Understanding its colonization, dispersal, and connectivity will be critical for mitigating negative economic and ecological impacts in the newly invaded areas. Characterization of spatial-genetic structure can contribute to this; however, previous studies have reached different conclusions about regional population genetic structure, with one study reporting a weak east-west pattern, and the most recent reporting an absence of structure. Here we systematically assessed several explanations for the absence of spatial-genetic structure. To do this, we developed nine new microsatellite markers and combined them with an existing 24-locus data matrix for the same individuals. We then reanalyzed this full dataset alongside datasets in which certain loci were omitted with the goal of creating more favorable signal to noise ratios. We also partitioned the data based on the sex of D. frontalis individuals, and then employed a broad suite of genotypic clustering and isolation-by-distance (IBD) analyses. We found that neither inadequate information content in the molecular marker set, nor unfavorable signal-to-noise ratio, nor insensitivity of the analytical approaches could explain the absence of structure. Regardless of dataset composition, there was little evidence for clusters  or clines , with one exception: significant IBD was repeatedly detected using an individual-based measure of relatedness whenever datasets included males . This is strongly indicative of broad-scale female-biased dispersal, which has not previously been reported for D. frontalis, in part owing to logistical limitations of direct approaches . Weak spatial-genetic structure suggests long-distance connectivity and that gene flow is high, but additional research is needed to understand range expansion and outbreak dynamics in this species using alternate approaches.The southern pine beetle Dendroctonus frontalis , and upon arrival by flight, they initiate an attack and release aggregation pheromones that attract males  might be detectable. Indeed, earlier work by There are several reasons to expect spatial-genetic structure across the range of  species , includi species . Also, go 3.4 km . While tD. frontalis by vs. 1,513 km, respectively). Also, in both studies, per-site sample sizes were quite large . Thus, it would seem that biologically meaningful genetic distance estimates should have been attainable. From an applied management perspective, it is important to reconcile these contrasting inferences about spatial-genetic structure among D. frontalis in eastern North America because they have different ramifications for impacts upon pine forest ecosystems in newly colonized regions. For example, if distinct genetic clusters exist, multiple gene pools may converge and recombine at the wave front, producing an invasive population with elevated genetic variation and adaptive potential  to be consistent with broad-scale panmixia , and acknowledging the potential for genuine panmixia , the goal here was to systematically explore evidence for each of them. This was approached by augmenting, sub-setting, and reanalyzing While the explanations for a lack of detectable spatial-genetic structure given above are not mutually exclusive .” As an alternative strategy, we attempted to enhance overall signal by retaining only those loci that maximized genetic differentiation, as measured by FST  by inherent features of the loci themselves. To d by FST  among san = 185 individuals from 9 sites) and female-only  partitions, in addition to combined datasets . To assess the suitability of these new loci, multiplex polymerase chain reaction (PCR) amplifications were performed for sets of three loci, each with a different 5′ fluorescent label on the forward primer and mostly non-overlapping allele size ranges , 1.5 µL dH2O, 0.75 µL of each primer (10 mM), and 1.5 µL genomic DNA. The following “touchdown” thermal cycling conditions were used: 95 °C for 2 min (1 cycle), 95 °C for 30 s, 61 °C minus 2 °C per successive cycle for 30 s, 72 °C for 45 s (5 cycles), 95 °C for 30 s, 51 °C for 30 s, 72 °C for 45 s (30 cycles), and 60 °C for 30 min (1 cycle).In the present study, we extended upon the assessment of a suite of microsatellite loci identified by e ranges . Reactioi.e., 27 individuals from Sicily Island, Louisiana, and 28 individuals from Tombigbee, Mississippi). For each locus and population, evidence for departures from Hardy-Weinberg Equilibrium (HWE) were examined via exact tests (O) and expected (HE) heterozygosity were also calculated. Independent segregation of alleles among loci was assessed using all D. frontalis samples  by testing for departures from Linkage Equilibrium (LE) for all possible pairs of 33 loci  with a Liz 500 ladder  at Yale University’s DNA Analysis Facility on Science Hill. Genotype scoring was performed using GENEIOUS v10.0.5 . Followict tests , with siP v4.5.1 , with seP v4.5.1  of