Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), one of the most important maize pests in the Mediterranean countries, using microsatellite markers for the first time in this species. Insects were collected in twenty-five locations in southwest and southeast France from cultivated and wild host plants . Contrary to what has been reported so far in France, we found that MCB populations could be locally abundant on wild poales plants. Analysis was carried out at 11 polymorphic microsatellite markers. Molecular variance was significantly determined by geography, then by host plant, with 17% and 4%, respectively, when considered as a major effect, and with 14% and 1%, respectively, when considered as a marginal effect in permutational analysis. Multidimensional scaling (MDS) and GENELAND Bayesian clustering suggested that populations infecting wild plants  were more structured locally than those affecting cultivated maize. In S. halepense, significant Isolation By Distance (IBD) indicated that this factor could explain genetic differentiation of the moth populations. In T. domingensis, local population differentiation was strong but did not depend on distance. The implication of this absence of population structure in maize and the heterogeneity of population genetics patterns in wild plants are discussed in the context of the population dynamics hypothesis and population management strategies.The population genetic structure of crop pest populations gives information about their spatial ecology, which helps in designing management strategies. In this paper, we investigated the genetic structure of the Mediterranean Corn Borer (MCB),  Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), also known as the Mediterranean Corn Borer (MCB), corn stalk borer or pink stem borer, is one of the most damaging pests of maize (Zea mays L.) in several southern European countries. With Ostrinia nubilalis Huebner (Lepidoptera: Crambidae), which has an overlapping but more northerly distribution, they can damage up to 80% of maize plants [Fusarium fungus, which is toxic for the cattle, often develops in larval tunnels [e plants . In Frane plants , in low e plants . The pes tunnels . The num tunnels . The num tunnels , to five tunnels . Very liet al. (1996), who analyzed European and Moroccan populations using allozyme polymorphism, suggested the existence of two population groups, one ranging from northwest Spain to southwest France and the second from Morocco and northeast Spain to southeast France [et al. (2007), using amplified fragment length polymorphism (AFLP), observed another population group in the south of Greece that was separated from the population grouping in Spain, southwest France, Italy, and Northern Greece [et al. (2008), on the basis of random amplified polymorphic DNA (RAPD), suggested that populations from Spain and the Southwest of France were separated from populations from Italy, Greece and Turkey [Phylogeographic analyses using one nuclear and two mitochondrial genes  suggestet France . In Euroet al. 196, who ad Turkey . It is sPoales [O. nubilalis) and noctuids ([Spodoptera frugiperda). It was also suggested for Sesamia calamistis [et al. (2006) on caterpillars collected from maize (Z. mays), cultivated sorghum (Sorghum sp), sunflower (Helianthus annuus L.), pepper (Capsicum frutescens L.) and cantaloupe (Cucumis melo L.) in southern France regions [Although the MCB is known to feed on a diversity of wild and cultivated Poales , studiesPoales –14 for Ooctuids  and Sorghum halepense, in 14 collection points in the Bouche du Rhône county  and in one collection point in the Rhone Valley area in the Ardèche county (Aubenas) in July 2012  on the same date. Even if we could not find places hosting MCB on both wild and cultivated plants, having samples on the same plant from different localities helped to determine the effect due to distance, and having some samples from the same locality on different wild plants helped to determine the effect from plants. A total of 451 caterpillars were sampled, and each individual was preserved in ethanol (95%) before DNA extraction.Larvae were collected from both wild and cultivated host plants in twenty-five locations in two regions of southern France . In the uly 2012 . In thiset al. in 2012 (unpublished data) using next generation sequencing, confirmed by amplification, which allowed us to identify 17 suitable markers . In order to develop efficient and reliable multiplex PCR amplifications, these 17 microsatellites markers were tested in combinations of three to four loci per amplification reaction. Five multiplexes that showed clear and unambiguous amplification profiles were selected: multiplex 1: SN01; SN16; SN20; SN44; multiplex 2:SN15; SN22; SN37; multiplex 3: SN18; SN25; SN34; SN42; multiplex 4: SN21; SN32; SN45; multiplex 5: SN68; SN59; SN61. The final PCR consisted of 5μl Platinum® Multiplex PCR Master Mix (Applied Biosystems®), 1μl Primer Mix, 3μl H2O and 1μl DNA.The DNA extractions were performed on third-stage larvae (whole body) using the Qiagen ® Dneasy Blood and Tissue Kit. The microsatellite loci used were developed by Capdevielle Genepop v4.0.1 [Amplifications were performed in 96-well thermocycler using the following program: 5 mn at 95° C to activate the taq polymerase, followed by 25 cycles of 30 s denaturation at 95° C, 30 s for primer annealing at 55° C and 30 s extension at 72° C, and final extension of 5 mn at 72° C. The amplified fragments were detected by a capillary sequencer ABI 3130xl (Applied Biosystems ®). Microsatellite profiles and allele scoring were made using GeneMapper Software (Version 4.0). The genotype scoring was manually checked for every individual. Two loci having too much missing data (SN32 and SN61) and two others, which were not polymorphic at all in our samples (SN25 and SN68), were removed. The remaining 13 microsatellite loci were analysed separately for conformity with Hardy–Weinberg (HW) equilibrium expectations using p v4.0.1 .Lepidoptera are known to have a high frequency of null alleles ,22. ThesAr), using Fstat v2.9.3.2 software [Ho) and unbiased expected heterozygosity (He) for each locus in each population, using Genetix v4.05 software [FIS values for each population and pairwise FST values between all pairs of populations, between regions, county, localities or plants, and overall FST values. FIS 95% confidence interval (95% CI) and FST significance were computed on the basis of 1,000 bootstraps over loci.Population genetic diversity was assessed for populations represented by at least 5 individuals, by estimating allelic richness  distances using POPULATIONS v 1.2.32 . The rel4 burn-in iterations followed by 7.106 iterations for STRUCTURE and 7.105 iterations for TESS, for each value of K). For STRUCTURE, we used a model with correlated allele frequencies [Although TESS and GENELAND can account for geographic information in the definition of clusters, this option was not used. It would have favored spatial factors to the detriment of the ecological factors that we intended to compare in our study. For STRUCTURE and TESS, the “Admixture” model was carried out, allowing K to range from 2 to 10  estimated by a_values, by the logarithm of geographical distances, as proposed by Rousset [Genepop v4.0.1 software [Isolation by distance (IBD) was tested using individuals as replication unit. It used a linear regression of F Rousset  using Gesoftware , while spost-hoc multiple comparison , using R [adonis function implemented in the vegan R package [Genetic diversity indices were used to investigate the differences between insects collected from different host plants or different locations. For that, we used Kruskal-Wallis non-parametric analysis of variance using populations as a repetition unit, followed by  using R . To dist using R  that all using R . Signifi package . Plant mAr) and expected heterozygosity (He) ranged from 1.32 to 1.56 (mean of 1.46) and from 0.32 to 0.56 (mean of 0.46), respectively (FIS varying from 0.15 to -0.19 (mean of 0.032). These within-population genetic diversity indices were not significantly different among insects collected from the two regions , from the different populations within regions  and from the three different host plants  or maize vs wild host plants  (Allelic richness (ectively . The numectively . The mea for Ar) .MCB specimens collected from very close localities  gave comparable results and differed from GENELAND . The optcalities . The rescalities . Individ0 and 29  with no 0 and 29 .−5, FST = 0.057 among host plants, −5; locality within county, P < 10−4), plants within localities (P = 0.0003) and reciprocally localities within plants (P < 0.0001). When considering genetic differentiation between localities within sub-samples of each host plant, geographic differentiation was significant for T. domingensis and S. halepense (P < 0.0001), but was not significant for maize, although the mean geographic distance between T. domingensis localities was smaller (16.2 km on average) than between S. halepense or maize localities . This difference in geographic structure according to the plant species can be visualized by MDS representations  have more geographic structure compared to individuals collected on maize, suggesting that host plant species may influence the moth’s propensity to disperse  alone, as marginal effect, or within sub-samples for each host plant  . We obsentations , where cdisperse .S. halepense but not on samples from T. domingensis (P = 0.23). Yet in T. domingensis, genetic differentiation was strong, even at a short distance (FST = 0.181 between Arles 3 and Arles 4 at a distance of 0.137 km) , and increased to 0.147–0.30 between Arles and Aubenas at a distance of 99–113 km. This is illustrated by the smaller intercept and the larger slope in this host species . Results are shown in .137 km) . This is.137 km) . By cont species . In maizOstrinia nubilalis [ST = 0.01, 4% of variance explained). It was still important in sympatry between S. halepense and T. domingensis (FST = 0.02 at Arles 12 and FST = 0.05 at Arles 16). It was observed in the AMOVA as a nested marginal effect within locality. Similar genetic differences between plants were observed by Leniaud (2006) on the basis of only 3 polymorphic allozyme markers between MCB populations collected from maize and Sorghum (FST = 0.01) in the South of France. As a comparison, the level of differentiation between mono- and dicotyledonous host plant variants of the European Corn Borer (ECB), now considered as different species  [ST = 0.016 [ST = 0.026 [ST between 0.032 and 0.053 for AFLP markers [Ostrinia species [S. nonagrioides on maize lays eggs beneath windings of young leaf sheets, and in our rearing units, specifically on paper sheet rolls serving as structural surrogates, that are absent in dicotyledons [S. nonagrioides oviposition and larval feeding preference traits may be transmitted between stages and generations not genetically, but by learning plant chemical cues transferred across stages and generation [al. (2015) reported that the most efficient transmission of preference was obtained in studies where the trait was adult oviposition preference and the exposure to the chemical cue at the larval stage [S. nonagrioides regarding transmission of preferences among development stages.The existence of host races, i.e. genetically differentiated populations specialized on different hosts, has been demonstrated in several Lepidoptera species  and in pubilalis ,14,18,43ctively)  ranges f markers . The low species  but did  species . In Afri species . The gentyledons ,46. The neration . This faneration ,49; it hneration ,51. In tal stage . This tral stage . Such tret al. showed by simulation that GENELAND is better at detecting permeable barriers than TESS and other clustering methods [et al. (2011) and Kalinowski (2011). GENELAND captured a local structure that was not captured by TESS and STRUCTURE. This local structure is confirmed by the FST values observed at short distance between populations. The two approaches provide complementary information on the population structure of the MCB and seem to work at different scales. We next discuss how the other statistics performed in this paper cast light on the reason for these differences and thereafter, the population biology of MCB.Our study showed contrasted results between GENELAND non-admixed and TESS-STRUCTURE admixed models. The GENELAND clusters in South East of France were exactly corresponding to local populations. The TESS and STRUCTURE clusters were more homogenous geographically. Only the Arles 4 population formed a separate cluster in the admixed analyses, whereas most of the locations on wild host plants formed different clusters in the GENELAND non-admixed analysis. Safner  methods . Kalinow methods . Our stuS. nonagrioides as a sedentary