Computational Methods for Investigating the Genetics of Gene Regulation

点击翻译按钮获取中文摘要

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.