Spartina alterniflora is an invasive plant that is a major threat to the economy and environment of the coastal regions in China. We analyzed the genetic structure and diversity of six populations of S. alterniflora differing in invasion histories in Guangxi, China. A total of 176 individuals from the six populations produced 348 AFLP fragments. The average heterozygosity was significantly lower than in the native population. And genetic bottlenecks were also detected in most populations. Standardized FST statistics (Φpt = 0.015) and AMOVA results indicated weak genetic differentiation. Genetic admixture and obviously isolation by distance indicated populations in Guangxi come from a pre-admixed population by a single introduction. High phenotypic variations of S. alterniflora in Guangxi influenced by soil salinity and temperature might be an important reason for the successful invasion.Genetic variation and population structure may reflect important information for invasion success of exotic plant species and thus help improve management of invasive plants.  Under the development of global trade and tourism, invasive alien species became one of the main direct drivers of biodiversity loss across the globe. The founding events cannot prevent some introduced species from successful colonization and rapidly spreading. They can become highly invasive despite having gone through genetic bottlenecks and low genetic diversity , 2. SuchSpartina alterniflora Loisel., native to the Atlantic and Gulf Coast estuaries of North America, is listed as one of the most invasive plants in China /2), and Nei’s genetic distance among populations. The analysis of molecular variance (AMOVA) was performed to estimate the allocation of genetic variation at three levels: among regions, among and within populations. The populations were partitioned into two regions:  and  with the Dandou Sea as a partial barrier to gene flow. The dendrogram was constructed by the neighbor-joining method using MEGA-X [H) between Chinese and native populations [The raw data were processed using the fragment analysis software GeneMarker V2.2.0. The chromatograms of the fragment peaks were scored as present (1) or absent (0). A binary qualitative data matrix was constructed. GENALEX 6.503  were useg MEGA-X , showingulations . The difK) was specified from 1 to 6 with 10 independent runs under the admixed model at 100 000 MCMC iterations and a 10 000 burn-in period. The most probable number of clusters (K) was selected by calculating an adhoc statistic △K based on comparing the log probability of the data (LnP(D)) for each value of K as described by Evanno et al.[K value was selected to determine the number of clusters. The software CLUMPP v1.1.2 [Q) for each individual, permute population membership coefficients matrices (Q-matrices) from 10 replicate cluster analyses and outputs a mean of the permuted matrices across replicates. Each individual’s probability of assignment to each cluster was visualized by Distruct software version 1.1 [Q ≥ 0.6. Individuals with Q < 0.6 in each cluster were considered admixed, as suggested by previous studies [K genetic pools from each population were displayed as pie charts, which were visualized with a partial map of Guangxi.Population structure was assessed using the Bayesian model-based clustering analysis with STRUCTURE 2.3 . The inino et al., and wasno et al.. The higP v1.1.2  was usedsion 1.1 . The memsion 1.1 . Individ studies , 43, 44.He) would be larger than the expected heterozygosity (Heq). We used the infinite alleles model (IAM) as the most appropriate evolutionary model for this study, with a sign test for each population and the entire range of S. alterniflora in Guangxi.We used STRUCTURE 2.3 to identify the potential migrants between populations. The GENSBACK was set to 2, which will test each individual for evidence of ancestry from any of the six populations for two generations before the present with the MIGRPRIOR = 0.05. We used BOTTLENECK v.1.2.02 software  to deterS. alterniflora in population 1-BJ, 4-QS, and 5-XC were evaluated at the same time of sample collection  thellection . The ave.cma.cn/ . ANOVA aThe associations between genetic (GD), phenotypic (PD), and geographic distances among populations were tested by using the Mantel test in GENALEX 6.503 , 46. TheA principal coordinate analysis (PCOA) of the genetic data was performed with the pairwise genetic distance matrix by GENALEX 6.503 . We alsoS. alterniflora produced 372 AFLP fragments, of which 348 (93.55%) were polymorphic. In each population, 310 (5-XC) to 322 (1-BJ) polymorphic fragments were identified, with the percentage of polymorphic bands (PPB) varying from 83.33% (5-XC) to 86.56% (1-BJ) (H) was 0.115, and Shannon’s information index (I) was 0.221. There was no obvious difference between the three genetic diversity indexes among the six populations . 1-BJ had the highest observed number of alleles (Na = 1.867) and the highest effective number of alleles (Ne = 1.160). H and I were the highest in 1-BJ , lowest in 5-XC , and intermediate in 6-DW . The number of private loci (PL) was the highest in population 5-XC (PL = 4), followed by 2-DS and 6-DW (PL = 2), whereas no private loci were detected in the populations 3-ST and 4-QS. Compared the genetic diversity between our study and the study of Utomo et al. [H) of S. alterniflora in Guangxi, China were significantly lower than S. alterniflora in undisturbed sites of Louisiana basins, USA . The population bottleneck sign test showed the number of loci with a heterozygosity excess was greater than the number of loci with a heterozygosity deficiency in population 1-BJ, 2-DS, and 3-ST .The 176 individuals of % (1-BJ) . Considehich 348 3.55% werand 3-ST . Five poK value was observed at K = 3  individuals from the six populations could be assigned to the three clusters with a membership coefficient (Q) more than 0.6, and 132 individuals were genetic admixed  between any two populations varied from 0.0022 to 0.0038, with an average distance of 0.0032. The closest genetic distance was observed between 4-QS and 5-XC (D = 0.0022), while the largest distance was observed between 4-QS and 2-DS (D = 0.0038)  . The six 0.0038) .Φpt) among all populations was 0.015. The gene flows of the six populations are provided as a heat map  was obvious higher and stouter than in Beijie Village (1-BJ) and Xicungang harbor (5-XC). Positive correlation between genetic distances and geographic distances  and between genetic distances and shoreline distances  were found among six populations  and between phenotypic (PD) distances and geographic distances of population 1-BJ, 4-QS and 5-XC   .P-value = 0.001) and the average annual mean temperature   , Fig 5. S. alterniflora in Guangxi, China. As genetic bottlenecks could result in decreased genetic diversity, low genetic diversity of S. alterniflora in Guangxi was not unexpected. Similar situations have also been revealed in other invasive regions of S. alterniflora in China [S. alterniflora. The newly-established population (6-DW) had similar genetic diversity index as the oldest population (1-BJ). These findings indicate that the low genetic diversity of S. alterniflora in Guangxi would not become an obstacle for population establishment and expansion. Moreover, genetic diversity could be successfully maintained at a certain level during the dispersal process of S. alterniflora.Genetic bottlenecks were detected in five of the six populations and the entire range of in China , 23, 24.S. alterniflora in Guangxi showed very low genetic differentiation (Φpt = 0.015). We also found that more than 99% of genetic variance was distributed within populations, whereas only 1% was between regions and populations. Despite high within-population genetic diversity was consistent with prior studies, the proportion of genetic variation among populations of S. alterniflora in its native range  [S. alterniflora. The low population differentiation between the two regions also confirmed that the Dandou Sea was not a partial barrier to gene flow between the earlier-established  and the later-established populations .The six populations of na, USA)  were allS. alterniflora populations and no correlativity between phenotypic differences and genetic differentiation might provide evidence of phenotypic plasticity. Phenotypic variation of S. alterniflora in China due to phenotypic plasticity rather than genetic variation was also revealed in the study of Liu et al. by a common garden experiment [Spartina, such as S. densiflora populations in North America [High levels of phenotypic variability among these genetically similar periment . Low gen America . Therefo America , 51, 52S. alterniflora populations in Guangxi most likely to be the result of a single introduction in Beijie from the Dandou Sea coast in 1994, and spread has occurred via natural pathways. This might be one reason why there were genetic bottlenecks and low genetic diversities in S. alterniflora populations in Guangxi, on account of multiple introductions from different sources could help invasive species to maintain a high level of genetic variation [A significant correlation between genetic distances and geographic distances was revealed in this study. Three clades in NJ dendrogram based on genetic distances between populations were also in accordance with the geographical distribution of the populations. The ages of the six populations and geographic distances among them were purely coincidental . This result implied that ariation , 54.S. alterniflora in Guangxi either. Although Liu et al. found S. alterniflora at low latitudes in China had low sexual reproductive activity [In addition, higher gene flows and more migrations were detected in adjacent populations than in non-adjacent populations implied the natural dispersal of activity , the migactivity , 55–57.S. alterniflora in China originated from the three ecotypes in Florida, Georgia, and North Carolina. Due to a lack of samples from the native region in this study, we could not confirm whether there was a one-to-one correspondence between the three clusters and the three ecotypes. We found high proportions of admixture individuals in all of the six populations even the early introduced site—Beijie Village (1-BJ), agreeing with previous studies showing that the hybrid and mixture of ecotypes coexist in China, especially in southern populations [S. alterniflora colonized in Guangxi [S. alterniflora spread to a new area in Guangxi. The proportion of admixed individuals in the newly-established population of 6-DW (100%) was much higher than that in the earliest population of 1-BJ (53%).The three genetic clusters revealed by STRUCTURE analysis concurred with previous findings that ulations , 24. It  Guangxi . We alsoThe admixture was revealed as the most important factors for successful invasion of invasive species , 21, 24.S. alterniflora was obviously impacted by soil salinities and the average annual mean temperature of these sites. Previous studies have also shown those plant traits of S. alterniflora such as height and biomass could respond to temperature change [S. alterniflora have been reported in studies of Nestler [S. alterniflora along the east coast of China exhibited linear relationships with latitude whereas our study did not support this inference due to population 1-BJ, 4-QS and 5-XC distributed in a small scale range. However, the study of Zhao et al. [The genetic differentiation between population 1-BJ, 4-QS, and 5-XC was not influenced by the four environmental variables. However, we found the phenotypic variation of e change , 60, 61. Nestler  and Huan Nestler . Height, Nestler . Liu et  Nestler  and Zhao Nestler  reportedo et al.  didn’t fS. alterniflora. However, high phenotypic variability influenced by environmental factors was also revealed. Therefore, we conclude that although S. alterniflora invaded into Guangxi by a single introduction from a pre-admixed population. High phenotypic plasticity would help this alien species overcome negative effect and colonized in a wide variety of environment. In spite of the previous study suspected S. alterniflora populations at low latitudes may spread slower [S. alterniflora in Guangxi, the population in this area should also be strictly monitored and managed to avoid its further spread.In Guangxi, we found obvious genetic bottlenecks, low genetic diversity, and low levels of genetic differentiation among populations of d slower , the newS1 FigSpartina alterniflora plots of the log-likelihood, ln P(D) (a), for ten runs at each value of K, and the second-order rate of change in ln P(D) (b), ΔK, as a function of the number of clusters, K, from the analyses of all samples.(TIF)Click here for additional data file.S2 FigP-value < 0.05, ANOVA) between population 1-BJ, 4-QS, and 5-XC.Different letters indicate significant differences ((TIF)Click here for additional data file.S3 FigThe correlations of genetic distances with geographic distances (A) and shoreline distances (B) by the Mantel test.(TIF)Click here for additional data file.S4 FigThe correlations of phenotypic distances (PD) with genetic (GD) (A) and geographic distances (B) by the Mantel test.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


