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

迄今为止，已经进行了4500多项遗传研究，确定了1800多种疾病和特征的近20万种遗传风险因素。然而，大多数这些遗传风险因素的生物学后果是未知的。预期它们通过控制或调节这种活性来影响何时何地（即哪个器官）基因活跃。技术的进步意味着我们现在可以以前所未有的细节描绘基因调控的复杂层次。有大量的数据可以用来探索基因调控作为一个生物系统是如何工作的。挑战在于如何有效地分析大量数据，并以有意义的方式表示出来。该研究金的目的是开发能够建立最全面的基因调控模型的工具，并灵活地适应不可避免地出现的新数据集。这些工具将利用多种不同但互补的数据类型，并将它们统一为一个系统。它将在这些数据类型中寻找模式，这些数据类型定义了基因调控的不同状态。这个项目的独特之处在于，它将针对大样本队列的分析进行优化。我的方法将扩展现有的研究，专注于识别系统在个体之间的差异。了解基因调控在哪里发生变化是理解它如何影响疾病发展的关键。该项目的最后一部分将集中于如何以可用的格式共享软件的输出，以便其他研究人员可以将其与自己的数据集成。具体来说，我将创建一个人类脑细胞类型的基因调控模型，这将提供一个独特的资源，以提高我们对影响大脑的疾病（如阿尔茨海默病和精神分裂症）的认识。这些数据将通过一个基于网络的应用程序共享，该应用程序是该奖学金的一部分。至关重要的是，研究人员将能够研究遗传风险因素的不同组合如何影响基因活性，并确定哪些基因受到影响。例如，他们可以确定哪些基因被遗传风险因素破坏，从而增加个体患阿尔茨海默病的风险。目前还没有方法可以提供这种洞察力。有许多研究小组和全球联盟产生数据，可以用计划中的软件进行分析。该方法具有前瞻性，侧重于从现有数据中最大限度地获取信息，适用于任何器官、疾病或生物体的研究。因此，这一研究金有可能改变我们对健康和疾病的理解。

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.