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.

摘要