Do complex population histories drive higher estimates of substitution rate in phylogenetic reconstructions?
Article first published online: 14 OCT 2009
© 2009 Blackwell Publishing Ltd
Volume 18, Issue 21, pages 4341–4343, November 2009
How to Cite
RAMAKRISHNAN, U. and HADLY, E. A. (2009), Do complex population histories drive higher estimates of substitution rate in phylogenetic reconstructions?. Molecular Ecology, 18: 4341–4343. doi: 10.1111/j.1365-294X.2009.04334.x
- Issue published online: 14 OCT 2009
- Article first published online: 14 OCT 2009
- Received 1 July 2009; revision received 20 July 2009; accepted 23 July 2009
- Ancient genetic data;
- molecular evolution;
- mutation rate;
- serial coalescent
Our curiosity about biodiversity compels us to reconstruct the evolutionary past of species. Molecular evolutionary theory now allows parameterization of mathematically sophisticated and detailed models of DNA evolution, which have resulted in a wealth of phylogenetic histories. But reconstructing how species and population histories have played out is critically dependent on the assumptions we make, such as the clock-like accumulation of genetic differences over time and the rate of accumulation of such differences. An important stumbling block in the reconstruction of evolutionary history has been the discordance in estimates of substitution rate between phylogenetic and pedigree-based studies. Ancient genetic data recovered directly from the past are intermediate in time scale between phylogenetics-based and pedigree-based calibrations of substitution rate. Recent analyses of such ancient genetic data suggest that substitution rates are closer to the higher, pedigree-based estimates. In this issue, Navascués & Emerson (2009) model genetic data from contemporary and ancient populations that deviate from a simple demographic history (including changes in population size and structure) using serial coalescent simulations. Furthermore, they show that when these data are used for calibration, we are likely to arrive at upwardly biased estimates of mutation rate.