Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologies

项目摘要/摘要 经过几十年的研究，全基因组关联研究(GWAS)已经确定了可遗传的编码和 导致阿尔茨海默氏症风险增加的非编码单核苷酸多态(SNPs) 疾病(AD)。然而，这些SNPs中的绝大多数在很大程度上仍然没有得到充分的描述，他们的 阿尔茨海默病的发病机制尚不清楚，这是我们理解AD的关键障碍 遗传学和发病机制。而APOE和TREM2基因中的SNP已经确定了AD的关键节点 生物学上，大多数与AD相关的SNPs驻留在非编码基因组中，使它们在 疾病不太清楚。邻近SNPs的共遗传(连锁不平衡)及其细胞类型特异性 非编码调控元件进一步使AD中非编码SNP的功能注释复杂化。作为以下内容的一部分 阿尔茨海默病测序项目功能基因组学联盟(ADSP FGC)，该项目将 提供与AD相关的非编码SNP的可靠和确凿的功能特征。要做到这一点，我们将 首先，创建一份关于基因表达和染色质可获得性的综合单细胞图谱 与AD相关的不同临床病理状态(目标1)。利用这些细胞类型特定的基因调控 ，我们将开发和实施创新的机器学习和统计基因组学方法来 预测功能非编码、剪接和编码SNP(目标2)。然后，我们将使用以下工具验证这些预测 大规模平行报告分析(MPRA)和大规模、无疤痕、单碱基的IPSCs CRISPR编辑 然后是特定细胞类型的分化(目标3)。综合起来(目标4)，这个项目将准确地指出 数十个AD相关风险基因的功能性SNP和靶细胞类型，并提供了前所未有的图景 阿尔茨海默病的基因调控图景。这项工作将作为斯坦福大学的联合合作进行 加州大学和加州大学旧金山分校的格莱斯通学院。我们的团队，通过许多长期的合作，已经 在长期参与DNA元素百科全书的联合体科学方面拥有丰富的经验， 癌症基因组图谱和基因类型-组织表达计划。因此，拟议的项目- 定位于融入高度协作的ADSP功能基因组学联盟。

PROJECT SUMMARY / ABSTRACT Through decades of research, genome-wide association studies (GWAS) have identified heritable coding and noncoding single-nucleotide polymorphisms (SNPs) that lead to an increased risk of developing Alzheimer's disease (AD). However, the vast majority of these SNPs remain largely under-characterized, and their contribution to AD pathogenesis remains unclear, marking a critical roadblock to our understanding of AD genetics and pathogenesis. While SNPs within the APOE and TREM2 genes have identified vital nodes in AD biology, most AD-related SNPs reside within the noncoding genome, making their functional roles in the disease less clear. Co-inheritance of nearby SNPs (linkage disequilibrium) and the cell type-specificity of noncoding regulatory elements further complicate functional annotation of noncoding SNPs in AD. As part of the Alzheimer's Disease Sequencing Project Functional Genomics Consortium (ADSP FGC), this project will provide a robust and conclusive functional characterization of AD-related noncoding SNPs. To do this, we will first create a comprehensive single-cell atlas of gene expression and chromatin accessibility across a cohort of diverse clinico-pathologic states related to AD (Aim 1). Using these cell type-specific gene regulatory landscapes, we will develop and implement innovative machine learning and statistical genomics methods to predict functional noncoding, splicing, and coding SNPs (Aim 2). We will then validate these predictions using massively parallel reporter assays (MPRAs) and large-scale, scarless, single-base CRISPR editing of iPSCs followed by cell type-specific differentiations (Aim 3). Taken together (Aim 4), this project will pinpoint the functional SNPs and target cell types for dozens of AD-related risk loci and provide an unprecedented picture of the gene regulatory landscape of AD. This work will be performed as a joint collaboration between Stanford University and the Gladstone Institutes at UCSF. Our team, with many long-standing collaborations, has extensive experience in consortium science with long-term involvement in the Encyclopedia of DNA Elements, The Cancer Genome Atlas, and The Genotype-Tissue Expression Project. The proposed project is thus well- positioned to integrate into the highly collaborative ADSP Functional Genomics Consortium.