Genetic information for chronic disease predictionTweet
Speaker: Michael A. Grasso
Start: Friday, September 23, 2011, 01:00PM
End: Friday, September 23, 2011, 02:15PM
Location: ITE 227
Abstract: 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.
Host: Yelena Yesha,