Learning the Regulatory Code of Alzheimer's Disease Genomes

随着世界范围内人口老龄化，神经退行性疾病正在日益增加。 长期福利、医疗费用和家庭生活的负担。尽管经过几十年的研究和 巨大的投资，没有疾病修饰治疗是最常见的这些 阿尔茨海默病（Alzheimer's，AD）迄今为止，这些努力中的大多数都没有成功， 一个可能导致AD的因素：β淀粉样蛋白聚集合并人口规模数据 收集，人类遗传学和机器学习提供了一种方法来发现和表征 参与AD的新的因果细胞过程。这将提供一系列潜在的治疗方法， 靶点，增加了比淀粉样蛋白-β途径更容易调节的机会。 正在积极收集规模空前的AD特异性基因组数据集：全基因组 来自~ 20 k个体的测序（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脑，外周和外周血中的单细胞转录组和类似模式 大脑固有免疫细胞（我们和其他人已经证明与AD相关）。有效 整合这些不同的数据以更好地理解AD代表了大量的计算 在数据规模和分析复杂性方面都面临挑战。该提案利用了 最先进的深度学习（DL）和机器学习（ML），结合人类遗传 分析，以应对这一挑战。我们将训练DL模型来预测表观基因组信号， 从基因组序列中剪接RNA，使计算机诱变能够估计 任何遗传变异的功能影响（“delta评分”）。Delta评分将用于 区分因果关系的遗传分析：驱动AD的细胞变化 而不是疾病的下游/副作用。德尔塔分数将有助于 将罕见和常见变体与AD相关联。为了获得足够的功率，必须 聚合（例如，对于基因）：delta评分将允许过滤掉许多可能的非功能性 （特别是非编码）变体。AD全基因组关联研究中最常见的变异 由于连锁不平衡（LD），GWAS（GWAS）与因果变异简单相关。三角洲 评分与跨种族GWAS相结合，将能够估计可能的因果变异。 这些分析将突出与AD相关的变异和基因。然而，基因并不以一种 真空，因此强大的概率ML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。检测到的网络将 与我们的遗传发现相结合，以发现网络邻居/路径，特别是 富含AD变体。这些途径将是未来功能性和 AD的治疗研究

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.