Quantifying the genetic diversity of human regulatory element activity

了解人类疾病的遗传原因对造福人类具有巨大的潜力 健康人类遗传学界投入了大量的资源来识别那些 原因，包括，最近，患者队列的全基因组测序。这些研究 发现基因组非编码区的遗传变异最常与 疾病和药物反应。不幸的是，由于遗传变异对基因的影响， 调控仍然知之甚少，难以在全基因组范围内进行研究， 其中大多数研究的效益尚未实现。我们的长期目标是了解 非编码遗传变异体通过基因调控元件影响表型。的 本提案的目标是朝着这一长期目标迈出一步， 经验和统计方法，以可靠和系统地确定监管 人类特征和疾病的潜在机制。具体而言，在目标1中，我们将使用高- 通量报告基因测定来量化数百万人类遗传变异对 调节元件活性。这些变体将代表不同的人类祖先，并将涵盖 超过60%的区域通过GWAS与性状或疾病相关。结果还不一定 最广泛的人类调节变异目录。在目标2中，我们将开发 新技术系统地将调节元件活动中的这些变化与 in gene基因expression表达.这项技术将联合收割机结合我们之前开发CRISPR-Cas9的工作， 基于靶向单细胞RNA-seq的表观基因组编辑筛选。在目标3中，我们将开发 统计分析，整合调控变异的影响，以推断基因的变化， 表达和个体之间表型的差异。生成的方法将是 类似于基于基因的关联测试，但用于非编码基因组。预期 这一项目的成果是：（一）大大提高了建立基本机制的能力， 与人类特征和疾病的非编码关联;（ii）更好地了解遗传 调控元件活性和基因调控的结构，这将指导设计和 对未来遗传关联研究的解释;以及（iii）新的试剂、方案，以及 其他实验室可以使用该软件来完成他们自己的模型系统的类似研究， 兴趣总的来说，我们期望这一项目将是朝着充分实现 全基因组和全基因组关联研究的潜力。

Understanding the genetic causes of human disease has immense potential to benefit human health. The human genetics community has devoted tremendous resources to identifying those causes, including, most recently, whole genome sequencing of patient cohorts. Those studies have found genetic variation in non-coding regions of the genome to be most often associated with diseases and drug responses. Unfortunately, since the effects of genetic variation on gene regulation remain poorly understood and difficult to study at the genome-wide scale, the full benefit of most of those studies has yet to be realized. Our long-term goal is to understand how non-coding genetic variants act through gene regulatory elements to influence phenotypes. The objective of this proposal, a step towards that long-term goal, is to develop a platform of empirical and statistical methods to reliably and systematically determine the regulatory mechanisms underlying human traits and diseases. Specifically, in Aim 1, we will use high- throughput reporter assays to quantify the effects of millions of human genetic variants on regulatory element activity. Those variants will represent diverse human ancestries, and will cover over 60% of all regions associated with a trait or disease via GWAS. The outcome will be the most extensive catalog of human regulatory variation every created. In Aim 2, we will develop new technologies to systematically relate those changes in regulatory element activity to changes in gene expression. That technology will combine our previous work developing CRISPR-Cas9- based epigenome editing screens with targeted single-cell RNA-seq. In Aim 3 we will develop statistical analyses to integrate the effects of regulatory variants to infer changes in gene expression and differences in phenotypes between individuals. The resulting method will be analogous to gene based association tests, but for the noncoding genome. The expected outcomes of this project are (i) dramatically improved ability to establish mechanisms underlying non-coding associations with human traits and diseases; (ii) better understanding of the genetic architecture of regulatory element activity and gene regulation that will guide the design and interpretation of future genetic association studies; and (iii) novel reagents, protocols, and software that other labs can use to complete similar investigations of their own model systems of interest. Taken together, we expect that this project will be a major step towards fully realizing the potential of genome wide and whole genome association studies.