Computational Methods for Investigating the Genetics of Gene Regulation

摘要 复杂疾病的基因诊断是现代医学的一个重要挑战。作为GWAS的大多数成员， hits涉及人类基因组的非编码区，调节元件如增强子已成为一种 主要集中在寻找因果机制。该提案的重点是发展计算 用于分析与基因调控机制和变体相关的实验数据的方法， 扰乱它们，导致疾病。我的实验室有能力对实验科学产生重大影响 在基因调控正在杜克和其他地方，通过现有的合作和成员在多个 财团。特别是，我的实验室一直在开发用于检测等位基因特异性基因的统计模型， 在个体和三人组中表达，以鉴定可能失调的基因。我的实验室还继续 开发用于分析来自大规模平行报告基因测定的遗传变异数据的方法，这是一个焦点， 我的博士学位论文我的实验室已经开始开发分析CRISPRi扰动的统计方法 在单细胞数据中识别基因-增强子关系。我们希望这些协同项目能带来更多 有效识别致病变异，提高目前未确诊患者的诊断率， 各种疾病，以及潜在的导致设计的治疗方法。

Abstract Genetic diagnosis of complex disease is an important challenge in modern medicine. As a majority of GWAS hits implicate noncoding regions of the human genome, regulatory elements such as enhancers have become a major focus in the search for causal mechanisms. This proposal focuses on the development of computational methods for analyzing experimental data relevant to gene regulatory mechanisms and the variants that can perturb them, leading to disease. My lab is well positioned to have a sizeable impact on the experimental science in gene regulation ongoing at Duke and elsewhere, via existing collaborations and memberships in multiple consortia. In particular, my lab has been developing statistical models for detecting allele-specific gene expression in individuals and trios, to identify genes that may be under dysregulation. My lab also continues to develop methods for analyzing genetic variant data from massively parallel reporter assays, which was a focus of my Ph.D. thesis. And my lab has begun developing statistical methods for analyzing CRISPRi perturbations in single-cell data to identify gene-enhancer relationships. We expect these synergistic projects to result in more effective identification of causal variants and a higher diagnosis rate for currently undiagnosed patients of a wide variety of diseases, as well as potential leads toward the design of therapeutics.