Defining the effects of noncoding genetic variation on human regulatory element activity

摘要 数以千计的遗传关联研究已经确定了基因组中有助于 常见病。绝大多数关联信号驻留在基因组的非编码部分， 这表明调控元件中的遗传变异对常见疾病有很大影响 通过改变基因表达模式的病因学。我的长期目标是(I)实现快速和常规 确定遗传关联背后的因果调控机制，以及(2)利用这一机制 在我职业生涯后期确定候选治疗目标优先顺序的信息。这样做仍然是一个重要的 然而，由于遗传关联信号的有限分辨率以及通常较低的 实验验证研究的吞吐量。这项提议目标是朝着长期目标迈出的一步 的目标是完成第一个全基因组和全人群的实验评估，非 编码基因调控元件活性的遗传变异。我还将评估几个基本的 关于非编码的频率、位置、基因组背景和组合相互作用的假设 最有可能导致疾病的等位基因。在目标1中，我将量化以下各项的监管效果 不同人类群体中数以千万计的基因变异。为了做到这一点，我将完成全基因组 高通量记者对约300个人的基因组进行分析，我将根据他们的 遗传多样性。在目标2中，我将预测已识别的调控变异对基因表达的影响。去做 因此，我将开发和应用新的统计方法来整合来自高通量报告分析的数据 来自遗传关联研究和染色质状态和基因调控的基因组分析的数据。在……里面 目标3，我将评估调控变体的添加组合在全基因组范围内的影响-通常 在遗传关联区域观察--对基因表达的影响；并测试这些影响是否可以 解释下游表型。要做到这一点，我将使用经验性测量的个人监管影响 用来估计它们在具有多个变体的单倍型中的联合效应，然后验证它们的功能 使用CRISPR基因组编辑对体外基因表达的影响。预期的结果将是第一 全面的数据集，详细说明了来自不同人类的调控变体的位置和功能影响 人口。通过在线公共数据库分发这些数据，研究人员将能够查询哪些 监管变异体可能会解释他们自己的关联研究结果。因此，这些结果将使 更容易识别因果遗传变异，使研究人员能够将重点转移到开发 为患者提供预防和治疗选择。在我未来的职业生涯中，我希望带领研究团队 使用这些信息来确定新基因的优先顺序，以便进行有针对性的机械性评估，理想情况下，还可以用于其 有可能成为治疗靶点。

ABSTRACT Thousands of genetic association studies have identified regions of the genome which contribute to common diseases. The vast majority of association signal resides in the noncoding portion of the genome, suggesting that genetic variation within regulatory elements significantly contributes to common disease etiology by altering gene expression patterns. My long-term goals are (i) to enable rapid and routine identification of the causal regulatory mechanisms underlying genetic associations, and (ii) to use that information to prioritize candidate therapeutic targets later in my career. Doing so remains a significant challenge, however, because of the limited resolution of genetic association signals and the typically low- throughput of experimental validation studies. The objective of this proposal, a step towards that long-term goal, is to complete the first genome- and population-wide experimental assessment of the effects of non- coding genetic variants on gene regulatory element activity. I will also evaluate several foundational hypotheses regarding the frequency, location, genomic context, and combinatorial interactions of non-coding alleles that are most likely to functionally contribute to disease. In Aim 1, I will quantify the regulatory effects of tens of millions of genetic variants across diverse human populations. To do so, I will complete genome-wide high-throughput reporter assays across the genomes of ~300 individuals that I will prioritize based on their genetic diversity. In Aim 2, I will predict the effects of identified regulatory variants on gene expression. To do so, I will develop and apply novel statistical methods to integrate the data from high-throughput reporter assays with data from genetic association studies and genomic analyses of chromatin state and gene regulation. In Aim 3, I will estimate the genome-wide impact of additive combinations of regulatory variants — commonly observed in regions of genetic association — on the expression of a gene; and test whether those effects can explain downstream phenotypes. To do so, I will use the empirically measured effects of individual regulatory variants to estimate their combined effect in haplotypes with multiple variants and then validate their functional impact on gene expression in vitro using CRISPR genome editing. The expected outcome will be the first comprehensive dataset detailing the location and functional effects of regulatory variants from diverse human populations. By distributing those data via an online public database, researchers will be able to query which regulatory variants may explain results from their own association studies. Thus, these results will enable easier identification of causal genetic variants and enable researchers to shift their focus toward developing prevention and treatment options for patients. In future work of my career, I hope to lead research teams in the use of that information to prioritize new genes for targeted mechanistic evaluations and, ideally, for their potential as therapeutic targets.