Genetic factors for heart failure and arrhythmias revealed

Analyzing cardiac measurements from large biobanks can uncover genetic predisposition to diseases like heart failure and arrhythmias.

MRI scan of the heart shows the left ventricle (green), which researchers studied to look for genetic markers for conditions that lead to heart failure.
Credit: Source: UK Biobank (Color added by James Pirruccello)
MRI scan of the heart shows the left ventricle (green), which researchers studied to look for genetic markers for conditions that lead to heart failure.

Two of the most common forms of heart disease are heart failure, in which the organ, often fatally, becomes too weak to pump enough blood throughout the body, and arrhythmias, where the heart beats abnormally. Identifying genetic factors underlying these disorders has been challenging, but two large genetic studies have taken a new approach and found hundreds of regions in the genome that contribute to these diseases. Many of the genomic regions had not previously been associated with heart disease.

Two teams led by researchers at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) each identified a set of genetic factors by studying data collected from many thousands of patients and looking for links between genetic variants, measurements of heart function and electrical activity, and cardiac disease.

The studies, recently published in Nature Communications, suggest newly recognized genetic relationships between heart function and disease, and provide clues about better ways of identifying and treating people at high risk of heart disease.

“This new approach to the study of cardiovascular genetics, using measurements of cardiac mechanics and electrical conduction, has allowed for the discovery of genetic regions contributing to cardiovascular disease susceptibility in a way that was not previously possible,” said Krishna Aragam, a senior author of one of the studies, who is a cardiologist at MGH and postdoctoral scholar in the Broad’s Program in Medical and Population Genetics.

Imaging Insight

In the past 10 years, researchers have identified common genetic variants that contribute to, or protect against, the onset of specific cardiac diseases. However, it has been difficult to find common variants associated with heart failure or arrhythmia, because the conditions encompass many subsets of disease, and genetic analysis often requires studying data from many thousands of patients. 

Instead of focusing on these broad diseases, the teams behind the new studies examined genetic data and cardiac measurements already collected by various biobanks. The approach allowed researchers to focus on specific disease pathways, disentangling them from the broader syndromes of heart failure and arrhythmia, and also to include data from people without disease, resulting in larger study sample sizes. 

In the study focused on heart failure, researchers used cardiac magnetic resonance imaging (MRI) data to quantify the mechanics of the left ventricle, the main pumping chamber of the heart. Using these measurements from 36,041 participants of the UK Biobank, the team conducted a genome-wide association study (GWAS) to search for common genetic variants associated with the structure and function of the left ventricle, which when compromised can lead to heart failure. 

“We came at the problem differently and thought: maybe if we look at normal variation in heart structure and function as assessed by cardiac imaging, and do so at scale, it'll yield promising genetic signals and insights about disease,” said Aragam, senior co-author of the study. “And in fact, it yielded many more signals and insights than we expected.”

The researchers found 45 novel genetic regions, or loci, associated with left ventricular structure and function, which appear to influence risk for various types of cardiomyopathy — leading causes of heart failure. 

According to the results, having a high burden of these common genetic variants can predispose people to dilated cardiomyopathy, which damages the organ’s muscle tone, diminishing its ability to pump blood. The team also reported that people with a very low burden of these variants were at increased risk for hypertrophic cardiomyopathy, a thickening of the heart muscle that is the most common cause of sudden death in athletes.

“What's really exciting to me is that these results emphasize that studying quantitative traits is a powerful tool for discovering genetic relationships, and that even though we were studying healthy people, this type of analysis identified genetic associations relevant to disease,” said James Pirruccello, the first author of the study, who is a cardiology fellow at MGH and postdoctoral fellow with the Broad’s Cardiovascular Disease Initiative

EKG Effort

In the second study, scientists from more than 140 institutions, including the Broad, used the same approach to perform a GWAS in search of genetic markers for abnormalities of a measurement on the electrocardiogram (EKG). The EKG, one of the oldest cardiac diagnostic tests, is both inexpensive and widely used. The investigators specifically studied the PR interval, which reflects atrioventricular conduction and is associated with a number of common electrical disorders such as atrial fibrillation and bradyarrhythmias. 

Studying a cohort of 293,051 people of European, African, Hispanic, and Brazilian ancestry, the researchers found 202 locations in the genome underlying cardiac conduction—141 of which had not been previously identified. The study, the largest genetic analysis of this EKG trait to date, more than tripled the number of known loci linked to atrioventricular conduction. Collectively, the loci explain about 62 percent of heritability of this trait. 

"That’s really a striking discovery that wouldn’t have been possible a few years ago,” said Steven Lubitz, an cardiac electrophysiologist at MGH and associate member of the Broad, who is a senior author on both papers. “But thanks to many studies, including the UK Biobank, we now have all this imaging and EKG data paired with genetic data, which has proven to be a really powerful combination.” 

Patricia Munroe of Queen Mary University of London is co-senior author of this study along with Lubitz.

The findings from both studies indicate that an individual's inherited predisposition to heart disease is not the result of single-gene mutations, but rather a cumulative effect of many variants across the genome. 

“I think what both of these studies now make very clear is that there's a polygenic contribution to heart function and structure, and that has important clinical implications for cardiac diseases,” said Lubitz. “Moreover, using imaging traits and EKG measurements from large-scale biobanks is a powerful and simple way to unlock insights into the biological causes of cardiac diseases.” 

The researchers say the results could one day lead to advanced screening methods to discern who is at greatest risk of developing disease, and could help reveal new genetic targets for disease research and potentially drug development.  

“We don’t know yet whether these loci contain genes that are druggable to potentially prevent the morbidity associated with cardiac disease, but this will be a really exciting avenue to explore down the line,” said Lubitz.

Support for the MRI study was provided in part by National Institutes of Health (RO1 HL127564), the National Human Genome Research Institute (5UM1HG008895), and the Ofer and Shelly Nemirovsky Research Scholar Award from Massachusetts General Hospital.

Support for the EKG study was provided in part by National Institutes of Health (1R01HL139731), the Medical Research Council UK and NIHR Barts Biomedical Research Centre, and the American Heart Association (17POST33660226, 18SFRN34250007 and 18SFRN34110082).

Paper(s) cited

Pirruccello JP et al. Analysis of cardiac magnetic resonance imaging traits in 36,000 individuals reveals genetic insights into dilated cardiomyopathy. Nature Communications. May 7, 2020. DOI: 10.1038/s41467-020-15823-7

Ntalla I et al. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction. Nature Communications. May 21, 2020. DOI: 10.1038/s41467-020-15706-x