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

回归结构的贝叶斯发现：遗传流行病学和综合基因组学分析的工具包

• 批准号: G1002319/1
• 负责人: Sylvia Richardson
• 金额: $ 65.93万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G1002319%2F1
• 关键词: Bayesian; Discovery; Regression; Structures; tool

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.

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；摄动也可能由暴露于生活方式和环境风险因素引起。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，即在大量可能性中找到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是很重要的，这样在许多队列研究中收集的昂贵数据才能充分发挥其潜力。该项目建议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。该项目将利用团队最近的工作成果，为此类分析创建一个新的框架，并交付一组技术，例如？工具吗?，以分析这些数据。采用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源环境中实现，因此它们可以很容易地适应广泛的问题。在该项目中，这些工具将应用于三个具体的案例研究：(i)在一个大型芬兰队列中分析脂质机制的调节；（ii）对乳腺癌多因子通路的分析；（三）分析基因对精神病患者大脑活动的影响。这些个案研究将作为这些方法的简介，展示它们的适用性并帮助它们的传播。正如应用范围所示，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。