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

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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.