Introduction

Many types of genetic analysis take advantage of microsatellite markers, which are highly polymorphic loci of simple sequence repeats located through the genome. For example, microsatellite analysis is useful in studies of paternity, population structure and history, as well as to make conservation decisions for the management of endangered species [1], [2].

Given the broad-scale utility of these markers, a large number of approaches have been developed for their isolation from genomic DNA [3]. These approaches typically involve some form of microsatellite enrichment, followed by time consuming and costly brute force sequencing. Aside for the labor and cost associated with traditional approaches, the microsatellite enrichment step sometimes fails. For example, for reasons not fully understood, isolation of microsatellites from Lepidopteran genomes is extremely difficult [4]–[6]. This problem is not confined to Lepidoptera, affecting bivalve mollusks [7], mosquitoes [8], mites [9], ticks [8], nematodes [10], [11] and birds [12], [13].

The increase in publicly available EST data for many species has made bioinformatic isolation of microsatellite markers increasingly commonplace (e.g., [14]–[17]). However, microsatellites isolated from EST libraries differ from those typically found in regions of the genome unassociated with genes. Gene-associated microsatellites are physically linked to particular alleles of a gene, and may hitchhike if the gene is under selection. Microsatellite variation in untranslated regions of transcribed DNA may affect the rates of gene expression or translation, and thus may be under selection. Indeed, EST-derived microsatellites almost universally show strong deviations from Hardy-Weinberg equilibrium. However, the relatively few studies that compare the performance of EST-derived microsatellites with that of other genotyping techniques have generally found comparable results [18]–[22]. Here we used the Roche 454 Titanium platform for transcriptional sequencing of Edith's checkerspot butterfly (Euphydryas editha), in order to rapidly isolate polymorphic microsatellite loci for a conservation genetics study. We then compared the estimates of population differentiation and biogeographic structure obtained by this approach with those from AFLP genotyping of the same set of populations [23].

Materials and Methods

Results

After quality filtering, the 454 run generated 864,056 reads, totaling 245,064,986 bases, which were assembled into 14,244 contigs with a threshold of 200 bp overlap and 95% identity. 49,937 singleton reads remained unassembled and were not included in the subsequent analysis, although if needed, they may be used for microsatellite mining. The assembled contigs contained 92 microsatellite loci, 72 of which were selected for microsatellite development. Of these, 36 loci amplified successfully and appeared polymorphic (see Table S2). Following the initial screening performed of eight individuals, we developed four multiplex PCR cocktails containing a total 10 polymorphic loci for large-scale genotyping (Table S1). Sequences for the other loci are available from the authors upon request. The reaction conditions were as above, but without fluorescently labeled dUTPs in reactions 1 and 2, and with primer concentrations as noted in Table S2. The 10 loci are deposited in GenBank under accession numbers GU997598-GU997607.

The markers show significant deviations from Hardy-Weinberg equilibrium in the many of the populations (Figure 1). The difference between observed and expected heterozygosities was positively correlated with the number of failed amplification for each locus, suggesting that null alleles may in part be responsible for driving this difference (r_s = 0.81, n = 10, p = 0.0042). However, estimates of pairwise population differentiation were concordant between microsatellite-derived data and an earlier AFLP analysis of the same samples by Wee [26] (r = 0.71, n = 23, p<0.00001).

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Figure 1. Hardy-Weinberg equilibrium statistics.

Significant deviations from Hardy-Weinberg (chi-squared test, p<0.05) are indicated in dark grey. Loci monomorphic in that population are shown in light grey. Every population is represented by a column, with each row corresponding to a microsatellite locus. The order of the populations is the same as in Table S1 (alphabetical).

https://doi.org/10.1371/journal.pone.0011212.g001

Raw microsatellite data generated in this study have been deposited in the Dryad database (www.datadryad.org) under accession number 1540.

Discussion

Microsatellite isolation from lepidopteran genomes has been difficult, possibly because microsatellite loci appear to be rare, and may have very similar flanking regions [6], which makes the design of primers problematic. We hypothesized that microsatellite loci isolated from non-translated transcripts may be less likely to exist as duplicate copies, and thus be more amenable to marker development. This has made microsatellite isolation relatively straightforward in our case. Given the decrease in next-generation sequencing costs, transcriptional re-sequencing will be a faster and cheaper way to isolate microsatellites, compared with traditional enrichment techniques. We were able to complete microsatellite development and screening in about three months of part time work by a single technician. Our actual cost of library construction and sequencing, was about US$15,000, is comparable to that charged by private companies for microsatellite enrichment [3]. Since then, the actual cost of library construction and next generation sequencing has dropped by at least 50%, and is decreasing further.

In this and several other studies, microsatellites derived from transcribed sequence data significantly depart from Hardy-Weinberg equilibrium (Figure 1) [14]–[17]. This could be due to selection on polymorphisms in untranslated gene regions where these microsatellites typically reside, or to non-neutral dynamics of the genes to which they are physically linked. In our study, percent reaction failure explained most of the variance in the differences between observed and expected heterozygosities (Table 1). Therefore, at least in our case, Hardy-Weinberg disequilibrium may be partially due to insufficient optimization of PCR conditions and allele dropout. Whether or not higher levels of null alleles are common in EST-derived microsatellites is not clear, since these data are not routinely reported with such studies. We strongly recommend further optimization of the reaction conditions for the loci presented here, especially since the manufacture of fluorescent dUTPs used in this study has been discontinued.

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Table 1. Primers used for large-scale genotyping.

https://doi.org/10.1371/journal.pone.0011212.t001

In principle, deviations from Hardy-Weinberg can create substantial biases [28], limiting the utility of such markers. The extent to which these issues may affect analysis with EST-derived microsatellites is presently unclear, but should be carefully investigated by future studies. Ideally, studies isolating microsatellites from ESTs should verify their performance by comparing results with another genotyping method, as we have done with AFLPs. Likewise, it would be useful to present an analysis of null allele presence.

Supporting Information

Acknowledgments

We are grateful to C. L. Boggs for her assistance with library construction and sequencing contracts, and to J. Strassmann and her lab for hosting this project. We thank E. Meglécz for reviewing the manuscript.