Our group is developing and applying methods to analyse large-scale human genetic data with the aim of improving the prediction, prevention and treatment of disease. We use genetics to both inform on underlying disease biology, including detecting evidence for natural selection, as well as identifying genetic variation which is informative for clinically relevant phenotypes.
Currently a focus of our work is in infectious disease, through collaborations with two consortiums: MalariaGEN in which we aiming to understanding the human and parasite genetic determinants of susceptibility to severe malaria in Africa;and STOP-HCV, where we are using large NHS patient cohorts to assess how best to use molecular analysis, of both the patient and viral genomes, to improve treatment for individuals who have hepatitis C.
More generally our group aims to understand how different diseases are connected through shared molecular mechanisms. As currently we have a focus on identifying the genetic basis to infectious and autoimmune diseases, we are interested in understanding how the human genome has evolved in response to a changing environment, in particular, to defend the host (us) against pathogens. We would like to understand how these pressures have shaped our immune system and physiology, and how this relates to disease.
To complement this research we’re also interested in looking for patterns of genetic diversity that exhibit tell-tale signs of an interesting evolutionary past. These might be signals that a recent mutation has spread rapidly through the population, or that it has been around for a suspiciously long time. These approaches can provide additional clues as to which genetic variants have been important for determining pathogen susceptibility in our ancestors. One way to try and understand the mechanisms is by looking for genetic risk factors in humans that are modified by the genetics of the pathogen. There are statistical challenges in how to look for these associations and interactions in the context of disease susceptibility, and how to use them to stratify patients or to infer underlying mechanisms.
Now is a very exciting time in human genetics. New technologies are driving an explosion of genetic data with which to address important questions. However, these are Big Data with complex patterns. To make inferences it is often helpful to think about the right approach given unlimited computation resources, and then to develop models which capture the essence of the approach, but allow computation in a reasonable time scale. Our group and others in the WTCHG are developing these methodologies.
We have various DPhil opportunities and post-doctoral positions coming up in my group, and in collaborator's groups. If you would like to be kept informed please send an e-mail to including "Research enquiry" in the subject heading.