"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

A Hierarchical Bayesian Approach to Quantify DNA Instability in the Inherited Neuromuscular Disease Myotonic Dystrophy

Catherine Higham, Biomedical & Life Sciences, University of Glasgow

Monday, July 26, 2010

3:00 p.m., room 223, Weber Bldg

ABSTRACT

About 20 human genetic diseases are associated with inheriting abnormally long, unstable DNA simple sequence repeats that mutate, by changing the number of repeats. These changes occur between generations but also during the lifetime of patients and have been termed ‘dynamic’ to distinguish them from much rarer static mutational events. By calibrating a new stochastic birth and death model to a recent data set comprising over 30,000 de novo dynamic blood DNA mutations, from myotonic dystrophy patients, we have shown that the evolution of repeat length can be explained using relatively few biological parameters. The model predicts that the observed expansion bias is actually the result of many expansion and contraction events.

However, applying the model to each patient does not provide a basis for inference about the population so we will present new work that investigates the distribution of DNA instability within the population using a hierarchical Bayesian approach. This is a richer statistical model that will provide more robust prognostic information for patients. DNA instability is also a quantitative trait that could be assessed in terms of its heritability and used as a biomarker to identify any trans-acting genetic, epigenetic or environmental effects. Our expectation is that these trans-acting genetic modifiers will also apply in the general population where they will affect ageing, cancer, inherited disease and human genetic variation