P-valP-values determined using 1,000 permutations. Frequency of the null allele (r) was then was estimated using the method of i.e., <20%) showed significantly (P < 0.05) positive FIS values. For each locus and population, the null hypothesis of panmixia was compared to an alternative hypothesis of heterozygote deficiency using a U-test .MICRO-CHECKER v2.2.3  was useda U-test , performi.e., a reference allele identified from the eastern United States gene pool as whole). Rank-ordering of loci based on the potential level of stochastic noise contributed by homoplasy followed the inferred number of repeats of each reference allele.The number of contiguous repeat units may be a useful proxy for microsatellite mutation rate, given that DNA structure affects the opportunity for slipped-strand mispairing (t-test (one-tailed) determined that a significant (P < 0.05) reduction in the mean value of the original metric used for rank-ordering (compared to that of the 33-locus dataset) had been achieved. To assess whether the low noise dataset was comprised of a subset of loci with inadvertently low information content, we calculated vs. full dataset, using a t-test as above.The omission of loci that may elevate overall noise was based on joint consideration of null alleles and homoplasy. Along each of these two axes, we iteratively omitted the lowest ranked locus one at a time, until a two-sample e.g., those displaying unusually strong differentiation, potentially due to effects of divergent selection on linked genomic regions) may be particularly useful for identification of population structure  may be slow to register in most loci, particularly when it is coupled with large effective populations sizes. To explore the potential benefit of focusing on loci with the strongest signatures of spatial-genetic structure in D. frontalis, we calculated global FST . These breaks defined the thresholds for retention vs. exclusion of loci from the high signal dataset.For species in which population divergence is shallow, “outlier” microsatellite loci  and their members, we analyzed microsatellite datasets using three approaches: STRUCTURE v2.3.4 .To infer the number of natural genetic groups  generations and run length of 200,000 MCMC generations were used, with other parameters set as default. The best-fit value of K was chosen using K method implemented in STRUCTURE HARVESTER v0.6.94  level of hierarchical structure (see K) plots, from which the smallest K that captured the major structure in the data was accepted, following  v0.6.94 . Howeverture see . AccordiBAPS. To enable spatial clustering of individuals, we created non-redundant GPS coordinates via the addition of fine-scale jitter . For spatial clustering of groups of individuals, each of the 8 or 9 sampling locations (depending on the dataset) defined an a priori group, and the original spatial coordinates were used. Accordingly, the complexity of Voronoi tessellations differed considerably between the two approaches. Spatial data were analyzed with associated microsatellite genotype data, with 10 replicates per run. The best-fit K was identified via log marginal likelihood scores  analysis to identify uncorrelated variables to be used for subsequent discriminant analysis. The best-fit number of clusters was determined via k-means clustering, with the lowest Bayesian Information Criterion (BIC) score used to choose K. Following de novo clusters that were inferred to be present at a given sampling location. DAPC was implemented using ADEGENET v2.1.2 , which has desirable properties for detecting IBD in the presence of null alleles (Ds), which increases linearly with geographic distance if mutation follows an infinite allele model and occurs at a constant rate; and FST linearized as FST/(1- FST) following  alleles ; Nei’s . Conversely, when comparing two mutlilocus genotypes, Dps is calculated as number of shared alleles summed over loci / (2 × number of loci compared). Unlike the other two metrics, e.g., a shared rare allele is considered more likely to be identical-by-descent than a shared common allele). Given that rˆ is asymmetrical, following ent Dkf, ; proportDkf, ps, , or relaDc, Dkf, and Dps, whereas GENALEX was used to calculate Ds, FST and rˆ, as well as linear pairwise geographic distance matrices based on latitude and longitude coordinates, using a modified Haversine Formula. For all datasets and distance metrics, the significance of IBD was assessed viaMICROSATELLITE ANALYZER v4.05  was usedD. frontalis samples was reasonably high (mean of 4.2% missing data per locus). Three loci showed significant departures from HWE in the direction of homozygote excess. However, in all cases this was limited to just one of the two local collection sites . Levels of with-population polymorphism were modest . Across all eastern United States D. frontalis samples, there was a mean of 8.1 alleles per new locus .For the nine new loci