In the past decade, biological and medical research has changed dramatically with the ability to sequence genomes in a cost-effective way and to measure thousands of biological markers that characterise normal or pathological processes. Such knowledge has a huge potential to improve our understanding of diseases such as cancer, diabetes, cardiovascular and infectious diseases. Particularly interesting is the exploration of these data for genetic, life-style and environmental causes of diseases. However, these new biotechnologies produce vast amounts of information, making their analysis difficult.
Statisticians are faced with the challenging task of finding specific combinations of genetic biomarkers and risk factors that are related to disease status amongst a vast array of possibilities. In order to develop effective treatments, the complex interactions of the thousands of components of a cellular system working together in a network need to be understood as well, at least to some approximation. Proposing and improving statistical tools for these tasks is important to ensure that these expensive datasets, which are now being collected in many clinical and epidemiological studies, are exploited to their full potential.
The Statistical Genomics research team here at the MRC Biostatistics Unit are developing new and improved techniques for finding important combinations of features in large genetic and genomics datasets that characterise or predict health outcomes and will therefore lead to a better understanding of the underlying biological mechanisms. We aim to develop all our methods in an open-source environment, thus allowing easy adoption by researchers throughout the field, and therefore their application to a wide range of questions. In order to aid the dissemination and increased utilisation of these methods, we work with collaborators to demonstrate their applicability through case studies related to (among others) autoimmune and infectious diseases, type 2 diabetes, coronary heart disease and a variety of cancers.
Other Research Themes:
- DART: Design and Analysis of Randomised Trials
- ESH: Evidence Synthesis to inform Health
- COLD: Methods for the Analysis of Complex Observational and Longitudinal Data