ST-based method (BayeScan) among four populations of Atlantic salmon with a common genetic origin. Several regions were identified for these techniques that harbored genes, such as kind1 and chp2, which have been associated with growth-related traits or the kcnb2 gene related to immune system in Atlantic salmon, making them particularly relevant in the context of aquaculture. Our results provide candidate genes to inform the evolutionary and biological mechanisms controlling complex selected traits in Atlantic salmon.Domestication of Atlantic salmon started approximately 40 years ago, using artificial selection through genetic improvement programs. Selection is likely to have imposed distinctive signatures on the salmon genome, which are often characterized by high genetic differentiation across population and/or reduction in genetic diversity in regions associated to traits under selection. The identification of such selection signatures may give insights into the candidate genomic regions of biological and commercial interest. Here, we used three complementary statistics to detect selection signatures, two haplotype-based (iHS and XP-EHH), and one F Domestication is a complex evolutionary process whereby wild animals or plant populations adapt to environmental conditions created by humans and so involves genetic and developmental changes over multiple generations . Since tOreochromis niloticus L), rainbow trout (Oncorhynchus mykiss W), coho salmon (Oncorhynchus kisutch W), and Atlantic salmon  among others  experienced by population undergoing selection will cause the frequency of alleles underlying favorable traits to increase rapidly. Linkage disequilibrium (LD) between favorable mutations and neighboring loci will increase and spread, given that there is little opportunity for recombination over the brief time since the onset of intense selection . AnalyseST,  derived from the Mowi strain. This strain comes from one of the first farmed Atlantic salmon populations, which was established with fish from west coast rivers in Norway, with major contributions from River Bolstad in the Vosso watercourse, River Årøy, and possibly from the Maurangerfjord area . Salmon All populations were genotyped using Affymetrix’s Atlantic salmon 200K SNP Chip described in O) and expected heterozygosity (HE) calculated with PLINK v1.09 (r2) for each population and within chromosomes using PLINK v1.09  over a subset of 120,316 SNPs that had MAF > 0.05 among all populations.(1) iHS. The iHS score for detecting selection is based on the ratio of EHH for haplotypes anchored with the ancestral versus derived allele. The ancestral allele state for our Atlantic salmon populations is unknown and so to avoid losing SNPs by trying to polarize them from publicly available outgroup references, we assumed that the major allele represented the ancestral state as in 10 of at least three  based on a one-tailed test assuming that the standardized iHS ∼ N.(2) XP-EHH. The XP-EHH statistic compares the integrated EHH between two populations at the same SNP, to identify selection based on overrepresented haplotypes in one of the populations (PopO/IPopR), where IPopO is the integrated EHH for the observed populations and IPopR is the integrated EHH value of the reference population. Negative XP-EHH scores suggest selection in the “reference” population, whereas positive scores suggest selection acting in the “observed” population. A -log10 of three  was used as the lower threshold for considering XP-EHH score as significant evidence of selection and at least two SNPs ≤ 500 kb apart.ulations . We eval(3) BayeScan. We used the Bayesian likelihood method implemented in BayeSCAN v.2.1 to estimate the posterior probability that loci are experiencing selection (10 = 1.5) and a positive value of α , corresponding to a posterior probability of 0.97 and considered as being “very strong” evidence of selection and as in iHS and XP-EHH, we required the candidate selected regions to have at least two SNPs ≤500 kb apart.election . This meDanio rerio) peptide reference database (downloaded from http://www.ensembl.org/) to determine gene identify. As evidence of homology, we used an e-value ≃ 0 and then retrieved the zebra fish gene identifiers information from the ensemble biomart database (http://www.ensembl.org/index.html). Functional annotation of detected genes was performed using DAVID (Danio rerio) as reference in Gene Ontology (GO) analysis.Genomic regions harboring SNPs showing evidence of selection were annotated based on the ICSAG_v2 reference genome . We defing DAVID  with genWe performed PCA based on genotypes to look at the genetic relationship among individuals in our sample. The first and second components accounted for 14.2% and 10.3% of the genetic variation, respectively  (see r2) of each chromosome and population are shown in Observed heterozygosity levels were similar across the four domestic populations and were slightly higher than expected for populations A, B, and C, and even more so for population D. All these genetic diversity measures were statistically significant (p ≤ 0.001) for Pop-A, -B, -C, and -D respectively  = 4.17] containing 26 genes including the soga1 gene. Pop-C had 23 candidate regions that were on average ∼370 kb long, and which spanned a total of 165 genes. The 1,570-kb-long region with one of the most significant iHS score was on Ssa22, and spanned the genes kcnkf, sc61a, and mstn1. Pop-D had the most significant number of SNPs (467) and had 83 putatively selected genomic regions under our criteria. Most of these regions were located on Ssa01, Ssa10, Ssa13, and Ssa26 and spanned genes, such as haus2, itfg1, and phkb. Details of the total regions and genes can be found in Pop-B had 12 regions with an average length of ∼ 250 kb putatively under selection distributed among five chromosomes. The highest iHS score was for a region found on Ssa13 [-log to detect regions having unusually high EHH and overrepresented haplotypes consistent with selection. In total, we detected 482 (Pop-B/Pop-A), 800 (Pop-C/Pop-A), and 207 (Pop-D/Pop-A) XP-EHH outlier SNPs indicative of selection . The candidate genes were enriched in 37 gene ontology (GO) terms overall, most of them population specific , probably related to a larger region under selection detected in this population. The results presented here also reinforce the notion that exposure to different management and environmental conditions over just a few generations (at least four in this particular case) is sufficient to generate large changes in the genetic structure of farmed Atlantic salmon populations with the same genetic origin.To examine genetic population structure and relationships among the major groups of salmon, we conducted an ADMIXTURE analyses based on high-quality SNP data. This analysis revealed that 12 ancestral lineages contribute to the modern gene pool represented by the four farmed populations, which was expected considering the admixed origin of these populations . The fouST-based methods we are unable to directly identify which population in a pairwise comparison is experiencing selection from the posterior odds alone. Low overlap in selected regions identified with haplotype-based and single-SNP FST-based approaches have been reported in other studies in Atlantic salmon  on Atlantic salmon , IGF binding protein 6 paralog A2 (igfbp-6a2), and IGF binding protein-related protein 1 precursor (igfbprp1) as being under selection. We hypothesize that these genes are all contributing to weight variation in farmed salmon. The GO analyses for our candidate genes also showed enrichment for categories related to metabolic and developmental processes, which could certainly affect growth.Geographical adaptation and selection in farmed Atlantic salmon has resulted in considerable differences between wild and farmed strains . Genomicc salmon . We detec salmon . It has c salmon . We detekcnb2, rlf, synrg, snx14, fbxl5, e2f4, blm) that were previously shown to be affected by parasite-driven selection  that have were associated with disease resistance in the face of a bacterial disease  in Coho salmon  gene, the differential expression of which has been associated to tameness in the red junglefowl  family, which was previously associated with age to maturity in Atlantic salmon , myo3a, which is involved in allowing dogs to sense local environmental stimuli .ML and JY conceived the research idea. ML drafted the manuscript and carried out the analyses. TL supervised the data analyses and contributed to discussion and writing. TL, AN, JL, RN, and JY reviewed the manuscript. All authors read and approved the final manuscript.This work has been conceived on the frame of the grant CORFO (11IEI-12843 and 12PIE17669), Government of Chile.AN was employed by Marine Harvest, Kindrum, Fanad, C. Donegal, Ireland. JL was employed by company Benchmark Genetics Chile, Puerto Montt, Chile. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


HERC2 and FADS responsible for blue eye color and synthesis of long-chain unsaturated fatty acids, respectively, when Slovenians were compared to Southern Europeans. While the comparison was done with Eastern Europeans, we identified significant signals in PKD2L1 and IL6R which are genes associated with taste and coronary artery disease, respectively.The Slovenian territory played a crucial role in the past serving as gateway for several human migrations. Previous studies used Slovenians as a source population to interpret different demographic events happened in Europe but not much is known about the genetic background and the demographic history of this population. Here, we analyzed genome-wide data from 96 individuals to shed light on the genetic role and history of the Slovenian population. Y chromosome diversity splits into two major haplogroups R1b and R1a with the latter suggesting a genetic contribution from the steppe. Slovenian individuals are more closely related to Northern and Eastern European populations than Southern European populations even though they are geographically closer. This pattern is confirmed by an admixture and clustering analysis. We also identified a single stream of admixture events between the Slovenians with Sardinians and Russians around ∼2630 BCE (2149-3112). Using ancient samples, we found a significant admixture in Slovenians using Yamnaya and the early Neolithic Hungarians as sources, dated around ∼1762 BCE (1099-2426) suggesting a strong contribution from the steppe to the foundation of the observed modern genetic diversity. Finally, we looked for signals of selection in candidate variants and we found significant hits in  The Slovenian territory is geographically located between the Alps, the Adriatic Sea and the Pannonian basin and as such it could have been used as a gateway for different populations over several periods of time. However, the presence of geographical and possibly cultural barriers could have led to a more puzzling role for this region. The