Quantifying the genetic diversity of human regulatory element activity

了解人类疾病的遗传原因具有造福人类的巨大潜力。 健康。人类基因学界投入了巨大的资源来鉴定这些基因 原因，包括最近对患者队列的全基因组测序。那些研究 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，很难在全基因组范围内进行研究， 这些研究中的大多数的好处还没有实现。我们的长期目标是了解 非编码的遗传变异通过基因调控元件影响表型。这个 这项提议的目标是为实现这一长期目标建立一个平台 可靠和系统地确定监管的经验和统计方法 人类特征和疾病的潜在机制。具体地说，在目标1中，我们将使用高- 吞吐量报告者分析以量化数百万人类基因变异对 调节元素的活动。这些变异将代表不同的人类祖先，并将涵盖 超过60%的所有地区都与一种性状或疾病有关，这是通过Gwas来实现的。结果将是 最广泛的人类调控变异目录。在目标2中，我们将开发 系统地将监管要素活动中的这些变化与变化联系起来的新技术 在基因表达上。这项技术将结合我们之前开发CRISPR-CAS9的工作- 基于具有靶向单细胞RNA序列的表观基因组编辑屏幕。在目标3中，我们将开发 整合调控变异的影响以推断基因变化的统计分析 个体间表型的表达和差异。生成的方法将为 类似于基于基因的关联测试，但适用于非编码基因组。预期中的 该项目的成果是：(1)极大地提高了建立相关机制的能力 与人类特征和疾病的非编码联系；(2)更好地理解基因 调控元件活性和基因调控的体系结构将指导设计和 对未来遗传关联研究的解释；以及(Iii)新的试剂、方案和 其他实验室可以用来完成对自己的模型系统的类似调查的软件 利息。综上所述，我们预计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.