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

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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.