To what extent do microsatellite markers reflect genome‐wide genetic diversity in natural populations?

Microsatellite variability is widely used to infer levels of genetic diversity in natural populations. However, the ascertainment bias caused by typically selecting only the most polymorphic markers in the genome may lead to reduced sensitivity for judging genome-wide levels of genetic diversity. To test this potential limitation of microsatellite-based approaches, we assessed the degree of nucleotide diversity in noncoding regions of eight different carnivore populations, including inbred as well as outbred populations, by sequencing 10 introns (5.4–5.7 kb) in 20 individuals of each population (wolves, coyotes, wolverines and lynxes). Estimates of nucleotide diversity varied 30-fold (7.1 × 10⁻⁵ –2.1 × 10⁻³), with densities of one single nucleotide polymorphism every 112–5446 bp. Microsatellite genotyping (10–27 markers) of the same animals revealed mean multilocus heterozygosities of 0.54–0.78, a 1.4-fold difference among populations. There was a positive yet not perfect ( r ²  = 0.70) correlation between microsatellite marker heterozygosity and nucleotide diversity at the population level. For example, point estimates of nucleotide diversity varied in some cases with an order of magnitude despite very similar levels of microsatellite marker heterozygosity. Moreover, at the individual level, no significant correlation was found. Our results imply that variability at microsatellite marker sets typically used in population studies may not accurately reflect the underlying genomic diversity. This suggests that researchers should consider using resequencing-based approaches for assessing genetic diversity when accurate inference is critical, as in many conservation and management contexts.     

Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites

Although growing numbers of single nucleotide polymorphisms (SNPs) and microsatellites (short tandem repeat polymorphisms or STRPs) are used to infer population structure, their relative properties in this context remain poorly understood. SNPs and STRPs mutate differently, suggesting multi-locus genotypes at these loci might differ in ability to detect population structure. Here, we use coalescent simulations to measure the power of sets of SNPs and STRPs to identify population structure. To maximize the applicability of our results to empirical studies, we focus on the popular STRUCTURE analysis and evaluate the role of several biological and practical factors in the detection of population structure. We find that: (1) fewer unlinked STRPs than SNPs are needed to detect structure at recent divergence times <0.3 N_e generations; (2) accurate estimation of the number of populations requires many fewer STRPs than SNPs; (3) for both marker types, declines in power due to modest gene flow (N_em=1.0) are largely negated by increasing marker number; (4) variation in the STRP mutational model affects power modestly; (5) SNP haplotypes (θ=1, no recombination) provide power comparable with STRP loci (θ=10); (6) ascertainment schemes that select highly variable STRP or SNP loci increase power to detect structure, though ascertained data may not be suitable to other inference; and (7) when samples are drawn from an admixed population and one of its parent populations, the reduction in power to detect two populations is greater for STRPs than SNPs. These results should assist the design of multi-locus studies to detect population structure in nature.