Bayesian Discovery of Regression Structures: a tool kit for genetic epidemiology and integrative genomics analyses

为了更好地了解癌症等多因素疾病，研究人员正在使用新的生物技术来探测遗传密码，并测量一系列对人类健康良好运作至关重要的生物机制。特别令人感兴趣的是，利用这些数据来发现遗传密码突变与可能导致疾病的生物过程扰动之间的潜在关联;这些扰动也可能是由于暴露于生活方式和环境风险因素造成的。这些新的生物技术产生了大量的信息，这些数据的庞大规模使其分析变得困难。因此，统计学家面临着在大量的可能性中找到与疾病状态相关的遗传和环境因素的特定组合的艰巨任务。提出更好的统计工具来完成这项任务是很重要的，这样在许多队列研究中收集的昂贵数据就能充分发挥其潜力。该项目提出开发改进的技术来发现数据中的结构，重点是大型遗传和基因组学数据集的多维和多变量方面。该项目将利用该团队最近的工作，为此类分析创建一个新颖的框架，并提供一套技术，例如？工具箱？分析这些数据。以全面的多元观点来分析大规模数据需要开发复杂的算法，这将是研究计划的一个关键方面。为了确保可行性，将利用最新的计算机技术使用图形卡。所开发的方法将在开放源代码环境中实施，以便它们可以很容易地适用于各种问题。在该项目中，这些工具将应用于三个具体的案例研究：（一）在一个大型的芬兰队列的脂质机制的调节分析;（二）乳腺癌的多因素途径的分析;和（三）对精神病患者的大脑活动的遗传影响的分析。这些案例研究将作为这些方法的小插曲，展示其适用性，并有助于其传播。正如一系列应用所证明的那样，该提案需要广泛的跨学科工作，结合统计建模、计算、遗传学和流行病学方面的专业知识。鉴于他们的互补技能和丰富的数据库，在帝国理工学院的研究团队是独一无二的，以成功实现这些目标。

To better understand multifactorial diseases such as cancer, researchers are using new biotechnologies that probe the genetic code and measure a range of biological mechanisms that are fundamental for the good functioning of human health. Of particular interest is to use these data to discover potential associations between mutations in the genetic code and perturbations of biological processes that can lead to disease; perturbations that may also result from exposure to life style and environmental risk factors. These new biotechnologies produce vast amounts of information and the sheer size of these data render their analysis difficult. Statisticians are consequently faced with the difficult task of finding specific combinations of genetic and environmental factors that are related to disease status amongst a vast array of possibilities. Proposing better statistical tools to do this task is important so that the expensive data that is being collected in many cohort studies are exploited to their full potential. This project proposes to develop improved techniques for discovering structures in the data, with a focus on multidimensional and multivariate aspects of large genetic and genomics data sets. The project will take advantage of recent work by the team to create a novel framework for such analyses and deliver a set of techniques, a ?tool kit?, to analyse such data. Taking a comprehensive multivariate point of view to analyse large scale data requires the development of sophisticated algorithms, which will be one key aspect of the research programme. To ensure feasibility, advances in the latest computer technology using graphics cards will be exploited. The methods developed will be implemented in an open-source environment so that they can be easily adapted to a wide range of questions. In the project, these tools will be applied to three specific case studies: (i) an analysis of the regulation of lipid mechanisms in a large Finnish cohort; (ii) an analysis of multifactorial pathways in Breast cancer; and (iii) an analysis of the genetic influence on brain activity of psychotic patients. These case studies will serve as vignettes for the methods, showcasing their applicability and helping their dissemination. As demonstrated by the range of applications, the proposal requires extensive interdisciplinary work, combining expertise in statistical modelling, computing, genetics and epidemiology. In view of their complementary skills and access to rich data bases, the team of investigators at Imperial College is uniquely placed to successfully achieve these objectives.