Summary: I am a biostatistician specializing in statistical genetics. I lead the LICAP Statistics group.
Location: Cancer Genetics Building
Teaching Commitments: I am module lead for “Introduction to Genetic Epidemiology” (EPIB5032M), which is offered as part of the MSc in Epidemiology and Biostatistics and the MSc in Molecular Medicine.
I have led the Statistics research group since 2000, after previous academic posts at the University of Manchester and an earlier spell at Leeds. After completing my undergraduate degree in Mathematics and Philosophy I worked for a number of years in secondary and adult education and with the Open University, mainly teaching mathematics, returning to higher education to study for an MSc and PhD in statistics once my children started school. Although I have a broader interest in biostatistics, most of my research has been focused on statistical genetics. I have worked on the genetic epidemiology of numerous complex diseases, including various cancers, musculoskeletal disease (primarily rheumatoid arthritis) and cardiovascular disease.
My main current research is in the genetic epidemiology of cancer and musculoskeletal disease. Much of this concerns the statistical aspects of the studies carried out by the multi-disciplinary teams within the Section of Epidemiology and Biostatistics. Key projects include large-scale international genome-wide studies of melanoma susceptibility and prognosis, through the GenoMEL, BioGenoMEL and MELGEN consortia. I also collaborate with other researchers in the Leeds Biomedical Research Centre on epidemiological and pharmacogenetic studies of rheumatoid arthritis and giant cell arteritis, and with other groups within the Institute or wider University carrying out clinical or population-based genetic research.
The statistical approaches involved in our work include highly specialized and evolving methods, and an important part of my role is to develop and lead research into the statistical methodology pertinent to the applied studies, reviewing existing methodology, and where relevant refining or adapting these specialised methods. Methodological interests include family-based genetic studies, investigations of genetic association including genome-wide association studies, the analysis of transcriptomic data, risk modeling and areas of increasing translational importance such as predictive biomarker studies.