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Abstract:
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Sequence data from a series of homologous DNA segments from related organisms are typically polymorphic at many sites, and these polymorphisms are the result of evolutionary processes. Careful characterization of the distribution of the substitution rate variation is essential for accurate estimation of evolutionary distances and phylogeny reconstruction among these sequences. We accommodate these correlations using a composite likelihood method, which is computationally tractable and more flexible than hidden Markov models, and we show that the estimates have good theoretical properties. We use data from the mitochondrial DNA of ten primates to obtain maximum composite likelihood estimates of the mean, overdispersion, and correlation parameters, and use these estimates in a parametric phylogenetic bootstrap to assess the impact of serial correlation on the estimates of mutation rates.
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