Improving Heart Health by Diversifying Genetic Data

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UNIVERSITY PARK, Pa. — On the first Friday of February, many Americans wear red to kick off American Heart Month and bring attention to the prevalence of heart disease — the leading cause of death in the United States. Many factors can contribute to a person’s risk of developing heart disease, including high blood pressure, high cholesterol, diabetes, smoking, and being overweight. But a less visible factor also impacts risk: a person’s genes. 

The genetics of heart disease are very complex. For example, there isn’t a single “heart attack gene.” Instead, many genes interact with each other and with environmental factors to contribute to a person’s overall risk of heart disease. But which genes are involved in these “polygenic” traits and their relative contributions can vary among individuals and from population to population. Penn State statistician Xiang Zhu works with international collaborations to study these genes and their role in heart disease in as diverse a sample as possible to improve our understanding of risk and ultimately to improve heart health for everyone.

“If we know a disease is highly genetic, we can make predictions about an individual’s risk of contracting that disease based on genetics in the form of polygenic risk scores,” said Zhu, assistant professor of statistics, member of the Huck Institutes of the Life Sciences and affiliate of the Institute for Computational and Data Sciences. “If our predictions are good enough, then doctors can use those predictions to make personalized recommendations to their patients. We cannot change a person’s genetics, but we can change other known risk factors like diet, exercise and smoking.”

Ongoing research around the genetics of heart disease has greatly improved researchers’ abilities to construct polygenetic risk scores based upon which versions of genes and other genetic elements are present in a person’s genome. However, the basis for how these risk scores are calculated is built on data primarily collected from people of European ancestry. 

“Good knowledge about the genetics of heart disease has really helped us improve prevention and treatment of the disease,” said Zhu. “But the genetic architecture of the same disease can be different in different populations, and right now our knowledge is largely limited to one population. That means we have to try to apply the data we have to other populations, which is not ideal and can exacerbate health disparities. One of our first goals was to create datasets that cover multiple populations so we can improve our methodology for creating risk scores for everyone.”

Working with international teams of researchers, Zhu has played important roles in two of the largest, most genetically diverse studies related to the genetics of two factors the contribute to heart disease: coronary artery disease — the most common form of heart disease, which can lead to heart attack — and cholesterol levels — a measurable risk factor for heart disease.

In the first study, the research team used genetic data about coronary artery disease from the Million Veteran Program, which includes a healthcare system that serves a diverse population, as well as data from recently published studies. This resulted in information from nearly a quarter of a million people with coronary artery disease, including the largest samples to date of Black and Hispanic people, which allowed the researchers to characterize the disease in these populations for the first time. The researchers also created polygenetic risk scores based on their data, which performed as well or better than previous scores based primarily on data from populations of European ancestry.

“We are very grateful to the participation of U.S. veterans in the Million Veteran Program,” said Zhu. “Without their participation, we would not have been able to do this work.”

This data will also be incorporated into the CARDIoGRAMplusC4D consortium, where it can be combined with other data to maximize the power for discovery.

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