Literature DB >> 19001634

Accommodating the effect of ancient DNA damage on inferences of demographic histories.

Andrew Rambaut, Simon Y W Ho, Alexei J Drummond, Beth Shapiro.   

Abstract

DNA sequences extracted from ancient remains are increasingly used to generate large population data sets, often spanning tens of thousands of years of population history. Bayesian coalescent methods such as those implemented in the software package BEAST can be used to estimate the demographic history of these populations, sometimes resulting in complex scenarios of fluctuations in population size, which can be correlated with the timing of environmental events, such as glaciations. Recently, however, Axelsson et al. (Axelsson E, Willerslev E, Gilbert MTP, Nielsen R. 2008. The effect of ancient DNA damage on inferences of demographic histories. Mol Biol Evol 25:2181-2187.) claimed that many of these complex demographic trends are likely to be the result of postmortem DNA damage, a problem that they investigate by removing all sites involving transitions from ancient sequences prior to analysis. When this solution is applied to a previously published data set of Pleistocene bison, they show that the demographic signal of population expansion and decline disappears. Although some apparently segregating mutations in ancient sequences may be due to postmortem damage, we argue that discarding the data will result in loss of power to detect patterns of population change. Instead, to accommodate this problem, we implement a model in which sequences are the result of a joint process of molecular evolution and postmortem DNA damage within a probabilistic inference framework. Through simulation, we demonstrate the ability of this model to accurately recover evolutionary parameters, demographic history, and DNA damage rates. When this model is applied to the bison data set, we find that the rate of DNA damage is significant but low and that the reconstruction of population size history is nearly identical to previously published estimates.

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Year:  2008        PMID: 19001634     DOI: 10.1093/molbev/msn256

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  53 in total

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