<p>Avinash Das Sahu is the lead author on a paper published in <em>Nature Communications</em> on a new method for identifying genetic factors that could contribute to risk for cardiovascular disease.</p>

Avinash Das Sahu is the lead author on a paper published in Nature Communications on a new method for identifying genetic factors that could contribute to risk for cardiovascular disease.

Heart disease is the leading cause of death in the United States, according to the Centers for Disease Control and Prevention.

Thanks to a new method developed by University of Maryland researchers, it might be possible to identify genetic factors that contribute to a person’s risk for heart disease.

“We wanted to find out: What are the genetic variants that underlie diseases?” said Sridhar Hannenhalli, cell biology and molecular genetics professor.

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Hannenhalli and Avinash Das Sahu, a computer science graduate student, looked at about 300 human heart samples in the course of their research — about 170 failing hearts and 130 healthy hearts. A paper detailing their research was published in October in the online edition of the Nature Communications journal.

Genetic variants, or diversities in gene sequence in an organism’s DNA, influence gene activities in various tissues that can manifest as disease, Hannenhalli said. A critical aspect of disease research lies in figuring out how a genotype, or the genetic makeup of an organism, relates to gene activity, which Hannenhalli and Sahu aimed to investigate.

Traditional methods of investigating this problem typically rely strictly on statistical relationships between the genetic variants and a phenotype, or an organism’s observable characteristics, for finding genotype and disease associations.

But these methods do not incorporate an understanding of how genetic variance affects gene activity, Hannenhalli said. Their new method uses multiple layers of additional biological information about genetic variants to help researchers pick specific variants out of a “bunch of good candidates,” which are likely contributors to the underlying causes of a particular disease, he said.

“[This method] identifies these causal genetic variants. To even address how to treat something, we need to know what causes it,” Hannenhalli said. “It is critical that the variants we latch onto are likely to be the actual causes … those are the better candidates to pursue.”

Eytan Ruppin, director of the Center for Bioinformatics and Computational Biology who was not involved with this research, said developing an understanding of the genetic factors that might affect heart functioning and predict heart disease is significant.

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“There is a serious drive to try and understand and characterize the genetic basis for heart disorders,” Ruppin said. “This research is an important step forward in understanding the regulatory processes that are shaping gene variation in healthy hearts and unhealthy hearts.”

There is another advantage of finding what genotype causes particular disease, Sahu said.

“Even if you identify [the disease], therapeutically there are few things you can do to intervene,” Sahu said. “But if you go through the systematic approach to also find what gene activity that eventually leads to disease, there is an additional level of information that can contribute to finding the right therapeutic tools.”

If researchers identify the specific genetic variant increasing a gene’s activity, they can look for drugs that inhibit the activity of that gene, Hannenhalli said.

Heart diseases do not have one genetic cause, Hannenhalli said, but are likely caused by mutations in multiple different genes. There is a large amount of data readily available on heart disease, which makes it easier to investigate, he said. But in terms of the future of this research, their new method could be applied when looking into many kinds of diseases.

“Once we have a correct framework,” Sahu said, “we can apply it to large number of complex diseases.”