territory of modern day Slovenia was settled during the 6th and 7th centuries AD by different Slavic tribes from at least two different directions: one from the north and one from the east . The oriPrevious genetic studies were only based on Y chromosome variation  showing All these elements highlight the necessity of a broader and more comprehensive genetic study on Slovenian population. Outcomes of this kind of study could be useful to several disciplines and not only for population genetics and linguistics. For example, in the context of genetic epidemiology, describing the genetic landscape of this population would be beneficial to understand the genetic background of disease-related loci and their distribution; in fact Slovenians show evidence of familial hypercholesterolemia (3.1%) , a diseaTherefore, our aims are to provide (i) a detailed characterization of the genetic structure of Slovenians in a broader European context using both Y chromosome and autosomal data, (ii) a description of the past and present admixture pattern, and (iii) a survey of variants putatively under selection and associated with different traits/diseases. For these purposes, we used genotype data from 96 Slovenian individuals and we analyzed them together with previously published modern and ancient samples.Overall, 96 samples ranging from Slovenian littoral to Lower Styria were genotyped for 713,599 markers using the OmniExpress 24-V1 BeadChips Figure  for a toFirst, 52 male samples were extracted from the dataset and Y chromosome haplogroups were assigned using AMY-tree v2.0 software . Input fPrincipal Component Analysis (PCA) was performed using the option –pca in PLINK v1.9 . PresencK) from K = 2 to 12. Cross validation error was also performed. Gene flow and time of admixture between Slovenian and modern populations in the Slovenian_HO_EU dataset was investigated using ALDER v.1.30  for modern populations with sample size greater than 10.We inferred the ancestral structure using ADMIXTURE v1.30 , using mR v.1.30  and MALDR v.1.30 . These tR v.1.30  using thp-value < 1e-8 were then selected. Furthermore, markers reported in Analyses looking for signals of selection were performed using PCAdapt implemented in the R package PCAdapt . A 2-popFirst, Y chromosome genetic diversity was assessed. A total of 52 Y chromosomes were analyzed for 195 SNPs. The majority of individuals  belong to the haplogroup R1a1a1a (R-M417) while the second major haplogroup is represented by R1b (R-M343) including 15 individuals (28.8%). Twelve samples are assigned to haplogroup I (I M170): five and two samples belong to haplogroup I2a (I L460) and I1 (I M253), respectively, while the remaining five samples did not have enough information to be further assigned. We then performed principal component analysis on autosomal data to further investigate the presence of structure within the Slovenian population. We did not find any clusters even when samples were highlighted by the region of origin , albeit showing a slightly higher amount of segments and total homozygosity , and blood pressure (ACAD10 and HECTD4) . Moreover, we found significant signals also using Bedouin, and Russian population as possible sources of gene flow. When tested for multiple admixture events (MALDER), we obtained evidence for one admixture event 165.391 ± 17.1918 generations ago corresponding to ∼2620 BCE (CI: 3101–2139) considering a generation time of 28 years , followed by MA1 with LBK_EN (Z-score -9.7) and Yamnaya with Stuttgart (Z-score = -8.6) used as possible source populations  while for Yamnaya and LBK_EN at 153.65 ± 22.19 generations ago . Outgroup f3 with Yamnaya put Slovenian population close to Hungarians, Czechs, and English, indicating a similar shared drift between these population with the Steppe populations . On the other hand, PCAdapt analysis using as reference South Europeans and Sardinians reveals significant differentiation for pigmentation allele rs12913832 (linked to blue eye color) in the HERC2 gene. The derived allele frequency of rs12913832 in Slovenians and Russians is 0.64 and 0.86, respectively, which is higher when compared to Greeks and Spanish showing a derived allele frequency of 0.25 and 0.29, respectively. Moreover, we found a significant differentiation for three polymorphisms  in FADS1 and FADS2 genes which are implicated in lipid traits showing higher derived allele frequency in Greeks when compared to the Slovenians. Interestingly, we identified a signal on BACE2 for rs6517656: this polymorphism shows a high derived allele frequency in Europe, Middle East and Asia while the ancestral allele is still predominant within Africa. A graphical representation of the worldwide allele variation is shown in Supplementary Figure Considering the pattern of admixture in Slovenian population, we searched for highly differentiated loci between Slovenians and specific reference populations. By comparing Russians and Slovenians we found that rs4129267 on Slovenia presents a peculiar landscape, composed by mountain regions on the North West changing to flat lands toward the East. The border with the West not only represents a geographical barrier but also a linguistic one. To date no previous studies have described the genetic variation in Slovenian population. We considered different measurements of population admixture, isolation, and selection. In this study, we addressed the genetic features of Slovenian genomes and how admixture and selection shaped the genetic diversity of this population. Analysis of Y chromosome variation showed a presence of two main haplogroups R1b and R1a, confirming previous findings and suggesting gene flow from the Steppe . AccordiFrom autosomal data our analyses on population structure revealed the absence of strong substructure within the Slovenian samples, although the samples came from different regions of the country. We discovered a strong affinity between Slovenians and Central-Eastern European populations such as Czechs and Hungarians. Slovenians are closer to North European samples respect to South European ones including the neighboring North Italian population. Our findings suggest that the Slovenian ancestry seems more closely related to population from Northern-Central Europe, respect to Western-South Europe.For purpose of further studies focused on genetic epidemiology, our analyses show that Slovenians have no evidences of isolation. Nevertheless, we found a specific pattern of ROH hotspot in our Slovenian samples, despite the limitation of the possibility to replicate this pattern in other populations of the dataset due to the difference of sample size. Some of these regions contains interesting genes linked to GWAS traits including Type 1 Diabetes that should be further investigated.Our analyses of admixture events using methods based on LD decay revealed that the modern Slovenian gene pool could be explained by several admixture events that happened in a single window of time. We also showed that that there is no support for multiple admixture events across time. Overall, the Slovenian genetic pool seems to have been formed during the Bronze Age period as admixture between North-Eastern European populations and Near-Eastern populations as proxy. This pattern has been further confirmed when we used ancient genomes. Specifically, we obtained the strongest signal for Slovenians using as references Yamnaya and Hungary Early Neolithic samples. The estimated admixture time falls within the range of that one obtained using modern populations. We can conclude that populations closely related to Yamnaya and early Neolithic Hungarians contributed during the Bronze age to the foundation of the modern genetic variability in Slovenians. We could make the hypothesis that also disease and specific traits-alleles were likely to have been introduced in the Slovenian genetic pool during this period, such as pigmentation alleles , lactose tolerance  and also immune related alleles such as rs4833095 in TLR1 .HERC2 for blue eye color and FADS1-FADS2 alleles; FADS genes are essential for the synthesis of long-chain unsaturated fatty acids  such as ty acids . These gty acids  and Greety acids , with alty acids . FinallyPKD2L1 which is a gene involved in taste (PKD2L1. Another signal has been found on IL6R: this gene was discovered as underlying coronary artery disease (IL6R was discovered to suppress feeding and improves glucose tolerance in mice (On the other hand, when we compared Slovenians with North-Eastern populations, such as Russians, Slovenians showed signature of selection in in taste  and alsoin taste  and ther disease  and harb in mice . OverallOne limitation of our study is the use of only SNP-chip data, future studies involving whole-genome sequencing would highlight more details the genetic features of the genes under selection, also enhancing the power to detect putatively deleterious rare variants.MM and PM designed the project, performed the analyses, and wrote the manuscript. MR-G and DG collected the samples. PG, MR-G participated in project coordination and helped to draft the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Emys orbicularis orbicularis, E. o. galloitalica, E. o. occidentalis). The Pyrenees are a biogeographically important region separating many lineages endemic to the Iberian Peninsula from their Western European counterparts. We discovered limited admixture, reflecting a complex biogeographic scenario. Simulations using Approximate Bayesian Computing supported that E. o. orbicularis invaded the Iberian Peninsula in the Holocene, circumventing the Pyrenees along the Mediterranean coast, and hybridized in the northern peninsula with the local coastal subspecies galloitalica, and to a lesser extent, with occidentalis. While E. o. occidentalis, and in particular E. o. orbicularis, expanded their ranges considerably during Holocene warming, E. o. galloitalica remained largely confined to its former Iberian refuge. Admixture among the three taxa is surprisingly low, and a future taxonomic investigation that includes the unstudied subspecies of E. orbicularis from North Africa, Eastern Europe and Asia has to determine whether their current status properly reflects their evolutionary divergence or whether certain taxa should be regarded as full species.Hybrid zones are natural laboratories allowing insights in genetic processes like lineage diversification, speciation and introgression. Using large sampling, 15 microsatellite loci and a mitochondrial marker, we examined the Pyrenean contact zone of three pond turtle taxa ( Introgression is defined as the introduction of foreign genetic material into the genome1, which happens in geographic regions where genetically distinct populations meet, mate and hybridize3. Such geographic contact zones correspond to two basic types3. Primary hybrid zones are understood as continuous populations diverging gradually along geographic clines, i.e. divergence is caused by isolation-by-distance with genetic divergence increasing with geographic distance. In contrast, secondary hybrid zones represent contact zones of genetically distinct populations that diverged in allopatry and came into contact again due to range shifts4. In the Western Palearctic, climatic changes during the Pleistocene triggered such processes6. During this period many thermophilic species retreated to southern glacial refugia7, e.g. common toads8, sand and green lizards10, pond turtles13 and grass snakes14, and recolonized more northerly regions with Holocene warming. Many refuge areas were located on the southern European peninsulas7, and this is particularly relevant for the Iberian Peninsula, which is separated from Western Europe by the Pyrenees. This mountain chain constitutes an important dispersal barrier and contact zone of Iberian and Western European lineages16. The Iberian Peninsula itself harbored several microrefugia or ‘refugia-within-refugia’17, in which distinct genetic lineages survived the last glacial. Holocene range expansions led to secondary contact and hybridization, for instance in tree frogs (Hyla spp.)18, ocellated lizards (Timon lepidus complex)19, Iberian wall lizards (Podarcis bocagei and P. carbonelli)20 and the Iberian hare and mountain hare 21. Another example is the European pond turtle (Emys orbicularis).Hybridization between closely related taxa is widespread and plays an important evolutionary role because introgression may lead to genetic enrichment22. Over this range, six named and two unnamed subspecies occur 24 meet in northern Spain. Emys orbicularis occidentalis, having an Ibero-Maghrebian range28, and E. o. galloitalica, distributed along the Mediterranean coast from northeastern Spain to Italy24, are thought to be old faunal elements of the Iberian Peninsula. Yet, the northern Iberian populations of E. o. occidentalis are derived from a recent range expansion from the south of the peninsula28. Emys o. orbicularis is considered a Holocene invader from Central and Southeastern Europe31. A pioneering study examining gene flow across the Iberian contact zone, using eight microsatellite loci and the mitochondrial cytochrome b (cyt b) gene of 146 samples from this region, found pronounced cytonuclear discordance, with mitochondrial introgression, but surprisingly low levels of nuclear admixture24. Because this result is unexpected for subspecies or recently diverged lineages, it required further examination. Here we use sequences of the mitochondrial cyt b gene and 15 highly polymorphic nuclear microsatellite loci of over 800 pond turtles, including 663 samples from the contact zone, to scrutinize the geographic and genetic extent of genetic admixture and introgression in the Iberian contact zone.The distribution ranges of three subspecies . Laboratory procedures for cyt b gene amplification and sequencing followed an established protocol13.We isolated total DNA using standard proteinase K and phenol chloroform protocolsb sequences that were merged with 101 previously published sequences40 using BIOEDIT 7.0.5.241. New sequences were trimmed to 1031 bp length to match previously published data. Using POPART (http://popart.otago.ac.nz) and the implemented parsimony network algorithm of TCS42, we built a haplotype network using these data. Haplotype nomenclature follows previous studies43 in that a letter follows a Roman numeral, corresponding to the individual haplotype and the mitochondrial lineage, respectively. The European Nucleotide Archive (ENA) accession number of a newly identified haplotypes is LS997573.If sampling sites were represented by more than twenty individuals, we randomly selected for mtDNA sequencing twenty samples. In total, we generated 564 new cyt b gene, we genotyped 744 samples using 15 previously characterized unlinked and highly polymorphic microsatellite loci44 according to procedures described elsewhere25  and south of the Pyrenees (599 samples), respectively.Because uneven sample sizes may bias STRUCTURE results53 for R 3.2.3.In addition to STRUCTURE analyses, we examined our microsatellite data with Principal Component Analyses (PCAs) using the package ADEGENET54 for determining the threshold for mixed ancestry in STRUCTURE analyses. One data set included 20 genotypes each of pure E. o. orbicularis and E. o. galloitalica as parental genotypes, another data set included 20 genotypes each of pure E. o. galloitalica and pure E. o. occidentalis as parental genotypes, and the third data set included 20 genotypes each of pure E. o. orbicularis and E. o. occidentalis as parental genotypes. Based on each data set, we modeled 20 genotypes of each hybrid class . We then subjected the simulated hybrid data to STRUCTURE analyses, together with the data of the 20 pure genotypes of each taxon. According to these STRUCTURE runs including modeled hybrid genotypes, we identified individuals with cluster membership proportions below 85% as having mixed ancestries.We used simulations with the software HYBRIDLAB 1.0We assigned our 71 individual collection sites to seven geographically coherent groups to achieve comparable sample sizes. For each individual subspecies, we lumped together collection sites representing pure populations (groups 1–3). According to the collection sites, the four remaining groups 4–7 corresponded to putatively admixed populations. Groups 4 and 5 represented populations in the west of the contact zone Fig. ; groups E. o. occidentalis , 2 (E. o. orbicularis) and the admixed groups 4 and 5 were compared. Then groups 1 , 2 (E. o. orbicularis), 3 , and the admixed groups 6 and 7 were compared. We calculated for the two approaches population genetic diversity indices, pairwise FST values and analyses of molecular variance (AMOVAs) using CONVERT 1.3155 and ARLEQUIN 3.5.1.256.Group 1 consisted exclusively of pure E. o. occidentalis through the contact zone to pure populations of E. o. orbicularis. A Mediterranean transect starts also inland in the Iberian Peninsula and runs across the contact zone of the three taxa at the Mediterranean coast to southeast France, i.e. the transect runs from pure populations of E. o. occidentalis to pure populations of E. o. galloitalica. The third transect connects the Atlantic and Mediterranean transects along the course of the Ebro valley. For analyzing the third transect, we used only microsatellite data to calculate the cline, because the wide syntopic occurrence of distinct mitochondrial lineages along the transect prevents the use of this marker system.To examine nuclear genomic and mitochondrial introgression patterns across the Pyrenean contact zone, we computed hybrid clines for three transects Fig. . An AtlaQ of cluster membership (as inferred by STRUCTURE) for each population19. For mtDNA data, we used the frequency of haplotypes of each mitochondrial lineage per population, that is the frequency of haplotypes of E. o. orbicularis (lineage II), E. o. galloitalica (lineage V) or E. o. occidentalis (lineage VI).For the cline analyses of microsatellite data, we calculated the mean proportion 57, corresponding to each transect. This R package fits molecular genetic data to classic equilibrium cline models using the Metropolis-Hastings Markov chain Monte Carlo algorithm. For cline fitting, we added geographical information of sites to genetic information. For this, we measured geographical distances between populations using ARCGIS 10.3 (www.esri.com/arcgis) and adjusted then the 15 HZAR models to the mean proportions of cluster membership or haplotype frequencies  and among E. o. occidentalis, E. o. galloitalica and E. o. orbicularis . For the Mediterranean range, we assumed two different admixture events: First, contact between E. o. occidentalis and E. o. galloitalica, and subsequently contact with E. o. orbicularis. The latter subspecies is thought to be a Holocene invader whereas E. o. galloitalica survived the last glaciation along the Mediterranean coast24 and, therefore, contact with the northwards expanding E. o. occidentalis28 should have established first. From the present to the past, these contacts have times t1 and t2 and admixture rates r1  and r2 (contribution of E. o. orbicularis).For dating and inferring the history of the secondary contact between the pond turtle taxa, we analyzed both microsatellite and mtDNA data using the Approximate Bayesian Computation approach of DIYABC 2.1.05 data sets for each lineage to build a reference table and used the closest 1% of the simulated data sets to the observed data to estimate demographic parameters. As summary statistics we used for mtDNA data: (i) the number of haplotypes, (ii) the number of segregating sites, (iii) the mean of pairwise differences, (iv) private segregating sites, (v) the mean of pairwise differences (W), (vi) the mean of pairwise differences (B), and (vii) FST; and for microsatellites: (viii) the mean number of alleles, (ix) the mean genetic diversity, and (x) FST. We accepted the default mutation rate suggested for microsatellites in the software (minimum of 10−4 and a maximum of 10−3). For mtDNA, we considered a minimum mutation rate of 10−9  and two orders of magnitude more as maximum. All other settings were left as suggested in the DIYABC 2.0 handbook. To convert the obtained estimates of admixture and divergence times from generations to years, we assumed an approximate generation time of 15 years, based on life history data of a pond turtle population from southwestern Spain .We set priors to follow uniform distributions ranging from 10 to 10,000 for effective population sizes and admixture times and for diversification times, from 10 to 100,000. We simulated 10Emys orbicularis orbicularis, E. o. galloitalica, and E. o. occidentalis, respectively25. This is in line with expectations from previous investigations27. In addition, we recorded a few turtles having haplotypes of lineages I or IV, which were not expected to occur in our study region and which are interpreted as introduced turtles (a common phenomenon in Europe39). The native distribution ranges of lineages I and IV are Eastern Europe and Western Asia as well as eastern Italy and the southwestern Balkans, respectively13. In parsimony network analyses  occurred in Morocco and the Iberian Peninsula  were distributed in our study region along the Atlantic coast of France through northern Spain to the Mediterranean coast of Spain and France. Along the Mediterranean coast of Spain haplotypes of lineage II occurred together with haplotypes of E. o. galloitalica (lineage V) and E. o. occidentalis (lineage VI). Moreover, haplotypes of E. o. orbicularis occurred together with haplotypes of E. o. occidentalis south of the Pyrenees. Furthermore, we found haplotypes of E. o. galloitalica (lineage V) along the Mediterranean coast of France and in one turtle , there was no obvious geographical pattern in haplotype distribution , there was no obvious pattern. All turtles harbored haplotype Va. In contrast, in lineage VI  some haplotypes were clearly geographically differentiated. Three haplotypes  were endemic to northern Morocco and showed mutually exclusive distributions with Iberian haplotypes. In the Iberian Peninsula, haplotype VIa was widespread and the most frequent haplotype. We found it in some sites together with rare Iberian haplotypes of lineage VI. Some western sites  in that haplotypes typical for E. o. galloitalica (lineage V) and E. o. orbicularis (lineage II) were combined with nuclear genotypes of E. o. occidentalis (blue cluster). Weak nuclear admixture also occurred in these regions.The 15 microsatellite loci were highly polymorphic. The number of alleles per locus varied between 12 and 29 Table , and the49, we ran STRUCTURE also for subsets from north and south of the Pyrenees, respectively. For both subsets, K = 2 was revealed as best solution , had mitochondrial haplotypes of lineage II, typical for E. o. orbicularis , were represented by turtles occurring on the Mediterranean coast of France  and E. o. galloitalica (green) were not differentiated, with widely overlapping individual values and 95% confidence intervals, matching the finding that these subspecies were lumped together in STRUCTURE analyses using the whole data set. In contrast, E. o. occidentalis (blue) was clearly distinct. Admixed individuals  were also highly distinct from E. o. orbicularis and E. o. galloitalica, but not clearly differentiated from E. o. occidentalis.The Principal Component Analyses (PCAs) corroborated these results Fig. . The twob gene and microsatellites, whereas only microsatellites were examined for the transect running through the Ebro valley, because the widely syntopic occurrence of distinct mitochondrial lineages prevents the use of this marker system for cline analyses in this region.The Atlantic and Mediterranean transects Fig.  were anab gene, the center was estimated to be 735.73 km  northeast of the reference site  was estimated to 0.26  and r2  and E. o. orbicularis (group 2) with admixed populations from the Atlantic coast north (group 4) and south (group 5) of the Pyrenees , we compared pure individuals of ees Fig. ; Table 1es Table , and the75 Table , and theE. o. orbicularis (group 2), E. o. galloitalica (group 3) and E. o. occidentalis (group 1) with admixed populations from the Spanish