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.

项目摘要/摘要