'Unlocking therapeutic innovation in heart failure through genomic data science

现有的分类系统不能充分描述人类疾病的多样性，也不能解释各种疾病的共同机制。由于潜在的机制通常预测治疗效果，因此需要一种基于机制的疾病分类法来揭示治疗进展。数据科学方法提供了一个框架，通过整合从基因到疾病结果的不同患者数据来解决这一未得到满足的需求。心力衰竭(HF)是一种复杂的临床综合征，其发病率的上升与缺乏有效的治疗选择形成了鲜明对比。该项目寻求利用新兴的基因组生物信息学和无监督学习方法，通过分析来自HDR London、UK Biobank和Hermes Consortium(由申请人创建的主要全球HF基因组学合作)的协调患者数据，根据潜在的因果机制对HF患者进行重新分类。总的目标是确定和验证分子通路作为新的治疗干预的候选目标，并在英国人类发展报告中为研究复杂疾病的新方法提供范例。目的1.明确常见的遗传变异对心力衰竭风险的贡献和临床确定的亚型。Hermes Consortium已经完成了对25项研究中32,000例心衰患者的全基因组关联荟萃分析(GWAMA)，在多个基因座产生了变异。为了研究次要关联信号，我将对60,000例心力衰竭患者(Hermes、HDR London和UK Biobank)进行扩展分析，并通过分层分析研究由共病(房颤、冠心病)和左心室表型定义的心力衰竭亚组。这些分析的结果可能有助于识别新的致病基因，并将形成研究临床和遗传异质性的起点(目标2)。此外，他们将为目标验证孟德尔随机化分析(目标3)提供HF结果数据集。目的2.利用临床、EHR和基因组数据识别和表征新的心力衰竭亚型。初步分析表明，心力衰竭风险变异在由疾病状态定义的心力衰竭亚型中表现出等位基因异质性。观察到的临床和遗传复杂性表明有机会定义一个新的、数据驱动的分类法。除了研究已建立的HF亚型外，我还将使用无监督学习方法(机器学习)根据表型相似性发现新的HF疾病簇。为了探索心力衰竭亚组的遗传异质性，我将使用当地可获得的个体参与者水平数据来测试目标1中心力衰竭相关等位基因频率的差异。为了验证这些发现，我与哈佛/麻省理工学院和波士顿大学的研究人员建立了合作关系。使用这些方法，我将寻求定义一个数据驱动的心衰亚型分类，并通过基因组分析提供对机制的见解。目的3.通过遗传因果推理分析发现并验证心力衰竭亚组的治疗靶点。改变基因转录本或其同源蛋白丰度或功能的遗传变异可以作为工具，利用孟德尔随机化分析来探索蛋白质在疾病结果中的因果作用。许多循环蛋白的遗传决定因素是已知的，可用于研究HF和/或HF亚型的潜在治疗靶点(来自目标2)。我将根据先前观察到的关联性或其他在疾病中起因果作用的证据，优先选择蛋白质进行研究。对于有因果作用证据的蛋白质，我将寻求通过探索遗传工具变异与相关的成像和EHR表型的关联来表征影响的中介因素，以便为临床试验的进行提供信息。

Existing classification systems do not adequately describe the diversity of human disease, nor do they account for common mechanisms across diseases. Since underlying mechanism often predicts therapeutic efficacy, a mechanism-based taxonomy of disease is required to unlock therapeutic progress. A data-science approach provides the framework to address this unmet need by integrating diverse patient data from genotype to disease outcomes. Heart failure (HF) is a complex clinical syndrome for which rising prevalence stands in contrast to a lack of effective therapeutic options. This project seeks to reclassify HF patients according to underlying causal mechanisms through analysis of harmonised patient data from HDR London, UK Biobank and the HERMES Consortium (a major global collaboration in HF genomics founded by the applicant) using emerging genome bioinformatics and unsupervised learning methods. The overall aim is to identify and validate molecular pathways as candidate targets for novel therapeutic intervention and to provide an exemplar within HDR UK for novel approaches to the study of complex disease. Objective 1. Define the contribution of common genetic variation to risk of HF and clinically established sub-phenotypes. A genome-wide association meta-analysis (GWAMA) of >32,000 HF cases from 25 studies has been completed by the HERMES Consortium, yielding variants at a number of loci. To investigate sub-significant association signals, I will perform an extended analysis with >60,000 HF cases (HERMES, HDR London and UK Biobank) and investigate HF subgroups defined by comorbidity (atrial fibrillation, coronary heart disease) and left ventricular phenotypes by stratified analysis. The results of these analyses may enable the identification of novel causal genes and will form the starting point for investigating clinical and genetic heterogeneity (Objective 2). In addition, they will provide the HF outcome dataset for target validation Mendelian randomisation analyses (Objective 3). Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data.Preliminary analyses indicate that HF risk variants exhibit allelic heterogeneity among HF subtypes defined by comorbid disease status. The observed clinical and genetic complexity suggest an opportunity to define a new, data-driven taxonomy. As well as studying established HF subtypes, I will use unsupervised learning methods (machine learning) to uncover new HF disease clusters based on phenotypic similarity. To explore genetic heterogeneity of HF subgroups, I will test for differences in HF-associated allele frequencies from Objective 1 using locally available, individual participant level data. To validate these findings, I have established a collaboration with investigators from Harvard/ MIT and Boston University. Using these approaches, I will seek to define a data-driven taxonomy of HF subtypes and to provide insights into mechanisms through genomic analysis.Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis. Genetic variants that alter the abundance or function of gene transcripts or their cognate protein can serve as instruments to explore the causal role of a protein in a disease outcome using Mendelian randomisation analysis. The genetic determinants of a many circulating proteins are known and can be used to investigate potential therapeutic targets in HF and/or HF subtypes (from Objective 2). I will prioritise proteins for study based on the presence of prior observational associations or other evidence of a causal role in disease. For proteins with evidence of a causal role, I will seek to characterise the mediators of effect by exploring the association of the genetic tool variants with related imaging and EHR phenotypes, to inform the conduct of clinical trials.