What follows are the replies that I received to my evoldir query regarding apparently non-neutral microsatellite loci. For privacy's sake, I have dropped people's names. Thanks to all who provided responses. My original post: "Most analyses of population genetic variation and structure in populations assume the neutrality of the markers used. There are a number of software tools that test for selection on loci, with LOSITAN (which uses the It uses the FST-outlier method (Vitalis et al. 2001; Beaumont 2005) my preferred tool. In a data set of 31 microsatellite loci, as many of as six test as being significantly under either balancing or positive selection across my samples. I would just like to get a feel for whether the community at large, faced with such a scenario, would 1) advocate dropping those loci from the data set, 2) leaving them in or 3) presenting analyses both with and without those loci included. My question is asked from the standpoint of presentation in publication - I intend to analyze both the full and trimmed data set for my own interests in how non-neutral loci affect population genetic analyses." Responses: I would be cautious with only using the classical Fst method (for example LOSITAN) for detecting loci under selection. The classical methods for detection of loci under selections are based on simulating a FST-null distribution across all loci and from this detect loci that lie outside the credibility region, and therefore assumed to be under selection. These methods apply a simple demographic model such as coalescent-based approach assuming genetic drift to be the contributor to differentiation among populations; outliers are therefore taken as evidence of selection. Novel methods (such as BAYESCAN ver. 2.01 (Foll and Gaggiotti, 2008)) extent the classical approach to include dynamic processes such as gene flow are based on detection of LD among pairs of loci. The demographic models are more advanced and more realistically describing ecological scenarios, including migration among subpopulations. The degree of differentiation (FST) decomposed into a locus¬-specific component (alpha), shared by all populations, and a population-specific component (beta), shared by all loci. Selection is assumed when alpha is necessary for explaining the observed pattern of diversity. For testing for loci under selection I have used both LOSITAN and BAYESCAN, the latter seemed to be more realistic, when the population structure and history was taken into consideration. I guess I'd say your results are probably evidence that the method used to identify "positive selection" is junk (like most such methods). Almost certainly false positives... I would look into how your loci are segregating your samples. You could do PCAs or correspondence analyses of the allele distributions among your samples and see how they segregate for each marker, then you can compare what your presumably neutral and what your presumably selected markers are doing. If segregation is the same among all markers (i.e. throughout the genome), then I would say you do not have selection, just some markers that are particularly good at picking the biological signal, and thus, in my opinion, all markers should be included into the estimation of differentiation. If you neutral markers all segregate your samples one way, but your outlier loci do it another (or several other ways), then you may have selection, and I would report your neutral differentiation, and the differentiation due to "selected" markers. It depends on the impact on the results and the amount of data you have. I definitely prefer to check it. Often the impact for the final conclusions is very limited and can be noted verbally. (Well, dendrograms are notoriously instable, but one does not need them anyway.) You might want to do some more realistic simulations to explore other confounding factors such as sampling from a spatially structured population, founding events and so on. I would suggest excluding these 6 loci since you will have many left. I would also check what each non neutral ones says. I would also check the repeat motive. Trinucleotides are more likely to be non neutral than dinucleotidic loci. I would also, if possible try to know where these loci come from in the genome (in a coding sequence etc...). I would strongly recommend to at least report the outlier results and subsequent results with and without them. I would definitely analyse data with and without. In my experience, this will likely change things. But it might only affect only some of the populations analysed (which is reasonable if any such locus is indeed affected by some local selective force specific to some area of your study). If the loci "potentially under selection" do change the picture quite a bit, you would need to check on BLAST if they are anywhere near a genomic region that has some functional implication. Please also note that you should consider also the fact that in certain conditions, when a population is quickly expanding its range at distribution margins, some rare alleles can suddenly increase in frequency as a result of these peripheral founding at the front wave of expansion (see recent papers by Excoffier & Co about the "allele surfing" hypothesis). Reviewers will almost certainly ask you to consider that. The idea of comparing the analyses with and without the selected loci seems interesting (just be aware of the smaller sample size, and thus lower power, in the "neutral" fraction compared to the whole list of loci). Also I have a feeling that outlier analyses such as LOSITAN are quite prone to giving false positive hits. Alan W. Meerow, Ph.D., Research Geneticist and Systematist USDA-ARS-SHRS, National Germplasm Repository 13601 Old Cutler Road, Miami, FL 33158 USA voice: 786-573-7075; FAX: 786-573-7102 email: alan.meerow@ars.usda.gov "Meerow, Alan"