developed here, amplification success across all eastern United States on sites , suggestew locus . Based oNull alleles. Two local populations had seven or more loci with significantly positive FIS values: Homochitto, MS, and Woolford, MD. These were considered inbred and therefore omitted from consideration when assessing null alleles. MICRO-CHECKER identified 13 loci that may have a null allele  for 10 of these. Of the remaining loci, two had null alleles in four populations, and one had nulls in two populations. Across the 20 cases of putative null alleles , mean estimated r was 0.245 (range: 0.139–0.431). Rank-ordering followed by iterative removal revealed that omission of the five worst loci was required to significantly reduce the mean rcumulative value . The removal of these loci did not cause a concomitant reduction in mean information content, as measured by PI .l allele . HoweverHomoplasy. For 31 loci, the available allele sequence trimmed at the 5′ ends of both primers yielded a predicted PCR product that was within the empirically determined size range for eastern United States D. frontalis. For two loci with unexpectedly short or long amplicons, an alternative priming site  was readily identifiable  repeat motifs located immediately adjacent to one other  repeat motif interrupted by a 2-bp , but their omission did not also reduce mean PI .tifiable . Three lrcumulative remained close to significant , and so did reduction in mean number of repeats . As before, there was no inadvertent change in mean PI . Although our low noise dataset happened to contain the same number of loci originally analyzed by Of the two sets of five omitted loci, one (Dfr-24) was common to both approaches used to reduce noise. Accordingly, the largest subset of loci with reduced null allele and homoplasy issues was made up of 24 loci. Reassessment of the impacts of our strategic removal of nine loci to create the low noise dataset showed that the reduction in mean FST plot, several inflection points marking pronounced decreases in FST among adjacent rank-ordered loci were apparent , there were two other inflection points: one at the transition from the 10th to 11th ranked locus, and another at the transition from 19th to 20th. Accordingly, we created two alternative versions of the high sensitivity dataset (HSDS). One contained 10 loci with the highest FST values, and the other was an expansion of this, where we retained the 19 highest ranked loci  there were five instances where the two alternative approaches for identifying the best-fit value of K were in conflict. In these cases, K = 1 whereas K method inferred K = 2, 3, or 4, depending in the dataset (K is not capable of assessing K = 1). More generally, irrespective of whether there was agreement or conflict between methods, wherever K>1 was inferred, the optimal clustering solution almost always contained “ghost clusters” . In some cases, all clusters were nonsensical. For instance, based on ΔK, the best-fit value was K = 3 for LNDS males plus females, and LNDS females-only, yet not a single individual was strongly assigned; K>1 there was also an absence of geographic localization of clusters. Ultimately, as with D. frontalis dataset, our reanalysis using augmented and/or sub-setted datasets did not provide any indications of discrete population structure.s” sensu; i.e., tBAPS. The optimal partition of geo-referenced molecular data was K = 1 for all datasets, using both individual- and population-based clustering approaches (P < 0.0005), indicating that eastern United States D. frontalis cannot simply be characterized as panmictic.proaches . Given tDAPC. Across all datasets, there were only two instances where K = 1 was inferred  = 0.036; augmented dataset with males only: slope = −2 ×10−6, r = 0.037; low noise dataset with males plus females: slope = −2 ×10−6, r = 0.020; low noise dataset with males only: slope = −2 ×10−6, r = 0.030; and even weaker for the 19-locus high signal dataset with females only: slope = −3 ×10−6, r = 0.001]. Overall, these outcomes are consistent with female-biased dispersal; however, males do not appear to be strongly philopatric.vs. human-mediated dispersal  when structure manifests as continuous gradients of genetic differentiation, and conversely, true signatures of IBD can be distorted if sampling traverses abrupt genetic breaks or is discontinuous and uneven  and number of independent introductions into newly invaded areas, and provide insights into the relative importance of natural t spread  and whict spread , or enhaen e.g., . While cen e.g., , an undei.e., migration rate, m ≥ 0.005), the best-fit number of clusters inferred using STRUCTURE may be systematically underestimated, leading to erroneous inferences of K = 1. Following the recommendations of those authors and others, we compared outcomes across several analytical methods with different assumptions and found that the inferred