species. In our study, significant genetic structure was observed among sampling sites . Mantel testing of genetic versus geographic distance was significant overall with extremely low slope indicating that distance plays a weak role in genetic differentiation , using AFLP markers, did not find genetic differences between populations collected at sites 1,800 km apart, from Axioupoli (Greece) to Toulouse (France), and De La Poza et al. (2008) did not find significant IBD across the MCB collected in Europe, using RAPD markers [et al. (1996) found weak differentiations at 12 allozyme loci among MCB populations (FST = 0.064) collected across Morocco, northern Spain, and southern France [et al. (2016) recorded flight performances of tethered insects in a windmill and compared 24 noctuid species. They found total flown distances overnight to vary from 597 m to 12,352 m with a mean value of 4,566 m [S. halepense and T. domingensis and highly regional on maize (one cluster over a maximum distance of 160 km). MDS representation also shows stronger geographic structure on T. domingensis and S. halepense than on maize. Lack of geographic distance effect on maize MCB differentiation and possible effect on wild plant MCB is congruent with Leniaud (2006), who sampled MCB on different host plants in southern France and found significant IBD within non maize plant groups and non-significant IBD within the maize group [Many authors ,56 have ntiation . Yet a nntiation  and the ntiation  did not  markers , 10. Sim 4,566 m . Even if 4,566 m , so longze group .Our results and other direct experiments therefore strongly support the strong dispersal abilities of maize MCB contrary to what is often alleged so far.Typha species [S. halepense or T. domingensis wild host plants . This suggests that populations may specialize locally on the most abundant resource. Overall, the result suggests relatively varied and separated population dynamics on the three studies host plants. Our hypothesis is that for maize populations, population sizes are large but crop rotation imposes insect dispersion, thus increasing the total effective number of migrants (Ne×m), the critical parameter in reducing population structure. In Camargue, S. halepense and T. domingensis represent more perennial settlements than maize, since they develop from rhizome in humid non-cropping areas and are both resistant to flooding and drying events. This situation may have allowed the establishment of a pattern of IBD as observed for the moth populations on S. halepense. In T. domingensis on the other hand, the moth population genetic structure is strong but does not depend on distance. More precise data on the evolution of both plants and moth populations around the year are needed to understand the factors of the observed geographic structure. The hypothesis that the presence of wild relatives does not increase or could even decrease population dynamics on maize through maladaptation remains to be confirmed. Sampling of the MCB in uncultivated areas must therefore be carried out all across Europe, especially because the biotope favorable to its development, i.e. river banks, ponds, wetlands, estuaries as occurring in the Rhone Valley, in Africa [Understanding the relationships between crop pest populations living in cultivated and uncultivated areas is essential for effective control at a landscape scale. To our knowledge, this is the first MCB population genetic study including non-cultivated plants. This moth is a serious maize pest in southern France, and one question linked to controlling it is its ability to use wild plants as an alternative host. In Africa, the MCB develops populations in many wild plants, including a  species . In soutn Africa  or in Irn Africa ,60 existS1 FigB- Value of the deviation information criterion as a function of K (TESS). K is the number of inferred genetic clusters.(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigEach thin vertical line corresponds to one individual. Coloured segments represent the proportion of each individual’s genome assigned to each genetic cluster.(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S1 TableInd: individual ID, Pop: Putative population of individuals, from Column 4, Microsatellite markers used.(DOCX)Click here for additional data file.S2 TablePop: Putative population, Loc: microsatellite loci.(DOCX)Click here for additional data file.S3 TableLoc.: Locality, Lo: Longages, Pou: Poucharamet, Cam: Cambernard, Lav: Lavergne, Ar: Arles. Dept.: Department, 07: Ardèche, 13: Bouches du Rhône, 46: Lot, 31: Haute Garonne. Reg.: Region. SW: Southwest, RV: Rhone Valley #: Population number.(DOCX)Click here for additional data file.


Nesticus barri and the troglobiotic beetle Ptomaphagus hatchi, each from four closely located caves (ranging from 3 to 13 km apart) in the cave-rich southern Cumberland Plateau of Tennessee, USA. Consistent with the hypothesis of highly restricted dispersal, we find that populations from separate caves are indeed highly genetically isolated. Our results support the idea of caves as natural laboratories for the study of parallel evolutionary processes.Caves offer selective pressures that are distinct from the surface. Organisms that have evolved to exist under these pressures typically exhibit a suite of convergent characteristics, including a loss or reduction of eyes and pigmentation. As a result, cave-obligate taxa, termed troglobionts, are no longer viable on the surface. This circumstance has led to an understanding of highly constrained dispersal capabilities, and the prediction that, in the absence of subterranean connections, extreme genetic divergence between cave populations. An effective test of this model would involve (1) common troglobionts from (2) nearby caves in a cave-dense region, (3) good sample sizes per cave, (4) multiple taxa, and (5) genome-wide characterization. With these criteria in mind, we used RAD-seq to genotype an average of ten individuals of the troglobiotic spider  Moreover, as photosynthesis is not possible, cave communities depend nearly entirely on trophic input from the surface. Caves are also more stable in temperature and humidity than surface habitats2. As a result, adaptation to life underground typically involves extensive evolutionary change3.Caves are unique habitats with environmental conditions fundamentally distinct from the surface. The most conspicuous of these is the complete absence of light, precluding the use of visual cues for hunting, foraging, locating mates, and evading predators2, often bear a suite of distinctive characteristics, including loss or reduction of eyes, pigment loss, elongated appendages, improved non-visual sensory mechanisms, reduced metabolic rates, longer lifespans, and lower rates of reproduction4. These features represent widely repeated cases of convergent evolution, that can be shaped even by fine-scaled niche partitioning within subterranean habitats5. Many of these phenotypes have obvious tradeoffs for fitness on the surface. For instance, pigment loss, selectively neutral or possibly even advantageous in the cave environment6, will decrease crypsis on the surface, a trait known to undergo particularly strong purifying selection7. While most studies the selective pressures on crypsis come from surface-dwelling species8, the importance of camouflage among other arthropods is suggestive that similar pressures would exist for troglobionts traversing surface terrain. Intolerance to variation in temperature and humidity may also preclude surface viability2. Hence, the surface is a hostile environment for troglobionts. With this idea in mind, Culver and Pipan2 pointed out that caves are like islands in a sea of surface habitat.Some organisms have evolved under these conditions to the extent that they are never found outside of caves. These organisms, termed troglobionts10. This is especially true in the eastern United States, where up to 45% of troglobionts are single-cave endemics11. These exceptional rates of endemism are consistent with restricted gene flow and frequent speciation. Population genetic studies lend further support. Examining COI in the troglobiotic spider Nesticus barri, Snowman et al.12 found extensive haplotype divergence and limited sharing of haplotypes between caves, indicating that migration was minimal to nonexistent over distances greater than 15 km. Another study, examining COI in several troglobionts, including N. barri and the beetle Ptomaphagus hatchi, provided similar findings13, indicating that restricted migration is likely general to terrestrial troglobionts.Previous studies have shown that troglobiont migration is indeed highly limited. For instance, at the species level, endemism is less the exception than the rule14  or troglobionts17. N. barri demonstrates typical troglomorphic features, lacking eyes and with reduced pigment, although it still possesses reproductive seasonality18. The genus Ptomaphagus includes about 60 species in North America, again with roughly one third either troglophiles or troglobionts4. Diversification of the genus throughout the southern Cumberland Plateau is thought to have occurred through progressive vicariance, as the Cumberland Plateau eroded over the last 6 million years19. Like other troglobiotic Ptomaphagus, P. hatchi has greatly reduced eyes and is wingless. On the southern Cumberland Plateau, N. barri and P. hatchi have largely overlapping ranges, with each species known from dozens of caves19, and both species are common in the caves they inhabit. Finally, where previous studies made use of one, or at most a handful loci, we take a genome-wide approach using 2bRAD20. This method has the advantage of interrogating thousands of loci across the genome, allowing for more confident estimates of population divergence and neutral diversity22. This is the first study to investigate these species at the genomic scale.Here, we evaluate the hypothesis that dispersal of terrestrial troglobiont invertebrates is extremely limited between caves. To emphasize the severity of isolation, we sampled individuals from caves located close together on the southern Cumberland Plateau, one of the most biodiverse karst areas in the United StatesST and dXY. An important corollary of our hypothesis is that intermediate subterranean connectivity, via habitats such as horizontal fissures5 or Mesovoid Shallow Substratum (MSS)24 is also highly limited or nonexistent.With this approach, we test several predictions of the hypothesis of extremely limited dispersal between caves. First, each cave should harbor a genetically distinct population. Individuals from any cave should be most similar to others from the same cave. Hence, unsupervised methods such as hierarchical clustering, and principal component analysis should sort samples based on their cave of origin. Second, when modeled as mixtures of ancestral populations, individuals should demonstrate limited admixture between caves. Third, we should observe extensive genetic differentiation between caves. We test this third prediction using pairwise estimates of FN. barri and P. hatchi25. Distances between the caves ranged from 3 to 13 km  were placed in the cave for 24 h and live specimens were subsequently collected at the baits. Sample size per cave ranged from 6 to 16 individuals per species (Table Sampling was conducted between 21 September and 25 October 2018. We collected Nesticus barri) the legs were saved while the cephalothorax and abdomen were used. QIAGEN’s DNeasy Blood & Tissue Kit (Cat. No. 69504 or 69506) was used following the kit’s protocol with the exception of using 50 µl Buffer AE for elution. Concentrations of each DNA isolation were initially checked by nanodrop and confirmed with the Quant-IT Picogreen DS DNA assay (Life Technologies cat. no. P7589). The 2b-RAD library preparation was carried out as described previously28. Briefly, DNA isolations were normalized to ~ 12.5 ng/µl. Samples with concentrations lower than 12.5 ng/µl DNA (~ 30 samples), were fully dehydrated in a vacuum centrifuge and resuspended to a target concentration of ~ 12.5 ng/µl. Digestion reactions had concentrations of 1 × NEB buffer #3 and 10 µM SAM mixed with 1 total U of BcgI restriction enzyme and 50 ng genomic DNA in a total volume of 6 µl. Digests were incubated at 37 °C for 1 h followed by 20 min at 65 °C for heat inactivation. Ligation reactions had concentrations of 1 × T4 ligase buffer, and 0.25 µM each adapter with 400 total U of T4 DNA ligase and 6 µl of digested DNA in a total volume of 20 µl. Ligation reactions were incubated at 4 °C overnight, followed by 20 min at 65 °C for heat inactivation. Inclusion of internal barcodes in the i7 adapter allowed for pooling sets of samples at this point. Amplification and additional barcoding reactions were performed on these pools. These reactions had