and French Mediterranean coast , we compared pure individuals of is group  with admes Table , and theEmys orbicularis orbicularis, E. o. galloitalica and E. o. occidentalis). This region was already examined in a previous study24, which found only few turtles with mixed ancestry, but extensive cytonuclear discordance, suggestive of incipient isolation mechanisms reducing gene flow. However, due to patchy sampling and less informative genetic markers compared to our study, only preliminary conclusions could be drawn.Our data set of more than 800 samples covers the entire distribution range of the European pond turtle in Morocco, the Iberian Peninsula and large parts of its range in France, with a focus on the Pyrenean contact zone of three subspecies , which have been used successfully to scrutinize fine-scale gene flow in another contact zone of the E. orbicularis complex in southern Italy13. Most notably, using approximately six-fold greater sampling and fine-scale analyses , gene flow was largely unidirectional from orbicularis into galloitalica . Gene flow among all three taxa was evident  combined with a mitochondrial haplotype of E. o. occidentalis . In contrast, there were many pure genotypes of E. o. occidentalis (blue columns) or admixed genotypes combined with haplotypes of E. o. orbicularis  or E. o. galloitalica . This provides evidence for unidirectional gene flow from E. o. occidentalis into E. o. orbicularis and E. o. galloitalica. This pattern was also supported by PCAs using microsatellite data in that admixed individuals clustered with E. o. occidentalis but are distinct from the two other taxa  or E. o. galloitalica (V) north of the Pyrenees. The vast majority of turtles there represented genotypically pure E. o. orbicularis or E. o. galloitalica and their hybrids , while the genetic impact of E. o. orbicularis commenced only approximately 5,700 years BP. During its northward range expansion, the topology forced E. o. occidentalis to follow two major routes, one along the Mediterranean coast to enter the Ebro valley from the east, and the other along the Atlantic coastal corridor to enter all the river basins dissecting the Spanish Meseta .South of the Pyrenees admixture with  VI Fig. . Neverthids Fig. . In the s24 Fig. . This scE. o. occidentalis and E. o. orbicularis lead to admixture of these subspecies in the Ebro valley and with E. o. galloitalica in its former glacial refuge at the Mediterranean coast of Spain (Fig. E. o. galloitalica was largely imprisoned in its refuge consisting of isolated short rivers, without many possibilities to disperse. This situation explains why until today largely pure E. o. galloitalica occur in some sites there.For the pond turtle, the antidromic range expansions of ain Fig. . In contE. o. occidentalis representing the most advanced stage. A future taxonomic investigation that includes also the subspecies from North Africa, Eastern Europe and Asia, not covered by the present study, has to resolve whether an elevation of one or more subspecies to full species level matches their evolutionary divergence better than lumping all together in one species, a situation reminiscent of the recent taxonomic break-up of smooth67 and crested newts68, common toads69, tree frogs71, slow worms72 and grass snakes74.The observed hybridization patterns across our study region suggest that the three taxa correspond to different stages in the speciation process, with Supporting Information


SNP array by combining three different statistical methods based either on allele frequencies  or haplotype differentiation (Rsb). We identified 337 and 270 SNPs potentially under divergent selection in wild and hatchery populations of Canadian and Scottish origins, respectively. We observed little overlap between results obtained from different statistical methods, highlighting the need to test complementary approaches for detecting a broad range of genomic footprints of selection. The vast majority of the outliers detected were population‐specific but we found four candidate genes that were shared between the populations. We propose that these candidate genes may play a role in the parallel process of domestication. Overall, our results suggest that genetic drift may have override the effect of artificial selection and/or point toward a different genetic basis underlying the expression of similar traits in different domesticated strains. Finally, it is likely that domestication may predominantly target polygenic traits  such that its genomic impact might be more difficult to detect with methods assuming selective sweeps.Selective breeding and genetic improvement have left detectable signatures on the genomes of domestic species. The elucidation of such signatures is fundamental for detecting genomic regions of biological relevance to domestication and improving management practices. In aquaculture, domestication was carried out independently in different locations worldwide, which provides opportunities to study the parallel effects of domestication on the genome of individuals that have been selected for similar traits. In this study, we aimed to detect potential genomic signatures of domestication in two independent pairs of wild/domesticated Atlantic salmon populations of Canadian and Scottish origins, respectively. Putative genomic regions under divergent selection were investigated using a 200K  This evSalmo salar L.], rainbow trout [Oncorhynchus mykiss W.], tilapia [Oreochromis niloticus L.], common carp [Cyprinus carpio L.]) in which selective breeding programs have been implemented, focusing on specific economic objectives      (BAYESCAN), (b) genomewide environmental association (LFMM), and (c) haplotype extended patterns (Rsb). From the set of candidate genes detected by these approaches, we identified their biological function to delineate their potential importance in the process of domestication of Atlantic salmon populations.Across the entire Atlantic salmon natural range, European and North American populations exhibit a deep genetic divergence and belong to two divergent glacial lineages which likely separated more than 1,000,000 years ago  and seven North American fish, as described by Yáñez et al. (Genomic DNA was extracted from fin clips using a DNeasy Blood & Tissue Kit (QIAGEN). For genotyping, we used an Affymetrix Axiomz et al. . SNP quaz et al. . Loci wiz et al. .2.3FST (1984) estimator across all loci among populations, using VCFTools  were estimated using PLINK v1.07  implemented in the R package adegenet . The optimal K was selected based on the lowest cross‐validation error and a visual inspection of the co‐ancestry values for each individual.To investigate population differentiation, we calculated the pairwise Jombart, . Second,Jombart,  software2.410 = 1–1.5) for one locus is considered putatively under divergent selection, while between 32 and 100 (log10 = 1.5–2) and Bayes factors above 100 (log10 > 2) are considered putatively under very strong and decisive divergent selection, respectively. We used a Bayes factor of 10 (log10 = 1.30) as threshold to select candidate loci under selection.Three methods were implemented to detect signatures of selection: (a) population differentiation, (b) environmental association, and (c) haplotype extended patterns. First, we used the population differentiation approach with a Bayesian likelihood method, implemented in BAYESCAN v.2.1, which identifies candidate loci under selection using differences in allele frequencies between populations. The algorithm uses a reversible‐jump Markov Chain Monte Carlo to explore models with or without selection. To estimate the probability that a locus is under selection, the program uses a Bayes factor for two models: one assuming selection and another assuming neutrality given the data . This method can efficiently estimate random effects due to population history and isolation‐by‐distance and can reduce false‐positive associations compared to genome scans  and as recommended by Frichot et al.  between pairs of populations (Rsb) analysis  for each SNP within each population. As the Rsb values are normally distributed, a Z‐test was applied to identify significant SNPs under selection between wild and domestic strains of both Canadian and Scottish origins. A positive value of Rsb indicate iESPOP1 is larger than iESPOP2; therefore, Pop1 has longer haplotypes than Pop2, and hence, positive Rsb scores suggest that selection occurred in the alternative population (domestic population), whereas the negative scores suggest that selection occurred in the reference population (wild population). One‐sided p‐values were obtained as −log10(1‐Φ(Rsb)), where Φ(Rsb) represents the Gaussian distribution function for the Rsb statistic. We used −log10 = 3 as a threshold to define significant Rsb values Standardized log‐ratio of integrated EHHS (iES) between pairs of populations using rehh R package , were interrogated for genes annotated to the Atlantic salmon genome reference ICSAG_v2 (GenBank: GCA_000233375.4) using SnpEff v4.3  peptide reference database (downloaded from http://www.ensembl.org). For each unique peptide in this species, the corresponding gene id and associated gene name were retrieved from ensembl biomart database (retrieved from http://www.ensembl.org/biomart). Enrichment analysis was conducted using the online tool Panther Classification System (http://www.pantherdb.org) with default parameters.Using the salmon transcripts, we performed a blastx on the zebrafish  poly high resolution  and (b) no‐minor allele homozygous (good cluster resolution with no‐minor homozygous genotypes). Thus, a total of 151,509 SNPs were anchored to a unique location on the last version of Atlantic salmon genome assembly (GenBank: GCA_000233375.4)  and expected heterozygosity (HE) were higher in Scottish than Canadian stocks. Overall, HO was slightly higher than HE in every population evaluated and heterozygosity levels were higher in both wild populations in comparison with their domesticated counterpart. Indeed, HO was 0.404 (CI = 0.400–0.407) versus 0.353 (CI = 0.350–0.356) and 0.297 (CI = 0.294–0.299) versus 0.266 (CI = 0.264–0.269) in wild versus domesticated Scottish and Canadian populations, respectively. A similar trend was observed for HE with wild Scottish exhibited the highest level , followed by domestic Scottish , while wild and domestic Canadian HE was lower . All these genetic diversity measures were statistically significant from each other .Using the set of markers with MAF > 5% common to all four populations revealed that measures of observed heterozygosity , although the FST between both wild populations was 0.286 (CI = 0.284–0.288). When combining wild and domestic individuals , Canadian and Scottish populations still showed a high level of genetic differentiation . A lower FST was observed between Canadian wild and domestic populations  which was twice as large as the FST estimated between wild and domestic Scottish populations .The highest level of differentiation was found between both domestic populations originating from Canada and Scotland , wild Scottish (Sct‐W), domesticated Canadian (Can‐D), and domesticated Scottish populations  and the second one of 2,672 Kb (26 SNPs), respectively. Chromosomes Ssa03 and Ssa04 also contained, respectively, 15 and 27 loci potentially under divergent selection, the majority with a −log10 ≥ 2. Other chromosomes with several outlier markers were Ssa13, Ssa15, and Ssa21, with 13, 10, and 15 loci, respectively.In Canadian populations, Bayescan analysis detected 160 SNPs putatively under divergent selection Figure a. These In Scottish populations, Bayescan detected 197 outliers SNP distributed along the vast majority of the chromosomes, similarly to what we observed for Canadian populations Figure b. Yet, i3.3.2z| scores higher than 10 as the threshold to select SNPs associated with domestication using this approach. This cutoff value indicated significant SNP effect at the level of p‐value < 10e‐7 after applying a standard Bonferroni correction for α = 0.05. We found 115 outliers in Canadian populations  equal to three