absence of discrete clusters was robust  assayed via sub-genomic sampling methods that are applicable to non-model species  or analyzed subsets of loci with more favorable signal-to-noise ratio , no spatially abrupt genetic subdivisions were detected. Using simulations, s robust . We intees e.g.,  could renderosae . That saae e.g., .D. frontalis to the northeastern United States is both recent and on-going: the species spread throughout New Jersey by the mid- to late-2000’s, to Long Island in New York by 2014, and was trapped in Connecticut, Rhode Island and Massachusetts by 2015–2016  in a flight mill experiment was attributed to sexual size dimorphism . Alternatively, a difference in the timescales over which spatial-genetic structure has evolved and/or is assessed may be responsible. Indeed, individual-based genetic distances may better reflect contemporary processes such as intra-generational dispersal, whereas population-based metrics primarily measure historical connectivity given that most signal comes from the accumulated multi-generational effects of dispersal and gene flow  had low power. One potential advantage of r is that loci with more alleles—particularly those with rare alleles—provide more information about relatedness, such that highly polymorphic genetic markers such as microsatellites are very powerful . Thus, although the overall spatial scale of sampling was similar between studies (see Introduction), effort allocation was not. Indeed, FST changes depending on the extent of gene flow limitation, with misallocations leading to either over- or under-estimation of FST, depending on the circumstances. Furthermore, direct comparison between studies may be impacted by the fact that D. frontalis during years of relatively low population densities  whereas our specimens were collected at a time of considerable increase in trap captures in Louisiana and Alabama, and an active outbreak in Mississippi .Our findings for D. frontalis outbreak years, pulse irruptions have unusual population dynamics where beetles emerge from infested trees in several waves and inter-tree distance is an important determinant of attack success, with crowded healthy trees being at greater risk owing to the spill-over effects of aggregation pheromones . Indeed, seasonal differences within a given year can also impact dispersal behavior, whereby individuals have the greatest potential for long-distance dispersal in Fall and the lowest potential in mid-Summer, owing to temporal differences in fat content levels  and/or on-going expansion processes underscores the importance of considering both and ecological and geographic context. For example, Choristoneura fumiferana), a cyclical irruptive pest in North America, exhibits weak spatial-genetic structure, there is considerable nuance to this general finding. For example, the legacy of high connectivity during past outbreaks can persist for some time afterwards, high admixture can be driven by migrant adults sourced from relatively few sites, and different life stages  can concurrently exhibit different degrees of spatial genetic structure. Although the periodicity of spruce budworm outbreaks is considerably longer than that of D. frontalis  and the synchrony of irruptive populations may occur at different spatial scales, there may be considerable similarity in the underlying processes that generate apparent weak spatial-genetic structure. Future work on D. frontalis could attempt to distinguish between migrant and resident individuals, and model range expansion dynamics and associated source–sink dynamics, explicitly incorporating temporal effects.The idea that preexisting structure may have been “overwritten” either by a change in outbreak status (D. frontalis in the eastern United States has implications for management as the species’ impacts spread north. In contrast to alien pests that invade a region via long distance introduction by human transport, D. frontalis is rapidly spreading into a previously unoccupied region contiguous to its native range. For alien species, gene flow between native and alien populations only occurs via additional introductions. Conversely, for D. frontalis, the gene flow between long established populations in the southeastern United States and invading populations in New Jersey, New York, and Connecticut is likely to be extensive and on-going. While it is still an open question whether D. frontalis had been present in the northeastern areas at low densities but is only now causing impacts due to population increases with less winter mortality, or if it truly represents a recent arrival, the management implications could rely less on looking for differences in beetle genetics, and more on environmental and host tree differences between the south and north. Indeed, this is an approach that seems to be working (The lack of discrete population differentiation and substantial IBD in  working .10.7717/peerj.11947/supp-1Supplemental Information 1https://datadryad.org/stash/share/3W7kH0cBmwhtt7H0U54kvnpnoctWlUww-zsqa81k8dcReviewer URL: Click here for additional data file.


