer's disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial heritability, genetic studies have emerged as indispensable for elucidating genes and biological pathways driving AD onset and progression. However, genetic and molecular mechanisms underlying AD remain poorly defined, largely due to the pronounced heterogeneity of AD and the intricate interactions among AD genetic factors. Notably, approximately 90% of AD-associated genetic variants reside in intronic and intergenic regions, yet their functional significance has remained largely uncharacterized. To address this challenge, we developed a deep learning framework combining bulk and single-cell epigenomic data to evaluate the regulatory potential (i.e., silencing and activating strength) of noncoding AD variants in the dorsolateral prefrontal cortex (DLPFCs) and its major cell types. This model identified 1,457 silencer and 3,084 enhancer AD-associated variants in the DLPFC and binned them into silencer variants only (SL), enhancer variants only (EN), or both variant types (ENSL) classes. Each class exerts distinct cellular and molecular influences on AD pathogenesis. EN loci predominantly regulate housekeeping metabolic processes, whereas SL loci (including the genes MS4A6A , TREM2 , USP6NL , HLA-D ) are selectively linked to immune responses. Notably, 71% of these genes are significantly upregulated in AD and pro-inflammation-stimulated microglia. Furthermore, genes associated with SL loci are, in neuronal cells, often responsive to glutamate receptor antagonists (e.g, NBQX) and anti-inflammatory perturbagens (such as D-64131), the compound classes known for reducing the AD risk. ENSL loci, in contrast, are uniquely implicated in memory maintenance, neurofibrillary tangle assembly, and are also shared by other neurological disorders such as Parkinson's disease and schizophrenia. Key genes in this class of loci, such as MAPT , CR1/2 , and CLU , are frequently upregulated in AD subtypes with hyperphosphorylated tau aggregates. Critically, our model can accurately predict the impact of regulatory variants, with an average Pearson correlation coefficient of 0.54 and a directional concordance rate of 70% between our predictions and experimental outcomes. This model identified rs636317 as a causal AD variant in the MS4A locus, distinguishing it from the 7bp-away allele-neutral variant rs636341. Similarly, rs7922621 was prioritized over its 54-bp-away allele-neutral rs7901634 in the TSPAN14 locus. Additional causal variants include rs6701713 in the CR1 locus, and rs28834970 and rs755951 in the PTK2B locus. Collectively, this work advances our understanding of the regulatory landscape of AD-associated genetic variants, providing a framework to explore their functional roles in the pathogenesis of this complex disease.

Absence of GluN2A in hippocampal CA1 neurons leads to altered dendritic structure and reduced frequency of miniature excitatory synaptic events

GluN2A is a NMDA receptor subunit postulated as important for learning and memory. In humans, heterozygous loss of function variants in the gene encoding it (GRIN2A) increase the risk of epilepsy, intellectual disability and schizophrenia. Haploinsufficient mouse models show electrophysiological abnormalities and thus to improve and widen understanding of the pathogenesis of GRIN2A-associated disorders in humans, this study aimed to assess the impact of Grin2a absence and haploinsufficiency on core neuronal and synaptic properties in genetically modified rats. Electrophysiological whole-cell current- and voltage-clamp recordings were made from CA1 pyramidal neurons in acute hippocampal slices from wild-type and Grin2a heterozygous (Grin2a+/- ) and homozygous (Grin2a-/- ) knock out rats aged postnatal day 27-34. While reduced levels or absence of GluN2A did not affect neuronal excitability or intrinsic membrane properties in both Grin2a+/- and Grin2a-/- rats, we found a reduced frequency of miniature excitatory post synaptic currents and a reduced density of proximal dendrites suggestive of a reduced number of excitatory synapses. Recordings from CA1 neurons in slices prepared from Grin2a+/- and Grin2a-/- rats revealed there was a reduced ratio of the current mediated by NMDA receptors compared to AMPA receptors, while in Grin2a-/- recordings, there was a slowing of the decay time-constant of the NMDA receptor-mediated excitatory postsynaptic currents. Moreover, neither summation of sub-threshold excitatory postsynaptic potentials nor summation of supra-threshold excitatory postsynaptic potentials to initiate action potential firing in CA1 pyramidal neurons indicated any dependence on GluN2A. We conclude that reduced levels of GluN2A alters the kinetics of NMDA receptor-mediated synaptic events and dendritic structure of CA1 neurons, but do not affect several other core neuronal functions. These relatively subtle changes are consistent with the largely intact neural functioning of the majority of humans carrying GRIN2A loss of function variants. Further research could explore whether the changes in synaptic properties we observed contribute to alterations in higher level circuit dynamics and computation, which may manifest as disorders of cognition and excitability in humans.

SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders

Most genetic risk variants for neurological diseases are located in non-coding regulatory regions, where they may often act as expression quantitative trait loci (eQTLs), modulating gene expression and influencing disease susceptibility. However, eQTL studies in bulk brain tissue or specific cell types lack the resolution to capture the brain's cellular diversity. Single-nucleus RNA sequencing (snRNA-seq) offers high-resolution mapping of eQTLs across diverse brain cell types. Here, we performed a meta-analysis, "SingleBrain," integrating publicly available snRNA-seq and genotype data from four cohorts, totaling 5.8 million nuclei from 983 individuals. We mapped cis-eQTLs across major brain cell types and subtypes and employed statistical colocalization and Mendelian randomization to identify genes mediating neurological disease risk. We observed up to a 10-fold increase in cis-eQTLs compared to previous studies and uncovered novel cell type-specific genes linked to Alzheimer's disease, Parkinson's disease, and schizophrenia that were previously undetectable in bulk tissue analyses. Additionally, we prioritized putative causal variants for each locus through fine-mapping and integration with cell type-specific enhancer and promoter regulatory elements. SingleBrain represents a comprehensive single-cell eQTL resource, advancing insights into the genetic regulation of brain disorders.

Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders

A meta-genome-wide association study across eight psychiatric disorders has highlighted the genetic architecture of pleiotropy in major psychiatric disorders. However, mechanisms underlying pleiotropic effects of the associated variants remain to be explored. We conducted a massively parallel reporter assay to decode the regulatory logic of variants with pleiotropic and disorder-specific effects. Pleiotropic variants differ from disorder-specific variants by exhibiting chromatin accessibility that extends across diverse cell types in the neuronal lineage and by altering motifs for transcription factors with higher connectivity in protein-protein interaction networks. We mapped pleiotropic and disorder-specific variants to putative target genes using functional genomics approaches and CRISPR perturbation. In vivo CRISPR perturbation of a pleiotropic and a disorder-specific gene suggests that pleiotropy may involve the regulation of genes expressed broadly across neuronal cell types and with higher network connectivity.

Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment

Exposure to traumatic stress is common in the general population. Variation in the brain's molecular encoding of stress potentially contributes to the heterogeneous clinical outcomes in response to traumatic experiences. For instance, only a minority of those exposed to trauma will develop post-traumatic stress disorder (PTSD). Risk for PTSD is at least partially heritable, with a growing number of genetic factors identified through GWAS. A major limitation of genetic studies is that they capture only the genetic component of risk, whereas PTSD by definition requires an environmental traumatic exposure. Furthermore, the extent, timing, and type of trauma affects susceptibility. Here, we discuss the molecular mechanisms of PTSD risk together with gene × environment interactions, with a focus on how either might inform genetic screening for individuals at high risk for disease, reveal biological mechanisms that might one day yield novel therapeutics, and impact best clinical practices even today. To close, we discuss the interaction of trauma with sex, gender, and race, with a focus on the implications for treatment. Altogether, we suggest that predicting, preventing, and treating PTSD will require integrating both genotypic and environmental information.

Massively parallel assessment of gene regulatory activity at human cortical structure associated variants

Genetic association studies have identified hundreds of largely non-coding loci associated with inter-individual differences in the structure of the human cortex, though the specific genetic variants that impact regulatory activity are unknown. We implemented a Massively Parallel Reporter Assay (MPRA) to measure the regulatory activity of 9,092 cortical structure associated DNA variants in human neural progenitor cells during Wnt stimulation and at baseline. We identified 918 variants with regulatory potential from 150 cortical structure associated loci (76% of loci studied), of which >50% showed allelic effects. Wnt stimulation modified regulatory activity at a subset of loci that functioned as condition-dependent enhancers. Regulatory activity in MPRA was largely induced by Alu elements that were hypothesized to contribute to cortical expansion. The regionally specific impact of genetic variants that disrupt motifs is likely mediated through the levels of transcription factor expression during development, further clarifying the molecular mechanisms altering cortical structure.

Just a SNP away: The future of in vivo massively parallel reporter assay

The human genome is largely noncoding, yet the field is still grasping to understand how noncoding variants impact transcription and contribute to disease etiology. The massively parallel reporter assay (MPRA) has been employed to characterize the function of noncoding variants at unprecedented scales, but its application has been largely limited by the in vitro context. The field will benefit from establishing a systemic platform to study noncoding variant function across multiple tissue types under physiologically relevant conditions. However, to date, MPRA has been applied to only a handful of in vivo conditions. Given the complexity of the central nervous system and its widespread interactions with all other organ systems, our understanding of neuropsychiatric disorder-associated noncoding variants would be greatly advanced by studying their functional impact in the intact brain. In this review, we discuss the importance, technical considerations, and future applications of implementing MPRA in the in vivo space with the focus on neuropsychiatric disorders.

