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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease patient Alzheimer&apos 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

项目摘要

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.

随着世界范围内人口老龄化，神经退行性疾病的发病率不断增加尽管进行了数十年的研究和研究，但仍对长期福祉、医疗费用和家庭生活造成负担。巨额投资，但对于其中最常见的疾病尚无缓解疾病的治疗方法疾病：阿尔茨海默氏症（AD），其中大多数迄今尚未成功，但已集中努力。 AD 的一个潜在原因：淀粉样蛋白-β 聚集。收集、人类遗传学和机器学习提供了一条前进的道路来发现和表征 AD 中涉及的新的因果细胞过程这将提供一系列潜在的治疗方法。目标，增加了一种比淀粉样蛋白-β途径更容易调节的机会。正在积极收集前所未有规模的 AD 特异性基因组数据集：全基因组对约 20k 个体进行测序 (WGS)、基因表达 (RNA-seq) 和表观基因组学 (ATAC-seq、组蛋白 ChIP-seq） > 1000 个死后 AD 大脑、单细胞转录组以及外周和外周组织中的类似模式大脑固有免疫细胞（我们和其他人已证明其与 AD 有效相关）。整合这些不同的数据以更好地理解 AD 代表着大量的计算该提案在数据规模和分析复杂性方面都面临着挑战。最先进的深度学习（DL）和机器学习（ML），与人类遗传相结合分析，以应对这一挑战。我们将训练深度学习模型来预测表观基因组信号和来自基因组序列的 RNA 剪接，能够通过计算机诱变来估计任何遗传变异的功能影响（“德尔塔分数”）德尔塔分数将用于。区分因果关系的遗传分析：导致 AD 的细胞变化 Delta 分数将有助于了解发病机制，而不是疾病的下游/副作用。将稀有变体和常见变体与 AD 相关联为了获得足够的功效，必须将稀有变体与 AD 相关联。聚合（例如对于基因）：增量分数将允许过滤掉许多可能的非功能性（特别是非编码）变异来自 AD 全基因组关联研究。（GWAS）由于连锁不平衡（LD）而与因果变异简单相关。分数与跨种族 GWAS 相结合，将能够估计可能的因果变异。这些分析将突出显示与 AD 相关的变异和基因。然而，基因并不在 AD 中发挥作用。真空如此强大的概率机器学习将用于学习细胞类型和疾病特异性基因来自分选的批量和单细胞 RNA-seq 的调控网络将是与我们的遗传发现相结合，特别是发现网络邻居/路径丰富的 AD 变体将成为未来功能和功能的主要候选途径。 AD 的治疗研究。