concentrations of 312 µM each dNTP, 0.2 µM each of p5 and p7, 0.15 µM appropriate TruSeq-Un primer, 0.15 µM appropriate primer, 1 × Titanium Taq buffer, and 1 × Taq polymerase mixed with 4 µl of pooled ligation in a total volume of 20 µl. Thermocycler conditions were 70 °C for 30 s, followed by 14 cycles of 95 °C for 20 s, 65 °C for 3 min, and 72 °C for 30 s. The final library was pooled into a single tube and sequenced at the University of Texas at Austin’s Genome Sequencing and Analysis Facility on a single lane on the Illumina Hiseq 2500 platform.Most of the DNA extractions were performed using the entire body of the individual. If a particular sample seemed large enough . Generation of de novo loci was performed using cd-hit29 and custom Perl scripts as described previously28. These tags were assembled into 30 equally sized pseudo-chromosomes for mapping. Re-mapping of reads to these de novo loci was done with bowtie230. Sorting and indexing of bam files in preparation for genotyping were done with samtools31.Raw reads were trimmed and demultiplexed based on internal ligation barcodes using custom Perl scripts32. A second, alternative approach is to estimate sample allele frequency spectra directly from base calls and quality metrics in the alignment data, allowing for population genetic inferences without making individual genotype calls32. We processed hard genotype calls primarily using VCFtools33. Estimation of genotype likelihoods, allele frequency spectra, and additional population genetic inferences were implemented using Angsd36. Throughout the manuscript we emphasize the results produced using Angsd, using analyses from hard genotype calls primarily for corroboration. All steps used for both sets of analyses, along with scripts for statistical analysis and figure generation are included in the git repository37.We analyzed the genotype data in two ways. The first method depended on hard genotype calls, in which the genotype of each individual at each site is either called exactly or filtered to missing data based on arbitrary cutoffs. While simpler to implement, these hard genotype calls can introduce biases, because they can fail to capture statistical uncertainty inherent to individual genotypes from Next-Generation Sequencing data38 using mpileup and bcftools39. Genotype calls with depth lower than 2, as well as indels, singletons, sites with more than two alleles, or less than 75% of samples genotyped were removed using VCFtools33. Sites with excess heterozygosity   were removed based on the—hardy output from VCFtools.Hard genotype calls were made as described previouslyp values, including the strand bias p value 40, the hetbias p value 41, and SNP p value , were set to 0.05.For the analyses performed with Angsd, quality filtering included a minimum mapping score of 20 (− minMapQ 20), a minimum quality score of 32 (− minQ 32), and minimum representation among samples > 80% (− minInd). Filters based on i and j (dij) is calculated as:M is the total number of sites with at least 1 read from each individual, and 1 − Ibj(bi) is the indicator function which is equal to one when the two individuals have the same base and zero otherwise34. This distance matrix was used for hierarchical clustering and multidimensional scaling. We determined the optimal number of mixture model components for this distance matrix based on BIC using the Mclust package42. Admixture analysis was performed on genotype likelihoods output by Angsd using NGSadmix43.To summarize genetic variation, we used Angsd to calculate pairwise differences between samples using the − IBS 1 option and a minimum minor allele frequency of 1% (− minMaf). Here pairwise distance between samples ST for each pair of populations using Angsd44. This method computes the posterior expectation of genetic variance between populations (designated A), and total expected variance (designated B). These values (A and B) are closely related to the alpha and beta estimates described in Reynolds et al.45. The unweighted FST is computed as the mean of the per-site ratios of A and B and the weighted FST is computed as the ratio of the sum of As to the sum of Bs34. The unweighted and weighted FST values reported in Table ST values tend to be too noisy36 we focus on the weighted FST values.We estimated FST and dXY were also calculated for each pair of caves based on hard genotypes. We used VCFtools to calculate pairwise Weir and Cockerham’s FST46, and a custom R script to calculate dXY for unphased data as:xi and yj are the frequencies of the ith allele from population X and the jth allele from population Y respectively, and kij is 1 when i and j differ, and 0 if they are the same47. The Weir and Cockerham’s FST and the dXY values reported in Table ST values to estimate pairwise migration rates according to the following formula47:Pairwise Fentclass1pt{minima28 to remove sites where the heterozygosity rate appeared higher than 50%, as these were likely paralogs spuriously lumped as single loci. We then estimated nucleotide diversity from the folded site frequency spectra using Angsd48. The value was then averaged across the 30 pseudochromsomes from the reference created during de novo locus generation described above. These averages are the values reported in Fig. To calculate nucleotide diversity using genotype likelihoods, we first generated genotype likelihoods as described above. We then used the custom Python script HetMajorityProb.pyh) for each site as:n is the number of sequences, and pi is the frequency of the ith allele at the site47. We then calculated π as the sum of expected heterozygosities across sites:S is the number of segregating sites and hj is the expected heterozygosity of the site47. We report this value per site by dividing by the total number of interrogated positions. Effective population size was estimated by dividing this per site value by four times the number of mutations per site per generation (4µ). The mutation rate estimate was based on Drosophila49.We also calculated nucleotide diversity (π) from hard genotype calls. Here we first determined the allele frequencies for each species in each cave using VCFtools. We then calculated the expected heterozygosity (P. hatchi (1.2 ± se 0.08 million reads) than N. barri (0.462 ± se 0.04 million reads). De novo locus generation produced 130,061 loci for N. barri and 264,486 loci for P. hatchi. Filtering of hard genotype calls from mpileup produced 7620 biallelic SNPs for N. barri and 12,879 for P. hatchi. Filtering of genotype likelihoods produced with Angsd identified 13,629 SNPs for N. barri and 31,000 for P. hatchi. Final sample sizes for each species are given in Table Sequencing produced 236 million raw reads. After demultiplexing and PCR duplicate removal, a total of 65.8 million reads remained. Mean fold coverage per sample tended to be higher for N. barri  ranged from 0.33 to 0.52 for N. barri, and 0.3 to 0.36 for P. hatchi. Unweighted FST (Angsd) and Weir and Cockerham’s FST estimated from hard genotype calls were lower, but still considerable, with a minimum value of 0.12 , the beetle P. hatchi had higher nucleotide diversity than the spider N. barri (Table 2 = 0.92), only cave length was significant when controlling for the other independent variables (p < 0.05). Assuming a mutation rate of 2.8 × 10–9 estimated for Drosophila49, the effective population sizes based on the π estimates for P. hatchi ranged from 1.4 × 105 in Solomon’s Temple to 2.2 × 105 in Buggytop, with a range of 1.0 × 105 to 2.3 × 105 for N. barri  and from 2.20 to 5.48 (mean = 3.71) for Ptomaphagus. Hence, the maximum migration rate m for any pair of caves was estimated to be less than 1.5 × 10–5. While our estimates of Ne are highly uncertain, this low value demonstrates that migration between caves is indeed highly restricted.In both species, estimates of nucleotide diversity based on genotype likelihoods indicated surprising levels of diversity that varied with cave length. For individual caves, per site nucleotide diversity (π) ranged from 1.17 × 10ri Table . In a muri Table . Based oST of 0.33 for N. barri and 0.30 for P. hatchi  from two Brazilian caves less than 1 km apart. While the number of individuals was small  they reported significant genetic differentiation, with FST = 0.043. They contrasted this with a similar FST value of 0.042, for populations of Drosophila americana located more than 200 km apart52. While it is difficult to directly compare FST between different types of markers, such as SNPs and microsatellites, these comparisons highlight the relatively strong genetic isolation between populations of terrestrial cave-dwelling species. The tendency of cave populations toward greater genetic differentiation was also shown in the Mexican blind cavefish complex (Astyanax mexicanus). Here, using 26 microsatellite loci, applied to 11 cave and 10 surface populations, Bradic et al.53 detected generally greater differentiation between cave populations than surface populations, with FST ranging from 0.2 to 0.5 between cave populations and a maximum of 0.09 for surface populations. These findings extend the idea, even among aquatic vertebrates, that cave populations exchange migrants much less readily than surface populations. These diverse cases reiterate the historical understanding that highly localized distributions of subterranean organisms are shaped by limited dispersal capabilities54.We used genome-wide genotyping to examine population structure of two troglobionts from the southern Cumberland Plateau in Tennessee. Despite relatively small distances between caves (no two caves were more than 15 km apart), we detected strong population structure for both species. Hierarchical clustering, PCA, and Admixture clearly identified each cave as a genetically distinct population Figs. , 3. Pairhi Table , indicatN. barri, but not for P. hatchi  was consistently lower within watersheds for P. hatchi.Based on hierarchical clustering and admixture analyses Figs. , 3, we echi Fig. . Althougchi Fig. , only ab2. Because Next-Generation sequencing reads originate from single molecules, they are subject to several sources of error such as DNA damage, PCR errors, and sequencing errors, which can inflate estimates of nucleotide diversity55. De novo locus generation likely further contributes to this bias. Fortunately, these error sources can be reasonably expected to influence all samples similarly22. Hence, while we believe absolute estimates of Ne reported here are likely inflated, and should be considered cautiously, relative comparisons of diversity are still reliable. This idea is illustrated by the concurrent associations between cave length and π estimated using Angsd and from hard genotypes, despite the roughly eightfold difference between them in absolute terms . These intermediate habitats can form as crevices beneath streambeds of temporary watercourses  and can harbor both epigean and subterranean fauna23. Consistent with this concept, hierarchical clustering of populations for both species paired caves by watershed, rather than physical distance (Figs. 19. These numerous populations, coupled with the extreme genetic differentiation observed here, suggests that extremely rare migration into an unoccupied cave can establish a population that subsequently becomes genetically distinct. Rare migrants may still occasionally reach the cave, but at such a low frequency that the population remains genetically distinct from other caves just one or a few km distant.Based on our results, we conclude that gene flow between caves is rare. This is consistent with the inability of terrestrial troglobionts to traverse even small distances between caves Fig. . Hence t56 reported a similar pattern, linking caves’ invertebrate species richness with their linear development. One explanation for this pattern is greater availability of food, and/or microhabitats in longer caves56. For instance, larger caves are known to harbor richer and more abundant bat populations57, that would provide larger guano deposits56. Another interesting possibility is that linear development increases caves’ exposure to small-scale interconnections, such as canaliculi56, or other intermediate subterranean environments24. This scenario would be particularly powerful in shaping richness if individuals in these intermediate connections were attracted into the larger caves by food sources such as guano56. At the regional scale, the number of caves58, rather than total karst area, is a significant predictor of troglobiont richness, indicating that while intermediate subterranean environments may be important for connectivity, caves are still a dominant factor shaping community diversity. The positive trend identified here (Fig. For both species, we detected a positive association between nucleotide diversity and cave length Fig. ; Fig. S2Supplementary Figures.


