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

摘要 晚发性阿尔茨海默病（LOAD）全基因组关联研究（GWAS）发现了许多基因座。 但是，仍然有一个未满足的需求，即通过基因工程将GWAS的发现转化为疾病机制。 鉴定所涉及的特定基因、致病变体以及它们的分子机制， 发挥其致病作用。大多数LOAD相关的SNPs位于非编码区， 作为一种重要的疾病机制。LOAD遗传学的另一个挑战是多样性，因为大多数 这些研究是在欧洲血统的受试者中进行的，而其他人群在很大程度上研究不足。 我们的中心假设是，LOAD特异性表观基因组特征，以及非编码功能性遗传特征， 变异导致在LOAD致病生物学途径中具有关键作用的基因的失调。虽然omics 使用来自欧洲血统捐赠者的大量脑组织进行的研究为一些人提供了信息数据， LOAD基因座的基因，来自不同人群的患者和对照组大脑的单细胞组学数据， 提供了新的知识，在前所未有的脑细胞亚型精度跨越多个种族和民族， 组我们将研究LOAD特异性基因表达、染色质可及性 和遗传变异性在欧洲和非洲的祖先通过单核多组学方法， 三个具体目标。目标1将使用以下方法生成匹配的单核（sn）RNA-seq和ATAC-seq数据集： 10 X Genomics平台（单细胞多组）用于表征转录组学中细胞亚型特异性变化 和染色质可及性景观，分别在负载相比，控制，这是共享的和不同的 欧洲和非洲的祖先。AIM 2将整合这些数据集以识别开放/闭合染色质 作为调控元件发挥作用以影响LOAD状态下的基因表达的位点，然后将其 通过CRISPR/Cas9基因组编辑使用等基因hiPSC衍生模型在相关细胞亚型中验证。 目标3将通过以下途径鉴定特定脑细胞亚型内的LOAD特异性基因调控变体： 整合性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，特别关注落在先前公布的GWAS区域内的QTL，以确定 GWAS信号是否可以通过识别的调节相互作用来解释。然后我们将对 确定为强且显著eQTL和cQTL的SNP，并优先考虑那些预测 影响转录因子结合位点。最后，我们将验证基因组编辑中最优先的变体， 等基因hiPSC衍生模型。成功实现这些目标预计将产生巨大影响，因为 将促进对不同人群中支持LOAD的遗传复杂性的理解，并将 破译调控元件和介导LOAD风险的相应基因。这些知识将 通过促进多基因风险评分的完善，以及新的治疗药物的开发， 基于对失调基因的操纵的LOAD靶点。

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.