It is becoming more commonplace (example) of sampling large numbers of ancient individuals from natural populations. Unfortunately, ancient DNA suffers from potential damage, so what appears to be an ancient DNA sequence may contain both differences from current sequences that are due to evolution (changes in the gene pool over time), but also due to damage (changes in the ancient population's DNA remnants over time).
A way to deal with damage is to limit oneself to the study of genetic changes that are resilient to it, e.g., transversions over the commoner transitions. But, by doing so, the statistical power is diminished (less data is used). What the authors of this paper do, is try to use the full ancient DNA data, while accounting for the effects of damage in a systematic way. Their conclusions -for their test case- is that its overall effect is not very pronounced.
Molecular Biology and Evolution, doi:10.1093/molbev/msn256
Accommodating the effect of ancient DNA damage on inferences of demographic histories
Andrew Rambaut et al.
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. (2008) claimed that many of these complex demographic trends are likely to be the result of post-mortem 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. While some apparently segregating mutations in ancient sequences may be due to post-mortem 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 post-mortem 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.