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Models of microsatellite evolution
 Statistical Methods in Molecular Evolution, Series: Statistics for Biology and Health
, 2004
"... Microsatellites are simple sequence repeats in DNA, for example the motif AT repeated twentyfive times in a row. Microsatellites mutate by changing the number of their repeats, for example the (AT)25 mentioned in the previous sentence might become an (AT)24 or (AT)26 in that individual’s offspring. ..."
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Microsatellites are simple sequence repeats in DNA, for example the motif AT repeated twentyfive times in a row. Microsatellites mutate by changing the number of their repeats, for example the (AT)25 mentioned in the previous sentence might become an (AT)24 or (AT)26 in that individual’s offspring.
Microsatellite evolution: Markov transition functions for a suite of models
 Theor. Popul. Biol
, 2007
"... This paper takes from the collection of models considered by Whittaker et. al. (2003) derived from direct observation of microsatellite mutation in parentchild pairs and provides analytical expressions for the probability distributions for the change in number of repeats over any given number of ge ..."
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This paper takes from the collection of models considered by Whittaker et. al. (2003) derived from direct observation of microsatellite mutation in parentchild pairs and provides analytical expressions for the probability distributions for the change in number of repeats over any given number of generations. The mathematical framework for this analysis is the theory of Markov processes. We find these expressions using two approaches, approximating by circulant matrices and solving a partial differential equation satisfied by the generating function. The impact of the differing choice of models is examined using likelihood estimates for time to most recent common ancestor. The analysis presented here may play a role in elucidating the connections between these two approaches and shows promise in reconciling differences between estimates for mutation rates based on Whittaker’s approach and methods based on phylogenetic analyses. Key words and phrases: microsatellites, Markov process, generating functions 1.
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, 804
"... Abstract: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they require sophisticated statistical techniques that have been made available to the biologist community (through compu ..."
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Abstract: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on Approximate Bayesian Computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and stepwise population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios, and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated