College of Science

New genetics research aims to help researchers and doctors better predict disease risk in individuals


Personalized medicine — where doctors will predict a patient’s risk of developing a disease and tailor that person’s treatment based on their unique genes, environment and lifestyle — is the future of healthcare.

Today, though, in most cases, the predictors are not accurate enough for clinical settings.

Clemson University Center for Human Genetics researcher Fabio Morgante is working to help change that. He has received a five-year, $1.25 million grant from the National Institutes of Health for research designed to improve the predictability of medically relevant complex traits.

Complex traits are traits controlled by many genes and environmental, or non-genetic, factors. 

Fabio Morgante

“Many traits of medical relevance fall under this category, including neurological disorders such as schizophrenia and Alzheimer’s disease, blood-related traits such as lipid panels or Type 2 diabetes, and risk factors for cardiovascular-related diseases such as [high] blood pressure,” said Morgante, who is an assistant professor in the Department of Genetics and Biochemistry and the Clemson Center for Human Genetics.

One problem with current genetic-based prediction models is that over 80 percent of genetic studies have been conducted on people of European descent, and discoveries in one ancestry rarely translates well in another ancestry.

“If we do a lot of research on people of European descent, we can get pretty accurate predictions for some traits in that ancestry. But when we make predictions for African Americans using models trained on individuals of European descent, for example, the accuracy is much lower, so you increase the inequality already present in medical care,” Morgante said.

Historically, even with data sets that included individuals from underrepresented minorities, researchers excluded those individuals to avoid heterogeneity that can cause spurious results. Spurious describes a relationship between two variables that, at first glance, appear to be causally related, but upon closer examination only appear so by coincidence or due to a third confounding factor.

Different approach

Morgante is taking a different approach. 

He is including gene-environmental interactions in his prediction model. Take, for instance, a gene that interacts with diet. The gene may increase the probability of developing a particular disease if a person eats a high-fat diet. But if a person eats a healthier diet, the gene’s influence  may not increase their likelihood of developing that disease at all, or if it does, it’s not by as much.

“So, the effect is not constant. It depends on the interacting factor,” Morgante said. “Different populations are exposed to different environmental conditions. If we don’t include these gene-environment interactions, we will not be able to achieve higher accuracy because we’re not capturing the difference in effect in the different populations.”

Morgante’s research will center on blood pressure traits such as systolic blood pressure, diastolic blood pressure, pulse pressure and hypertension.

Novel strategy

“This research will provide a novel analytical strategy and methodology that can apply to any trait of interest and will be a significant advance toward effective and equitable personalized medicine,” he said. “If you know you are at a higher risk of developing a particular disease, your doctor can monitor you more often. The doctor can prescribe preventive actions, such as modifying your diet or exercising more. There may even be some preventive drug.”

Man wearing a suit and sunglasses stands in front of a sign at the Clemson University Center for Human Genetics
Clemson University Center for Human Genetics Assistant Professor Fabio Morgante’s research involves statistical genetics of human populations. Photo by Robert Bradley.

Morgante had been a part of the Center of Biomedical Research Excellence (COBRE) in Human Genetics, a partnership between Clemson Center for Human Genetics and the Greenwood Genetic Center (GCC) established last year through a $10.6 million NIH COBRE grant. 

The NIH COBRE program provides long-term investment in the advancement of medical research around a central theme. Part of that money is used to launch research programs of junior faculty until they can “graduate” by receiving their own external funding. With his NIH MIRA grant, Morgante became the first graduate of the COBRE in Human Genetics.

Leading edge

“Fabio’s work is at the leading edge of research in statistical genetics of human populations. Genotype by environment interactions are pervasive for all quantitative traits, including disease susceptibility in humans, and taking them into account in sophisticated prediction models has great potential to advance precision medicine,” said Trudy Mackay, the director of the Clemson Center for Human Genetics and director of the COBRE in Human Genetics.  

Morgante earned a bachelor’s degree in agricultural science and a master’s degree in animal science from the University of Florence in Italy. His master’s thesis was in animal breeding and genetics. He went on to get a master’s in that field from the University of Edinburgh in Scotland, the same school where Mackay earned her doctorate and had taught years earlier.

The animal breeding and genetics program was part of a suite of master’s degrees called quantitative genetics and genome analysis. Morgante realized he liked quantitative genetics, so he went to North Carolina State University to pursue a Ph.D. in quantitative genetics under Mackay. He earned a master’s in statistics at the same time. Morgante did a postdoctoral fellowship in statistical genetics at the University of Chicago.

Morgante said his background in animal breeding helps his human genetics research because many of the models used for human predictions come from animal breeding.

“I think I can contribute because I have had training that many human geneticists don’t,” he said.

Morgante has been at Clemson since August 2020.

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