# Deep learning to enable the genetic analysis of aorta

> **NIH NIH K08** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $169,560

## Abstract

Project Summary / Abstract
Aortic disease is an important contributor to cardiovascular morbidity and sudden death. Key discoveries,
including identification of the causal gene for Marfan’s syndrome (FBN1), have advanced our knowledge of
syndromic aneurysm and dissection, but to date there remains insufficient information on sporadic thoracic aortic
disease. For example, despite growing knowledge of the importance of aortic disease, there is no guideline for
screening for ascending aortic disease, and no therapy to treat its underlying molecular mechanisms. While there
is likely some overlap between thoracic and abdominal aortic disease, they are embryologically distinct and likely
have different genetic and clinical risk factors.
In Dr. Pirruccello’s preliminary work, he developed an automated deep learning model to quantify the diameter
of the thoracic aorta using cardiovascular magnetic resonance imaging (MRI). He applied the model in the UK
Biobank and conducted a genome-wide association study for the diameter of ascending and descending thoracic
aorta in nearly 40,000 participants. These results cemented the feasibility of the approach of (1) training deep
learning models to extract biologically relevant information from imaging, and (2) conducting genetic analyses
on these deep learning model-based phenotypes. This now paves the way for a more comprehensive analysis
of additional aortic traits, and downstream evaluation of genetic risk factors for both thoracic and abdominal
aortic disease.
First, Dr. Pirruccello proposes to develop models for additional aortic traits including thoracic aortic strain and
distensibility, and abdominal aortic diameter. Second, after developing additional models to extract those
features, Dr. Pirruccello proposes to conduct genetic analyses on these traits in the UK Biobank, elucidating the
common and rare genetic variation that leads to variability in the aorta’s size and distensibility at several levels.
Third, he proposes to produce polygenic scores, permitting modeling of the clinical and genetic risk for
abnormalities in aortic size and distensibility that may predispose to aortic aneurysm and dissection.
This work will take place in the Division of Cardiology at the Massachusetts General Hospital, and at the Broad
Institute of MIT and Harvard. Dr. Pirruccello will perform this research under the mentorship of Dr. Patrick Ellinor,
the Director of the Cardiovascular Disease Initiative at the Broad Institute, and Dr. Mark Lindsay, an expert in
genetic aortic disease at the Massachusetts General Hospital Thoracic Aortic Center.
Dr. Pirruccello’s goal is to become a computational cardiovascular geneticist with expertise in machine learning.
He is dedicated to becoming an independent investigator and to use the research performed for the K08 as a
springboard for an R01.

## Key facts

- **NIH application ID:** 10283972
- **Project number:** 1K08HL159346-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** James Pirruccello
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $169,560
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10283972

## Citation

> US National Institutes of Health, RePORTER application 10283972, Deep learning to enable the genetic analysis of aorta (1K08HL159346-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10283972. Licensed CC0.

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