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

使用高通量和单细胞技术对新型 AD 变体进行多组学功能评估

• 批准号: 10436207
• 负责人: Anshul Kundaje
• 金额: $ 166.92万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10436207
• 关键词: Address; Adult; Affect; Alleles; Alzheimer' s Disease; Alzheimer' s disease risk; Atlases; Biological Assay; Biology; Brain; Brain region; Catalogs; Cell Nucleus; Cell physiology; Cells; Chromatin; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Cognitive; Collaborations; Communities; Data; Data Set; Dementia with Lewy Bodies; Development; Disasters; Disease; Encyclopedia of DNA Elements; Environment; Etiology; Exhibits; Foundations; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Diseases; Genetic Polymorphism; Genetic Predisposition to Disease; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Heritability; Inherited; Institutes; Investigation; Joints; Lead; Link; Linkage Disequilibrium; Machine Learning; Maps; Methods; Modeling; Multiomic Data; Mutation; Neurodegenerative Disorders; Pathogenesis; Pathologic; Phenotype; Positioning Attribute; Public Health; RNA Splicing; Regulator Genes; Regulatory Element; Reporter; Research; Resolution; Risk; Risk Factors; Role; Science; Scientist; Single Nucleotide Polymorphism; Small Nuclear RNA; Specificity; TREM2 gene; The Cancer Genome Atlas; Therapeutic Intervention; Training; United States; Universities; Untranslated RNA; Validation; Variant; Work; analytical method; base; brain cell; cell type; cohort; data resource; data sharing; deep learning model; experience; functional genomics; gene function; genome editing; genome sequencing; genome wide association study; improved; in vitro Model; induced pluripotent stem cell; innovation; member; mortality; multiple omics; novel; presenilin-1; rare variant; risk variant; single cell technology; statistical and machine learning; syntax; transcription factor; whole genome

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.

项目概要/摘要 经过数十年的研究，全基因组关联研究 (GWAS) 已经确定了可遗传的编码和 非编码单核苷酸多态性 (SNP) 会增加患阿尔茨海默病的风险 疾病（AD）。然而，这些 SNP 中的绝大多数仍然没有被充分表征，并且它们的特征 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。 APOE 和 TREM2 基因内的 SNP 已识别出 AD 中的重要节点 生物学上，大多数与 AD 相关的 SNP 位于非编码基因组内，使其在 病情不太清楚。附近 SNP 的共同遗传（连锁不平衡）和细胞类型特异性 非编码调控元件使 AD 中非编码 SNP 的功能注释进一步复杂化。作为一部分 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 AD 相关非编码 SNP 的稳健且结论性的功能表征。为此，我们将 首先创建一个全面的单细胞基因表达和染色质可及性图谱 与 AD 相关的多种临床病理状态（目标 1）。利用这些细胞类型特异性基因调控 景观，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）。然后我们将使用以下方法验证这些预测 iPSC 的大规模并行报告分析 (MPRA) 和大规模、无疤痕、单碱基 CRISPR 编辑 其次是细胞类型特异性分化（目标 3）。综上所述（目标 4），该项目将确定 数十个 AD 相关风险位点的功能性 SNP 和靶细胞类型，提供了前所未有的图像 AD 的基因调控景观。这项工作将由斯坦福大学和斯坦福大学联合合作进行 大学和加州大学旧金山分校格拉德斯通研究所。我们的团队拥有许多长期的合作关系， 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项目。因此，拟议的项目是很好的 定位于融入高度协作的 ADSP 功能基因组学联盟。