Function-based exploration of genetic variation at genome-scale

项目总结 全基因组关联研究发现了数千种与表型相关的遗传变异 疾病风险等特征。大多数相关的变异位于基因组的非编码区，并且 这些变异的因果效应在很大程度上仍不清楚。关于交互作用的知识稀疏性 基因组的编码和非编码调节部分之间的差异使得对变异功能的预测 仅从基因组序列和位置是不可能的。我们建议通过实验来揭示功能 大规模的遗传变异的相关性，通过干扰变异和含有变异的遗传元素， 并读出这些扰动对基因调控的直接后果。为此，我们建议 应用我们最新开发的CRISPR/Cas9功能基因组学靶向单细胞筛选技术 转录读数(目标干扰序列或TAP-SEQ)，以实现对非 编码区和其中的遗传变异。首先，我们将应用我们的目标扰动序列来解密 一种编码在整个人类染色体上的调节电路，通过系统地干扰所有主要的基因 元素(增强子、蛋白质编码和lncRNA基因)。这一庞大的数据集将使我们能够破译 控制所选染色体上基因表达的复杂调控网络。接下来，我们将揭开 通过将高通量的精确基因组编辑与 同时进行单细胞基因组和转录读出。使用这种新颖的方法，我们将能够 破译基因变异对基因表达的功能影响，并得出遗传变异的规则 扰乱了基因调控过程。我们将把生成的数据与可用的功能基因组学相结合 数据，如转录因子结合(CHIP-SEQ)、染色质可及性(ATAC-SEQ、DNase-SEQ)和 3D中的交互(Hi-C)，以便训练机器学习模型以导出观察到的监管的规则 互动。这些模型将被用于破译调控逻辑背后的分子机制， 并预测整个基因组和不同细胞类型之间的调控相互作用和变异。已选择 预测将使用已建立的微扰技术进行实验验证，以进行临床验证 相关预测，提高预测模型的性能。总而言之，这个项目将 回答基因调控中的基本问题，揭示基因变异影响的机制 基因表达，并创建数据集和计算模型作为解释结果的有价值的工具 Gwas、eQTL和临床基因组研究。

PROJECT SUMMARY Genome-wide association studies have discovered thousands of genetic variants associated with phenotypic traits such as disease risk. Most of the associated variation lies within non-coding regions of the genome and the causative effects of those variants remain largely unknown. The sparsity of knowledge on interactions between the coding and non-coding regulatory parts of the genome makes the prediction of variant function solely from genome sequence and location impossible. We propose to experimentally uncover the functional relevance of genetic variants at a large scale, by perturbing variants and genetic elements containing variants, and reading out the direct consequences of those perturbations on gene regulation. To this end, we propose to apply our recently developed CRISPR/Cas9 functional genomics screening technology with targeted single-cell transcriptomic readouts (targeted Perturb-seq or TAP-Seq in short) to enable systematic interrogation of non- coding regions and genetic variation therein. First, we will apply our targeted Perturb-seq to decipher the regulatory circuitry encoded on an entire human chromosome by systematically perturbing all major genetic elements (enhancers, protein-coding and lncRNA genes). This extensive data set will enable to decipher the complex regulatory networks controlling gene expression on the selected chromosome. Next, we will uncover causal regulatory variants in these regions by coupling high-throughput precision genome editing to simultaneous single-cell genomic and transcriptomic readout. Using this novel approach, we will be able to decipher the functional impact of genetic variants on gene expression and derive rules by which genetic variation perturbs gene regulatory processes. We will integrate the generated data with available functional genomics data, such as transcription factor binding (ChIP-seq), chromatin accessibility (ATAC-seq, DNAse-seq) and interactions in 3D (Hi-C), in order to train machine learning models to derive rules of the observed regulatory interactions. These models will be applied to decipher the molecular mechanisms underlying the regulatory logic, and to predict regulatory interactions and variants throughout the genome and across cell types. Selected predictions will be experimentally validated using the established perturbation technologies, to verify clinically relevant predictions and improve the performance of the predictive models. Taken together, this project will answer fundamental questions in gene regulation, uncover the mechanisms by which genetic variation impacts gene expression, and create datasets and computational models as valuable tools for interpreting results from GWAS, eQTL and clinical genomic studies.