Dynamic genetic regulation of gene expression in diverse differentiation trajectories with human embryoid bodies

了解遗传变异的功能影响，特别是其对 疾病，仍然是人类遗传学中的一个关键挑战。大多数与疾病相关的遗传位点位于 人类基因组的调节区，并被怀疑通过调节表达而起作用 附近基因的水平。然而，尽管人们做出了广泛的努力来描述基因调控效应 在基因表达方面，大多数疾病基因座还没有被解释为与表达有关 级别。无处不在的背景--这些监管效应的特殊性带来了一个关键的瓶颈 造成这种差异的原因之一。检测调控效应需要观察基因 在效应活跃的特定细胞状态中表达，并且大规模的努力 主要集中在测量健康成人组织中的表达水平。刻画 因此，在日益多样化和动态的细胞环境中的基因调控将揭示 可能有助于阐明疾病分子机制的新的基因调控效应 精神错乱。 为了分析不同细胞环境中基因表达的遗传调节，我们将 利用来自多个人类捐赠者的单细胞RNA测序。 类胚体是诱导的多能干细胞的三维聚集体， 自发分化为数十种非离散的细胞类型。分析《红楼梦》中的表情 单细胞水平将使我们能够切开这些复杂聚集体的异质性，并 将每个细胞映射到明确定义的差异化环境中的唯一位置。拟胚状体 因此，机体为研究不同的分化提供了统一的实验框架 轨迹，加速探索跨许多细胞状态的基因调控 可能会导致人类疾病。 在这项拟议的研究中，我将开发新的计算和统计工具 利用蜂窝分化的扩展和多分支格局来改善我们的 能够解决特定背景和动态的遗传调控效应。我将开发一种 比较个体间基因表达动态的概率模型，以及 将个体间差异归因于遗传变异的假设检验框架。这就做 用体外和体内染色质验证我们发现的动态遗传调控效应 辅助功能数据。最后，我将搜索新发现的监管变体之间的重叠 和已知的疾病基因，以揭示对介导遗传效应的目标基因的新见解 关于常见疾病的研究。

Understanding the functional impact of genetic variation, particularly its impact on disease, remains a key challenge in human genetics. Most disease-associated genetic loci lie in regulatory regions of the human genome and are suspected to act by regulating the expression levels of nearby genes. Yet despite extensive efforts to characterize genetic regulatory effects on gene expression, most disease loci have not been explained by association with expression levels. Pervasive context-specificity of these regulatory effects introduces one key bottleneck contributing to this discrepancy. Detecting regulatory effects requires observation of gene expression in the specific cell states where the effect is active, and large-scale efforts have primarily focused on measuring expression levels in healthy adult tissues. Characterizing genetic regulation in increasingly diverse and dynamic cellular contexts will therefore reveal novel genetic regulatory effects which may help elucidate molecular mechanisms of disease loci. To analyze genetic regulation of gene expression in diverse cellular contexts we will leverage single-cell RNA-sequencing of embryoid bodies from multiple human donors. Embryoid bodies are three-dimensional aggregates of induced pluripotent stem cells that spontaneously differentiate into dozens of non-discrete cell types. Analysis of expression at the single-cell level will enable us to cut through the heterogeneity of these complex aggregates and map each cell to a unique position within a clearly defined differentiation landscape. Embryoid bodies thus offer a unified experimental framework for the study of diverse differentiation trajectories, accelerating the exploration of genetic regulation across the many cell states that may contribute to human disease. In this proposed research, I will develop novel computational and statistical tools that leverage the expansive and multifurcating landscape of cellular differentiation to improve our ability to resolve context-specific and dynamic genetic regulatory effects. I will develop a probabilistic model to compare gene expression dynamics between individuals, and a hypothesis testing framework to attribute inter-individual differences to genetic variation. I will validate the dynamic genetic regulatory effects we discover using in vitro and in vivo chromatin accessibility data. Finally, I will search for overlap between newly discovered regulatory variants and known disease loci to reveal novel insights into the target genes mediating genetic effects on common diseases.