Atrial fibrillation (AF) is the most common heart rhythm disorder. With an aging population, the number of people with AF is expected to rise dramatically. People with AF are twice as likely to die, are five times more likely to have a stroke, can develop worsening heart muscle function, and have a lower quality of life. We have learned that a person's genetic makeup, or DNA, has a major impact on their risk of developing AF; but we have a limited understanding of why, or how to use this information to treat people in a safer and more effective way. People with AF first receive drugs to control their irregular heart rhythm. Even people who have procedures to treat AF are also prescribed drugs. This is particularly important in the group of patients who have persistent AF, who require electrical or chemical therapy to change their heart rhythm, as the success of surgical procedures in this population is well below 50%. Unfortunately our current drugs are generally ineffective, and can be unsafe, with little progress in drug development over the last two decades.
With these challenges in mind, the first goal of my research program is to identify and understand the genes that play a role in the development and progression of AF, and determine which are most common and most important in the Canadian population. To do this, I am gathering a biobank of AF patients and performing the largest scale detailed genetic testing in this population to date. I am also focused on understanding the effect that genes can have on the safety and efficacy of rhythm controlling drugs, and have already started a trial, funded by the Canadian Cardiovascular Society, that will link a person's genetic makeup to these important outcomes. I will then be able to take this large clinical and genetic data set to the laboratory where we have developed the unique ability to generate patient-specific stem cell disease models of AF. The ultimate goal of my research program is to directly tailor therapy for AF patients based on their genetic makeup, using information from clinical research and personalized disease modeling.