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Jackson Reyes
Jackson Reyes

Fst 7 Refined: A Complete Guide to Building Muscle and Definition with FST-7


TopSpin offers a fully workflow-oriented user interface and leverages the latest 64-bit features of modern Windows / CentOS / macOS operating systems for optimal performance. The software is designed to accelerate the operation and throughput of sample analysis for increased cost efficiency.




Fst 7 Refined Torrent Full



Herein, we apply a comparative landscape genetic approach to two closely related, geographically proximate, and ecologically similar torrent salamander species (Rhyacotritonidae) across multiple portions of their heterogeneous geographic ranges. Torrent salamanders are endemic to the U.S. Pacific Northwest (Oregon, Washington, and northern California), occurring in small, cool, shaded stream habitats in moist coniferous forests (Sheridan and Olson 2003; Olson and Weaver 2007). They are highly sensitive to desiccation, warm temperatures, and ground disturbances that result in sedimentation of their instream breeding habitats, stressors which have been coincident with past timber harvest practices in the region (Adams and Bury 2002). Adverse effects of timber harvest on torrent salamanders have been reported in multiple studies (Diller and Wallace 1996; Welsh and Lind 1996; Sheridan and Olson 2003; Olson and Weaver 2007; Olson and Burton 2014).


We focus our comparison on the Columbia torrent salamander, Rhyacotriton kezeri, which is endemic to coastal southwest Washington and northwest Oregon and has a restricted geographic range compared to other amphibians (Whitton et al. 2012), and its congener the southern torrent salamander, R. variegatus, which has a somewhat larger yet still restricted range extending south to northern California from their shared range boundary in coastal Oregon (Fig. 1). Both species are of conservation concern (ODFW 2008; CDFW BDB 2011), with R. kezeri proposed for listing under the U.S. Endangered Species Act (WSDOT 2017). Forest disturbances are key concerns for both species, as their ranges are highly managed for wood production in the Pacific Northwest, where timber harvest and associated roads may degrade and fragment stream breeding and upland dispersal habitats (Bury and Corn 1988; Corn and Bury 1989). Other main land uses in these species' ranges include paved roads and rural development. Also, fire has periodically affected forest habitat in these species' ranges. For example, with the 1987 Silver Fire Complex (566 km2) and 2002 Biscuit Fire (2000 km2) in the R. variegatus range in Oregon, and four fires from 1933 to 1951 collectively known as the Tillamook Burn (1400 km2) in the R. kezeri range in Oregon. Approximately 28% of the historical area of old-growth conifer forest remains in this region of the U.S. as of 2000, which is estimated to be 46,656 km2 (Strittholt et al. 2006). More recently, climate change projections have raised concerns for R. variegatus, as its southern and interior distribution may be limited by historically warm, dry climates that may become warmer and dryer in the future (Bury 2015). A comparative landscape genetic study of R. kezeri and R. variegatus may help inform management efforts for these species of concern by revealing landscape-scale associations with genetic structure, which can be used to identify forest landscape planning priorities in the area.


Our comparison of two closely related, ecologically similar species suggests that landscape genetic patterns can be consistent across broad spatial scales, yet ecological differences in habitat characteristics and disturbance can differentially affect landscape genetic structure at small scales. Overall, we determined that land cover and roads are the strongest predictors of genetic distance in these two torrent salamanders, but within the genetic clusters in each species there is variation in the relative importance of these and other variables related to minimizing desiccation. Our results also suggest that decreased genetic connectivity and lower genetic diversity in R. kezeri may be associated with disturbances that reduce forest cover, however, similar patterns of forest fragmentation were found in the northern portion of the study areas for both species. Forest cover can be affected by timber harvest, other land uses, landslides, and wildfire, and in the area analyzed, timber harvest is the most pervasive disturbance to landscape-scale forest cover (Nickerson et al. 2011). Given the smaller geographic range of R. kezeri compared to R. variegatus, the effects of fragmentation in a large proportion of this area may have stronger negative effects on observed genetic diversity. Furthermore, the specific landscape context, variation in degree of human development, biotic interactions, or evolutionary history may play a role in shaping genetic diversity.


