A DNA Sequence Evolution Analysis Generalized by Simulation and the Markov Chain Monte Carlo Method Implicates Strand Slippage in a Majority of Insertions and Deletions

2002 ◽  
Vol 55 (6) ◽  
pp. 706-717 ◽  
Author(s):  
Manami Nishizawa ◽  
Kazuhisa Nishizawa
Genetics ◽  
2001 ◽  
Vol 158 (2) ◽  
pp. 885-896 ◽  
Author(s):  
Rasmus Nielsen ◽  
John Wakeley

Abstract A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).


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