Learning the Regulatory Code of Alzheimer's Disease Genomes

学习阿尔茨海默病基因组的调控密码

• 批准号: 10471969
• 负责人: David Arthur Knowles
• 金额: $ 113.32万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10471969
• 关键词: ATAC-seq; Address; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease patient; Alzheimer' s disease risk; Amyloid beta-Protein; Architecture; Autopsy; Base Sequence; Biological; Brain; Cell physiology; Cells; ChIP-seq; Chromatin; Clinical Data; Code; Computer Models; DNA; DNA Sequence; Data; Data Collection; Data Set; Development; Disease; Family; Frequencies; Future; Gene Expression; Genes; Genetic; Genetic Risk; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Health Care Costs; Histones; Human Genetics; Immune; Individual; Investments; Learning; Life; Link; Linkage Disequilibrium; Machine Learning; Maps; Medical; Meta-Analysis; Methods; Microglia; Modality; Modeling; Molecular; Multiomic Data; Mutagenesis; Neighborhoods; Neurodegenerative Disorders; Nucleic Acid Regulatory Sequences; Pathogenesis; Pathway interactions; Peripheral; Personal Satisfaction; Persons; Population; Post-Transcriptional Regulation; Quantitative Trait Loci; RNA; RNA Processing; RNA Splicing; Regulation; Research; Signal Transduction; Single Nucleotide Polymorphism; Statistical Models; Susceptibility Gene; Techniques; Testing; Therapeutic Studies; Training; Untranslated RNA; Vacuum; Variant; Work; abeta accumulation; case control; causal variant; cell type; deep learning; deep learning model; diverse data; endophenotype; epigenomics; exome sequencing; frontal lobe; functional genomics; gene network; gene regulatory network; genetic analysis; genetic architecture; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; in silico; insertion/deletion mutation; insight; large scale data; mRNA Expression; machine learning algorithm; machine learning method; molecular phenotype; monocyte; multi-ethnic; new therapeutic target; novel; novel diagnostics; novel therapeutic intervention; novel therapeutics; protective allele; protein aggregation; rare variant; risk variant; side effect; single-cell RNA sequencing; therapeutic development; therapeutic target; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; whole genome

With ageing populations world-wide, neurodegenerative diseases are placing an ever increasing burden on long- term well-being, healthcare costs and family life. Despite decades of research and enormous investment, no disease-modifying treatment is available for the most common of these diseases: Alzheimer’s (AD). The majority of these, to-date unsuccessful, efforts have focused on one potential cause of AD: amyloid-β aggregation. Combining population-scale data collection, human genetics and machine learning provides a way forward to uncover and characterize new causal cellular processes involved in AD. This would provide an array of potential therapeutic targets, increasing the chance that one will be more easily modulated than the amyloid-β pathway. AD-specific genomic datasets of unprecedented scale are being actively collected: whole genome sequencing (WGS) from ~20k individuals, gene expression (RNA-seq) and epigenomics (ATAC-seq, histone ChIP-seq) from >1000 post-mortem AD brains, single-cell transcriptomes and similar modalities in peripheral and brain-resident innate immune cells (which we and others have shown to be AD-relevant). Effectively integrating these diverse data to better understand AD represents a substantial computational challenge, both in terms of data scale and analysis complexity. This proposal leverages state-of-the-art deep learning (DL) and machine learning (ML), combined with human genetic analyses, to address this challenge. We will train DL models to predict epigenomic signals and RNA splicing from genomic sequence, enabling in silico mutagenesis to estimate the functional impact (a “delta score”) of any genetic variant. The delta scores will be used in genetic analyses that distinguish causal associations: cellular changes that drive AD pathogenesis rather than downstream/side effects of disease. Delta scores will aid in associating both rare and common variants to AD. To achieve sufficient power, rare variants must be aggregated (e.g. for a gene): delta scores will allow filtering out many likely non-functional (particularly non-coding) variants. Most common variants from AD Genome Wide Association Studies (GWAS) are simply correlated with the causal variant due to linkage disequilibrium (LD). Delta scores, combined with trans-ethnic GWAS, will enable estimation of the likely causal variant(s). These analyses will highlight variants and genes involved in AD. However, genes do not operate in a vacuum so robust probabilistic ML will be used to learn cell-type and disease-specific gene regulatory networks from sorted bulk and single-cell RNA-seq. The detected networks will be integrated with our genetic findings to discover network neighborhoods/pathways especially enriched in AD variants. Such pathways will be prime candidates for future functional and therapeutic studies of AD.

随着全球衰老的衰老，神经退行性疾病正在增加 长期福祉，医疗保健成本和家庭生活负担。尽管进行了数十年的研究和 大量投资，最常见的疾病修改治疗 疾病：阿尔茨海默氏症（AD）。大多数这些，急诊赛的努力不成功 关于AD的一个潜在原因：淀粉样蛋白-β聚集。结合种群规模的数据 收集，人类遗传学和机器学习为揭露和特征提供了前进的道路 AD涉及的新因果细胞过程。这将提供一系列潜在疗法 目标，增加了比淀粉样蛋白β途径更容易调制的机会。 积极收集了前所未有的量表的广告特异性基因组数据集：整个基因组 来自〜20K个体的测序（WGS），基因表达（RNA-SEQ）和表观基因组学（ATAC-SEQ，， Hisstone chip-seq）来自 > 1000个验尸广告大脑，单细胞转录组和外围和类似方式 脑居住的先天免疫细胞（我们和其他人已证明与AD相关）。有效地 整合这些多样的数据以更好地理解广告代表了一个实质性的计算 挑战，无论是数据量表还是分析复杂性。该提议利用 最先进的深度学习（DL）和机器学习（ML）与人类通用结合 分析，以应对这一挑战。我们将训练DL模型以预测表观基因组信号和 从基因组序列中剪接的RNA剪接，使得硅诱变以估计 任何遗传变异的功能影响（“增量得分”）。增量分数将在 区分因果关系的遗传分析：驱动AD的细胞变化 发病机理，而不是疾病的下游/副作用。增量分数将有助于 将稀有和常见变体与AD相关联。为了获得足够的力量，稀有变体必须是 聚合（例如，对于基因）：增量分数将允许过滤许多可能的非功能 （部分非编码）变体。 AD基因组广泛关联研究的最常见变体 （GWAS）仅与因子二动disequilibrium（LD）引起的因果变体相关。三角洲 分数与跨种族GWAS结合在一起，可以估算可能的因果变体。 这些分析将重点介绍与AD有关的变体和基因。但是，基因不在 真空如此强大的有问题的ML将用于学习细胞类型和疾病特异性基因 来自分类的散装和单细胞RNA-seq的调节网络。检测到的网络将是 与我们的遗传发现集成在一起，尤其是发现网络社区/途径 富含广告变体。这样的途径将成为未来功能的主要候选人 AD治疗研究。