Genomic laboratory protocols have been set up and optimized through years by introducing modifications on the original RAD-seq methodology to get better results using different laboratory protocols for different scenarios (e.g. samples with low DNA quality, genome size, etc.; see Fig. 5 in [8]). Similarly, the bioinformatic pipelines starting from raw data, a critical issue in RAD-seq methodologies, have undergone an important refinement and diversification. Nevertheless, there is not a consensus about what is the best strategy for each scenario, despite the increasing number of studies addressed to evaluate the impact of technical and/or bioinformatic protocols [9, 10]. In a typical 2b-RAD library, hundreds of millions of reads are generated, and they need to be allocated to each multiplexed individual (dozens to hundreds in the same lane) and to each genomic position or locus in the reference genome (or RAD-tag catalogue). The rationale behind this is stacking raw reads belonging to the same locus, while discerning and separating at the same time the reads belonging to different loci. Results could be improved if a reference genome, belonging to the species itself or to other congeneric species, is available. This would enable to avoid mixing of reads pertaining to paralogous loci. In November 2020, there were reference genomes for 25 bivalve species and subspecies (22 genera) and 583 fish species (338 genera) with different assembly confidence at the NCBI database ( ). Nevertheless, there are about 9200 species within the 1260 bivalve genera [11] and 35,672 recognized species within the 5212 documented fish genera [12]. All in all, less than 0.2% of the genomes of the known eukaryotic species have been sequenced to date [13]. Although full genome sequencing assembly is becoming progressively more robust thanks to the long-read sequencing methods and assembling strategies, most of the species will have to wait for long before their genomes are assembled. Therefore, de novo approaches (i.e. stacking reads without a reference genome) will be the only option for many studies, although some initiatives are trying to change this perspective (e.g. Earth Biogenome Project; ). For this reason, one of the strengths of a RAD-based method is its applicability without a reference genome [14].


Attention should be paid to the order of the different filtering steps because this can alter the final SNP panel. When adjusting the filtering parameters, it would be advisable to consider not exclusively the number of removed SNPs at each step separately, since they could result from the interaction among filtering steps. For instance, the coverage filter determines the increase of missing data which influences the percentage of SNPs eliminated by MAC and population representation filters, according to the stringency of the coverage threshold used. Furthermore, missing data may be due to a lower coverage than the selected threshold or for not being genotyped by the building-loci software with the genotyping options selected (e.g. previously selected nucleotide frequencies range to genotype in ALT pipeline). We found that the last could be the main source of COM SNPs genotyping differences between both building-loci pipelines excluding Manila clam. This means that the ALT pipeline genotyping parameter should be improved by choosing appropriate ranges for each species. The objective of any filtering strategy is removing SNPs that are not reliable without losing informative SNPs. Different factors can influence the filtering criteria, e.g. to achieve the number of SNPs required to meet the research goals. In this sense, a panel made up with markers found by two different pipelines should ensure reliability. It was found that 67% of SNPs from STACKS panel were common with UNEAK panel, using a de novo approach in soybean (Glycine max L.) data [21]. With reference genome the overlap percentages among STACKS and other building-loci pipelines ranged from 76 to 96% [21]. Using a reference genome approach the percentages of shared SNPs between STACKS with SAMtools and GATK ranged from 7.3 to 71.4% [25]. The lowest values could be partially explained because STACKS panel recruited many more SNPs than the other building-loci pipeline. The lowest percentage of COM SNPs taking STA panel as genotyping reference in our study (i.e. 23.9% in small-spotted catshark and 43.0% in Manila clam) were in panels with less than 1000 SNPs. These low values may be explained by a strong filtering effect, on shared SNPs between pipelines. The highest number of COM SNPs were detected when STA panels included the highest number of SNPs, around 74% in brown trout and 81% in silver catfish. Despite including a lower number of SNPs, the COM panels provided roughly similar results to the larger ones. This suggests that most informative markers are retained downstream, with the advantage of working with a reduced panel that can simplify and speed-up analyses. In the study by Díaz-Arce et al. [10] the possible effect of SNP number on FST estimation using reduced SNPs subsets was tested and similar values regarding the full panel were obtained. Moreover, estimated genotyping accuracy may be higher with SNPs shared by more than one building-loci pipeline according to Torkamaneh et al. [21]. The impact of genotypic differences between shared SNP panels was low, such as those obtained by Wright et al. [25].


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