Maximum Likelihood Estimation of Population Growth Rates Based on the Coalescent

Department of Genetics, University of Washington, Seattle, Washington 98195, USA.
Genetics (Impact Factor: 5.96). 06/1998; 149(1):429-34.
Source: PubMed


We describe a method for co-estimating 4Nemu (four times the product of effective population size and neutral mutation rate) and population growth rate from sequence samples using Metropolis-Hastings sampling. Population growth (or decline) is assumed to be exponential. The estimates of growth rate are biased upwards, especially when 4Nemu is low; there is also a slight upwards bias in the estimate of 4Nemu itself due to correlation between the parameters. This bias cannot be attributed solely to Metropolis-Hastings sampling but appears to be an inherent property of the estimator and is expected to appear in any approach which estimates growth rate from genealogy structure. Sampling additional unlinked loci is much more effective in reducing the bias than increasing the number or length of sequences from the same locus.

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Available from: Mary K Kuhner, Jul 11, 2014
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    • "Most methods used to calculate population size changes are based on statistical deviation from a specific model (e.g., constant size, Rogers 1995) by permutations (Excoffier et al. 2005), and fluctuations of effective population size (N e ) need to be assessed independently (Kuhner et al. 1998); however, it is now possible to estimate population sizes and fluctuations through time without a priori limitations from multiple loci (Ho and Shapiro 2011). We tested the population expansion hypothesis using EBSPs (Extended Bayesian Skyline Plots; Heled and Drummond 2008) implemented in BEAST 1.7 with three independent runs of 100 9 10 6 generations and a burn-in of 10 9 10 6 steps. "
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    • "This effective population size parameter need not be a static number—it can itself be a value that increases or decreases through time (Griffiths & Tavare 1994). By discretizing historical periods and allowing different periods to exhibit patterns of population increase or decrease, one can model complex histories of effective population size increase or decrease that can be compared using likelihood and model comparison methods (Griffiths & Tavare 1994; Kuhner et al. 1998; Pybus et al. 2000; Drummond et al. 2005; Minin et al. 2008). Blair et al. (2013) used such an approach to determine that population sizes in lineages of Asian tree frogs (Polypedates leucomystax) had recently increased, helping them to understand how climate has affected these frogs through time. "
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    • "Effective population size measures genetic diversity present in the population and relates to census population size if certain assumptions are met [Wakeley and Sargsyan, 2009]. Many early coalescent-based phylodynamic methods required strict parametric assumptions about the effective population size trajectory, such as constant through time [Griffiths and Tavaré, 1994] or exponential growth [Drummond et al., 2002, Kuhner et al., 1998]. A major alternative arose with the advent of nonparametric methods, one of the earliest and most influential being the piecewise constant classical skyline model [Pybus et al., 2000]. "
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