Capturing the phenotypic landscape of single-nucleotide variation via systematic genome editing

项目总结 理解表型多样性、对进化中的选择建模以及 发展精确医学正在增强我们目前有限的预测疾病和表型的能力 基于基因组序列和环境暴露的结果。全面了解 遗传变异及其在条件性表型中的作用需要系统的、基于扰动的测试 在多个环境和同基因背景下跨越基因组的遗传变异。上一首 系统性的基因组干扰工作主要集中在工程功能丧失上，但这是自然而然的 发生的变异与理解医学上相关的表型最相关，比如人类的特征 和疾病。这些变异已经通过全基因组关联研究(GWAS)和定量 性状基因座(QTL)分析，但这些方法仅限于研究中出现的单倍型 只有在极少数情况下才能确定实际的致病变异体。基因组研究进展 编辑技术使工程特定的基因变异在大范围内可行。这项建议 目标是系统地设计并从功能上描述全基因组的“变异集合”，分为三个阶段 涵盖酵母菌中所有自然单核苷酸变体(SNV)的基因独特的菌株 酿酒酵母物种以及与人类疾病有关的SNV。该集合将由 利用内部序列解析技术(重组酶)的高吞吐量CRISPR方法 定向索引，或REDI)，这将允许快速、廉价地分离经序列验证的变异菌株 在将产生的数百万美元中。因为有些变种只在某些情况下发挥作用 环境，这种菌株收集将在数百种条件下进行分析，包括暴露在各种 压力和毒品。集成到每个菌株基因组中的DNA条形码将使集合、竞争 生长，并允许全面识别基因组中调节给定适合度的变异 在一次实验中的状态。最后，剖析疾病潜在途径的遗传结构 并确定关键相互作用，将分析携带SNV组合的菌株。菌种收集将 供社区进行进一步的表型调查。除了基因x环境之外 (GxE)可能是迄今为止产生的最大数据集、技术、分析和可视化 管道将公开共享，并整合到社区资源中。这项工作将构成一个 史无前例地研究遗传变异的后果及其对 环境，同时为科学界提供宝贵的资源。它将铺设技术和 基于系统扰动的人类细胞遗传变异研究的概念基础 提供基于基因组序列的疾病风险预测和治疗策略设计的信息。

PROJECT SUMMARY A major challenge common to understanding phenotypic diversity, modeling selection in evolution, and developing precision medicine is enhancing our currently limited ability to predict disease and phenotypic outcomes based on genome sequence and environmental exposures. A comprehensive understanding of genetic variation and its role in conditioning phenotypes requires systematic, perturbation-based testing of genetic variants across the genome in multiple environments and in an isogenic background. Previous systematic genome perturbation efforts have focused primarily on engineering loss-of-function, but naturally occurring variants have the most relevance to understanding medically relevant phenotypes like human traits and disease. Such variants have been studied via genome-wide association studies (GWAS) and quantitative trait locus (QTL) analysis, but these approaches are limited to the haplotypes that appear in the study population, and only in few cases have the actual causative variants been identified. Advances in genome editing technologies have made engineering specific genetic variants feasible at a large scale. This proposal aims to systematically engineer and functionally profile a genome-wide `variation collection' in three genetically distinct strains that cover all natural single-nucleotide variants (SNVs) in the Saccharomyces cerevisiae species as well as SNVs associated with human diseases. The collection will be constructed by a high-throughput CRISPR approach, leveraging an in-house sequence parsing technology (Recombinase Directed Indexing, or REDI) that will allow rapid, inexpensive isolation of sequence-verified variant strains among the millions that will be generated. Because some variants only exert their effects in certain environments, this strain collection will be profiled in hundreds of conditions, including exposure to various stresses and drugs. DNA barcodes integrated into the genome of each strain will enable pooled, competitive growth, and allow the comprehensive identification of variants in a genome that modulate fitness in a given condition in a single experiment. Finally, to dissect the genetic architecture of pathways underlying diseases and identify key interactions, strains carrying combinations of SNVs will be analyzed. The strain collection will be made available to the community for further phenotypic investigations. In addition to the gene x environment (GxE) dataset that will likely be the largest produced to date, the technological, analytical, and visualization pipelines will be publicly shared and integrated into community resources. This work will constitute an unprecedented investigation of the consequences of genetic variation and their dependence upon environment, while providing valuable resources for the scientific community. It will lay technological and conceptual groundwork for systematic perturbation-based studies of genetic variation in human cells that will inform the prediction of disease risk and the design of therapeutic strategies based on genome sequence.