Mapping gene-by-environment interactions using multiplexed single cell RNA-sequencing

摘要 虽然已知常见的变异对基因表达的变异有额外的贡献，但一直有限。 人类基因与环境相互作用(GxE)的统计证据。这是因为即使是最大的 表达数量性状座位(EQTL)研究几乎没有统计能力来检测GxE交互作用，因为 环境暴露中存在大量的分离位点和广泛的变异性。我们假设 多重干扰和单细胞RNA测序相结合是定位GxE的有效策略 在大的人群队列中的相互作用。然而，目前的方法不能扩展到对107个细胞进行测序 需要104个样本(即103个供体按10个条件)进行足够强大的扰动筛查 人类群体。在这项提案中，我们将首先开发一种成本效益高的单细胞RNA测序方法，称为 DIT-SEQ，将测序成本降低到0.06美元/细胞(目标1)。然后，我们将制定编码策略 使用样本多路传输来定位和表征人类中GxE相互作用的环境扰动 免疫应答(目标2)。最后，我们将开发一个新的统计模型和一个计算管道 使用数千万个细胞进行有效的假设检验(目标3)。实验和计算 所提出的技术有可能创造研究基因-表型的基本新方法 关系和获得的生物学见解可能有助于阐明基因表达的遗传结构 并促进从大规模基因组和外显子组测序研究中解释疾病关联。

ABSTRACT While common variants are known to additively contribute to gene expression variation, there has been limited statistical evidence of gene-by-environment interactions (GxE) in humans. This is because even the largest expression quantitative trait loci (eQTL) studies have little statistical power to detect GxE interactions given the large number of segregating loci and extensive variability in environmental exposure. We hypothesize that the integration of multiplexed perturbations and single-cell RNA-sequencing is an efficient strategy for mapping GxE interactions in large population cohorts. However, current approaches are not scalable to sequencing 107 cells across 104 samples (i.e. 103 donors by 10 conditions) needed for sufficiently powered perturbation screens in human cohorts. In this proposal, we will first develop a cost-effective single-cell RNA-sequencing approach called DIT-seq that reduces the cost of sequencing to $0.06/cell (Aim 1). We will then develop strategies for encoding environmental perturbations using sample multiplexing to map and characterize GxE interactions in the human immune response (Aim 2). Finally, we will develop a new statistical model and a computational pipeline for efficient hypothesis testing using tens of millions of cells (Aim 3). The experimental and computational technologies proposed have the potential to create fundamental new ways to study genotype-phenotype relationships and the biological insights gained could shed light on the genetic architecture of gene expression and facilitate the interpretation of disease-associations from large-scale genome and exome sequencing studies.