Untangling the diversity in the genetic architecture of late-onset Alzheimer's disease using single cell multi-omics

利用单细胞多组学揭示迟发性阿尔茨海默病遗传结构的多样性

基本信息

批准号：
10452296
负责人：
Ornit Chiba-Falek
金额：
$ 233.39万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10452296
关键词：
ATAC-seq Affect African ancestry Alzheimer&apos s disease patient Alzheimer&apos s disease risk Architecture Astrocytes Autopsy Binding Sites Biological Brain CRISPR/Cas technology Catalogs Cell Nucleus Cells Chromatin Chromatin Structure Clinical Data Data Set Development Disease Ethnic group European Event Freezing Gene Expression Gene Expression Regulation Genes Genetic Genetic Diseases Genetic Transcription Genomics Genotype Goals Hi-C Individual Intervention Knowledge Late Onset Alzheimer Disease Maps Mediating Mediation Medical Microglia Modeling Molecular Neurofibrillary Tangles Neurons Outcome Pathogenicity Pathway Analysis Pathway interactions Patients Population Population Group Population Heterogeneity Positioning Attribute Preventive therapy Process Publishing Quantitative Trait Loci Race Regulator Genes Regulatory Element Risk Sampling Signal Transduction Site Subgroup Symptoms Testing Tissues Translating Untranslated RNA Variant XCL1 gene base brain cell brain tissue causal variant cell type differential expression epigenome epigenomics falls functional genomics gene function genetic architecture genetic association genetic variant genome editing genome wide association study genomic platform molecular targeted therapies multiple omics neuropathology new therapeutic target polygenic risk score pre-clinical programs risk variant transcription factor transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

ABSTRACT Late Onset Alzheimer's Disease (LOAD) genome wide association studies (GWAS) discovered numerous loci. But there remains an unmet need to translate the GWAS findings to disease mechanisms through the identification of the specific genes involved, the causal variants, and the molecular mechanisms by which they exert their pathogenic effects. Most LOAD-associated SNPs are in noncoding regions pointing to gene regulation as an important disease mechanism. Another challenge in LOAD genetics is diversity, as most studies were conducted in subjects from European ancestry, while other populations are largely understudied. Our central hypothesis is that LOAD-specific epigenomic signatures, as well as noncoding functional genetic variants result in dysregulation of genes with key roles in LOAD pathogenic biological pathways. While omics studies using bulk brain tissue from European ancestry donors have produced informative data for a few genes at LOAD loci, single-cell omics data from brains of patients and controls from diverse populations will provide new knowledge in unprecedented brain cell-subtype precision across multiple racial and ethnical groups. We will investigate the relationships between LOAD-specific gene expression, chromatin accessibility and genetic variability in European and African ancestries by single-nuclei multi-omics approaches following three specific aims. Aim 1 will generate matched single-nuclei (sn)RNA-seq and ATAC-seq datasets using the 10X Genomics platform (Single Cell Multiome) to characterize cell-subtype specific changes in transcriptomic and chromatin accessibility landscape, respectively, in LOAD compared to control, that are shared and distinct across European and African ancestries. Aim 2 will integrate these datasets to identify open/closed chromatin sites that function as regulatory elements to impact gene expression in LOAD state, which will be then validated in the relevant cell-subtype using isogenic hiPSC-derived models by CRISPR/Cas9 genome editing. Aim 3 will identify LOAD specific gene regulatory variants within specific brain cell-subtypes through integrative single-cell genomics. We will perform expression(e)QTL and chromatin(c)QTL analyses by cell- subtype focusing specifically on the QTLs that fall within previously published GWAS regions to determine whether GWAS signals can be explained by the identified regulatory interactions. We will then catalogue the SNPs that identified as both strong and significant eQTL and cQTL and prioritize those that predicted to affect transcription factor binding sites. Last, we will validate the top prioritized variants in genome edited isogenic hiPSC-derived models. Successful accomplishment of these aims is expected to be high impact as it will advance the understanding of the genetic complexity underpinning LOAD in diverse populations and will decipher the regulatory elements and the corresponding genes mediating LOAD risk. This knowledge will be translational by promoting the refinement of Polygenic Risk Scores, and the development of novel therapeutic targets for LOAD based on manipulation of dysregulated genes.

抽象的阿尔茨海默氏病晚期（负载）基因组广泛的关联研究（GWAS）发现了许多基因座。但是仍然需要将GWA的发现转化为疾病机制鉴定所涉及的特定基因，因果变异和它们的分子机制发挥其致病作用。大多数与负载相关的SNP都位于指向基因的非编码区域调节是重要的疾病机制。负载遗传学的另一个挑战是多样性，因为大多数研究是在欧洲血统的受试者中进行的，而其他人群则在很大程度上进行了研究。我们的中心假设是负载特异性的表观基因组特征以及非编码功能遗传变体导致在负载致病生物学途径中具有关键作用的基因失调。而omics 使用来自欧洲血统捐助者的散装脑组织的研究已经产生了一些信息数据负载基因座的基因，来自患者大脑的单细胞OMIC数据以及来自不同人群的对照在多种种族和种族的脑细胞囊型精度中提供新知识组。我们将研究负载特异性基因表达，染色质可及性之间的关系以及通过单核多态方法在欧洲和非洲祖先中的遗传变异性。三个具体目标。 AIM 1将使用该匹配的单核（SN）RNA-Seq和ATAC-Seq数据集生成匹配 10倍基因组学平台（单细胞多组）以表征转录组的特定变化与对照相比，分别与对照相比，分别在负载和染色质可及性景观中共享且独特遍布欧洲和非洲的祖先。 AIM 2将集成这些数据集以识别开放/封闭的染色质作为调节元素影响基因表达在负载状态下的位点，那将是通过CRISPR/CAS9基因组编辑使用ISEOGE HIPSC衍生的模型在相关的细胞量表中验证。 AIM 3通过综合单细胞基因组学。我们将通过细胞进行表达（E）QTL和染色质（C）QTL分析亚型专门针对列在先前发表的GWAS区域的QTL以确定是否可以通过确定的调节相互作用来解释GWAS信号。然后，我们将分类识别为强和重要的EQTL和CQTL的SNP，并优先考虑那些预测的影响转录因子结合位点。最后，我们将验证编辑的基因组中的顶级优先级变体等源HIPSC衍生的模型。预计成功实现这些目标将会产生很大的影响将进一步了解不同人群中负载的遗传复杂性的理解，并将破译调节元件和相应的基因介导载荷风险。这些知识将是通过促进多基因风险评分的完善和新型治疗的发展来翻译基于操纵失调基因的负载靶标。