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

通过系统基因组编辑捕获单核苷酸变异的表型景观

基本信息

批准号：
9978073
负责人：
Lars M Steinmetz
金额：
$ 62.75万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-18 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9978073
关键词：
Bar Codes Biological Biological Assay Cells Clustered Regularly Interspaced Short Palindromic Repeats Code Collection Communities Complex DNA Data Data Set Dependence Detection Development Disease Disease susceptibility Engineering Environment Environmental Exposure Evolution Exposure to Foundations Genes Genetic Genetic Engineering Genetic Variation Genetic study Genome Genomics Growth Guide RNA Haplotypes Human Individual Investigation Libraries Maps Measures Medical Metabolic Modeling Nucleotides Organism Outcome Pathway interactions Pharmaceutical Preparations Pharmacotherapy Phenotype Post-Translational Protein Processing Quantitative Genetics Quantitative Trait Loci RNA Sequences Research Personnel Resources Role Saccharomyces Saccharomyces cerevisiae Single Nucleotide Polymorphism Stress Technology Testing Therapeutic Tissues Untranslated RNA Variant Visualization Work Yeasts base causal variant cell type combinatorial conditioning cost effective deletion library design disease phenotype disorder risk experience experimental study fitness functional genomics genetic analysis genetic architecture genetic resource genetic variant genome database genome editing genome wide association study genome-wide high throughput technology human disease indexing insight internal control loss of function mutant precision medicine recombinase repository screening study population trait

项目摘要

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.

项目概要理解表型多样性、进化中的建模选择以及发展精准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调节表型中的作用需要系统的、基于扰动的测试在多种环境和同基因背景下跨基因组的遗传变异。以前的系统的基因组扰动工作主要集中在工程功能丧失上，但自然发生的变异与理解医学相关表型（如人类特征）最相关和疾病。此类变异已通过全基因组关联研究（GWAS）和定量研究进行了研究性状位点（QTL）分析，但这些方法仅限于研究中出现的单倍型人群中，只有在少数情况下才确定了实际的致病变异。基因组的进展编辑技术使得大规模改造特定的遗传变异变得可行。这个提议旨在系统地设计和功能分析三个基因组范围的“变异集合” 遗传上不同的菌株，涵盖了所有天然单核苷酸变异（SNV）酿酒酵母物种以及与人类疾病相关的 SNV。该系列将通过高通量 CRISPR 方法构建，利用内部序列解析技术（重组酶定向索引，或 REDI）将允许快速、廉价地分离经过序列验证的将产生数以百万计的变异菌株。因为某些变体仅在以下情况下发挥作用在某些环境下，该菌株集合将在数百种条件下进行分析，包括暴露于各种压力和药物。整合到每个菌株基因组中的 DNA 条形码将能够汇集、竞争性生长，并允许全面识别基因组中调节适应性的变异在单个实验的给定条件下。最后，剖析潜在途径的遗传结构疾病并确定关键的相互作用，携带 SNV 组合的菌株将被分析。应变收集品将提供给社区进行进一步的表型研究。除了基因 x 环境 (GxE) 数据集可能是迄今为止最大的数据集，技术、分析、可视化管道将公开共享并集成到社区资源中。这项工作将对遗传变异的后果及其依赖性进行了前所未有的调查环境，同时为科学界提供宝贵的资源。它将奠定技术以及基于扰动的系统性人类细胞遗传变异研究的概念基础将为疾病风险的预测和基于基因组序列的治疗策略的设计提供信息。