Actinidia chinensis Planch. at broad and fine scales, China. The broad-scale sampling included five mountain populations and one oceanic island population (Zhoushan Archipelago), and the fine-scale sampling covered 11 lake islands and three neighboring land populations in Thousand-Island Lake (TIL). These populations were genotyped at 30 microsatellite loci, and genetic diversity, gene flow, and genetic differentiation were evaluated. Genetic differentiation was positively related to geographical distance at the broad scale, indicating an isolation-by-distance effect of habitat fragmentation on genetic diversity. The oceanic population differed from the mainland populations and experienced recent bottleneck events, but it showed high gene flow with low genetic differentiation from a mountain population connected by the Yangtze River. At the fine scale, no negative genetic effects of habitat fragmentation were found because seed dispersal with water facilitates gene flow between islands. The population size of A. chinensis was positively correlated with the area of TIL islands, supporting island biogeography theory, but no correlation was found between genetic diversity and island area. Our results highlight the scale-dependent effects of habitat fragmentation on genetic diversity and the importance of connectivity between island-like isolated habitats at both the broad and fine scales.Spatial scale partly explains the differentiated effects of habitat fragmentation on plant biodiversity, but the mechanisms remain unclear. To investigate the effects of habitat fragmentation on genetic diversity at different scales, we sampled  Habitat fragmentation affects biodiversity by increasing isolation between populations and decreasing effective population size, which alters inbreeding and genetic drift within populations, as well as gene-flow frequency between populations5. This mechanism can be explained by island biogeography theory7.Habitat fragmentation caused by anthropogenic activities is considered a key factor threatening biodiversity8. Populations in island-like habitats are usually thought to have low genetic diversity10, which is explained by the species–area relationship from island biogeography theory11. Population size is positively related to island area, so small islands are likely influenced by genetic drift, which usually leads to low genetic diversity13. Habitat fragmentation can decrease gene flow among populations because low connectivity between habitats limits seed dispersal and insect pollination16. If gene flow is too low to buffer the negative effects of inbreeding and genetic drift in fragmented populations17, then habitat fragmentation may decrease population fitness and increase the risk of extinction19. Toczydlowski and Waller found high levels of inbreeding and genetic differentiation between isolated populations of Impatiens capensis20.Habitat fragmentation changes continuous habitats to isolated patches and results in the formation of island-like habitats. Guo defined island-like habitats as island entities or areas, such as mountains, oases, or discontinuous forests, isolated by farmlands22. The differentiated effects of habitat fragmentation on genetic diversity may be associated with species characteristics , environmental features 25, or the scale of habitat fragmentation27. Compared to monoecious plants, dioecious plants have higher potential for adaptation and higher genetic diversity because they have higher selection efficacy both against deleterious mutations and for beneficial mutations28. Long-lived species have higher potential against genetic erosion from habitat fragmentation than short-lived species because of the occurrence of gene flow among populations29. In a broad-scale study across Tibet, Sichuan, and Yunnan provinces in China (ca. 1 × 106 km2), habitat fragmentation was found to weaken gene flow between populations and cause isolation-by-distance effects30, while there was a lack of evidence of isolation-by-distance effects in a fine-scale-fragmented habitat (ca. 1.5 km2) on Stephens Island, New Zealand31. Long-distance dispersal of seeds can promote gene flow and reduce the genetic distance between populations on a broad scale32. Thus, spatial scale and species characteristics, such as dioecy, a long lifespan, and long-distance dispersal ability, should be considered when studying the effects of habitat fragmentation on genetic diversity.However, some studies did not find negative effects of habitat fragmentation on genetic diversityActinidia chinensis in China across its national distribution range, considering geographic distance at a broad scale (ca. 1 × 106 km2) and a fine scale (ca. 30 km2). This study aimed to investigate (1) whether island-like isolated habitats limit gene flow and cause differentiation between populations, (2) whether the correlation between genetic diversity and island area in fragmented habitats supports island biogeography theory, and (3) whether the genetic effect of habitat fragmentation is scale-dependent. We employed microsatellites known as simple sequence repeats (SSRs) for genetic diversity analysis because they are codominant loci and have the advantages of a low cost, high polymorphism, high reliability, and a low requirement for DNA quality34, which facilitates the analysis of population demography in fine-scale regions35. These findings are expected to provide useful information for the conservation of genetic diversity.Here, we investigated the genetic diversity of A. chinensis (2n = 2X = 58) is a perennial, dioecious, and deciduous liana36 with a wide distribution in China , located in the East China Sea, was originally a part of mainland China and formed by rising sea levels ca. 7000–9000 years ago27  islands, where population size was estimated by multiplying the average number of plants in three randomly sampled bays by the number of bays  were sampled around the TIL islands  and one oceanic island (ZSA) in China  solution for 10–15 min. Ploidy analysis was conducted by flow cytometry (BD LSRFortessa) with FSC 488, SSC 290, and PI 411–432 41.To determine the genetic background of A. chinensis was extracted from 25 mg of dry leaves using a Hi-DNA Secure Plant Kit , using a modified version of the SDS method5. The genetic diversity of A. chinensis populations was detected using 30 microsatellite markers described by Huang et al.42 .Whole-genomic DNA of 43. Linkage disequilibrium and deviation from Hardy–Weinberg equilibrium were tested in Arlequin v. 3.544. No scoring errors or allele dropout was detected by MICRO-CHECKER. Five of 30 loci were null alleles, and no pattern of linkage disequilibrium was found among any loci (P > 0.05) , effective number of alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (FIS), were calculated for the 278 plants sampled from 19 populations by GenAlEx version 6.50345. Analysis of variance (ANOVA) was employed to investigate the influence of isolation on genetic diversity, with Tukey’s HSD test and 95% confidence intervals, by using SPSS46.Genetic diversity indices, including the number of alleles (45. The genetic differentiation coefficient (FST) was calculated by analysis of molecular variance (AMOVA)47. The significance of FST values was tested with 999 permutations, followed by Bonferroni correction. Gene flow (Nm) was estimated based on the formula 47.We separately calculated genetic differentiation and gene flow at the broad and fine scales using GenAlExFST/(1 − FST) and logarithmic geographic distance48. Since oceanic isolation is the main reason for the genetic divergence of the ZSA population from the mountain populations, we excluded the ZSA population from the Mantel test to remove the interference of oceanic isolation on IBD analysis. The pairwise matrix of geographic distances was calculated based on geographic coordinates, which were identified by a global positioning system (GPS) device (Garmin Oregon 450). All calculations above were performed by GenAlEx45.We performed a Mantel test to detect the correlation between the genetic differentiation matrix and geographic distance matrix, using 999 permutations, 49. We set the number of groups (K) from 1 to 19 with ten independent runs using an admixture ancestry model and 100,000-step Markov chain Monte Carlo (MCMC) replicates after a 10,000-step burn-in for each run. The best K value was inferred by delta K in STRUCTURE HARVESTER51. A cluster analysis based on genetic distance was performed by the unweighted pair-group method with arithmetic mean (UPGMA) approach in MEGA 6.052. We used the TREEMIX program to detect historical migration among populations53.To infer population structure, Bayesian clustering analysis was conducted in STRUCTURE version 2.3.454 under the TPM (two-phase model), which allows multiple-step mutations55, with a 10% infinite allele mutation and 90% stepwise mutation model and 1000 replicates56. The significance of heterozygote excess was determined by Wilcoxon’s signed-rank test (P < 0.05). Departures from mutation-drift equilibrium were detected by a mode-shift test57.BOTTLENECK was used to examine recent bottleneck eventsA. chinensis populations58. According to the phylogenetic relationships reconstructed by STRUCTURE and TREEMIX, we set a demographic scenario to infer the effective population size and divergence time of four clusters, i.e., TIL islands, neighboring land populations, mountain populations, and the ZSA population. We set the number of simulated data sets to 100,000, and the number of selected data sets to 1000. The historical model, genetic data, and summary statistics were set to default values.We applied the program DIYABC to infer the divergence history of A. chinensis and features of TIL islands , distance to the nearest island (DTI), and distance to the nearest mainland (DTL), were measured with a geographic information system (GIS). The island-shape index (SI) was calculated as follows: 59.To identify the relationships between genetic diversity and population size in Actinidia chinensis plants in the TPZ, XHZ, SYH, YAC, and TIL-M populations were diploid, while 18 plants in the ZSA population were tetraploid , with the lowest FST index in the comparison of ZSA and SYH populations (Table Nm) between populations ranged from 1.786 to 20.833, with the highest Nm in the comparison of the ZSA and SYH populations    of the total genetic variance was attributed to the differences between the two groups. The vast majority  of the variance occurred among individuals within populations, whereas only 7% of variance occurred among populations  Fig. , and UPG 4) Fig. . AnalysiNe) in ZSA than in the mountain populations  coincided with the formation of ZSA islands (ca. 7000–9000 years ago). The ZSA population differentiated a mean of 2450 generations ago, later than the formation time of the mountain populations  revealed a smaller effective population size  and perimeter  . The maximum DBH of sampled A. chinensis plants on the TIL islands was 30.8 cm in the MGI population .With a hypothesis of plants migrating from old to young populations, we can detect possible dispersal pathways of nds Fig. . The difnds Fig. , indicatA. chinensis was not related to TIL island features or geographic distance between islands (Supplementary Table A. chinensis populations in the neighboring mainland populations and on large islands (JSI and MGI) was higher than that on other small