Analysis of microsatellite loci in tree of heaven (Ailanthus altissima (Mill.) Swingle) using SSR-GBS

Abstract

Microsatellite markers are still the marker of choice for many research questions in the field of forest genetics. However, the number of available markers is often low for species that have not been studied intensively like the tree of heaven (Ailanthus altissima). During the last decade, next-generation sequencing (NGS) has offered advanced techniques for efficiently identifying microsatellite markers and accurately genotyping samples. Here, we identify new microsatellite markers for the tree of heaven by applying an NGS-based method using the Illumina MiSeq platform. NGS technology was proved to be an effective method for fast and cost-efficient identification of microsatellite markers by implementing a genotyping-by-sequencing approach based on Illumina amplicon sequencing (SSR-GBS). We screened three populations from Eastern Austria for genetic variation at 19 newly identified microsatellite loci. We tested two different genotyping approaches: (1) considering only allele lengths (forming a socalled Ballele length dataset^), (2) taking also single nucleotide polymorphisms (SNPs) within the amplified fragments into account (forming a so-called BSNP dataset^). The results revealed higher values for all genetic diversity parameters, as well as a better resolution of genetic assignment, when the latter approach was followed. Thus, by taking advantage of sequence information which is provided by SSR-GBS, one may achieve considerable gains in performance using the same marker set. The developed markers provide a cost-efficient tool for genotyping populations of tree of heaven and the approach presented here promises to be of high value for medium throughput genotyping applications in non-model forest tree species. We will use this method to widen the perspectives for further population genetic investigations of the tree of heaven.

Resolving fine‐scale population structure and fishery exploitation using sequenced microsatellites in a northern fish

“…Effective fisheries management and population persistence hinge upon understanding population structure and stock composition (Hilborn, Quinn, Schindler, & Rogers, 2003;Ruzzante et al, 2006;Schindler et al, 2010), particularly in northern species facing increased exploitation and growing pressure from climate change. Yet the paucity of genomic resources for many northern species and Our results build directly on recent studies developing panels of sequence-based microsatellites in salmonids and other taxa (Darby et al, 2016;Neophytou et al, 2018), the development of software for sequence-based microsatellite genotyping (e.g., Zhan et al, 2013) and clearly illustrates the large potential for sequence-based microsatellite genotyping to other northern fish and/or understudied species.…”

Fast sequence-based microsatellite genotyping development workflow

“…Starting from a reasonable number of candidate loci that were identified using readily available or newly generated genomic resources, resulted in an average of 75% of the loci included in the PCR multiplex generating reliable genotypic data. This result compares favourably with previous studies where success rates were lower (47% , 53% (Neophytou et al 2018)) or similar (78% (Tibihika et al…”

“…Previous studies have demonstrated the added benefit of analysing different types of polymorphism within sequences compared to using amplicon size only (as in traditional capillary electrophoresisbased genotyping) to differentiate alleles (Darby et al 2016;Neophytou et al 2018;Barbian et al 2018;Tibihika et al 2018). Size homoplasy, due to alleles identical by size but not by sequence, ranges from to 32% and 44% (Darby et al 2016;Vartia et al 2016;Barbian et al 2018).…”

“…Sequence data thus reduces allele homoplasy because alleles of the same size may contain molecular variation that does not translate into size variation such as SNP, indels masking variation in repeat number, or presence of two adjacent SSR motifs with complementary size variation (Darby et al 2016). As a result, SSRseq offers refined genetic diversity estimation and population structure inference (Darby et al 2016;Bradbury et al 2018;Neophytou et al 2018;Viruel et al 2018).…”

“…Studies published so far have explored specific technical or analytical aspects of SSRseq. Several bioinformatics approaches have been developed (Hoogenboom et al 2016;Suez et al 2016;Zhan et al 2017; Barbian et al 2018), different laboratory protocols tested (Vartia et al 2016;Pimentel et al 2018) and means to account for molecular variation on population genetic inference compared (Neophytou et al 2018;Viruel et al 2018). Together, these studies explored numerous issues surrounding the technical and analytical advantages of SSRseq over traditional methods, each of them focusing on a given model species making it difficult to assess finding generalization to other biological models.…”