within a region of 1,269 Kb long. The region with the second highest score was on Ssa27 with 14 significant markers inside a 280 Kb region. For the comparison between wild and domestic in Scottish populations, Rsb detected 67 SNPs under selection  in Atlantic salmon genome annotation. In Scottish populations, three shared SNPs belong to two predicted genes (brwd3‐like and sh3bgrl3‐like). Only one common outlier on chromosome 25 was shared between the two geographical origins .We uncovered a total of 337 and 270 SNPs putatively under divergent selection for Canadian and Scottish populations, respectively, according to the three complementary methods , collagen alpha‐1XIII chain (coda1), autism susceptibility 2 protein‐like (LOC106585083), and transient receptor potential cation channel subfamily M member 3‐like (LOC106580056). A list of the most important genes is described in Tables Overall, we found a total of 138 and 121 genes among the 337 and 270 outlier sequences detected as being potential targets of selection between wild versus domesticated Atlantic salmon, for Canada and Scotland, respectively. Among these sets of genes, we discovered four genes overlapping continents: ubiquitin‐conjugating enzyme E2F putative  mapping have already reported a large number of different QTLs for growth  than wild populations (FST = 0.286). This outcome is in agreement with the work of Mäkinen et al. (FST = 0.171) between domestic and wild Scottish populations showed that using a wild population from a nearby geographical region as a genetic proxy was appropriate, in the absence of a true ancestral population.Of the 151,509 SNPs successfully genotyped, 61,199 and 130,586 SNPs were polymorphic for Canadian and Scottish populations, respectively. This striking difference on the number of polymorphic SNPs is in accordance with earlier studies based on various types of markers  showing that North American Atlantic salmon populations have lower genetic diversity than European populations  were identified in both comparisons. There is evidence that collagen genes may be involved in the response of Atlantic salmon to sea lice as initiators of inflammatory cytokine signaling , coding for ubiquitin‐conjugating enzyme E2‐F. Growth and development of skeletal muscle undergo breakdown and replacement of proteins during different periods, and in teleost fish, this process involves E2‐ubiquitin‐conjugating enzymes and E3‐ubiquitin ligases  has been associated with autism and mental retardation in humans  included supervillin‐like gene, which is a protein involved in actin and myosin II assembly that promotes cell growth  and the zona pellucida (ZP) sperm‐binding protein 3‐like gene (Zp3) in Canadian and Scottish populations, respectively, both localized in chromosome Ssa27. The bromodomain‐containing 2 gene may be involved in spermatogenesis or folliculogenesis (Rhee, Brunori, Besset, Trousdale, & Wolgemuth, No other overlaps were found with previous studies related to sexual maturity in Atlantic salmon, for both Canadian and Scottish populations. Nevertheless, we found putative outliers in the bromodomain‐containing two genes (5Domestication events in Atlantic salmon have produced pronounced phenotypic changes in domesticated fish compared to their wild counterpart, and the selection process has left important signals throughout the genome of these populations. This study highlighted the existence of nonparallel and parallel signatures of selection between domesticated versus farmed fish, detected by different statistical approaches. A small number of parallel genomic regions putatively under selection belong to genes with molecular functions that might be associated with traits under domestication, such as behavior, immune response, and reproduction. However, the vast majority of genomic regions detected as being putatively under selection are not shared either among method or lineages, suggesting that domestication may involve a different set of genes, perhaps because of the distinct evolutionary history and very different standing genetic variation of Canadian and Scottish populations. Another possibility is that alternative methods to genome scan that test for genomic signal of polygenic adaptation are required for revealing genetic architecture underlying trait of interest. Indeed, the most important trait for which Atlantic salmon has been artificially selected is growth, whereas few selection signatures possibly associated with this trait have been detected, perhaps due to the polygenic nature of this trait. Nevertheless, our results contribute to the detection of variants that may underlie important traits of practical interest for aquaculture and to fully understanding the early patterns and effect of domestication in Atlantic salmon.None declared.https://doi.org/10.5061/dryad.60b9p56.Data available from the Dryad Digital Repository:  Click here for additional data file.


HCG22rs2535307 and CCNG1kgp22385164) significantly associated with HIV-1 acquisition and progression. High genetic diversity, multi-wave genetic mixture and low linkage disequilibrium of Southern African populations constitute a challenge in identifying genetic variants with modest risk or protective effect against HIV-1. We therefore posit that it is compelling to assess genome-wide contribution of ancestry to HIV-1 infection. We further suggest robust methods that can pin-point population-specific variants that may contribute to the control of HIV-1 in Southern Africa.The interesting history of Southern Africa has put the region in the spotlight for population medical genetics. Major events including the Bantu expansion and European colonialism have imprinted unique genetic signatures within autochthonous populations of Southern Africa, this resulting in differential allele frequencies across the region. This genetic structure has potential implications on susceptibility and resistance to infectious diseases such as human immunodeficiency virus (HIV) infection. Southern Africa is the region affected worst by HIV. Here, we discuss advances made in genome-wide association studies (GWAS) of HIV-1 in the past 12 years and dissect population diversity within Southern Africa. Our findings accentuate that a plethora of factors such as migration, language and culture, admixture, and natural selection have profiled the genetics of the people of Southern Africa. Genetic structure has been observed among the Khoe-San, among Bantu speakers, and between the Khoe-San, Coloureds, and Bantu speakers. Moreover, Southern African populations have complex admixture scenarios. Few GWAS of HIV-1 have been conducted in Southern Africa, with only one of these identifying two novel variants ( The clatatistic . The vallations) . Geograplations)  as illusin silico variants that confer susceptibility or resistance to a particular disease through statistical association models  are approaches of scanning the genome to identify n models . Unlike mic loci . Althougmic loci  have notmic loci . The ovemic loci . Like otmic loci .Confounders such as population structure can affect GWAS results. These have to be controlled to avoid spurious results . MoreoveMost GWAS were performed in non-African populations  in whichMigration Into Southern Africa section) was created using maps package in R and edited using MacOS Preview software. We conclude with a discussion of research areas where further work on GWAS of HIV-1 is needed.We used PubMed search engine to retrieve HIV-1 GWAS studies which have been published in the past 12 years (2007–2019); species was restricted to the human species. The specific search terms were the following:((“genome”[MeSH Terms] OR “genome”[All Fields]) AND wide[All Fields] AND (“association”[MeSH Terms] OR “association”[All Fields]) AND (“hiv-1”[MeSH Terms] OR “hiv-1”[All Fields])) AND (“2007/01/01”[PDat]: “2019/04/30”[PDat] AND “humans”[MeSH Terms]). Ninety-eight items were retrieved; articles relevant to Southern Africa were used in the review. Cited studies which were not in the search results were directly searched for. To review population structure and admixture in Southern Africa, a relaxed search of the terms  was performed in PubMed, and relevant articles were selected for this review. SNP annotations were confirmed on dbSNP . A map oDemic diffusion has been shown to have a paramount role in genetic drift and gene flow, the two most important events that can trigger population structure. The major expansions of humans date as far back as 100,000 years ago (the Out of Africa (OOA) model) . One of Migration routes of the Bantu populations have been well described . AccordiIn more recent history, Southern Africa has seen an influx of migrants from other continents such as Europe and Asia. By 1750 the Dutch and Portuguese had colonized Southern Africa on the southwest and east coasts, respectively . These cThe inhabitants of Southern Africa could be distinctly classified according to ways of subsistence and food production: hunters and gatherers (and fishers) and the agro-pastoralists . It has In general, African populations can be classified into four main ethnolinguistic groups: Niger-Kordofanian, Afroasiatic, Nilo-Saharan, and Khoisan . Over 20Khoisan language families include Khoe, !Xun (Ju), Ta’a (including !Xo), !Wi, and Southwestern or Cape /Xam. The Khoe and the San intermixed resulting in linguistic affinities and diffusion of culture between the two groups; this led to them collectively being referred to as the Khoe-San . The preUsing autosomal SNPs, the Pygmy , the Hadza, and Sandawe clustered together with Southern African Khoe-Sans. This suggested that the Pygmy, Southern African Khoe-San, Hadza, and Sandawe populations are a remnant of a proto-Khoe-San-Pygmy population . AlthougThe Southern African Khoe-San underwent a population bottleneck due to barriers such culture, language, and geographic isolation . The Khoe-San also underwent genocide at the hands of Bantu speakers and European settlers. This stemmed from conflicts over land which the colonists had invaded while the Khoe-San also hunted the colonists’ cattle, as they used to hunt antelopes and elands . The popBantu speakers were agro-pastoralists, while the Khoe were pastoralists and the San were hunter-gatherers . There hThe gene pool of Southern Africa is a mosaic of alleles from multiple ancestries . Past hiConversely, genome-wide studies analyzing millions of SNPs revealed that the Khoe and the San are a genetically diverse group . SchlebuThe Khoe-San are genetically distinct from the Bantu populations . LikewisIn another study, SNPs associated with lactase persistence (LP) from eight Khoe-San and seven Bantu-speaking groups from Botswana, Namibia, and Zambia were characterized . LP is a-6) between the Xhosa and Sotho groups   . SoutherT ≥ 0.3) . AdmixtuT ≥ 0.3) .Bantu groups reached Southern Africa 1,200–2,000 years ago  and reacL0d and L0k mtDNA haplogroups have been previously shown to be localized in the Khoe-San . BarbierY-chromosome and mtDNA analyses of Southern African populations suggest patterns of sex-biased migration and admixture . The BanPrecolonial admixture of indigenous Southern African populations with populations of west Eurasian ancestry has also been documented . Due to The “Coloured” of South Africa (SAC) are another popular, well-studied population in Southern Africa . ConsistRecent direct admixture involving Eurasian groups has been detected using GLOBETROTTER, an R program,  in the ‡Mycobacterium tuberculosis  also known as zidovudine, was approved in 1987. This was an anti-cancer drug that was also found to inhibit reverse transcriptase . AZT andSouthern African countries concomitantly bear the brunt of both HIV and TB . In 2017CCR5-Δ32 . The minor allele G of HCP5 rs2395029 led to a reduction in viral load and delayed progression  which accounts for multiple testing of about one million SNPs. As a result of this, SNPs with small effect sizes are not detected by GWAS and thousands of samples are required to effectively perform GWAS. In a study of progression in 404 Europeans, no SNP reached genome-wide significance  showed improved viral control of the same magnitude with that observed in Europeans carrying the HLA-B*5701 allele , this indicating ethnic specificity of the variant associations . The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [grant # 107752/Z/15/Z] and the UK government. The views expressed in this publication are those of the authors and not necessarily those of AAS, NEPAD Agency, Wellcome Trust, or the UK government. The authors would also like to thank the National Research Foundation of South Africa for funding (NRF) [grant # RA171111285157/119056].