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面临的重要挑战。作为GWA的大多数成员 HIT涉及人类基因组的非编码区，增强子等调控元件已成为 主要集中在寻找因果机制上。本提案侧重于计算技术的发展。 分析与基因调控机制相关的实验数据的方法和可以 扰乱它们，导致疾病。我的实验室处于有利地位，将对实验科学产生重大影响 在杜克大学和其他地方正在进行的基因监管中，通过现有的合作和多个 财团。特别是，我的实验室一直在开发用于检测等位基因特定基因的统计模型 在个体和三个组中的表达，以确定可能处于失调状态的基因。我的实验室也在继续 开发从大规模平行报告分析中分析遗传变异数据的方法，这是一个重点 我的博士论文。我的实验室已经开始开发统计方法来分析CRISPRi扰动 在单细胞数据中识别基因-增强子关系。我们预计这些协同项目将产生更多 有效识别原因变异，提高目前未诊断的广泛性脑出血患者的诊断率 各种各样的疾病，以及潜在的导致治疗设计的因素。

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.