Genomic and Phenomic Architecture of Heart Failure

该项目的首要目标是改善心力衰竭（HF）患者的护理。HF，是否与 射血分数降低（HFrEF）或保留（HFpEF）与显著的发病率、死亡率和 成本仅在美国，HF就影响超过500万成年人，预计到2010年患病率将超过800万。 2030. HF是医疗保险接受者中最常见的住院原因，导致超过300亿美元 每年的医疗保健支出。管理方面的进步，特别是HFrEF， 死亡率随着时间的推移，但死亡率仍然很高，大约一半的患者在5年内死亡 诊断。此外，药物发现的步伐一直很慢，也没有经过验证的治疗方法可用于患者 患有HFpEF。在已确诊的HF患者中，疾病严重程度、程度 心脏重塑、疾病进展和对治疗的反应。这些观察突出了 HF综合征的异质性，并表明存在不同临床和潜在的亚型 遗传特征，随后在下游疾病机制、总体风险和治疗方面存在差异， 反应但 了解HF的表型、遗传和病理生理异质性 不完整 本项目研究HF的表型亚结构和遗传结构， 来自范德比尔特大学医学中心（VUMC）的相互关联的数据集的独特集合，包括de- 电子健康记录（EHR）和BioVU，一个链接的DNA生物库。EHR包含约260万个 患者，包括约35，000名HF患者，BioVU目前拥有超过225，000份DNA样本。密集基因型数据 在> 28，000名受试者中可用，一个机构基因分型项目将在2015年中期将其增加到> 125，000名。 2017年;这包括> 13，000例HF受试者。拟议的研究将：1）从密集的HF中识别HF亚型， 仅使用先进的、无偏的深度学习算法的临床数据（目标1），2）定义遗传结构 通过使用推断的基因表达，一般线性混合模型，遗传风险评分， 传统的关联检验用于量化HF亚型之间的遗传性和遗传相关性， 已建立的危险因素对HF亚型的贡献，和3）发现亚型特异性遗传危险因素（目的 2）发现HF亚型特异性临床结果、疾病关联和药物反应表型 使用先进的表型组扫描和网络分析（目标3）。

The overarching goal of this project is to improve care for patients with heart failure (HF). HF, whether with reduced (HFrEF) or preserved (HFpEF) ejection fraction, is associated with significant morbidity, mortality, and cost. In the U.S. alone, HF affects over 5 million adults, and the prevalence is projected to exceed 8 million by 2030. HF is the most frequent cause of hospitalization among Medicare recipients and results in over $30 billion in health care expenditures each year. Advances in management, especially for HFrEF, have modestly reduced death rates over time, but mortality continues to be high, with approximately half of patients dying within 5 years of diagnosis. Moreover, the pace of drug discovery has been slow, and there are no proven therapies for patients suffering with HFpEF. Among patients with established HF there is substantial variation in illness severity, degree of cardiac remodeling, disease progression, and response to therapy. These observations highlight the heterogeneity of the HF syndrome and suggest existence of subtypes with differing clinical and potentially genetic profiles, with subsequent differences in downstream disease mechanisms, overall risk, and therapeutic response. However, the understanding of the phenotypic, genetic, and pathophysiological heterogeneity of HF is incomplete. This project investigates the phenotypic substructure and genetic architecture of HF by leveraging a unique collection of interrelated datasets from Vanderbilt University Medical Center (VUMC), including the de- identified electronic health record (EHR) and BioVU, a linked DNA biobank. The EHR contains ~2.6 million patients, including ~35,000 with HF, and BioVU currently houses >225,000 DNA samples. Dense genotype data are available in >28,000 subjects and an institutional genotyping project will increase this to >125,000 by mid- 2017; this includes >13,000 subjects with HF. The proposed research will: 1) identify HF subtypes from dense clinical data alone using advanced, unbiased, deep learning algorithms (Aim 1), 2) define the genetic architecture of HF and HF subtypes by using inferred gene expression, general linear mixed models, genetic risk scores, and traditional association testing to quantify heritability of and genetic correlations among HF subtypes, define the contribution of established risk factors to HF subtypes, and 3) discover subtype-specific genetic risk factors (Aim 2), and discover HF subtype-specific clinical outcomes, disease associations, and drug response phenotypes using advanced phenome scanning and network analysis (Aim 3).