Genetic markers are used for in modern population genetics/genomics to uncover the past neutral and selective history of population and species. Besides Single Nucleotide Polymorphisms (SNPs) obtained from whole genome data, microsatellites (or Short Tandem Repeats, SSR) have been common markers of choice in numerous population genetics studies of non-model species with large sample sizes . Microsatellites can be used to uncover and draw inference of the past population demography (e.g. expansion, decline, bottlenecks…), population split, population structure and gene flow, but also life history traits and modes of reproduction (e.g. [2,3]). These markers are widely used in conservation genetics  or to study parasites or disease vectors . Microsatellites do show higher mutation rate than SNPs increasing, on the one hand, the statistical power to infer recent events (for example crop domestication, [2,3]), while, on the other hand, decreasing their statistical power over longer time scales due to homoplasy .
To perform such analyses, however, an excellent and reliable quality of data is required. As emphasized in the article by De Meeûs et al.  three main issues do bias the observed heterozygosity at microsatellites: null alleles, short allele dominance (SAD) and stuttering. These originates from poor PCR amplification. As a result, an excess of homozygosity is observed at the microsatellite loci leading to overestimation of the variation statistics FIS and FST as well as increased linage disequilibrium (LD). For null alleles, several methods and software do help to reduce the bias, and in the present study, De Meeûs et al.  propose a way to tackle issues with SAD and stuttering.
The authors study a dataset consisting of 387 samples from 61 subsamples genotyped at nine loci of the species Ixodes scapularis, i.e. ticks transmitting the Lyme disease. Based on correlation methods and FST, FIS they can uncover null alleles and SAD. Stuttering is detected by evaluating the heterozygote deficit between alleles displaying a single repeat difference. Without correction, six loci are affected by one of these amplification problems generating a large deficit of heterozygotes (measured by significant FIS and FST) remaining so after correction for the false discovery rate (FDR). These results would be classically interpreted as a strong Wahlund effect and/or selection at several loci.
After correcting for null alleles, the authors apply two novel corrections: 1) a re-examination of the chromatograms reveals previously disregarded larger alleles thus decreasing SAD, and 2) pooling alleles close in size decreasing stuttering. The corrected dataset shows then a significant excess of heterozygotes as could be expected in a dioecious species with strong population structure. The FDR correction removes then the significant excess of homozygotes and LD between pairs of loci. FST on the cured dataset is used to demonstrate the strong population structure and small effective subpopulation sizes. This is confirmed by a clustering analysis using discriminant analysis of principal components (DAPC).
While based on a specific dataset of ticks from different populations sampled across the USA, the generality of the authors’ approach is presented in Figure 6 in which they provide a step by step flowchart to cure microsatellite datasets from null alleles, SAD and stuttering. Several criteria based on FIS, FST and LD between loci are used as decision keys in the flowchart. An excel file is also provided as help for the curation steps. This study and the proposed methodology are thus extremely useful for all population geneticists working on non-model species with large number of samples genotyped at microsatellite markers. The method not only allows more accurate estimates of heterozygosity but also prevents the thinning of datasets due to the removal of problematic loci. As a follow-up and extension of this work, an exhaustive simulation study could investigate the influence of these data quality issues on past demographic and population structure inference under a wide range of scenarios. This would allow to quantify the current biases in the literature and the robustness of the methodology devised by De Meeûs et al. .
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 Koffi, M., De Meeûs, T., Séré, M., Bucheton, B., Simo, G., Njiokou, F., Salim, B., Kaboré, J., MacLeod, A., Camara, M., Solano, P., Belem, A. M. G. and Jamonneau, V. (2015). Population genetics and reproductive strategies of African trypanosomes: revisiting available published data. PLoS neglected tropical diseases, 9(10), e0003985. doi: 10.1371/journal.pntd.0003985
 Estoup, A., Jarne, P., & Cornuet, J. M. (2002). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology, 11(9), 1591-1604. doi: 10.1046/j.1365-294X.2002.01576.x
 De Meeûs, T., Chan, C. T., Ludwig, J. M., Tsao, J. I., Patel, J., Bhagatwala, J., and Beati, L. (2019). Deceptive combined effects of short allele dominance and stuttering: an example with Ixodes scapularis, the main vector of Lyme disease in the USA. bioRxiv, 622373, ver. 4 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/622373
Dear Thierry (and co-authors),
I thank you for answering all comments by the reviewers. Many thanks also for clarifying your position regarding issues of using other software, which has been most helpful to me.
Can I ask you to add some of the info your gave as a reply to reviewer 2 and myself (as 2-3 sentences in the manuscript)?
Providing this last minor revision, I would accept and write a recommendation of the preprint.
