Development of software to model multi-modal genomic data as an integrated system: application to understanding the gene regulatory landscape

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To date over 4,500 genetic studies have been performed identifying almost 200,000 genetic risk factors for more than 1,800 diseases and traits. However, the biological consequences of the majority of these genetic risk factors are unknown. They are anticipated to influence when and where (i.e. which organ) genes are active by controlling or regulating this activity. Advances in technology mean we can now profile the complex layers of gene regulation in unprecedented detail. There is a wealth of data available to explore how gene regulation works as a biological system. The challenge is how to efficiently analyse this huge quantity of data and represent it in a meaningful manner. The aim of this Fellowship is to develop tools that are capable of building the most comprehensive model of gene regulation and is flexible to accommodate new data sets as they inevitably arise. These tools will take advantage of multiple different yet complementary data types and unite them as a single system. It will look for patterns across these data types which define different states of gene regulation. What makes this project unique is that it will be optimised for the analysis of large sample cohorts. My approach will extend existing research by focusing on identifying where the system varies across individuals. Knowing where gene regulation varies is the key to understanding how it influences the development of disease. The final part of the project will focus on how to share the output of the software in a useable format, so that other researchers can integrate it with their own data. Specifically, I will create a model of gene regulation for human brain cell types that will provide a unique resource to improve our knowledge of diseases that affect the brain (e.g. Alzheimer's disease and schizophrenia). The data will be shared through a web based application, developed as part of the Fellowship. Crucially, researchers will be able to investigate how different combinations of genetic risk factors influence gene activity and identify which genes are affected. For example, they could identify which genes are disrupted by genetic risk factors that increase an individual's risk of developing Alzheimer's disease. At present there is no method available to provide this kind of insight. There are a number of research groups and global consortium generating data that could be analysed with the planned software. The methodology is forward thinking and focused on maximising the information gain from existing data and is relevant for the study of any organ, disease or organism. This Fellowship, therefore, has the potential to transform our understanding of health and disease.