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

现代遗传学的工具现在使科学家能够在前所未有的规模和细节上探索和探测人类基因组。如今，微型化和并行化使遗传学家能够在一个手掌大小的芯片上进行成千上万的实验。然而，遗传学家现在面临着前所未有的大量数据，人类无法对其进行分析和解释。先进的基于计算的分析方法提供了一个解决方案，但现有的算法通常是基于特别的启发式，无法结合人类解释器可能包含的生物知识和结构。这可能导致许多发现在后续研究中被发现是错误的——这是一个非常浪费时间和资源的过程。本研究旨在通过发展正式的统计方法来应对现代遗传学带来的挑战，这些方法能够处理大量数据集，利用已知的生物结构，并协助临床医生和实验科学家的决策过程。使用复杂的数据分析方法和先进的基因组技术将最大限度地减少错误的发现，并使努力集中在那些可能导致进一步了解人类疾病的发现上。

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.