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

项目总结/摘要 通过几十年的研究，全基因组关联研究（GWAS）已经确定了遗传编码， 非编码单核苷酸多态性（SNPs）导致阿尔茨海默氏症风险增加 疾病（AD）。然而，这些SNP中的绝大多数在很大程度上仍然是特征不足的，并且它们的 对AD发病机制的贡献仍不清楚，这是我们理解AD的一个关键障碍 遗传学和发病机制。虽然APOE和TREM 2基因内的SNP已经确定了AD中的重要节点， 在生物学中，大多数AD相关的SNP位于非编码基因组中，使它们在基因组中的功能作用成为可能。 疾病不太清楚。邻近SNP的共遗传（连锁不平衡）和细胞类型特异性 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。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 提供了AD相关非编码SNP的可靠和决定性的功能表征。为此，我们将 首先创建一个全面的单细胞图谱的基因表达和染色质的可及性在一个队列的 与AD相关的多种临床病理状态（目的1）。使用这些细胞类型特异性基因调控 我们将开发和实施创新的机器学习和统计基因组学方法， 预测功能性非编码、剪接和编码SNP（目标2）。然后，我们将验证这些预测， 大规模平行报告基因分析（MPRA）和大规模、无瘢痕、单碱基CRISPR编辑iPSC 随后是细胞类型特异性分化（Aim 3）。综合考虑（目标4），该项目将确定 功能性SNP和靶细胞类型的几十个AD相关的风险基因座，并提供了前所未有的图片 AD的基因调控景观。这项工作将作为斯坦福大学和 加州大学旧金山分校的格莱斯顿研究所。我们的团队与许多长期合作， 长期参与DNA元素百科全书，在联盟科学方面拥有丰富的经验， 癌症基因组图谱和基因型组织表达计划。因此，该项目是很好的- 定位于整合到高度协作的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.