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lymantria dispar, is among the most destructive quarantine pests of forests. Here, we reconstructed the genetic structure and determined the population differentiation of gypsy moths across its distribution range at different times. This information could be used to both improve the prevention and detection of gypsy moths in the field. Using 31 newly designed species-specific primers targeting fragments of 216–1102 bp, we identified 103 full-length cytochrome oxidase subunit I (COI) gene sequences from eight fresh samples and 95 L. dispar specimens collected between 1955 and 1996, mainly in China. Combining 103 full-length COI gene sequences with 146 COI gene sequences from Genbank or DNA barcode libraries, we analyzed the genetic differentiation, gene flow and haplotypes between gypsy moth populations in order to reflect the genetic structure and population dynamics of gypsy moths. We discovered 25 previously unknown haplotypes from old gypsy moth specimens. We found that the genetic diversity among gypsy moth populations (collected in the same region at different time points) was relatively high. Furthermore, the genetic structure of Chinese geographical populations  in different years was distinct. Our results suggested that some gypsy moths in China showed the genetic affinity with European gypsy moths (a sub-species of gypsy moths found mainly in Europe).The gypsy moth,  Lymantria dispar  (Insecta: Lepidoptera: Erebidae: Lymantriinae) [Lymantria dispar dispar Linnaeus, Lymantria dispar asiatica Vnukovskij and Lymantria dispar japonica Motschulsky [L. d. dispar is found mainly in Europe, including most countries and regions in Western Europe, North Africa, France and North America. L. d. asiatica occurs from Central Asia to East Asia, including Russia, China and the Korean peninsula, while L. d. japonica is restricted to Japan  [L. d. asiatica and L. d. japonica have long been thought to pose greater global threats because of the superior flight ability of the females [The gypsy moth, triinae) ,3, is amtriinae) ,5,6,7. Tschulsky . L. d. dokkaido) ,10,11,12 females ,15,16,17 females ,19.L. dispar to areas where it is not already present causes unpredictable effects upon the economic benefits and ecological stability of forest ecosystems [L. dispar over a wide geographic distribution by using various methods, such as allozyme detection, amplified fragment length polymorphism, restriction fragment length polymorphism, sequence-based analysis and microsatellites [Over recent years, increased levels of international trade and tourism have facilitated the inadvertent dispersal of gypsy moths beyond their native ranges. However, introducing osystems . For quatellites ,26,27,28COI, COII, NDI)  were investigated in order to perform cluster analysis for gypsy moths from Japan, Europe, Tunisia and North America. This strategy revealed four populations: Okinawa in Japan; Hokkaido in Japan; Honshu and Kyushu in Japan and the Asian continent; Europe, Tunisia and North America. Later, Keena et al. [COI), the nuclear FS1 marker and four microsatellite loci. In the first such attempt, by Bogdanowicz et al. , three ma et al.  separateL. dispar by analyzing microsatellite loci and the mitochondrial DNA sequences of 1738 gypsy moths, with three clusters corresponding to the three known subspecies ; the North American populations formed a distinct fourth cluster. Based on the DNA barcode sequences of gypsy moth specimens sourced from 16 sites in China, Japan, Russia, Europe and the USA, Chen et al. [L. dispar populations from Asia were analyzed by Kang et al. [More recently, Wu et al.  identifin et al.  divided g et al. , and revg et al. ,29.L. dispar, a subspecies that is particularly prone to taxonomic confusion. For example, some individuals of L. dispar asiatica that were collected from the southern coastal area of Korea reportedly had morphological characteristics similar to those of L. d. japonica [L. dispar [In most studies, however, only recently formed population genetic structures have been revealed; consequently, we know little about the precise history of gypsy moth populations in the field. In light of this, further investigations are required to identify the historical genetic structure and demographic history of japonica . Chen etjaponica  further . dispar . L. dispar populations based on intensive sampling was deemed necessary. To this end, we investigated the genetic structure of gypsy moth specimens (collected over the past 10 years or even decades ago) in several regions of the world, by using their COI (cytochrome oxidase subunit I) sequences. We also used several dried and formalin-fixed specimens, collected between 1955 and 1996, instead of fresh samples. Worldwide, museums and other natural history collections, house millions of animal and plant specimens, making these institutions a treasure trove of biological diversity [To resolve this taxonomic confusion between gypsy moth subspecies, a demographic history of iversity . The comiversity ,34,35. Fiversity ,37. Amoniversity ,48,49,50COI gene sequences of gypsy moth specimens were amplified and spliced; along with the COI gene sequences of gypsy moths deposited in GenBank or DNA barcode libraries, we hoped to enhance our understanding of gypsy moth dynamics. For instance, our aim was to use this robust dataset to characterize the variation in the genetic structure of populations over different years and genetic differentiation in populations across different years. We particularly focused upon the mitochondrial cytochrome oxidase subunit I (COI) gene, which has been successfully used in the past to investigate phylogenetic relationships in the three L. dispar sub-species and widely used to study the genetic structure and genetic differentiation of the gypsy moth populations [Building upon this foundation, in the present study, we have attempted to link molecular data from museum specimens, as well as recently collected fresh samples in order to assess the genetic associations of these specimens. In short, this study made use of a large number of specimens which had been collected over the past 53 years (1955–2008). Importantly, these included many specimens taken at the earliest known time of gypsy moth distribution in China, as well as specimens collected from the USA in 1996. All ulations ,29,30.In 2017, we obtained fresh larval samples from the Insect Virus Research Centre of the Chinese Academy of Forestry. Several gypsy moth egg masses were collected in Liaoning Province in 2008 , and wheA total of 75 dried gypsy moth specimens, and 20 formalin-fixed larval specimens, were analyzed . All of All laboratory equipment and work areas were first cleaned with 75% ethanol to limit potential contamination before extraction. We also used new consumables and reagents to prevent possible cross-contamination of DNA extraction from other species. All specimens were briefly treated to remove surface contamination before DNA extraction. For fresh samples, larval samples were first washed with sterile distilled water, and part of their abdomen was ground in liquid nitrogen. Sterilized brushes were used to remove bristles and dust from the old dried specimens prior to DNA extraction. All dried specimens were also washed in sterile distilled water to remove surface contamination, and then air-dried. For dried specimens, part of their abdomen was ground in liquid nitrogen, without causing any damage to other external morphological features. The ground tissue (40 mg) of both fresh samples and dried specimens was then used for DNA extractions. The tissue was then lysed, and the lysate purified with an E.Z.N.A.™ Insect DNA kit  in accordance with the manufacturer’s instructions, but with a slight modification, i.e., lysate was incubated at 60 °C for 2 h.For formalin-fixed larval specimens, another DNA isolation protocol was required. Approximately 60 mg of tissue from each specimen was cut into pieces and centrifuged at 10,000 g for 2 min. The supernatant was then discarded. Tissues were then rinsed overnight in xylene at room temperature (10–25 °C), centrifuged at 10,000 g for 1 min and the supernatant was discarded. Next, all tissue samples were treated with a gradient of ethanol concentrations  for 2 hours, and centrifuged at 5000 g for 10 min to gradually break protein-DNA cross-linkages caused by the long-term exposure to formalin. Lastly, the specimens were left to dry overnight at room temperature (10–25 °C). After completing this drying process, the E.Z.N.A.™ Insect DNA kit was used in accordance with the manufacturer’s instructions, but with a slight modification: lysate was incubated at 60 °C for 2 h. Extracted DNA was stored in an elution buffer (EB) at –20 °C. Samples from different years were individually extracted, to eliminate any cross-contamination between DNA extracts. After extractions, a DS-11 Spectrophotometer (DeNovix) was used to determine the DNA concentration and absorbance ratio (A260/A280) of each sample. Generally, the expected ratios for extracted DNA samples from insects should range from 1.7 to 2.0 according to the manufacturer’s protocol for the E.Z.N.A.™ Insect DNA kit.COI sequences from L. dispar , 31 new species-specific primers , 1 µL of each primer (10 µM), 1 µL of DNA Template (25.0–125.0 ng/µL), with ddH2O added to 25 µL. The PCR conditions were as follows: 3 min at 94 °C; followed by 30 cycles for 30 s at 94 °C, 30 s at 48.0–56.8 °C , 100 mM KCl, 3 mM MgClCOI sequence. To verify the authenticity of COI sequences obtained from old specimens, we crosschecked the new sequences with established reference sequences .All sequences for a given specimen were first aligned separately in BioEdit v7.2 software  and thenCOI gene sequences of gypsy moths, from various countries and different sites, were previously amplified and uploaded into Genbank or DNA barcode libraries (Bold Systems V4). Such sequences can provide crucial information for defining genetic structure and demographic history in terms of its area of origin and adjacent areas [COI gene sequences of gypsy moth specimens collected during different periods. Combining this information with the COI gene sequences of gypsy moths published in Genbank or the DNA barcode library (Bold Systems V4), we identified the genetic structure of gypsy moths in specific periods and discussed associated changes in genetic structure at different times. Therefore, in order to reveal the historical genetic structure of gypsy moths, in addition to the specific gene sequences obtained in this study, we also downloaded the following gene sequences from Genbank or the DNA barcode library (Bold Systems V4): (1) 11 full-length COI gene sequences from gypsy moths from around the world , combined with the COI gene sequences of gypsy moths obtained in this study, both of which may perhaps reveal previously undetected haplotypes; (2) 86 COI gene sequences from gypsy moths in China  combined with the COI gene sequences of gypsy moths obtained in the present study, which could similarly reveal previously undetected haplotypes of the Chinese gypsy moth populations; (3) 63 COI gene sequences collected from Japan, Russia, the USA, Greece, Lithuania and different regions of China in the 1990s , which revealed the genetic structure and population differentiation of the gypsy moth population in the 1990s; and (4), recently published COI gene sequence from Chinese gypsy moth populations (GMCF001-14~GMCF074-14). It was hoped that these sequences (including COI gene sequences of the gypsy moth