The Confounding Effects of Population Structure, Genetic Diversity and the Sampling Scheme on the Detection and Quantification of Population Size Changes

By Chikhi, Lounès; Sousa, Vitor C. et al. | Genetics, November 2010 | Go to article overview

The Confounding Effects of Population Structure, Genetic Diversity and the Sampling Scheme on the Detection and Quantification of Population Size Changes


Chikhi, Lounès, Sousa, Vitor C., Luisi, Pierre, Goossens, Benoit, Beaumont, Mark A., Genetics


ABSTRACT

The idea that molecular data should contain information on the recent evolutionary history of populations is rather old. However, much of the work carried out today owes to the work of the statisticians and theoreticians who demonstrated that it was possible to detect departures from equilibrium conditions (e.g., panmictic population/mutation-drift equilibrium) and interpret them in terms of deviations from neutrality or stationarity. During the last 20 years the detection of population size changes has usually been carried out under the assumption that samples were obtained from populations that can be approximated by a Wright-Fisher model (i.e., assuming panmixia, demographic stationarity, etc.). However, natural populations are usually part of spatial networks and are interconnected through gene flow. Here we simulated genetic data at mutation and migration-drift equilibrium under an n-island and a stepping-stone model. The simulated populations were thus stationary and not subject to any population size change. We varied the level of gene flow between populations and the scaled mutation rate. We also used several sampling schemes. We then analyzed the simulated samples using the Bayesian method implemented in MSVAR, the Markov Chain Monte Carlo simulation program, to detect and quantify putative population size changes using microsatellite data. Our results show that all three factors (genetic differentiation/gene flow, genetic diversity, and the sampling scheme) play a role in generating false bottleneck signals. We also suggest an ad hoc method to counter this effect. The confounding effect of population structure and of the sampling scheme has practical implications for many conservation studies. Indeed, if population structure is creating "spurious" bottleneck signals, the interpretation of bottleneck signals from genetic data might be less straightforward than it would seem, and several studies may have overestimated or incorrectly detected bottlenecks in endangered species.

THE idea that molecular data should contain information on the recent evolutionary history of populations is not new and traces back to the beginning of the 20th century (e.g., Hirschfeld and Hirschfeld 1919). However, much of the work carried out today owes to the seminal work of the statisticians and theoreticians who demonstrated that it was possible to detect departures from equilibrium conditions (e.g., panmictic population/mutation-drift equilibrium) and interpret them in terms of deviations from neutrality (Watterson 1975; Tajima 1989b) or stationarity (Nei et al. 1975; Tajima 1989a). Following this period most studies have primarily been concerned with the statistical properties of relatively simple models such as the Wright-Fisher (WF) or Moran models (Ewens 2004). During the last 20 years the detection of population size changes (e.g., Tajima 1989b; Slatkin and Hudson 1991; Rogers and Harpending 1992; Cornuet and Luikart 1996; Beaumont 1999; Garza and Williamson 2001; Storz and Beaumont 2002) has usually been carried out under the assumption that samples were obtained from populations that can be approximated by a WF model. However, natural populations are usually part of spatial networks and are interconnected through gene flow. They are hence rarely isolated as in the WF model. To be clear, structured models with several populations or demes such as the n-island (Wright 1931) or the stepping-stone models (Kimura and Weiss 1964) have been proposed decades ago in population genetics. Also, a number of authors have proposed methods to infer parameters under structured models (Wakeley 1999; Beerli and Felsenstein 2001; Chikhi et al. 2001; Hey and Nielsen 2004; Excoffier et al. 2005; Beerli 2006; Becquet and Przeworski 2007; Bray et al. 2009). However, the number of populations involved is generally limited compared to the n-island and stepping- stone models (but see Beerli and Felsenstein 2001; De Iorio et al. 2005). …

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