Statistical Genetics and Bioinformatics

Statistical Genetics & Bioinformatics

With the ever increasing deluge of genetic data, understanding the structures within the data is increasingly important. Statistical genetics has a broad range of application from quality control and processing of raw data to uncovering association between genes and disease. Population genetics, which studies the shared variation between members of a species, is of particular interest in our group as it can shed light on demographic history and natural selection. To this end, we are involved in building cutting edge statistical models that are informed by the mechanisms of mutation and recombination and can scale to large modern datasets.

The gene regulatory network (GRN) is a complex control system and plays a fundamental role in the physiological and development processes of living cells. Focusing on the ordinary differential equation (ODE) modeling approach, our group implements and develops a novel pipeline for constructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. This pipeline consists of a five-step procedure, i.e., detection of temporally differentially expressed genes, clustering genes into functional modules, identification of network structure, parameter estimate refinement and functional enrichment analysis. Our group has published intensively in this area with applications in influenza infection, HIV and cancer.

People

Dr Michael Salter-Townshend
Research Interests‌: Bayesian Statistics, Statistical Modelling, Statistical Genetics.

Group Contact (Email): (opens in a new window)Dr Michelle Carey