islands  (Supplementary Table K = 2), and the nearby B2I, B3I, B4I, and B5I islands exhibited frequent gene flow   to differences between TIL island and neighboring land populations, with 5% among populations and 94% within populations  scale.Habitat fragmentation has been found to reduce genetic diversityA. chinensis population size and island area in TIL supported island biogeography theory64, while the relationship between the genetic diversity of A. chinensis and island area did not. Landscape attributes are key factors determining the genetic diversity effects of habitat fragmentation67. Small habitat area and island isolation have been found to decrease the genetic diversity of plant species68. Landscape connectivity is beneficial to the conservation of genetic diversity and helps reduce demographic bottlenecks in natural populations66. We found most seedlings on the edges of TIL islands, and the ripe fruits of A. chinensis are able to float in freshwater. The upstream Xin’an River is located west of the TIL and leads to northern and southern flow from west to east, which likely explains the northern and southern clusters . These results indicate that seed dispersal with lake water flow facilitates gene flow between islands.The positive correlation between ers Fig. . The resA. chinensis may buffer the area, isolation, and edge effects resulting from the fine scale of habitat fragmentation in TIL70. Dioecy can decrease the inbreeding rate and counteract the negative effects of isolation due to forced outcrossing71. Dioecious species experience lower frequencies of genetic drift than monoecious species28. Species with short lifespans in highly fragmented landscapes may experience genetic degradation due to the loss and fixation of alleles29, while long-lived species could resist the genetic erosion caused by habitat fragmentation60. The age of A. chinensis plants was greater than 20 years  in ZSA island and mountain populations.However, the buffering mechanisms discussed above were limited at the broad scale because geographic isolation resulted in low gene flow and high differentiation between mountain populations, and led to an isolation-by-distance effect. This was consistent with the findings of other studies on a broad scale of habitat fragmentationFIS values and bottleneck events (Table A. chinensis. As discussed above, in TIL, seed dispersal via floating on water is the main dispersal method of A. chinensis. Gene flow through long-distance pollen or seed dispersal could disrupt isolation and weaken the negative effects of habitat fragmentation69. In fact, some studies revealed gene flow and introgression between diploid and tetraploid populations of A. chinensis72. Breeding experiments showed that diploid A. chinensis could crossbreed with tetraploid A. chinensis and produce fertile offspring73. The existence of transitional phenotypes of A. chinensis in the wild proves the occurrence of gene flow and introgression between the two cytotypes in nature74. This phenomenon suggests that the design of corridors is very important for the conservation of genetic diversity.The ZSA population, on an oceanic island, significantly differed from the mainland populations Fig. , with hits Table . FounderA. chinensis plants in ZSA were tetraploid, and their origin was unclear. Diploid A. chinensis is thought to be the progenitor of tetraploid A. chinensis76. We detected historical migration events from TPZ to ZSA, indicating that the ZSA population may have come from northern populations, but the mechanism of polyploidization is still unknown. Generally, polyploidization can increase biological diversity, but the genetic diversity of the tetraploid ZSA population was lower than that of the diploid populations. This could be explained by treating tetraploid plants as diploid plants to score bands, the high inbreeding rate of the ZSA population, and bottleneck events resulting from the small population size. Llorens et al. reported bottleneck events and decreased genetic diversity in the shrubs Grevillea caleyi and G. longifolia, indicating that they had experienced long-term habitat isolation77. Moreover, the detrimental effects of long-term local adaptation likely decrease genetic diversity78. ZSA is an oceanic island that has undergone long-term isolation of ca. 7000–9000 years27. Long-term oceanic isolation decreased the genetic diversity and increased the genetic differentiation of insular populations, while there were small genetic effects on the TIL islands that experienced short-term isolation by water. This is likely to support a time-delayed effect of habitat fragmentation. Such time delays tend to mask the genetic effects of habitat fragmentation79.A. chinensis populations, with an isolation-by-distance effect at the broad scale. The connectivity between populations explains the scale-dependent effect because it facilitates the seed dispersal of A. chinensis and buffers against the negative effects of habitat fragmentation, not only at the fine scale but also at the broad scale. Therefore, our results highlight the importance of constructing corridors for biodiversity conservation, particularly in broad-scale island-like fragmented habitats and on long-term isolated islands.In conclusion, we found a scale-dependent effect of habitat fragmentation on the genetic diversity of Supplementary materials


FUT3 (Lewis) gene is responsible for the expression of Lewis fucosyltransferase, which is required for the synthesis of the structural determinants of both Lewisa and Lewisb specificity. These factors play an important role not only in clinical but also in medico-legal investigations. The gene sequence is highly polymorphic and ethnically specific. In the current study, we performed systematic sequence analysis of the coding region of FUT3 by DNA sequencing to investigate the genetic variations of FUT3 and the molecular basis of the Lewis phenotype in the Sindhi and Punjabi populations of Pakistan. Twenty-three point mutations were observed, including 7 unreported mutations, among which two missense mutations (490 G > A and 959 T > C) were predicted to be deleterious to enzyme activity by software assessment. In total, we observed 24 Lewis alleles, including 11 novel ones. However, all unreported missense mutations were present in Lewis-negative alleles confirmed previously. According to genotypic data, the Lewis-negative phenotypic frequencies were 11.5% and 22.93% in the Sindhi and Punjabi ethnic groups, respectively. Moreover, we found that le202,314 and le,106759 were predominant among Lewis-negative alleles, while the frequency of le59,1067 in the Punjabi population was significantly higher than that in the Sindhi population. In summary, our study revealed that there is a relatively high degree of sequence variation of the Lewis gene in Pakistani populations and provided the first genetic data on FUT3 in these two ethnic groups from Pakistan. The allele types and their frequencies showed that these ethnic groups exhibit more Caucasian components.The  FUT3 (Lewis) gene, regulates the expression of Lewis antigens in the human Lewis blood group system. The human Lewis blood group system is mainly composed of two Lewis antigens, Lea and Leb. These were initially identified as red blood cell (RBC) antigens, but were later discovered in exocrine secretions that were not inherent to RBCs but were absorbed against erythrocyte membranes from plasma1. In this context, there are some complications in regard to the phenotyping of the Lewis system using the haemagglutination test due to poor antibody specificity2. Other phenotyping errors with physiological and pathological causes include the following: (1) some individuals whose erythrocytes are typed as Lewis positive on can subsequently show a Lewis-negative erythrocyte phenotype during disease or pregnancy3; (2) because of the gene dosage effect causing FUT3 heterozygous individuals to exhibit lower α1-4 FucT activity in secretions than homozygous wild-type individuals, genuine Lewis-positive individuals /le  may erroneously be typed as showing a Lewis-negative phenotype on erythrocytes4; (3) the histo-blood group A-glycosyltransferase, B-glycosyltransferase and Lewis fucosyltransferase act on a common precursor substance, H type-1, and this competition will result in a reduction of the Leb antigen in A and B individuals compared to individuals with blood group O. Thus, some Leb antibodies may falsely type Le(a-b+) as Le(a-b−)5. In addition, if we want to acquire phenotype information on the Lewis blood system from biological materials other than whole blood, such as bloodstains (which we used in this study), body fluid, or hair, we may need to determine the genotype of the Lewis blood group at the DNA level to infer the corresponding phenotype. Therefore, it is important to determine the genotypes of the Lewis system. α  FucT (fucosyltransferase), encoded by the 7. Lewis antibodies have also been implicated in mild symptoms of HDN because Lewis phenotypes might be falsely typed in red cells from women and infants8. In addition, the identical donor–recipient pairs based on Lewis phenotypes were shown to have better graft survival than Lewis-incompatible pairs9. Therefore, it is necessary for the Lewis blood group system to be appropriately considered in transfusion and transplantation.Despite being relatively uncommon, in contrast to the ABO and RH systems, the Lewis blood type displays clinical significance. A few examples of haemolytic transfusion reactions have been attributed to improper phenotyping by using Lewis antibodies2-terminal cytoplasmic tail, a transmembrane region, a stem region and a COOH-terminal catalytic domain10. The point mutations in the coding sequence of FUT3 affecting Lewis enzyme activity depend on the corresponding amino acid characteristics and protein structure. The mutations like T202C, G508A, G667A, G808A, A1007C, T1067A will completely lose enzyme activity, while also some mutations like T59G, G484, C478T, G968C will lead to portion loss of it13. The G47C and G1022T mutations are also predicted to inactivate the enzyme15. The alleles composed of inactivating mutations are referred to as Lewis-negative alleles (le). An individual homozygous for le loses the ability to express the Lea and Leb antigens and presents the Lewis-negative phenotype Le (a-b-) on the red blood cell membrane. The alleles with mutations that do not influence enzyme activity are referred to as Lewis-positive alleles (Le).The protein structure of the Lewis enzyme is composed of an NHle202,314, le59,1067, le59,508, le484,667, and other rare Lewis-negative alleles originating from them13. Moreover, these alleles show racial differences and specificity. For instance, le202,314 and le59,1067 are found mainly in European populations, while le59,508 is common in east Asian and African populations16. To date, le484,667 has been detected only in African populations13. Previous studies have focused on Lewis-negative genes in many countries in Asia, including East Asian countries such as China2, Japan17, Korea18, and Mongolia13; Southeast Asian countries such as the Philippines11, Thailand11, and Indonesia19; and South Asian countries such as Sri Lanka14. However, there are no reported genetic data on FUT3 from Pakistan, which is a multi-ethnic country located in South Asia. Thus, in this study, we performed a systematic sequence analysis of the