Adding loci improves phylogeographic resolution in red mangroves despite increased missing data: comparing microsatellites and RAD-Seq and investigating loci filtering

Abstract

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. One challenge, however, is that RAD-Seq generates complete genotypes for only a small subset of loci or individuals. 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. 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. For all datasets, we calculated population genetic statistics and evaluated population structure, and for RAD-Seq datasets, we additionally examined population structure using coalescence. 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. Analyses of RAD-Seq datasets resolved the classic Gulf-Atlantic coastal phylogeographic break, which was not significant in the microsatellite analyses. 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.

Population genomic analysis of mango (Mangifera indica) suggests a complex history of domestication

“…() estimated gene diversity from transcriptome‐derived single nucleotide polymorphism (SNP) markers in mango to have a median value of 0.28–0.43, roughly 1.5–2‐fold higher than the average values calculated here. The explanation for this discrepancy is not immediately clear; however, more recent empirical work indicates that missing data may not inflate diversity indices in empirical datasets as much as was proposed initially (Hodel et al ., ). One possibility for the observed differences in gene diversity between studies is that low sequence coverage and low tolerance for missing data at the interspecific level in the present study produced a dataset of highly conserved genomic regions, which are inherently less diverse (Huang & Knowles, ).…”

Application of a SSR‐GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics

“…The high number of loci recovered with RAD‐seq allows for the recovery of population genetic differentiation patterns (Schopen, Bovenhuis, Visker, & Van Arendonk, ). However, there are some limitations associated with RAD‐seq, such as the difficulty in detecting paralogs without a reference genome, the high amount of missing data, and biases caused by the use of restriction enzymes that influence heterozygosity estimates, especially when stringent data filtering is implemented (Hodel et al, ). Further, SSR markers’ costs and data collection efforts do not increase linearly as a function of sample size.…”

“…Amplicon sequencing has a special role in SSR analysis (de Barba et al, 2017;Farrell, Carlsson, & Carlsson, 2016;Vartia et al, 2016;Šarhanová et al, 2018), and microsatellite amplification is the method of choice for population genetics, due to the ability to recover multiple alleles per locus, resulting in a high statistical power with a low number of sequenced markers (Ellegren, 2004;Schlotterer, 2000). Despite the obvious advantages of whole-genome sequencing approaches, genotyping-specific loci is more cost-effective and more easily implemented, which is also one of the arguments found in recent reviews for the use of microsatellites in place of RAD/GBS (Hodel et al, 2017(Hodel et al, , 2016. Second-generation sequencing methods facilitate new, more powerful applications using microsatellite loci by increasing the data collected and the possibility to reach high statistical power by increasing the number of markers per sample and the number of alleles per marker (de Barba et al, 2017;Tibihika, Curto et al, 2018;Vartia et al, 2016).…”

“…However, there are some limitations associated with RAD-seq, such as the difficulty in detecting paralogs without a reference genome, the high amount of missing data, and biases caused by the use of restriction enzymes that influence heterozygosity estimates, especially when stringent data filtering is implemented (Hodel et al, 2017).…”

A 3K Axiom®SNP array from a transcriptome-wide SNP resource sheds new light on the genetic diversity and structure of the iconic subtropical conifer treeAraucaria angustifolia(Bert.) Kuntze

“…2 and S5 File). A number of previous studies in plant and animal species have in fact shown higher F st estimates with SNPs when compared to microsatellites and a superior ability of SNPs to resolve fine-scale population structure and phylogeographic signals [87,91,92].…”