For Non-Scientists

Many common human diseases, for example type 2 diabetes, involve complex interplay between genes and non-genetic risk factors such as diet and exercise, and represent a considerable drain on public health resources. Thus, the identification of the underlying susceptibility genes is an important and challenging issue for the research community. Recently, there has been massive investment in international projects to discover how humans differ in their genetic make-up, and to investigate how these differences relate to disease risk. One of the most widely publicized innovations, whole-genome association (WGA) studies, make use of many thousands of DNA markers as signposts of common human genetic variation, and have huge potential to discover genes predisposing to complex diseases. However, there is currently a lack of statistical tools for the efficient analysis of WGA studies, meaning that full exploitation of this potential may never be achieved. The aim of the research performed by our group is to use our statistical expertise in genetic epidemiology to address the urgent challenge posed by this analytical bottleneck by providing novel methods for WGA studies. This will inform future study design, and ultimately provide a better understanding of the genetic mechanisms underlying complex diseases, potentially aiding counselling and development of drug therapies.