Lewis gene coding region by sequencing to investigate the genetic variations of FUT3 and the molecular basis of the Lewis phenotype in Sindhi and Punjabi populations from Pakistan and to better understand the genetic origin of the Pakistani population, in combination with other reports about ancestry-informative markers.The most common and important Lewis-negative alleles are FUT3 alleles was in Hardy-Weinberg equilibrium. Here, we defined mutations with an rs number in the dbSNP database (https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?geneId=2525) and defined mutations without population data from published reports and in the 1000 Genomes browser (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/?assm=GCF_000001405.25) as unreported mutations.In the current study, the distribution of FUT3 locus. Among the 18 SNPs found in the Sindhi population, 14 were reported previously, while 4 were unreported, among which G146A (rs1263565737) and G490A (rs767305253) were missense mutations, and G381A (rs144354196) and G561A (rs747036561) were synonymous mutations. Moreover, in the Punjabi population, in addition to 9 previously reported mutations, we observed 3 unreported mutations, among which 2 were synonymous mutations (G24C (new mutation submitted to dbSNP database) and C876T (rs3934140)), and 1 was a missense mutation (T959C (rs762649552)). All SNPs identified in this study and their corresponding frequencies are summarized in Table We found 18 single nucleotide polymorphisms (SNPs) in the Sindhi population and 12 in the Punjabi population of Pakistan as a result of DNA sequencing from the whole coding region of the Lewis haplotypes of 11 individuals who carried unreported mutations and undefined haplotypes via PCR cloning and allele-specific PCR. Finally, we identified eleven novel alleles, among which seven were defined by seven unreported mutations, one resulted in a new combination involving the previously reported mutation C882T, and the remaining three were characterized by the presence of G113A, G1061A, and T645C, which have been reported in the 1000 Genomes database, but haplotypes consisting of these three mutations are still unknown. Among these eleven alleles, seven were non-functional, and four were functional, which are summarized in Table le59,508; G490A and T959C located on the chromosome of le59,1067; and G113A located on le202,314. Under these conditions, the Lewis enzyme activities generated from the non-functional alleles with these mutations were not examined further. However, we can speculate about the impact of these nonsynonymous substitutions on enzyme activity on the basis of the positions of mutations and the associated changes in amino acids using the PolyPhen and PROVEAN programs. The G113A (R38Q) and G146A (S49N) mutations lie in the stem region of the encoded protein structure, showing a relatively minor effect on enzyme activity, and both were predicted to be benign  by PolyPhen and to be neutral by PROVEAN. The other three missense mutations, G490A (D164N), T959C (F320S) and G1061A (R354H), are located in the catalytic domain of the enzyme and are more likely to influence enzyme activity. However, the G1061A mutation was predicted to be benign (score: 0) by PolyPhen and to be neutral by PROVEAN, possibly because this mutation does not change the biochemical properties of the amino acids . The G490A (D164N) and T959C (F320S) mutations are predicted to be damaging  by PolyPhen and lethal  by PROVEAN. A mutation with a score ≤ −2.5 is considered lethal, while a score > −2.5 indicates a neutral mutation.We identified the Le, Le59, le59,1067, le202,314, le202,314,47, le59,202,1067, le59,508, and Le645 were found in both the Sindhi and Punjabi populations, whereas the le1007, le1067, le13,484,667, and Le612 alleles were only present in the Sindhi population, and le202 and le59,445 were only found in the Punjabi population . In the Punjabi population, 43.63% of alleles were non-functional, and le59,1067 was the most frequent (17.52%), showed significant differences between the two populations (P < 0.05). Moreover, the 3 alleles le47,202,314, le59,202,1067 and le202 were relatively rare, but le47,202,314 (2.25% in Sindhi and 4.78% in Punjabi) and le59,202,1067 (1.25% in the Sindhi and 1.27% in the Punjabi populations) presented a considerable frequency in these studied populations. According to the genotypic data, the frequency of the Lewis-negative phenotype was 11.5% in the Sindhi population and 22.93% in the Punjabi population, indicating a higher frequency of the Lewis-negative phenotype in the Punjabi population.As shown in Table 20. These invaders contributed to the ethnic variety of the Pakistani populations. There are many ethnic groups inhabiting different parts of Pakistan. In this study, the systematic sequencing analysis of the coding region of the FUT3 gene was performed in two ethnic groups, Punjabis, representing 62% of the Pakistani population, and Sindhis representing 18%. In the context of the Lewis blood type and genetic polymorphism mentioned above, these two groups were appropriate for in the current study because they represent>78% of the total Pakistani population. The Lewis blood group system is not only highly polymorphic but also ethnically and geographically specific. Many different sequence variations have been observed in different populations around the world. According to our results, the studied populations exhibit higher sequential variation and a wide variety of alleles at the FUT3 locus.Pakistan lies in a region that has been invaded by several different groups in the past, including Greeks, Aryans, Macedonians, Arabs and Mongols22, but in the currently studied populations, the frequency of this mutation only accounts for 3.75% (Sindhi) and 3.82% (Punjabi). On the other hand, the T1067A mutation, which is frequent in Japanese17, Sinhalese14, Southeast Asian11, and Caucasian populations13, also represents 14.25% in Sindhis and 18.79% in Punjabis. The T202C and C314T mutations, which were found predominantly in the Sindhi (21.75% and 20.25%) and Punjabi (21.66% and 19.75%) populations, have most commonly been found in Caucasian populations13. In most cases, T202C and C314T are in complete linkage, but it was interesting that the T202C singleton, which has been previously identified in Xhosa and Caucasian populations1, was also found in a heterozygous individual from the Punjabi population. It is worth noting that the G47C mutation, which has only previously been identified in the Sinhalese population of Sri Lanka in South Asia14 and the Caucasian panel of Coriell Cell Repositories, was found at a notable frequency in the Sindhi (2.25%) and Punjabi (4.78%) populations, indicating that G47C of FUT3 may be more specifically present in South Asian populations. In addition, some other rare mutations were sporadically identified in the investigated populations. For example, the G13A mutation, which was originally found in African Americans and was common in native Africans, was also present in the Sindhi population15. C445A was originally observed in Denmark4 and A1007C has only been reported in Japanese populations12; both enzyme-inactivating mutations were also seen in one heterozygous individual in each of the Punjabi and Sindhi populations. The A612G mutation was found only in Mongols and was identified in 4 heterozygous individuals from the Sindhi population13, while the T645C mutation was shared by both the Sindhi and Punjabi populations. The distribution characteristics of common and rare mutations at the Lewis locus suggested the existence of extensive sequence diversity in the Lewis coding region in the Sindhi and Punjabi populations, and the FUT3 SNPs and alleles shared with other racial populations indicated a mixed trait in the two investigated populations of Pakistan.Initially, the most frequent mutations identified in the studied populations were T59G, T202C, C314T, G508A, T1067A and G47C . Furthermore, the Lewis-negative phenotype frequency of the Punjabi population (22.93%) was twice that of the Sindhi population (12%). Therefore, the genetic profile of the Lewis blood type system in these two groups is somewhat similar.According to the frequency distribution of the Lewis allele and the negative phenotype in the Sindhi and Punjabi populations, the frequency of 22  and West Eurasia (35.7%)20. Likewise, the Punjabi mtDNA gene pool is primarily a composite of considerable proportions of South Asian haplotypes (65%) and West Eurasian (29%) haplogroups25. Therefore, based on the distribution of alleles at the FUT3 locus, we can conclude that the Punjabi and Sindhi populations from Pakistan are more closely related to Sinhalese and Caucasian population, and the present results conform to those of many other studies in Sindhi and Punjabi populations.The type and frequency distribution of the Lewis alleles, especially non-functional Lewis alleles, are race-specific among many populations22 Table . le59,50FUT3 has been raised by genome-wide association studies (GWASs), which have suggested inactivating polymorphisms  of the gene to be associated with the prevalence of ulcerative colitis (UC)26, Crohn’s disease (CD)27 and coronary artery disease29. Moreover, these two SNPs also influence the lesion location in UC and CD. In the Sindhi and Punjabi populations, our results demonstrated that T59G, G508A, T1067A, T202C, and C314T, as tag SNPs of Lewis-negative alleles, will be useful for large-scale association studies of Lewis-negative phenotypes with diseases in the future.In recent years, new interest in the polymorphisms of Norovirus30, Rotavirus31 and Helicobacter pylori32. The Lewis-negative phenotype is resistant to norovirus (GI) and rotavirus (P8), which are the leading causes of acute gastroenteritis in children worldwide. According to a previous report, the Lewis-negative phenotype varies from 7% (Asians) or 8% (Europeans) to 19% (Africans)21. Our results showed that the frequencies of the Lewis-negative phenotype were 12% (Sindhi) and 22.93% (Punjabi). Thus, a stable proportion of the Lewis-negative phenotype is maintained under long-term natural selection. The reason may be associated with microorganism infection, possibly related to a protective strategy against widespread disease.On the other hand, recent studies have suggested that the Lewis phenotype is associated with susceptibility to infection by In the past, it was relatively difficult to accurately classify Lewis phenotypes using the haemagglutination test in medico-legal investigations. This method only works for whole blood samples, not for special materials such as body fluid, hair, and bloodstains that are commonly found at crime scenes. However, in this study, we successfully typed the Lewis phenotype using bloodstain samples.FUT3, were identified by systematic sequencing analysis in Punjabi and Sindhi populations. These populations were not previously studied in reference to FUT3, and our present study revealed that the Sindhi and Punjabi populations are a mixture of South Asian and Caucasian ancestry. Thus, a genetically better understanding of the origins of these two ethnic groups is presented in this research.In conclusion, multiple sequence variations and a wide variety of alleles, including eleven novel alleles of In the current study, we collected bloodstains on FTA cards from 357 (200 Sindhi and 157 Punjabi) unrelated individuals residing