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Do complex population histories drive higher estimates of substitution rate in phylogenetic reconstructions?
Author(s) -
RAMAKRISHNAN UMA,
HADLY ELIZABETH A.
Publication year - 2009
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/j.1365-294x.2009.04334.x
Subject(s) - coalescent theory , biology , population , mutation rate , phylogenetic tree , evolutionary biology , molecular clock , effective population size , phylogenetics , ancient dna , substitution (logic) , genetic variation , genetics , demography , computer science , sociology , gene , programming language
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.