Dissecting the molecular aetiology of complex traits using high dimensional omic data

与复杂性状相关的遗传变异的发现正以指数速度增长。现在至关重要的是发展我们对分子机制的理解，这有助于解释这些发现，以提高我们预防和治疗疾病的能力。高通量测序技术的进步为解决这一挑战和确定全基因组关联研究（GWAS）结果的生物学和临床相关性提供了前所未有的机会。然而，在不同类型的分子“组学”特征上产生了越来越多的数据，伴随着部分重叠和经常未经测试的方法的快速发展。为了克服这一挑战，需要集中研究最适当和有效的方法，利用大规模的基因组数据来阐明复杂疾病的分子决定因素。研究概述在这个奖学金提案可以分为五类，利用大规模的数据，以改善病人的医疗保健符合英国的产业战略的总体目标。HDR-UK提供的机会将使我能够解决分子病因学中一些最关键的限制。具体而言，需要广泛研究组学性状的组织特异性，适当评估分子中介的系统框架，以及在这种范式中改进因果推理的方法。我还打算将新颖的、最先进的方法应用于现有的基因组学数据，以阐明对治疗评估具有转化价值的发现。最后，我将构建可公开访问的计算工具，以自动执行该范式中的基本分析，并提供资源，以传播本研究的发现。利用健康信息学来利用大规模、高通量的数据来发展我们对从遗传变异到复杂疾病的因果途径的理解是本研究项目的首要主题。这项研究将导致一些高影响力的出版物，以提高我们对疾病的分子决定因素的理解，以及有助于传播该项目产品的网络工具，并协助同事在该领域的努力。因此，这项工作与HDR-UK在卫生信息学和加速药物发现方面的优先事项最为一致。

The discovery of genetic variants associated with complex traits is increasing at an exponential rate. It is now of vital importance to develop our understanding of the molecular mechanisms which can help explain these findings, in order to improve our capability to prevent and treat disease. Advancements in high-throughput sequencing technologies present an unprecedented opportunity to address this challenge and ascertain the biological and clinical relevance of results from genome-wide association studies (GWAS). However, there is an increasing abundance of data being generated on diverse types of molecular "omic" traits, accompanied by the rapid development of partially overlapping and often untested methodologies. To overcome this challenge, there needs to be focused research into the most appropriate and efficient manner to harness large-scale 'omic data to elucidate the molecular determinants of complex disease.The research outlined in this fellowship proposal can be delineated into five categories, with the overall aim of harnessing large-scale data to improve patient healthcare in-line with the UK's industrial strategy. The opportunity presented by HDR-UK will allow me to address some of the most crucial limitations in molecular aetiology. Specifically, there needs to be extensive research into tissue-specificity for 'omic traits, systematic frameworks to appropriately appraise molecular mediation and methods to improve causal inference in this paradigm. I also intend on applying novel, state-of-art-methods to available 'omic data to elucidate findings which have translational value for therapeutic evaluation. Finally, I will build publicly accessible computational tools to automate fundamental analyses in this paradigm and resources to disseminate the findings of this fellowship. Using health informatics to harness large-scale, high throughput data to develop our understanding of the causal pathway from genetic variation to complex disease is the overarching theme of this research project. This research will lead to several high impact publications to improve our understanding of the molecular determinants of disease, as well as web tools that should help disseminate the product of this project and also assist colleagues with their endeavors in this field. As such, this work most closely aligns with the HDR-UK priorities concerning health informatics and accelerating medicines discovery.