Genomics of Arrhythmias and Sudden Death

Project: SFRN

Investigators

  • Elizabeth M. McNally (PI)

Description

One in every 2,000 children and young adults lives with an increased risk of sudden cardiac death due to potentially heritable causes. Genetic testing for highly penetrant monogenic alleles is not sufficiently sensitive to clinically stratify risk for arrhythmia and sudden cardiac death. Clinical stratification tools remain imperfect and subject to both false positive and false negative identification of sudden death risk. Advanced genetic stratification of arrhythmia risk represents a new frontier for predicting clinical outcomes. A genetic model, validated across a broad spectrum of arrhythmia disease, will be invaluable for patients at risk for sudden cardiac death, first-degree relatives, and anyone who must consider implantable cardioverter-defibrillator (ICD) implantation. We will use whole genome sequencing (WGS) to develop ARREST (Arrhythmia Risk Evaluation and Stratification Tool). ARREST will be a quantitative risk stratification tool that identifies subjects with a higher-risk for life-threatening arrhythmias and sudden death. We have already generated and analyzed WGS data from 900 subjects without cardiac disease, 250 subjects with genetic cardiomyopathies, and 150 subjects with an ICD or sudden death. We will sequence an additional 200 pediatric and young adult subjects for a total sample population of 1,500 clinically characterized subjects. ARREST will be developed by assembling phenotype and WGS data on the 1,500 subjects spanning the arrhythmia spectrum. We will divide subjects into lower -risk categories that includes subjects with no known cardiac risk factors or those with risk factors but no documented life-threatening arrhythmias. Higher -risk subjects are those who have had an ICD placed for secondary prevention, those who have received an appropriate ICD shock; and decedents who have suffered sudden death. We will then distill common and rare genetic variation into annotation categories that will be tested in both univariate and multivariate models for association with higher and lower risk arrhythmia phenotypes. ARREST will be built using the genetic annotation categories that best predict arrhythmia risk. The sensitivity and reliability of ARREST across different data types will be assessed. We will validate ARREST across other Network site populations.
Award amount$1,100,000.00
Award date07/01/2019
Program typeStrategically Focused Research Network
Award ID19SFRN34910009
Effective start/end date07/01/201906/30/2023
StatusActive