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

• Published in: Genetics (Austin) Vol. 186; no. 3; pp. 983 - 995
• Main Authors: Chikhi, Lounès, Sousa, Vitor C, Luisi, Pierre, Goossens, Benoit, Beaumont, Mark A
• Format: Journal Article
• Language: English
• Published: United States Genetics Society of America 01.11.2010; Oxford University Press
• Subjects: Animals; Bottlenecks; Computer Simulation; Confounding Factors (Epidemiology); Cyprinidae - genetics; Endangered & extinct species; Endangered species; Gene flow; Gene Flow - genetics; Genetic diversity; Genetic Loci - genetics; Genetic Variation; Genetics; Genetics, Population; Investigations; Life Sciences; Markov Chains; Methods; Models, Genetic; Monte Carlo Method; Monte Carlo simulation; Mutation; Natural populations; Pongo - genetics; Population Density; Population Dynamics; Population genetics; Population number; Population structure; Sampling Studies; Studies
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1534/genetics.110.118661

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.