in Sindh and Punjab provinces of Pakistan. All participants gave their informed consent either orally and with a thumb print (if they could not write) or in writing after the study aims and procedures were carefully explained to them. The study was approved (2019/060) by the ethical review board of China Medical University, Shenyang, Liaoning Province, People’s Republic of China, and was performed in accordance with the standards of the Declaration of Helsinki.Genomic DNA was isolated from FTA bloodstain cards using a modified phenol-chloroform method developed by our group  containing the open reading frame (1086 bp) was first amplified by PCR in a 20 μl system including 10 μl of 2×Power Taq PCR MasterMix , 40 ng genomic DNA, and 5 μmol of each primer. The primer sequences are shown in Table 33.The synthesized PCR products were directly sequenced using Sanger sequencing with the sequencing primers , 2 μl of the 1000-fold-diluted first PCR product and 5 μmol of each primer in 11 individuals, which showed unreported and rare point mutations. The primer sequences are shown in Table To verify our results for individual haplotypes, we also performed allele-specific PCR in a 20 μl system containing 2 μl of 10× PCR buffer, 1 μl dNTP mix, 5 μmol of each primer, 2.5 units rTaq DNA polymerase and the 1000×-diluted first PCR products as templates. We designed a total of 4 upstream primers and a common downstream primer (Nest-FUT3) for amplification in all individuals. The primer sequences are shown in Table The DNA sequences were analysed by using DNAMAN8 software with the NCBI sequence NG_007482 as a reference. Allelic frequencies and genotypes were calculated by the direct counting method, while Hardy-Weinberg equilibrium (HWE) was assessed with the chi-square test. The differences in the allele frequency distribution between the currently studied populations and reference populations were calculated by using SPSS version 21.0 software. The effect of point mutations on enzyme activity was inferred with the PolyPhen and PROVEAN programs.Supplementary information


Neurergus derjugini, we employed phylogeography patterns and genetic variability of this species by analyzing partial ND4 sequences (867 bp) of 67 specimens from 15 sampling localities from the whole species range in Iran and Iraq. Phylogenetic trees concordant with haplotype networks showed a clear genetic structure among populations as three groups corresponding to the populations in the north, center, and south. Evolutionary ages of clades north and south ranging from 0.15 to 0.17 Myr, while the oldest clade is the central clade, corresponding to 0.32 Myr. Bayesian skyline plots of population size change through time show a relatively slight increase until about 25 kyr  and a decline of population size about 2.5 kyr. The presence of geographically structured clades in north, center, and south sections of the species range signifies the disjunct populations that have emerged in three different refugium. This study illustrates the importance of the effect of previous glacial cycles in shaping the genetic structure of mountain species in the Zagros range. These areas are important in terms of long‐term species persistence and therefore valuable areas for conservation of biodiversity.Phylogeography is often used to investigate the effects of glacial cycles on current genetic structure of various plant and animal species. This approach can also identify the number and location of glacial refugia as well as the recolonization routes from those refugia to the current locations. To identify the location of glacial refugia of the Yellow‐spotted mountain newt,  To identify the location of glacial refugia of the Yellow‐spotted mountain newt, Neurergus derjugini, we employed phylogeography patterns and genetic variability of this species by analyzing partial ND4 sequences from the whole species range in Iran and Iraq. Phylogenetic trees concordant with haplotype networks showed a clear genetic structure among populations as three groups corresponding to the populations in north, center, and south. The presence of geographically structured clades by AMOVA analysis indicated the disjunct populations of N. derjugini may have been survived in three different glacial refugia in the Zagros mountains. Finally, we (e) match phylogeographic results with available Quaternary range dynamic outputs.The present study analyzed spatial variation of mtDNA sequences, aiming to investigate the genetic structure within species, as well as attempting to trace the recent demographic and phylogeographical history of the 22.1Neurergus derjugini from 15 localities in Iran and Iraq during 2012–2014 . Values for the numbers of haplotypes, polymorphic sites, parsimony informative site, haplotype diversity (Hd), nucleotide diversity (Pi) and number of transitions and transversions were calculated using the software Dnasp v 5.10.01  phylogeny was produced from the ND4 gene haplotypes by means of the program MrBayes, v 3.2.2 program  with 10,000 permutations in Arlequin version 3.0 . In the phylogenetic trees recorded three groups corresponding to the populations north, center, and south , . Within N. derjugini, there is little support for relationships northern populations sampled, with bootstrap 38%. The southern populations of N. derjugini from a well‐supported clade, with bootstrap 74%. To evaluate the phylogenetic analyses, 867 base pairs of ND4 sequences gene were combined with 718 base pairs of D‐loop and 1036 base pairs of ND2 sequeces gene. Based on the combined sequences, Bayesian inference trees showed that N. derjugini haplotypes form a monophyletic group with a three populations in north, center, and south , and genetic difference was detected among populations within regions  indicating populations sharing the same haplotypes or closely related haplotypes. FST values between populations range from −0.166 to 1  = .1) and sum of squared deviations (SSD)  = .1) were not significant, indicating population expansion. Results of Tajima's D test was positive and not significant  = .10). However, negative value  for Fu's Fs, which is more sensitive to recent population expansions, suggested the recent demographic expansion. Bayesian skyline plots suggest that population size was increased very slightly 25 kyr (around LGM),  and environmental distances remained significant .The analyzed populations revealed significant levels of isolation by distance  explained 72.51% and 22.17% of the total variation and did not separate the northern, central, and southern localities along temperature and precipitation gradients Table . The occ3.6N. derjugini haplogroups is presented in Figure N. derjugini between the north‐central haplogroups and southern haplogroups was 0.46 Myr and divergence between northern and central haplogroups took place in 0.39 Myr (middle Pleistocene). Evolutionary ages of clades north and south range from 0.15 to 0.17 Myr, while the age of clade center is higher, corresponding to 0.32 Myr.Dating of major mtDNA cladogenetic events within 42). A total of 3 allopatric and 11 parapatric clades were identified for ND4, most of them with a very restricted distribution in northern, central, and southern parts of the distribution. Of 11 haplotypes identified by ND4 in present the study, 3, 4 and 4 haplotypes are reciprocally monophyletic for northern, central, and southern sections of the species range. Previous studies on monophyly in N. derjugini as evidence for refugia matching include six haplotypes identified in the control region (D‐loop) which form a well‐supported monophyletic clade within northern and southern populations  and Hendrix et al. (2014): Neuregus derjugini derjugini and N. d. microspilotus. Frequently, number and size of yellow spots on the dorsal section of body have been used to distinguish different forms of the Yellow‐spotted mountain newts  change drastically during first three years before sexual maturation and remain fairly constant thereafter. In another study on configuration of yellow spots in a free living N. derjugini based on photo‐identification method a complete spatial randomness (CSR) test using nearest neighbors distances for the spots over dorsal part in adult newt showed a reasonable match with the Poisson distribution indicating that spots are distributed randomly over body of the newt  and 5.4%–5.5% (N. s. barani) of the pairwise uncorrected p‐distances of N. crocatus. The p‐distances between these two subspecies of N. strauchii ranged from 0.48% to 1.2%. Pairwise uncorrected p‐distances between clades in present study range from 0 to 0.002 in ND4 and from 0 to 0.004 in ND2 and 0 to 0.001 in D‐Loop. Average values of p‐distance for these genetic markers is not a level of divergence typically found between recognized species of the genus Neurergus; therefore, there may not be adequate evidences to support that N. derjugini constitutes a species complex.The finding of present genetic divergence among geographical clusters of populations of N. derjugini with three mitochondrial markers have yielded two (D‐Loop) and three (ND2 and ND4) reciprocally monophyletic lineages. Investigation on mitochondrial DNA variation and reconstruction of the potential current and past distribution of N. derjugini together with landscape connectivity analysis showed higher gene flow between the breeding streams in the southern part of the range, while the northern populations are more isolated  and the Last Glacial Maximum  shows that the potential distribution area of N. derjugini during the last LGM is characterized by low elevations and dry habitats  in N. derjugini (Figure A study on species distribution modeling of i Figure  confirme5N. derjugini provides evidence for a history of isolation and divergence in allopatry resulting in the diversification of three monophyletic and geographically separate clades. These diversifications are largely concordant with the last glacial maxima. Positive and significant correlation between geographical, environmental, and genetic distances suggests a possible impact of geographical and environmental divergence in shaping the genetic variation of N. derjugini that have emerged in three different refugia. Principal component analysis did not separate climatic profiles of the distribution range of N. derjugini and demonstrated the homogeneity in different bioclimatic variables in the distribution range of N. derjugini. The populations occurring in northern, central, and southern sections of the species range have been separated from one another since 0.46 Myr between the north‐central haplogroups and southern haplogroups and 0.39 Myr between northern and central haplogroups took place in middle Pleistocene. Results obtain from current study, also, signifies the role of refugia in conserving populations as well as genetic diversity of N. derjugini.Phylogeographic analysis based on mitochondrial markers in various populations of The authors declare that they have no competing interests.SV and MSH conceived the main idea with input from MM and SV collected the data; and MM and SV analyzed the data with support from MSH. Sampling was done by SV and MSH. The manuscript was written by SV, MSH, and MM. All authors made substantial contributions to the interpretation of results and the editing of the manuscript.This study complied with the appropriate institutional, national, and international guidelines.


