'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，也不能说明各种疾病的共同机制。由于潜在的机制通常预测治疗效果，因此需要基于机制的疾病分类来解锁治疗进展。数据科学方法提供了一个框架，通过整合从基因型到疾病结局的各种患者数据来解决这一未满足的需求。心力衰竭（HF）是一种复杂的临床综合征，其患病率上升与缺乏有效的治疗方案形成鲜明对比。该项目旨在通过使用新兴基因组生物信息学和无监督学习方法分析来自HDR伦敦、英国生物库和爱马仕联盟（由申请人创立的HF基因组学的主要全球合作）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。总体目标是确定和验证分子途径作为新的治疗干预的候选目标，并在HDR UK内为复杂疾病研究的新方法提供范例。目的1.定义常见遗传变异对HF风险和临床确定的亚表型的贡献。爱马仕联盟已经完成了对来自25项研究的> 32，000例HF病例的全基因组关联荟萃分析（GWAMA），在许多位点产生了变异。为了研究次显著相关信号，我将对> 60，000例HF病例（爱马仕、HDR伦敦和英国生物样本库）进行扩展分析，并通过分层分析研究由合并症（房颤、冠心病）和左心室表型定义的HF亚组。这些分析的结果可能使新的致病基因的鉴定，并将形成调查临床和遗传异质性的起点（目标2）。此外，他们将提供HF结局数据集，用于目标验证孟德尔随机化分析（目标3）。目标2.利用临床、EHR和基因组数据识别和鉴定新的HF亚表型。初步分析表明，HF风险变异体在共病状态定义的HF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）来发现基于表型相似性的新HF疾病集群。为了探索HF亚组的遗传异质性，我将使用当地可用的个体受试者水平数据检验目标1中HF相关等位基因频率的差异。为了验证这些发现，我与来自哈佛/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，我将试图定义一个数据驱动的分类HF亚型，并通过基因组分析提供深入的机制。通过遗传因果推断分析发现和验证心力衰竭亚组的治疗靶点。改变基因转录物或其同源蛋白质的丰度或功能的遗传变异可以用作使用孟德尔随机化分析探索蛋白质在疾病结果中的因果作用的工具。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）。我将根据先前的观察关联或疾病因果作用的其他证据的存在，优先研究蛋白质。对于具有因果作用证据的蛋白质，我将通过探索遗传工具变体与相关成像和EHR表型的关联来寻找效应介质，以指导临床试验的进行。

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.