Linking Iris Cis-Regulatory Variants to Primary Angle-Closure Glaucoma Via Clinical Imaging and Multiomics

Purpose

To elucidate the genetic basis of primary angle-closure glaucoma (PACG) by identifying pathogenic tissue and critical tissue-specific variants.

Methods

The correlations among PACG susceptibility, axial length (AL), and anterior chamber depth (ACD) were evaluated using meta-analyses. Propensity score matching was utilized on 2161 participants from the Handan Eye Study to determine the risk factors independent of ACD and AL for PACG. Subsequently, we employed the assay for transposase-accessible chromatin with sequencing (ATAC-seq) and allele-specific self-transcribing active regulatory region sequencing (STARR-seq) to screen 202 PACG genome-wide association study (GWAS) variants for chromatin accessibility and functional roles.

Results

The meta-analysis found that PACG susceptibility loci are not associated with ACD or AL. However, abnormal iris phenotypes emerged as significant independent risk factors for primary angle-closure disease (PACD), unrelated to ACD and AL. Substantial enrichment of PACG heritability was observed in the open chromatin regions of the human iris. Within the iris-relevant cellular context, 22 out of the 202 PACG GWAS variants could influence enhancer activity. Two variants in the iris open chromatin regions were implicated in the modulation of PLEKHA7 and C10orf53 expression. The downregulation of these two genes affects cytoskeletal organization.

Conclusions

Our findings underscore the importance of the iris in the pathogenesis of PACG and identified iris-specific, enhancer-modulating variants that may influence disease risk. Our approach also provides a generalizable framework for studying ocular diseases from the perspective of enhancer-modulating variants.

Active enhancers: recent research advances and insights into disease

Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies.

Deciphering the functional impact of Alzheimer's Disease-associated variants in resting and proinflammatory immune cells

Genome-wide association studies have identified loci associated with Alzheimer's Disease (AD), but identifying the exact causal variants and genes at each locus is challenging due to linkage disequilibrium and their largely non-coding nature. To address this, we performed a massively parallel reporter assay of 3,576 AD-associated variants in THP-1 macrophages in both resting and proinflammatory states and identified 47 expression-modulating variants (emVars). To understand the endogenous chromatin context of emVars, we built an activity-by-contact model using epigenomic maps of macrophage inflammation and inferred condition-specific enhancer-promoter pairs. Intersection of emVars with enhancer-promoter pairs and microglia expression quantitative trait loci allowed us to connect 39 emVars to 76 putative AD risk genes enriched for AD-associated molecular signatures. Overall, systematic characterization of AD-associated variants enhances our understanding of the regulatory mechanisms underlying AD pathogenesis.

Schizophrenia genomics: genetic complexity and functional insights

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

Identification, Design, and Application of Noncoding Cis-Regulatory Elements

Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.

MotifbreakR v2: extended capability and database integration

MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.

Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters

The inability to scalably and precisely measure the activity of developmental cis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.

Decoding polygenic diseases: advances in noncoding variant prioritization and validation

Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Overlapping binding sites underlie TF genomic occupancy

Sequence-specific DNA binding by transcription factors (TFs) is a crucial step in gene regulation. However, current high-throughput in vitro approaches cannot reliably detect lower affinity TF-DNA interactions, which play key roles in gene regulation. Here, we developed PADIT-seq ( p rotein a ffinity to D NA by in vitro transcription and RNA seq uencing) to assay TF binding preferences to all 10-bp DNA sequences at far greater sensitivity than prior approaches. The expanded catalogs of low affinity DNA binding sites for the human TFs HOXD13 and EGR1 revealed that nucleotides flanking high affinity DNA binding sites create overlapping lower affinity sites that together modulate TF genomic occupancy in vivo . Formation of such extended recognition sequences stems from an inherent property of TF binding sites to interweave each other and expands the genomic sequence space for identifying noncoding variants that directly alter TF binding.

One-Sentence Summary

Overlapping DNA binding sites underlie TF genomic occupancy through their inherent propensity to interweave each other.

[A role of transcription factors in pathogenic processes associated with schizophrenia]

Schizophrenia is a severe mental illness, the etiology and pathogenesis of which are significantly contributed by hereditary factors. Genome-wide association analysis shows that the majority of genetic variants associated with a high risk of schizophrenia are located in regulatory regions of genes. In this brief review, data on the overall structure of the major regulatory regions of genes are summarized. The main focus is on enhancers, which are regions of the genome that enhance gene activity. The participation of transcription factors interacting with gene regulatory regions in various molecular and cellular processes, the disruption of which is associated with the pathogenesis of schizophrenia, is discussed in detail.

Common genetic variation impacts stress response in the brain

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.