specimens) could reveal temporal changes in the genetic structure of the gypsy moth population. The information of all the downloaded gene sequences is shown in The nt areas ,29,30. ICOI gene sequences obtained during the same time period were correspondingly assigned to the same group according to the time of collection. For instance, 28 COI gene sequences from the HN55, HLJ56, NM56 and DX57 populations were used to analyze the genetic structure among four geographic populations in the 1950s. Similarly, 18 COI gene sequences from three regions  were used to assess genetic structure among these three populations in the 1970s. Combining this data with COI gene sequences from the 1990s and 2010s from Heilongjiang, Liaoning and Beijing, enabled us to analyze changes in genetic structure across different years. Furthermore, we had hoped that DNA barcode sequences from the same region, but from different years, may well provide information relating to the population dynamics of gypsy moths in that region. For example, using this model, we analyzed the COI sequences for the Beijing populations over several years .Compared with the recently published datasets from fresh samples, the gene sequences used in this study provide additional valuable information relating to species history. However, this heterochroneous dataset, which features population-level information spanning 53 years (1955–2008), intrinsically violates the assumption of constant sampling time; hence, heterochrony had to be explicitly avoided for in further analyses. To remove such heterochrony-driven bias, the FST) and gene flow (Nm) are two key indices that reflect genetic structure. The gene differentiation coefficient is often used to reveal variation in allele frequency among different populations, along with the degree of genetic differences between disparate species or populations, which is an important parameter that expresses the evolutionary rate of colonies. As gene flow can maintain genetic similarity between populations through the migration of gametes, individuals, or even entire populations, this represents a key factor in the homogenization of population genetic structure. Generally, the species populations with high gene flow have a lower degree of genetic differentiation, whereas populations with low gene flow will exhibit greater genetic differentiation. According to gene flow studies, genetic drift becomes the main factor driving genetic differentiation among populations when Nm < 1. In contrast, when Nm > 4, gene exchange between populations is sufficient to counteract the effects of genetic drift, thereby preventing genetic differentiation between populations [FST values and Nm values between L. dispar populations were calculated using the Kimura two-parameter model in Arlequin v3.0  [COI gene sequences.Genetic differentiation coefficients   based onCOI gene sequences of gypsy moths, using the program NETWORK v4.6.1.3 [The median-joining haplotype network can be useful for bothconveying population structures and estimating the genealogical relationships among various haplotypes. For this reason, a median-joining network was also derived based on the v4.6.1.3 . Moreovev4.6.1.3 .NJ) phylogenetic approach in MEGA v7.0 software [COI sequence from Lymantria albescens (Genbank ID: NC035627) serving as an outer group. The best evolutionary model was chosen, and its phylogeny was tested by the bootstrap method. Rates of change among lineages were presumed to be gamma distributed (G), and the bootstrap of each branch of the phylogenetic tree was subjected to n = 1000 replications.Phylogenetic trees were constructed using the neighbor-joining  of 1.8–2.0. DNA extracts from formalin-fixed specimens exhibited a DNA concentration of 18.7–28.3 ng/µL; here 260/280 ratios mostly ranged from 1.8 to 2.0. These results indicated that DNA extracts for the majority of samples were pure and could be sequenced. To do this, 31 newly designed primer pairs were tested with fresh samples obtained in 2018; 30 of these successfully generated COI gene sequences obtained from old specimens and 11 published COI gene sequences, we discovered 25 new haplotypes , LN11 (Liaoning in 2011) and GZ (Guizhou in 2011) populations. Chen et al. [COI genotype of LN11 and GZ populations was similar to that of the Siberian gypsy moth.In a parsimonious network built with 103 full-length plotypes . Compariplotypes . Moreoveplotypes . Fourteeplotypes , based oplotypes . We alson et al.  indicateCOI sequence over time and to provide insight into gypsy moth dynamics in Heilongjiang, China. We first analyzed the genetic diversity among Heilongjiang populations, and found that the mean number of nucleotide differences (K), haplotype diversity (Hd) and nucleotide diversity (Pi) were 1.0593, 0.6719 and 0.0016, respectively. In addition, four haplotypes were identified in the Heilongjiang population (p > 0.05), whereas it differed significantly among the other populations ; however, the other populations (except for the BJ87 and BJ92 populations) showed significantly genetic differentiation  = 0.9293) between the BJ79 population (collected in 1979) and other populations was the greatest, but the lowest (FST (average) = 0.6078) was observed between the BJ87 population and other populations . Although the collection times of the three populations were not consistent, the differences in gene sequence were almost negligible because of the small difference between the two collection times (2011 and 2012). For Liaoning populations, mean K, Hd and Pi values were 1.3297, 0.659 and 0.0021, respectively. For Beijing populations, mean K, Hd and Pi values were 1.6316, 0.658 and 0.0025, respectively. For Heilongjiang populations, mean K, Hd and Pi values were 0.4412, 0.441 and 0.0007, respectively.FST, K, Hd and Pi were 0.9279, 1.3726, 0.6863 and 0.0021, respectively. The differences in FST values among the three populations were significant   . The Nm  < 0.01) , which w < 0.01) .COI gene sequences downloaded from GenBank and Bold Systems V4, from 13 regions in the 1990s, were obtained in this study. With these, the genetic structure and demographic history of gypsy moths in China, and adjacent areas, were revealed. Firstly, we calculated pairwise genetic differentiation coefficients (FST) and gene flow (Nm) between all populations; values for the 1990s are shown in A total of 85 DNA barcode sequences, including 62 FST values are standardized according to the following formula, i.e.,FST values between populations, FST values, FST values. The degree of genetic differentiation between populations is positively correlated with the z-value.In a cluster heat map, all We identified 17 haplotypes for gypsy moths in the 1990s . In thisCOI gene sequences of the gypsy moth specimens from more than a decade, or even several decades. These data would be used to supplement existing data for future evolutionary research of gypsy moths. In addition, our findings provide information and insight into the historical haplotypes of different geographic populations of gypsy moth, which revealed many previously unknown gypsy moth haplotypes. The abundance of haplotypes may be related to the strong adaptability of gypsy moths to changing environments. We also found that haplotypes of the gypsy moth populations in different periods were different; this may be related to genetic flow between different populations or the genetic diversity of gypsy moths under environmental pressure. In short, the extensive variation in haplotype observed in this study may complicate efforts to control and detect gypsy moths.In previous studies, a large proportion of research focusing on the analysis of haplotypes from several geographic populations of gypsy moth has relied on fresh samples. In the present study, we analyzed the haplotypes of several gypsy moth populations by using museum specimens collected in different eras. We first supplemented the 2 and O3 concentrations, rising temperatures, and an uneven distribution of rainfall has been found to profoundly impact upon the composition of insect communities and the interactions between host plants and pest insects, and their natural enemies [Herein, we provide the first report on the gypsy moth dynamics in Heilongjiang, Liaoning, Hebei, and Beijing based on DNA barcode sequences from different years (1956–2012). We found that nucleotide polymorphism and genetic diversity among these four populations had, at different time points, remained relatively high, indicating that substantial genetic variability exists among these populations collected in the same region at different time points. Probably some of this variability relates to environmental changes that occurred between 1956 and 2012. For example, elevated atmospheric CO enemies . To succFST(average) = 0.9732) between the HLJ79 population and other Heilongjiang populations was greater than that between the HLJ56 population and other Heilongjiang populations  were relatively close, probably as a consequence of their close geographical distance. This indicated that the gypsy moth populations were likely to spread between adjacent areas, a finding which also directly relates to the strong flight ability of the Asian moths. In addition, based on the genetic differentiation and gene flow between populations, we suspected that increasing levels of international trade and tourism in the 1990s facilitated the overseas dispersal of gypsy moth populations from Honshu (Japan), Primorsky Krai (Russia) and China.COI genotype of two Chinese populations (Guizhou and Liaoning) was similar to that of the Siberian gypsy moths, though not identical. Our study re-visits the issue of whether there is genetic affinity between some of the gypsy moths from China and those found in Siberia [Chen et al.  indicate Siberia . In a neLastly, we found that one haplotype was shared by both the USA96 population and the Asian gypsy moth populations; furthermore, in the phylogenetic tree, these two populations were clustered into one as a single group. Consequently, two factors may have contributed to the genetic affinity between some of the gypsy moths from USA and those found in China. On the one hand, American gypsy moths may have been introduced into China, and mixed with those already there so went undetected. On the other hand, Chinese gypsy moths may have been introduced into USA. This matter could be readily resolved with more molecular data obtained by testing additional samples from these two latter regions. Furthermore, since mtDNA sequences only reflect the movements of females, datasets consisting of both mitochondrial and nuclear sequences are essential to reliably distinguish subspecies of this moth.COI gene sequences, which, when considered alone, are insufficient to reveal further information relating to gypsy moth history, and clearly, in future studies, a wider range of samples and molecular markers are required to reveal additional features of the history and dynamics of this important pest.Several studies have revealed the genetic structure of gypsy moths, but such analyses have primarily sought to determine the lineage of the gypsy moth in its native range and adjacent areas ,29,30. FCOI data in gypsy moth specimens from disparate locations revealed previously unknown genetic relationships in populations across space and time. Gypsy moth populations (collected in the same region at different time points) showed relatively high nucleotide polymorphism and genetic diversity. The Beijing gypsy moth population may be more conducive to produce genetic variation at different times based on our results of both nucleotide polymorphism and genetic diversity. We also found that the genetic structure among several Chinese geographical populations in different years was distinct. In addition, the development of international trade and tourism has facilitated genetic flow between gypsy moth populations in parts of China, Japan and Russia. Last but not least, our results suggested that some gypsy moths in China showed the genetic affinity with European gypsy moth (a sub-species of gypsy moth found mainly in Europe).Our analyses of mtDNA