Strategy for analysing epidemiological data involving genetic, endogenous, environmental factors and their interactions

众所周知，许多慢性病，特别是癌症、糖尿病和心脏病，是多因素的，涉及遗传易感性和其他风险因素（如饮食、生活方式特征和物理环境）之间的复杂关系。为了研究这些问题，目前在全球范围内进行的一些大型前瞻性队列研究（即跟踪一组个体的研究）所采用的策略是进行一系列专注于特定疾病结局的子研究，其中记录了越来越多的实验室分析和问卷调查数据。然而，解释如此丰富的数据的统计工具尚未跟上步伐，产生的昂贵数据没有得到充分利用。该项目将开发更好的技术，用于同时分析衡量各种风险因素的数据，并评价其相互作用。这些工具将应用于与乳腺癌和肺癌有关的两个具体案例研究，这两个案例研究是大型欧洲癌症和营养前瞻性调查的一部分（跟踪了50多万人）。除了这些具体分析外，流行病学界对一般战略和方法的发展也有广泛的兴趣，有可能对了解复杂疾病的原因和改善人类健康产生深远的影响。为了帮助解释我们的模型产生的丰富输出，我们还将开发可视化工具，这些工具可以由不同领域的广泛专家（临床医生，流行病学家，公共卫生专家）轻松使用，从而促进重要研究的传播。该提案要求开展广泛的跨学科工作，结合统计建模、遗传学、流行病学和计算方面的专门知识。鉴于他们的互补技能，帝国理工学院的研究人员团队处于独特的地位，能够成功实现这些目标。

It is well known that many chronic diseases, in particular cancer, diabetes and heart disease, are multifactorial, involving complex relationships between genetic predisposition and other risk factors such as diet, life style characteristics, and the physical environment. To study these, the strategy adopted in a number of large prospective cohort studies (i.e. studies that follow a group of individuals through time) currently underway worldwide is to conduct a series of sub-studies focussed on specific disease outcomes where increasingly large quantities of data from laboratory analysis and questionnaires are recorded. However, statistical tools to interpret such rich data have, as yet, failed to keep pace and the expensive data being produced are not exploited to their full potential. This project will develop improved techniques for simultaneous analysis of data measuring a wide diversity of risk factors and for evaluating their interactions. These tools will be applied to two specific case studies related to breast and lung cancer that are part of the large European Prospective Investigation on Cancer and Nutrition (over 500,000 people followed). Besides these specific analyses, the generic strategy and methodological developments are of broad interest throughout the epidemiological community, with the potential to have a profound impact on the understanding of the causes of complex diseases and on the improvement of human health. To aid the interpretation of the rich output produced by our models, we will also develop visualisation tools that could be used easily by a wide community of experts in different domains (clinicians, epidemiologists, public health specialists), thus facilitating dissemination of important research. The proposal requires extensive interdisciplinary work, combining expertise in statistical modelling, genetics, epidemiology and computing. In view of their complementary skills, the team of investigators at Imperial College is uniquely placed to successfully achieve these objectives.