Challenges in analysis and interpretation of microsatellite data for population genetic studies

Abstract

Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (FST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.

Rivers shape population genetic structure in Mauritia flexuosa (Arecaceae)

“…Under a model of mutation‐drift equilibrium, populations that have experienced a recent reduction in effective population sizes may present higher observed than expected heterozygosity (Maruyama & Fuerst, 1985). Although various models exist for microsatellites (Putman & Carbone, 2014), the SMM mutation model can implement equal probability of gaining or losing repeats, therefore accounting for homoplasy. We used the SMM model at 100%; the two‐phase mutation model allows for mutations of a larger magnitude than SMM but retains the mutation model and was used at 70% (Di Rienzo et al., 1994).…”

A nuclear DNA barcode for eastern North American oaks and application to a study of hybridization in an Arboretum setting

“…However, ascertainment bias (Putman & Carbone, 2014), reproducibility issues, and species specificity (Twyford & Ennos, 2012) often limit their utility across a range of species. Moreover, diagnosis of hybrids often requires a high number of loci.…”

Patterns of population structure at microsatellite and mitochondrial DNA markers in the franciscana dolphin (Pontoporia blainvillei)

“…Moreover, it is important to emphasize that among our microsatellite data set, we did not find null alleles that may lead to an overestimation of population genetic differentiation by reducing gene diversity (Chapuis & Estoup, 2007; Putman & Carbone, 2014). …”