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

摘要 成千上万的遗传关联研究已经确定了基因组中有助于 常见疾病。绝大多数关联信号位于基因组的非编码部分， 这表明调控元件内的遗传变异对常见疾病有显著影响， 通过改变基因表达模式来致病。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节机制，以及（ii）使用 在我的职业生涯后期优先考虑候选治疗目标的信息。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和典型的低分辨率， 实验验证研究的吞吐量。这项建议的目标是， 目标是完成第一个全基因组和全人群的实验评估， 编码基因调节元件活性的遗传变体。我还将评估几个基本的 关于非编码基因的频率、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 不同人群中数以千万计的遗传变异。为了做到这一点，我将完成全基因组 在约300个人的基因组中进行高通量报告分析，我将根据他们的优先顺序进行优先排序。 遗传多样性在目标2中，我将预测已鉴定的调控变体对基因表达的影响。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基因检测的数据 从遗传关联研究和染色质状态和基因调控的基因组分析数据。在 目标3，我将估计调控变异的加性组合的全基因组影响-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。为了做到这一点，我将使用经验衡量的影响，个别监管 变异，以估计它们在具有多个变异的单倍型中的组合效应，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个 一个全面的数据集，详细描述了来自不同人类的调节变体的位置和功能效应。 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 调节变体可以解释其自身关联研究的结果。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方案。在我未来的职业生涯中，我希望领导研究团队， 使用这些信息来优先考虑新基因，以进行有针对性的机制评估，理想情况下， 作为治疗靶点的潜力。

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.