Genomic and Phenomic Architecture of Heart Failure

该项目的首要目标是改善对心力衰竭(HF)患者的护理。高频，无论是否带有 射血分数减少(HFrEF)或保留(HFpEF)与显著的发病率、死亡率和 成本。仅在美国，心力衰竭就影响了500多万成年人，预计到2019年，患病率将超过800万 2030年。心衰是医疗保险接受者中最常见的住院原因，导致超过300亿美元的收入 每年的医疗保健支出。管理方面的进步，特别是对HFrEF的进步，略有下降 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 诊断的结果。此外，药物发现的步伐一直很慢，而且还没有对患者有效的治疗方法 患有HFpEF。在确诊为心衰的患者中，疾病的严重程度、程度有很大的不同。 心脏重塑、疾病进展和对治疗的反应。这些观察结果突显了 心力衰竭综合征的异质性，并提示存在临床和潜在不同的亚型 基因图谱，以及下游疾病机制、总体风险和治疗方面的后续差异 回应。然而， 了解心衰的表型、遗传和病理生理学异质性 是不完整的。 本项目研究了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)的独特的相关数据集集合，包括 识别电子健康记录(EHR)和连接的DNA生物库BioVU。电子病历包含约260万 患者，包括大约35,000名心力衰竭患者，和BioVU目前储存了225,000个DNA样本。密集的基因数据 在28,000名受试者中可用，一个机构基因分型项目将在年中将这一数字增加到125,000名。 2017年；这包括13,000名心力衰竭受试者。拟议的研究将：1)从致密型中识别出HF亚型 仅使用先进的、无偏见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、遗传风险分数和 传统的关联检验用于量化心力衰竭亚型之间的遗传力和遗传相关性，定义 已确定的危险因素对心力衰竭亚型的贡献，以及3)发现亚型特有的遗传风险因素(目的 2)，并发现心力衰竭亚型特有的临床结果、疾病相关性和药物反应表型 使用先进的现象组扫描和网络分析(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).