Buccinum undatum is a subtidal gastropod that exhibits clear spatial variation in several phenotypic shell traits  across its North Atlantic distribution. Studies of spatial phenotypic variation exist for the species; however, population genetic studies have thus far relied on a limited set of mitochondrial and microsatellite markers. Here, we greatly expand on previous work by characterizing population genetic structure in B. undatum across the North Atlantic from SNP variation obtained by RAD sequencing. There was a high degree of genetic differentiation between Canadian and European populations  consistent with the divergence of populations in allopatry . In addition, B. undatum populations within Iceland, the Faroe Islands, and England are typified by weak but significant genetic structuring following an isolation‐by‐distance model. Finally, we established a significant correlation between genetic structuring in Iceland and two phenotypic traits: shell shape and color frequency. The works detailed here enhance our understanding of genetic structuring in B. undatum and establish the species as an intriguing model for future genome‐wide association studies. Buccinum undatum, from across the North Atlantic. We described strong genetic structuring between Canadian and European populations concurrent with the divergence in allopatry, as well as associations between genotypic and phenotypic structuring in Iceland. The works detailed here enhance our understanding of genetic structuring in B. undatum across multiple geographical scales and establish the species as an intriguing model for future genotype–phenotype associative studies. The current study described novel trends in population genetic structure of Common Whelk,  Across the North Atlantic , England , and Faroe Islands . Geographical coordinates for all sample sites are listed in Table Live 2.2https://www.omegabiotek.com/product/e‐z‐n‐a‐mollusc‐dna‐kit/). Double digest RAD sequencing libraries were constructed by digesting whole genomic DNA with Sau3AI and ApeKI and selecting fragments 390 bp to 430 bp using a Pippin Prep (Sage Science). Individuals were assigned a molecular identification tag using combinatorial barcoding of forward (5bp) and reverse (6bp) reads, respectively, then pooled into four separate libraries before size selection and amplification using 10 PCR cycles. Resulting library concentrations were quantified using SYBR Gold double‐stranded DNA assay measured on a TECAN GENios plate reader , and the sizes estimated by 2% agarose gel. Library quality was assessed using two pilot sequencing runs on Illumina MiSeq (paired‐end 2 × 150 bp). Libraries were sequenced using Illumina HiSeq 2500 (paired‐end 2 × 125 bp) across four lanes, generating an average of 4 ± 3.5 SD million paired raw reads per individual.Fixed foot tissue was dried briefly  and cut into small pieces that underwent DNA isolation using an Omega Bio‐Tek E.Z.N.A. Mollusc DNA Kit  using the process_radtags program in Stacks v.2.0b  and the ‐‐inline_inline, –r, –c, –q, –t = 119 options enabled .Raw reads were demultiplexed in paired mode  = 19; England(n) = 14; Faroe Island(n) = 17; Iceland(n) = 150. Following cstacks catalog construction (–n 6 option enabled), the remaining de novo stacks pipeline proceeded using the original n = 383 samples. Sstacks and tsv2bam steps proceeded using default settings, while gstacks was run in de novo mode (‐P option enabled). Finally, populations analyses were run with –p 3 and –r 0.5 options enabled, where the ‐‐popmap designated individuals based on their country of origin .The initial pass through the de novo  Catchen . To avoiAndrews, ) was calpopulations .vcf file was imported into Radiator  = 28; England(n) = 14; Faroe Island(n) = 18; and Iceland(n) = 117.The resulting stacks pipeline using only the optimized n = 177 whitelisted sample set. Again, Stacks parameters were selected with the aim of retaining high numbers of SNPs, with more rigorous filtering occurring downstream in Radiator. Initially, the cstacks catalog was reassembled (–n 6 option enabled) using all n = 177 individuals. Sstacks and tsv2bam steps were run using default settings, and gstacks run in de novo mode (–P option enabled), yielding 900,825 loci, with an effective per‐sample coverage of 17.7 (± 6.8 standard deviation). Populations analyses were again run with –p 3 and –r 0.5 options enabled, and ‐‐popmap designating individuals based on their country of origin. Low‐quality individuals and markers were again filtered using Radiator (parameters and outputs listed in Table Radiator was 18.6 (±6.3 standard deviation).Primary analyses were initiated by undertaking a second pass through the de novo 2) of expected genotype frequencies in the R‐package Pegasχ  were observed . Notably, departure from HWE was assessed on a scale‐specific manner; firstly, at the North Atlantic scale where all individuals from all populations  were included and, secondly, at the Icelandic scale where only individuals from Iceland were assessed (again with population designated relative to sample site in Breiðafjörður). Adaptive SNPs (putative loci under selection) were identified either at the North Atlantic or Icelandic scale using BayeScan  was assessed using a chi‐squared test , expected heterozygosity (HE), and inbreeding coefficient (FIS) per population were calculated using Hierfstat  within the genomic data was estimated using the find.clusters function of Adegenet  was selected by computing the value of K with the lowest associated goodness of fit value estimated with BIC.The estimation of genetic variation and divergence was undertaken in both North Atlantic and Icelandic datasets using the neutral SNP dataset exclusively. Mean observed heterozygosity  assignments. Population admixture was estimated using the Discriminant Analysis of Principal Components (DAPC) in Adegenet  assignments. Isolation‐by‐distance was assessed by analyzing the association of genetic and geographic distances for all individuals using a Mantel test in Ade4  in Populations derived haplotype.tsv files were converted to a fineRADstructure . Haplotype coancestry of North Atlantic populations was calculated using fineRADstructure and visualized in R using the scripts provided in the fineRADstructure manual (http://cichlid.gurdon.cam.ac.uk/fineRADstructure.html).Finally, genomic divergence across the North Atlantic was investigated by assaying haplotype coancestry. 2.5Comparisons of genetic and phenotypic distances were performed for the Icelandic dataset only. Phenotypic data corresponding to each of the Icelandic sites was sourced from Magnúsdóttir et al. . No phenvegan  with phenotypic distances was tested on a per‐sample site basis, using Mantel's test, with 1,000 permutations in the R‐package K = 2) population assignments. Correlation between putative population clusters and shell shape differences were assessed using permutational multivariate analysis of variance of shape distances, implemented using the adonis function in vegan  and Radiator quality filtering, North Atlantic‐specific analyses  yielded a total of 35,836 common putative single locus SNP across Canada (n = 28), Iceland (n = 104), Faroe Islands (n = 18), and England (n = 14). A further n = 314 loci significantly deviated from HWE and were removed from the dataset, as were n = 2 loci flagged as putative adaptive SNP candidates . All downstream analyses were performed on the remaining n = 35,518 putative neutral loci  retained postfiltering.Following genotype calling (HO = 0.107). None of the North Atlantic populations displayed signals of inbreeding with FIS ranging from 0.043 to 0.068. At the North Atlantic scale, calculations of pairwise FST identified significant genetic divergence for all pairwise comparisons , with the Faroe Islands and England whelk displaying the greatest degree of genetic similarity, followed by Iceland, while Canada represented the most genetically divergent population relative to ENA populations  were observed between Canada, Iceland, Faroe Islands, and England , with observed heterozygosity ranging from 0.146 to 0.158 Table , while gs Figure . No signd Figure .K = 3 population clusters. Canadian and Icelandic populations formed distinct putative populations relative to the Faroe Islands and England whelk .The Bayesian information criterion estimated optimal goodness of fit value at k Figure . No signHaplotype coancestry analyses indicated a lack of shared genetic ancestry between CAN and all ENA individuals, with the split of CAN and the ENA lineages occurring before any subsequent ENA divergence Figure . Regardi3.2n = 9), BRJ (n = 9), HVA (n = 34), HRU (n = 18), ODD (n = 4), SKO (n = 24), and RAU (n = 6). Following the preparation of the Icelandic‐specific dataset , quality filtering removed a further n = 268 loci, which significantly deviated from HWE, as well as a further n = 175 putatively adaptive SNP candidates . All downstream Iceland‐specific analyses proceeded with the remaining n = 35,393 putative neutral loci .Quality filtering reduced sample representation across all sample sites (particularly for ODD), with Iceland‐specific analyses being conducted across BJA (FIS) ranging from −0.019 to 0.040 , with individuals within the outermost  and innermost  regions of Breiðafjörður, generally, displaying the greatest degree of genetic similarity  and innermost  bay sites  population assignments, admixture plots indicated minimal gene flow between the putative outer‐ and innermost bay populations  when considering individuals on a site‐specific basis, regardless of correction for spatial autocorrelation (geographic distance or depth). Sites were grouped into putative population clusters based on K‐means clustering (K = 2) and analyzed for correlations between genetic distance and phenotypic traits, shell shape distances, and color frequency  and shape distances  were observed between the two putative genetic clusters. The outer bay cluster consisted primarily of lighter shell colors, with orange and white being the most common , bolstering previous claims that the two lineages constitute cryptic species that diverged under allopatric speciation  identified two putative population clusters composed exclusively of individuals sampled from the bay's outer‐ and innermost regions. Significant correlation between shell shape variation and shell color frequency were observed in Iceland, with phenotypic traits showing concordance with the denotation of Breiðafjörður whelk as distinct outer‐ and innermost bay population clusters.The current study undertook a comprehensive analysis of North Atlantic population genetic structure in the common whelk utilizing a RAD sequencing approach. WNA (Canada) and ENA  populations were highly divergent, with an B. undatum described genetic structuring following an isolation‐by‐distance model across the North Atlantic  fall within the ranges previously estimated for Breiðafjörður using mtDNA (ranging from 0.0003 to 0.145)  is similar, albeit higher on average, to those derived for British  and Irish  B. undatum populations , increasing the frequency of rafting events, and thus, admixture between coastal sites. Surface waters in Breiðafjörður are considered to be well mixed, with nearshore currents flowing in a continuous clockwise fashion  assignments. Divergence in phenotypic shell traits between putative populations can reflect random fluctuations  in separate populations with limited demographic connectivity, which may be further augmented by selection due to different environmental settings. Previous genetic data are congruent with a model of recent population expansion into Breiðafjörður following the last glacial maximum , Green crab (Carcinus maenas), and Atlantic Rock crab (Cancer irroratus). Thicker, more elongate shells with smaller apertures were hypothesized to occur in association with the predatory crab species in the innermost regions of Breiðafjörður  and large‐scale (North Atlantic) geographical distances. Clear population genetic structure was defined for B. undatum within Breiðafjörður, Iceland, constituting two distinct putative genetic clusters. The frequency distribution of shell color and shell shape distance was significantly correlated with the two putative genetic clusters in Breiðafjörður. The work detailed here provides a rigid framework for ongoing associative studies of genetic variation in the subtidal mollusk, B. undatum, while laying the foundations for future comparative studies aimed at characterizing genotype–phenotype associations in common whelk.The current study undertook the first genome‐wide analysis of population structure and divergence in None to declare.Jake Goodall: Conceptualization ; Data curation ; Formal analysis (lead); Methodology ; Project administration ; Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Kristen M. Westfall: Conceptualization ; Data curation ; Formal analysis (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration ; Validation (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Hildur Magnúsdóttir: Conceptualization ; Data curation ; Formal analysis (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Snæbjörn Pálsson: Conceptualization ; Formal analysis (supporting); Funding acquisition (supporting); Methodology ; Project administration ; Resources (supporting); Supervision ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Erla Björk Örnólfsdóttir: Conceptualization ; Funding acquisition (lead); Project administration ; Resources (lead); Supervision ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Zophonías O. Jónsson: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration ; Resources (supporting); Supervision ; Writing‐original draft (supporting); Writing‐review & editing (supporting).SupinfoClick here for additional data file.