I copy below some parts of your reply which could be added in the manuscript to justify the difference of your aim/results to using Structure or other software. I would personally add the DAPC figure you suggested in your reply to me, but I leave it up to you if you wish to do so.
“By contrast to the aim of this study, clustering techniques are useful to detect a Wahlund effect. Structure (and other software) can be very helpful to estimate the race or species assignment of different individuals of a population, but this was not the aim of the study. The fact that we obtain, with the cured data set, substantially negative FIS and substantially high FST estimates obviously argues in favour of a strong population subdivision. The estimates of Nm in an Island model (here Nm=1 and N e =7) illustrate this point and support the idea that this tick population is strongly subdivided. This results is corroborated by a DAPC graphic (see Additional Figure XX), based on cured data, which provides quite a strong structure (mean assignment is 0.96), but, even if some geographic concordance can be noticed (Cluster8 is mainly Wisconsin), many individuals that belong to the same cluster originated from remote sites.”
I look forward to accept the article and to write a recommendation,
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You have addressed satisfactorily most points from the first two reviewers, and I am pleased to say that the paper has gained in clarity. The Figure 6 has proven to be very effective in summarizing the steps of data curation. However, as you want to stay with the format of a short communication, the study still reads quite narrow in focus and appears as a specific problem arising from this particular dataset.
The lack of generality of the study is highlighted by the new reviewer 2. This reviewer has suggestions which would require work beyond the scope of a short communication namely 1) to conduct in depth study of spatial structure/past demographic history (emphasize the biological results), or 2) perform a simulation study (emphasize the methodological results). As you have rebuked my suggestion to perform such additional work after the first round of review, I will not insist.
I nevertheless recommend to add one paragraph and one figure of population structure analysis with one of the classically used software as suggested by the new reviewer 2. These new results and the comparison to the Fst/Fis computed values can thus be discussed and provide additional evidence for the strong population structure in this species. This would reinforce and clarify the biological conclusion of the paper. Such addition would be also valuable to enlarge the conclusion of the paper, for example as a warning/word of caution on the influence of data curation on results obtained by classic methods (structure,...). To avoid the multiplication of figures, a possibility for a short communication article could be to group Figure 2, 3 and 5 in a single multiple panel figure.
Providing this additional result part and adequate reply/changes to the last minor comments by both reviewers, I believe that the article should qualify for acceptance in PCI Evol Biol in the very near future.
Best regards, and looking forward to the hopefully last version of the manuscript.
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Both reviewers and myself do find the topic of the study and the results to be of interest and relevant. Citing reviewer 1, the quest for interpreting difficult microsatellite data indeed deserves attention. It is thus of special interest to understand the biases which can be introduced during the curation steps of these datasets. If the aims, methods and interpretations are clear, the study would benefit from two major improvements. These would enhance the generality of the paper and its relevance for the wider community.
First, both reviewers point out the lack of theoretical “a priori” expectations in the paper. In comments 1-3, reviewer 1 asks to describe the rationale behind the idea that experimental artifacts should increase LD. Reviewer 2 would like to understand in a more quantitative manner the rationale behind pooling alleles close in size and the effect of the sample size on the results. The latter is important as in the study the authors chose a small sample size, while microsatellites have bene recently applied to much larger datasets (at least on many fungal pathogen species for example). A more thorough comparison with other existing curing methods could be provided. I would suggest as a possible solution to indeed build simulated datasets and apply curing methods revealing the different experimental artifacts. It would thus be possible to reveal general rules and outcomes of applying different curing approaches (including yours), such as changes of basic statistics and LD estimates. The effect of the sample size could also be tested on the same pseudo-observed data by subsampling. This general “theoretical” set-up would allow an in depth discussion of the mechanisms involved and make the article more general in scope. The biological dataset of the tick Ixodes scapularis analyzed here would then be used as an application of these general principles.
If it is not possible to perform such theoretical analysis of the curing of pseudo-observed datasets, several in depth descriptions answering comments of both reviewers should be added to the manuscript.
Second, as reviewer 1 points out (comment 4), most researchers move to other type of markers (GBS, RADseq,…) and it would be helpful to discuss if the effect of curing datasets also apply to those data. As a matter of curiosity, a focus could be on highlighting how population genetics inference combining different types of markers (SSRs, GBS, RADseq) can be affected by curing some markers but not others?
Several minor points are also suggested by the reviewers and need to be addressed for the revision. These include restructuring/reorganizing some parts and providing a flowchart (a schematic description) of the analysis/curing steps (reviewer 2).
I look forward to receive your revised version, and believe that this improved contribution would fit into the scope of PCI Evol Biol and be of general interest to the community.