High dimensional statistical data modeling and integration for studying regulatory variation

项目摘要 哺乳动物细胞的基因调控程序在很大程度上受到远程影响 染色质相互作用。我们建议开发健壮且可伸缩的统计方法 关于两个关键的基因组推断问题，取决于长程染色质 互动。首先，在单细胞水平上研究与3C-2的远程相互作用。 基于Schi-C的方法是全面理解细胞类型特异性基因的基础 监管。根据Schi-C的测量，这里蕴藏着未开发的生物多样性。然而，这些 测量容易受到极端稀疏、技术偏差和噪声的影响。虽然初始 推理方法只关注Schi-C数据的低维表示， 缺乏能够利用数据去噪中的非线性的可扩展框架 阻碍了这些实验的关键推理任务。我们将解决这些关键问题 为Schi-C数据开发一个新的深度生成模型的不足之处。作者：De- 通过对数据加噪声，这些方法将提高感兴趣信号的功率 被研究。第二，虽然测序和大规模可获得性的进展 表观基因组数据提高了全基因组关联的能力和解释能力 研究(GWAS)，在识别哪些基因可能是非编码SNPs方面存在缺陷 通过长距离染色质相互作用的影响阻碍GWAs的翻译 将发现转化为临床干预措施。利用现有的大规模多样性研究 杂交小鼠，我们将开发一个严格的框架，集成多种组学功能 精细绘制模式生物分子数量性状基因座和转移的数据模式 将非编码GWASSNPs连接到它们的效应器的结果对人类来说，即， 易感性，基因。具有2型糖尿病(T2D)特征的大规模应用程序将提供 候选的T2D效应基因及其调节基因 试验性随访。这两个目标将通过以下方式相结合实现 方法论发展、理论分析、数据驱动模拟、计算 分析和实验验证。本项目产生的统计资源 将以开源软件的形式传播。该项目的成功完成将 帮助确保从强大的Schi-C实验中获得最大信息 并建立了生物多组学数据模型。

Project Summary Gene regulatory programs of mammalian cells are largely influenced by long-range chromatin interactions. We propose to develop robust and scalable statistical methods for two critical genomic inference problems hinging upon long-range chromatin interactions. First, the study of long-range interactions at the single cell-level with 3C- based method scHi-C is fundamental to fully understanding cell type-specific gene regulation. scHi-C measurements harbor unexplored biological diversity. However, these measurements are prone to extreme sparsity, technological bias, and noise. While initial inference methods simply focused on lower dimensional representations of scHi-C data, lack of a scalable framework that can exploit nonlinearities in de-noising of the data impedes key inference tasks from these experiments. We will address these critical shortcomings by developing a novel deep generative model for scHi-C data. By de- noising the data, these methods will improve the power with which signals of interest can be studied. Second, while advances in sequencing and large-scale availability of epigenome data improved the power and interpretation of genome-wide association studies (GWAS), shortcomings in identifying which genes noncoding SNPs might be impacting through long-range chromatin interactions hinder the translation of GWAS findings into clinical interventions. Leveraging existing large-scale studies of diversity outbred mice, we will develop a rigorous framework that integrates multi-omics functional data modalities to fine-map model organism molecular quantitative trait loci and transfer the results to humans for linking noncoding GWAS SNPs to their effector, i.e., susceptibility, genes. Large-scale application with type 2 diabetes (T2D) traits will deliver candidate T2D effector genes and their regulatory loci that are amenable for experimental follow-up. Both aims will be accomplished through a combination of methodological development, theoretical analysis, data-driven simulation, computational analysis, and experimental validation. Statistical resources generated from this project will be disseminated as open-source software. Successful completion of the project will help to ensure that maximal information is obtained from powerful scHi-C experiments and model organism multi-omics data.