Computational Methods for Investigating the Genetics of Gene Regulation

研究基因调控遗传学的计算方法

• 批准号: 10708664
• 负责人: William Majoros
• 金额: $ 36.7万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708664
• 关键词: Alleles; Biological Assay; CRISPR interference; Cells; Collaborations; Complex; Computing Methodologies; Data; Development; Diagnosis; Disease; Doctor of Philosophy; Enhancers; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Human Genome; Individual; Methods; Modern Medicine; Mutation; Patients; Positioning Attribute; Regulator Genes; Regulatory Element; Reporter; Science; Statistical Methods; Statistical Models; Therapeutic; Untranslated RNA; Variant; Work; causal variant; design; genetic disorder diagnosis; genetic variant; genome wide association study

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.

抽象的 复杂疾病的遗传诊断是现代医学的重要挑战。作为GWA的大多数 命中暗示了人类基因组的非编码区域，调节元素（例如增强子）已成为一种 寻找因果机制的主要重点。该建议重点是计算的发展 分析与基因调节机制相关的实验数据的方法以及可以 扰动他们，导致疾病。我的实验室对实验科学有很大的影响很大 在Duke和其他地方正在进行的基因调节中，通过多次合作和会员资格 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 在个体和三重奏中的表达，以鉴定可能在失调下的基因。我的实验室也继续 开发用于分析大量平行记者测定法的遗传变异数据的方法，这是一个重点 我的博士学位论文。我的实验室已经开始开发用于分析CRISPRI扰动的统计方法 在单细胞数据中以识别基因增强剂关系。我们希望这些协同的项目能够带来更多 有效鉴定因果变异的因果变异和较高的诊断率目前未诊断的患者 各种疾病以及潜在的疗法设计。