# Machine Learning Guided Precision Genetic Testing for Identification of Monogenic Cardiovascular Disorders

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $756,895

## Abstract

Monogenic disorders individually are rare but in aggregate are common, with estimates of up to 1 in 40
individuals afflicted. In monogenic cardiovascular (CV) disease, early identification of at-risk individuals has
proven utility for prevention. Unfortunately, there is substantial clinical variability not only in identification of
monogenic CV diseases but also in therapeutic interventions for these patients, in part because of their
underdiagnosis. As such, there is a need for development of more targeted approaches for identification of
monogenic CV disorders, but that go beyond traditional diagnostic models that result in underdiagnosis. There
is an opportunity and need to integrate cutting-edge, high-throughput phenotyping approaches with
genetics to identify patients at high risk, or already showing evidence of monogenic CV disorders, and
determine if this approach can improve the care of these patients. The overall objective for this proposal is to
develop, validate and determine clinical utility of a precision genetic testing approach guided by machine
learning (ML)-based models. We hypothesize that ML electronic health record (EHR), genetic and
imaging algorithms coupled with precision genetic testing will lead to enhanced diagnosis of
hypertrophic cardiomyopathy (HCM) and transthyretin amyloidosis (ATTR-CM). We will accomplish this
objective through the following aims: Aim 1. Refine, validate and determine generalizability of imaging-based
deep learning (DL) algorithms for HCM and ATTR-CM using echocardiograms across four health systems
caring for diverse patients (Duke, MUSC, Mayo Clinic, Cedars-Sinai); Aim 2. Aim 2. Develop, validate and
determine generalizability of an EHR-based computable phenotype for HCM and ATTR-CM in four health
systems; Aim 3. Identify HCM and ATTR-CM genetic VUS with high evidence for pathogenicity using a ML-
algorithm; and Aim 4. Determine feasibility and clinical utility of a precision genetic testing approach integrating
a DL-imaging algorithm, an EHR computable phenotype and genetic testing. This proposal holds great
potential for demonstrating clinical utility of this approach, with an output of a coordinated system that could be
scaled within other health systems.

## Key facts

- **NIH application ID:** 10978288
- **Project number:** 1R01HL168940-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Svati H. Shah
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $756,895
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978288

## Citation

> US National Institutes of Health, RePORTER application 10978288, Machine Learning Guided Precision Genetic Testing for Identification of Monogenic Cardiovascular Disorders (1R01HL168940-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10978288. Licensed CC0.

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