Predicting the Impact of Genomic Variation on Cellular States

项目摘要 通过预测基因组变异的功能效应将基因型与表型联系起来，对于实现基因组变异的功能效应是至关重要的。 基因组医学的潜力。在过去的二十年里，联合会的努力具有共同和共同的特点， 罕见的群体规模遗传变异和跨细胞类型的功能性基因调控元件。最近， 单细胞技术使得能够对分子细胞状态进行生物体规模的调查。网站或资源的可用性 三种数据类型意味着通用模型预测变量影响的目标终于可以实现。 目前，整合这些不同的生物数据集来构建预测模型是困难的。而资源 例如RegulomeDB帮助研究人员注释具有假定调节功能变体，它们通常缺乏细胞 类型特异性和预测一般意义上的变体功能。类似地，GTEx有效地将特定变体 基因表达的变化，但这些变异主要是SNP，预测的影响主要是 两两互动此外，以前的努力主要依赖于批量测量，探索有限 基因组变异在单细胞水平上的影响。我们提出了细胞状态的定量变化， 定义和预测变异函数的新范式。单细胞转录组学和表观基因组学数据来自 健康个体提供细胞状态的参考图谱。通过比较细胞状态分布与此 参考，我们可以确定由遗传变异引起的定量变化，并探索这些偏差， 潜在的疾病状态。然后，我们将建立模型，通过结合单细胞数据和 背景生殖系遗传变异、染色质结构和支持功能数据。

Project Summary Linking genotype to phenotype by predicting the functional effects of genomic variation is crucial to realizing the potential of genomic medicine. Over the past two decades, consortium efforts have characterized common and rare population-scale genetic variation and functional gene regulatory elements across cell types. More recently, single-cell technologies have enabled organism-scale surveys of molecular cell states. The availability of these three data types means that the goal of general models to predict the effects of variants is finally within reach. Currently, integrating these diverse biological data sets to build predictive models is difficult. While resources such as RegulomeDB help researchers annotate variants with putative regulatory function, they often lack cell type specificity and predict variant function in a general sense. Similarly, GTEx effectively links specific variants to changes in gene expression, but these variants are primarily SNPs, and the predicted effects are mostly pairwise interactions. Furthermore, previous efforts rely primarily on bulk measurements, with limited exploration of the impact of genomic variation at the single-cell level. We propose quantitative shifts in cellular state as a new paradigm for defining and predicting variant function. Single-cell transcriptomic and epigenomic data from healthy individuals provide a reference atlas of cell states. By comparing cell state distributions against this reference, we can identify quantitative shifts resulting from genetic variation and explore these deviations as potential disease states. We will then build models to predict shifts in cell state by combining single-cell data with background germline genetic variation, chromatin structure, and supporting functional data.