# Genomic and Phenomic Architecture of Heart Failure

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $395,000

## Abstract

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).

## Key facts

- **NIH application ID:** 10076848
- **Project number:** 5R01HL140074-04
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Quinn Stanton Wells
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $395,000
- **Award type:** 5
- **Project period:** 2018-01-15 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10076848

## Citation

> US National Institutes of Health, RePORTER application 10076848, Genomic and Phenomic Architecture of Heart Failure (5R01HL140074-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10076848. Licensed CC0.

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