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: 10684210
• 负责人: Anshul Kundaje
• 金额: $ 166万
• 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/10684210
• 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; Dementia with Lewy Bodies; Development; Disasters; Disease; Encyclopedia of DNA Elements; Environment; Etiology; Exhibits; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Diseases; Genetic Polymorphism; Genetic Predisposition to Disease; Genome; Genome Mappings; Genomics; Genotype-Tissue Expression Project; Goals; Heritability; Inherited; Investigation; Joints; Link; Linkage Disequilibrium; Machine Learning; Methods; Modeling; Multiomic Data; Mutation; Neurodegenerative Disorders; Pathogenesis; Pathologic; Phenotype; Positioning Attribute; Public Health; RNA Splicing; Regulator Genes; Regulatory Element; Reporter; Research; Resolution; Resources; Risk; Risk Factors; Role; Science; Scientist; Single Nucleotide Polymorphism; 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; comparison control; 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; neuropathology; novel; presenilin-1; rare variant; resilience; risk variant; single cell technology; single nucleus RNA-sequencing; 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.

项目摘要 /摘要 通过数十年的研究，全基因组协会研究（GWAS）已经确定了可遗传的编码和 非编码单核苷酸多态性（SNP），导致患阿尔茨海默氏症的风险增加 疾病（AD）。但是，这些SNP中的绝大多数在很大程度上仍然不足，它们的 对AD发病机理的贡献尚不清楚，这标志着我们对AD的理解的关键障碍 遗传学和发病机理。虽然APOE和TREM2基因中的SNP已鉴定出AD中的重要节点 生物学，最与广告相关的SNP位于非编码基因组中，使其在功能中发挥作用 疾病不太清楚。附近SNP（连锁不平衡）和细胞类型特异性的共同传播 非编码调节元素进一步使非编码SNP的功能注释复杂化。作为 阿尔茨海默氏病测序项目功能基因组学联盟（ADSP FGC），该项目将 提供与广告相关的非编码SNP的强大且结论性的功能表征。为此，我们将 首先创建一个全面的基因表达单细胞图集和染色质的可及性 与AD相关的各种临床病理状态（AIM 1）。使用这些细胞类型特异性基因调节 风景，我们将开发和实施创新的机器学习和统计基因组学方法 预测功能非编码，剪接和编码SNP（AIM 2）。然后，我们将使用 大规模平行的记者测定法（MPRA）和IPSC的大规模，无疤，单基CRISPR编辑 其次是细胞类型特异性的分化（AIM 3）。总的来说（AIM 4），该项目将确定 数十个与广告相关风险基因座的功能性SNP和目标细胞类型，并提供前所未有的图片 AD的基因调节景观。这项工作将作为斯坦福之间的共同合作。 UCSF的大学和Gladstone Institutes。我们的团队以及许多长期的合作 长期参与DNA元素百科全书，在财团科学方面的丰富经验， 癌症基因组图集和基因型 - 组织表达项目。因此，拟议的项目很好 可以集成到高度协作的ADSP功能基因组学联盟中。