Developing novel statistical methodology incorporating biological structure for high-throughput genomic data analysis

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The tools of modern genetics now enable scientists to explore and probe the human genome at unprecedented levels of scale and detail. Miniaturisation and parallelisation now enable geneticists to perform many thousands of experiments on a single chip that can fit into the palm of the hand. However, geneticists are now faced with unprecedented quantities of data that defy human efforts to analyse and interpret them. Advanced computational based methods of analysis offer a solution but existing algorithms are frequently based on ad-hoc heuristics and fail to incorporate biological knowledge and structure that a human interpreter might. This can lead to many discoveries that are subsequently found to be false in follow-up studies - a process which is highly wasteful of time and resources. This study seeks to meet the challenges posed by modern genetics by developing formal statistical methods that are able to handle massive data sets, exploit known biological structure, and assist in the decision making process of clinicians and experimental scientists. The use of sophisticated data analysis methods with advanced genomic technologies will minimise false discoveries and allow efforts to be focused on those discoveries that may then lead to furthering the understanding of human disease.