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给人们带来越来越多的 对长期福祉、医疗保健费用和家庭生活造成负担。尽管几十年的研究和 巨大的投资，没有治疗这些最常见的疾病的方法 疾病：阿尔茨海默病(AD)。到目前为止，这些努力中的大多数都集中在 AD的一个潜在原因：淀粉样蛋白-β聚集。结合人口规模数据 收藏、人类遗传学和机器学习提供了一种发现和表征 阿尔茨海默病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 靶点，增加了比淀粉样蛋白-β途径更容易调节的机会。 正在积极收集史无前例规模的广告特定基因组数据集：全基因组 来自20,000个个体的测序(WGS)、基因表达(RNA-SEQ)和表观基因组学(ATAC-SEQ， 组蛋白芯片-序号)来自 &gt；1000死后AD脑、单细胞转录本和类似模式的外周和 常驻大脑的先天免疫细胞(我们和其他人已经证明它与AD相关)。有效地 集成这些不同的数据以更好地理解AD意味着大量的计算 在数据规模和分析复杂性方面都面临挑战。这项提议充分利用了 最先进的深度学习(DL)和机器学习(ML)，结合人类基因 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和 从基因组序列中剪接RNA，使在电子诱变中能够估计 任何基因变异对功能的影响(“增量分数”)。增量分数将用于 区分因果关系的遗传分析：驱动AD的细胞变化 发病机制，而不是疾病的下游/副作用。达美航空的得分将有助于 将罕见和常见的变种与AD联系起来。为了获得足够的能量，罕见的变种必须是 聚合(例如，针对某个基因)：Delta分数将允许筛选出许多可能的非功能 (尤其是非编码)变体。AD基因组广谱关联研究中最常见的变异 (Gwas)与因连锁不平衡(LD)而产生的因果变异简单相关。德尔塔 分数，结合跨种族的GWA，将能够估计可能的因果变量(S)。 这些分析将突出阿尔茨海默病的变异和基因。然而，基因并不是在 所以稳健的概率ML将被用来学习细胞类型和疾病特异性基因 来自分选的散装和单细胞RNA序列的调控网络。检测到的网络将是 与我们的基因发现相结合，特别是发现网络邻居/路径 富含AD变种。这些途径将是未来功能和 阿尔茨海默病治疗研究。

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.