Genetic information for chronic disease prediction

Michael A. Grasso, MD, PhD
University of Maryland School of Medicine

1:00pm Friday 23 September 2011, 227 ITE

Type 2 diabetes and coronary artery disease are commonly occurring polygenic-multifactorial diseases, which are responsible for significant morbidity and mortality. The identification of people at risk for these conditions has historically been based on clinical factors alone. However, this resulted in prediction algorithms that are linked to symptomatic states, which have limited accuracy in asymptomatic individuals. Advances in genetics have raised the hope that genetic testing may aid in disease prediction, treatment, and prevention. Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We present an overview of genetic issues involved in polygenic-multifactorial diseases, and summarize ongoing efforts use this information for disease prediction.

Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine, and an Assistant Research Professor of Computer Science at the University of Maryland Baltimore County. He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, and the William Beaumont Medical Research Honor Society. He completed a residency at the University of Maryland School of Medicine, and currently works in the Department of Emergency Medicine at the University of Maryland Medical Center. He has been awarded more than $1,200,000 in grant funding from the National Institutes of Health, the National Bureau of Standards and Technology, and the Department of Defense, and has authored more than 35 scholarly papers and abstracts. His research interests include clinical decision support systems, clinical data mining, clinical image processing, personalized medicine, software engineering, database engineering, and human factors. He is also a semi-professional trumpet player and is interested in the specific medical needs of performing artists, especially instrumental musicians.

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