This ultimately aims to improve our understanding of susceptibility to complex diseases as well as their diagnosis, prognosis and prevention, as well as identifying predictors of treatment response.
Our research falls into two broad categories:
1. The development and evaluation of novel methods for the analysis of genetic data.
2. The application of robust and appropriate methods to analyse genetic datasets.
Both are focussed on the common aims of improving our understanding of susceptibility to diseases; diagnosis, prognosis and prevention of disease and predictors of treatment response (termed ‘pharmacogenetics’).
The methods that form the basis of our research include statistical methods, encompass both established statistical methods and those perceived as cutting-edge statistical methods such as machine learning. Our research also includes methodologies aimed at ensuring the robust design and conduct of genetic studies. These methods are applied acorss a broad and diverse range of diseases, including complex and infectious disease. We also develop software to accompany our methods to ensure these methods are easily accessible.
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