Predicting the Impact of Genomic Variation on Cellular States

项目概要 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 基因组医学的潜力。在过去的二十年里，财团的努力具有共同和 跨细胞类型的罕见群体规模遗传变异和功能基因调控元件。最近， 单细胞技术使生物体规模的分子细胞状态调查成为可能。这些的可用性 三种数据类型意味着通用模型预测变异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很困难。同时资源 例如RegulomeDB帮助研究人员注释具有假定调节功能的变体，它们通常缺乏细胞 类型特异性并预测一般意义上的变异功能。同样，GTEx 有效链接特定变体 基因表达的变化，但这些变异主要是SNP，预测的效果大多是 成对相互作用。此外，以前的努力主要依赖于批量测量，探索有限 在单细胞水平上研究基因组变异的影响。我们提出细胞状态的数量变化作为 定义和预测变体功能的新范式。单细胞转录组和表观基因组数据 健康个体提供细胞状态的参考图集。通过将细胞状态分布与此进行比较 参考，我们可以识别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，我们将建立模型，通过将单细胞数据与 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.