Prediction of age of onset and penetrance and its application to clinical trial design for Huntington Disease

Huntington Disease (HD) is a progressive, neurodegenerative disorder caused by a CAG repeat expansion. The disease presents with motor disturbances, psychiatric symptoms, and cognitive decline. At the start of this thesis, there was no reliable method for predicting the age-specific likelihood of onset of HD. I hypothesized that being able to predict age of onset would be useful for both patients at risk for HD (patients) and clinical studies. For patients it would provide knowledge about their future age of onset, and clinically it would aid creation of clinical risk groups for stratifying patients in clinical trials and in the design of treatment regimes (in the case of a potentially hazardous therapy for HD). I first used data from the University of British Columbia HD clinic to demonstrate the utility of CAG-specific survival analysis. I then assembled what is believed to be the largest cohort of HD patients analyzed to date (3452 individuals from 40 centers worldwide) and developed a novel parametric survival model to estimate the agespecific likelihood of onset. The probability estimates of the model proved to be very accurate with a mean 95% confidence interval of 2%. I also developed a nonparametric survival model to predict the age-specific likelihood of death from HD. I used the parametric model to estimate the age and CAG specific penetrance of HD and demonstrated how my analyses might be used to aid in the design of presymptomatic clinical trials. Specifically I investigated how using the model can reduce sample size, cost and time necessary to conduct a trial. Further my analyses indicated a larger variance in age of onset for lower CAG repeat lengths, which could be of importance in future studies for factors that modify onset of HD.Medicine, Faculty ofMedical Genetics, Department ofGraduat