# Genetic Basis of Early Onset Bicuspid Aortic Valve Disease

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $385,000

## Abstract

Bicuspid Aortic Valves (BAV) can cause premature deaths due to aortic stenosis,
aortic regurgitation or Thoracic Aortic Aneurysms leading to acute aortic Dissections
(TAAD). The genetic causes of BAV remain largely unknown due to the substantial
genetic and clinical heterogeneity of complications related to BAV. We determined that
rare Copy Number Variants (CNVs) are significantly enriched in BAV patients who
experienced early onset clinical complications. The overall goal of my research is to
identify causal genetic variants that are responsible for both the BAV and its
complications, and to establish the clinical phenotypes that are associated with each
new gene. The specific aims of my proposal are: 1) to characterize a cohort of BAV
patients with severe and early onset complications, 2) to identify rare CNVs that are
associated with severe BAV-related complications and 3) to identify rare exome
sequence variants in patients with severe phenotypes or distinctive clinical features who
have available parents or affected relatives. Our discoveries could provide new insights
into the etiology of BAV disease and may also be useful for risk stratification or clinical
decision-making about surveillance and elective interventions for BAV patients.

## Key facts

- **NIH application ID:** 9898441
- **Project number:** 5R01HL137028-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** SIDDHARTH KUMAR PRAKASH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $385,000
- **Award type:** 5
- **Project period:** 2017-05-05 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898441

## Citation

> US National Institutes of Health, RePORTER application 9898441, Genetic Basis of Early Onset Bicuspid Aortic Valve Disease (5R01HL137028-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9898441. Licensed CC0.

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