Comparative and Population Genomics
Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.
Our overall goal is to understand complex genetic variants that underlie human disease. We are particularly interested in repetitive DNA variants known as short tandem repeats (STRs) as a model for complex variation. Our work focuses on developing computational tools for analyzing and visualizing complex variation from large-scale sequencing data and applying these tools to learn about the contribution of repetitive variation to human disease.
The Knight lab has broad interests in the human microbiome, the collection of trillions of microbes that inhabits our bodies, especially in developing techniques to read out these complex microbial communities and use the resulting data to understand human health, links between humans and the environment, and to prevent and cure disease. We offer a fast-paced environment with many collaborative opportunities on different projects.
The McVicker laboratory aims to understand how chromatin state and organization are encoded by the human genome. Our approach to this problem is to exploit naturally occurring human genetic variation to identify sequence variants that disrupt chromatin function. We are currently focused on chromatin within immune cells and we are also interested in how variants that affect chromatin and gene regulation lead to disease risk. The problems that we work on often require the development of sophisticated computational and statistical methods that can extract subtle signals from noisy experimental data.
Research in our lab is focused on developing computational methods for the discovery and analysis of human genetic variation using high-throughput sequencing technologies. We develop algorithms and software tools for accurate and comprehensive assembly of human genomes and identifying disease associated variants.
The Viral Evolution Group (located near the UCSD hospital in Hillcrest) studies computational and statistical models of evolution, with applications to viral pathogens and other systems. Solid programming skills and knowledge of probabilistic modeling (e.g. as covered in CSE250A and CSE250B) are required. There is no wet lab component.