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，这是实现 基因医学的潜力。在过去的二十年里，财团的努力具有共同和 罕见的种群规模的遗传变异和跨细胞类型的功能基因调控元件。最近， 单细胞技术使得对分子细胞状态的生物体范围的调查成为可能。这些产品的可用性 三种数据类型意味着，预测变量影响的一般模型的目标终于触手可及。 目前，集成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，它们通常缺少细胞 一般意义上的类型特异性和预测变异功能。同样，GTEx有效地链接了特定的变体 基因表达的变化，但这些变异主要是SNPs，预测的影响大多是 成对互动。此外，以前的努力主要依赖于批量测量，勘探有限。 基因组变异在单细胞水平上的影响。我们认为细胞状态的数量变化是一种 定义和预测变量函数的新范例。单细胞转录和表观基因组数据 健康人提供了细胞状态的参考图谱。通过将小区状态分布与此进行比较 作为参考，我们可以识别由遗传变异引起的数量变化，并探索这些偏差 潜在的疾病状态。然后，我们将构建模型，通过将单个单元格数据与 背景种系遗传变异、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.