Optimal estimation of transposition rates of insertion sequences for molecular epidemiology

Outbreaks of infectious disease can be confirmed by identifying clusters of DNA fingerprints among bacterial isolates from infected individuals. This procedure makes assumptions about the underlying properties of the genetic marker used for fingerprinting. In particular, it requires that each fingerprint changes sufficiently slowly within an individual that isolates from separate individuals infected by the same strain will exhibit similar or identical fingerprints. We propose a model for the probability that an individual's fingerprint will change over a given period of time. We use this model together with published data in order to estimate the fingerprint change rate for IS6110 in human tuberculosis, obtaining a value of 0.0139 changes per copy per year. Although we focus on insertion sequences (IS), our method applies to other fingerprinting techniques such as pulsed-field gel electrophoresis (PFGE). We suggest sampling intervals that produce the least error in estimates of the fingerprint change rate, as well as sample sizes that achieve specified levels of error in the estimate.