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生物机制，这些机制对人类健康的良好运作至关重要。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的扰动之间的潜在联系；也可能是由于暴露在生活方式和环境风险因素中的扰动。这些新的生物技术产生了海量的信息，而这些数据的巨大规模使它们的分析变得困难。因此，统计学家面临着一项艰巨的任务，即在众多可能性中寻找与疾病状况有关的遗传和环境因素的具体组合。提出更好的统计工具来完成这项任务非常重要，这样才能充分利用在许多队列研究中收集的昂贵数据的潜力。该项目建议开发发现数据结构的改进技术，重点放在大型遗传和基因组数据集的多维和多变量方面。该项目将利用该小组最近的工作，为这种分析建立一个新的框架，并提供一套分析这种数据的技术--“工具包”。从全面的多变量角度分析大规模数据需要开发复杂的算法，这将是研究方案的一个关键方面。为了确保可行性，将利用使用显卡的最新计算机技术的进步。所开发的方法将在开放源码环境中实施，以便它们可以很容易地适应各种问题。在该项目中，这些工具将被应用于三个具体的案例研究：(I)对芬兰一个大型队列中的血脂调节机制的分析；(Ii)对乳腺癌的多因素途径的分析；以及(Iii)对精神病患者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，展示它们的适用性，并帮助它们的传播。如应用范围所示，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访问丰富的数据库，帝国理工学院的调查团队在成功实现这些目标方面处于独特的地位。

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.