A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length.

Huntington's disease (HD) is a neurodegenerative disorder caused by an unstable CAG repeat. For patients at risk, participating in predictive testing and learning of having CAG expansion, a major unanswered question shifts from “Will I get HD?” to “When will it manifest?” Using the largest cohort of HD patients analyzed to date (2913 individuals from 40 centers worldwide), we developed a parametric survival model based on CAG repeat length to predict the probability of neurological disease onset (based on motor neurological symptoms rather than psychiatric onset) at different ages for individual patients. We provide estimated probabilities of onset associated with CAG repeats between 36 and 56 for individuals of any age with narrow confidence intervals. For example, our model predicts a 91% chance that a 40-year-old individual with 42 repeats will have onset by the age of 65, with a 95% confidence interval from 90 to 93%. This model also defines the variability in HD onset that is not attributable to CAG length and provides information concerning CAG-related penetrance rates. Huntington's disease (HD, MIM 143100) is a progressive, neurodegenerative disorder that presents with motor disturbances, psychiatric symptoms, and cognitive decline. HD has been reported worldwide with an overall prevalence of about 10 per 100,000 in Caucasian populations (1, 2). The mutation associated with clinical manifestations of HD is a CAG trinucleotide repeat expansion in the HD gene (3). Persons affected with HD have a CAG repeat length (CAG) between 36 and 250, though some individuals with a CAG less than 42 will never show symptoms (4–8). Numerous studies have described a significant inverse relationship between CAG and the age of onset (9–17), with CAG length accounting for up to 73% of the variation in the age of onset (4, 9–11). However, these studies have been of limited clinical use in predicting the mean age of onset for a particular CAG, because the precision of the predictions is relatively low, with 95% confidence limits up to 20% (4). Using the largest worldwide cohort of both affected and at-risk individuals to date, we have now developed a parametric survival model that significantly reduces the average confidence limits of the predictions. Almost all of the previous reports (4–19) concerning age of onset and HD have been based only on onset ages for CAG-expanded individuals with manifest disease, leading to possible bias from ignoring age distributions among those who have not developed the illness (4). Our new model, incorporating information from those with onset and those still at risk, may be useful for predicting risk of onset for any person at risk of HD at any age. It also provides insight into lifetime penetrance at various CAG lengths