Methods and tools for structural models integrating multiple high-throughput omics data sets in genetic epidemiology

近年来，使用尖端技术进行生物测量的新方法使同时测量数千种潜在的疾病分子生物标记物成为可能。这些生物标志物的范围从为每个人的一生固定的遗传变异，到动态产生的、随时间变化的蛋白质等基因产品，以及更密切地反映正常功能和疾病发展过程的大分子代谢物。许多生物标志物反映了环境暴露，包括生活方式、职业和饮食因素，因此可以用来全面研究基因和环境之间的相互作用与疾病结局的关系。这些数据集的复杂性和规模使得它们的分析变得困难。传统多变量统计方法的局限性意味着现有的大多数分析都依赖于单变量方法，这种方法分别考虑每种类型的生物标记物，实际上是每个特定的分子。这意味着关于不同分子如何共同变化的重要信息丢失了。生产能够连贯地分析这些大规模数据集的强有力的统计工具，对于确保最有效地利用这些昂贵的数据十分重要。在我们的项目中，我们建议使用结构方程模型，它能够在一个模型中模拟几种不同类型的生物标记物和疾病途径之间的关系。传统上，这些模型要么用在非常小的数据集上，只有几十个变量，要么用在数百个变量的集合上，但都是同一类型的。我们建议开发能够一起分析多个高维生物标记物数据集的结构模型，从而使这些模型能够用于现代流行病学数据集。该项目将利用我们最近在分子生物标记数据集的高维统计建模方面的工作，并将我们的进展扩展到更复杂的结构模型，用于同时分析几个生物标记集。方法将参考来自北芬兰出生队列的案例研究，该队列的首席研究员是该项目的联合研究员。我们还将受益于与空气电波健康监测研究和欧洲癌症和营养队列前瞻性调查的合作，这两项研究都在帝国理工学院主办。该项目需要广泛的跨学科工作，结合统计学、流行病学、遗传学和计算方面的专业知识。鉴于他们的技能互补和访问数据库，调查组处于独特的地位，可以相继实现这些目标。

In recent years, new methods for biological measurements using sophisticated technologies have enabled the simultaneous measurement of thousands of potential molecular biomarkers of disease. These biomarkers range from genetic variants which are fixed for each person throughout their life, through gene products such as proteins which are produced dynamically and vary with time and across different cells in the body, to large molecule metabolites which more closely reflect the processes involved in both normal functioning and disease development. Many biomarkers reflect environmental exposures, including lifestyle, occupational and dietary factors, and thus serve to study in a comprehensive way the interaction between genes and environment in relation to disease outcomes.The complexity and size of these data sets render their analysis difficult. Limitations of traditional multi-variate statistical methods have meant that the majority of existing analyses rely on univariate methods, which consider each type of biomarker, and in fact each particular molecule, separately. This means that important information on how different molecules co-vary is lost. Producing robust statistical tools capable of analysing these large-scale data sets coherently is important to ensure the best exploitation of these expensive data. In our project we propose to use structural equation models, which are able to model the relations between several different types of biomarkers and disease pathways in a single model. Traditionally these models have either been used on very small data sets, numbering tens of variables, or on sets of hundreds of variables but all of the same type. We propose to develop structural models which are capable of analysing multiple high-dimensional biomarker data sets together, thus enabling these models to be used on modern epidemiological data sets. The project will take advantage of our recent work in high-dimensional statistical modelling of pairs of molecular biomarker data sets, and extend our advances to the more complex structural models for analysing several biomarker sets together.The methods will be developed with reference to case studies from the North Finnish Birth Cohort, whose Principal Investigator is a co-Investigator on this project. We will also benefit from collaborations with the Airwave Health Monitoring Study and the European Prospective Investigation into Cancer and Nutrition cohort, both of which are hosted at Imperial. The project requires extensive interdisciplinary work, combining expertise in statistics, epidemiology, genetics and computation. In view of their complementary skills and access to data bases, the team of investigators is uniquely placed to successively achieve these objectives.