RAD-Seq uses restriction enzymes (REs) to create a reduced representation library of the genome single-nucleotide polymorphisms (SNPs) in regions of DNA between restriction sites are used to distinguish between individuals 8. The use of RAD-Seq data has increased greatly over the past decade, largely because thousands of loci can be generated simultaneously for hundreds of individuals for a fixed, known cost 7. Furthermore, microsatellites can be just as expensive to implement as newer high-throughput sequencing (HTS) techniques if there are no existing genetic resources (e.g., no primers already developed, or no available transcriptomic or genomic resources) 6. Thus, the property that makes microsatellites excellent for distinguishing different individuals may inflate statistics such as F ST and heterozygosity relative to the rest of the genome. Also, the mutational properties of SSRs are unusually high and almost certainly do not reflect those of the genome as a whole. Perhaps most importantly, a limited number of loci (usually < 25) can feasibly be employed in a typical microsatellite study. However, there are caveats to using SSRs. Additionally, if initial results necessitate adding a few additional individuals and/or loci, project costs will increase linearly with microsatellites. If primers are already developed for the taxa of interest, microsatellites can be inexpensive to implement. In addition, many user-friendly software packages are available for all aspects of microsatellite analysis, from loci development to population genetic inference 3. Microsatellites are a known quantity hundreds of thousands of studies that use SSRs are in the literature-primers are already available for many groups. There are advantages and disadvantages to using microsatellites in phylogeographic studies 2, 3, 5. Microsatellite markers are now being gradually replaced by RAD-Seq data for phylogeographic inference 4. Microsatellites (or simple sequence repeats, SSRs) have been one of the workhorses of phylogeographic studies for over two decades-their high variability made them popular for distinguishing between closely related conspecific or congeneric individuals 1, 2, 3. In phylogeographic studies, theoretical considerations impact decisions regarding whether to include more individuals or more loci. Applying multiple levels of filtering to RAD-Seq datasets can provide a more complete picture of potential biases in the data and elucidate subtle phylogeographic patterns.Ĭhoice of molecular markers remains a critically important consideration when designing a phylogeographic, phylogenetic, or population genetic study, as researchers must optimize the amount of informative genetic data they can obtain for a fixed and typically modest cost. ![]() Analyses of RAD-Seq datasets resolved the classic Gulf-Atlantic coastal phylogeographic break, which was not significant in the microsatellite analyses. We found higher F ST using microsatellites, but that RAD-Seq-based estimates approached those based on microsatellites as more loci with more missing data were included. For all datasets, we calculated population genetic statistics and evaluated population structure, and for RAD-Seq datasets, we additionally examined population structure using coalescence. Here we compare microsatellite data (8 loci) and RAD-Seq data (six datasets ranging from 239 to 25,198 loci) from red mangroves ( Rhizophora mangle) in Florida to evaluate how different levels of data filtering influence phylogeographic inferences. Simulations indicate that loci with missing data can produce biased estimates of key population genetic parameters, although the influence of such biases in empirical studies is not well understood. One challenge, however, is that RAD-Seq generates complete genotypes for only a small subset of loci or individuals. The widespread adoption of RAD-Seq data in phylogeography means genealogical relationships previously evaluated using relatively few genetic markers can now be addressed with thousands of loci.
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