R. aureum from northeast China, (Ⅱ) reveal the relative contribution of geographical and environmental impacts on the distribution and genetic differentiation of R. aureum, (Ⅲ) identify outlier loci under selection and evaluate the association between outlier loci and environmental factors, and (Ⅳ) exactly calculate the development trend of population of R. aureum, as it is confronted with severe climate change and to provide information for designing effective conservation and management strategies. We found high genetic variation (I = 0.584) and differentiation among populations (STΦ = 0.703) and moderate levels of genetic diversity within populations of R. aureum. A significant relationship between genetic distance and environmental distance was identified, which suggested that the differentiation of different populations was caused by environmental factors. Using BayeScan and Dfdist, 42 outlier loci are identified and most of the outlier loci are associated with climate or relief factors, suggesting that these loci are linked to genes that are involved in the adaptability of R. aureum to the environment. Species distribution models (SDMs) showed that climate warming will cause a significant reduction in suitable areas for R. aureum, especially under the RCP 85 scenario. Our results help to understand the potential response of R. aureum to climatic changes and provide new perspectives for R. aureum resource management and conservation strategies.Understanding genetic variation and structure, adaptive genetic variation, and its relationship with environmental factors is of great significance to understand how plants adapt to climate change and design effective conservation and management strategies. The objective of this study was to (I) investigate the genetic diversity and structure by AFLP markers in 36 populations of  R. aureum. Our analyses revealed high genetic variation and differentiation among populations and moderate levels of genetic diversity within populations of R. aureum. A significant correlation between genetic distance and environmental distance was identified, which suggested that environmental factors were the primary cause of the population differentiation. A significant correlation between genetic distance and environmental distance was identified, which suggested that environmental factors were the primary cause of the population differentiation.Using AFLP markers, landscape genetic, and species distribution modeling analysis together, we are able to identify many environmental factors that have influenced the genetic diversity and genetic structure, and we can predict the potential distribution area of  This pl) Figure . In Chinl. Kudo, . The R. R. aureum during the last glacial maximum (LGM) and the future. The objective of this study was to (a) investigate the genetic variation and genetic structure of R. aureum; (b) reveal the relative contribution of geographical and environmental impacts on the distribution and genetic differentiation of R. aureum, (c) identify outlier loci under selection and assess the association between outlier loci and climate, and (d) calculate development trend of population of R. aureum, as it is confronted with severe climate change and to provide information for designing effective conservation and management strategies.In this study, we adopted AFLP markers for characterizing the adaptive loci under selection using BayeScan and Dfdist and employed multiple linear regression (MLR) to detect potential adaptive loci that are under selection from existing environmental factors, and used species distribution models (SDMs) to predict the potential distribution of 22.1Changbai Mountain is generally recognized as the highest peak in northeast China, with obvious mountain climate characteristics. This environment is a local variable as small changes in altitude can lead to large changes in temperature, humidity, exposure, and other types of changes , 1 U of MseI , 1.0 μM of MseI‐Adapter, and 0.1 μM of EcoRI‐Adapter and double‐distilled water. The reaction was incubated at 37°C for 8 hr, 16°C for 4 hr, inactivated at 65°C for 10 min, and stored at 4°C. Pre‐amplification was performed in a 25 μl containing 2.5 μl of 10× PCR buffer and 0.5 U of Taq polymerase , 2.5 μl of diluted digestion–ligation product, 0.2 μM of dNTPs, and 0.3 μM each of primers with a single selective nucleotide. The pre‐amplification conditions were as follows: predenatured at 94°C for 5 min and 30 cycles of 94°C for 30 s, 56°C for 60 s, and 72°C for 80 s, with a final extension for 10 min at 72°C. The pre‐amplification products were diluted 1 to 40 (v/v) with ddH2O. The selective amplification was essentially the same as that for pre‐amplification except that 2 μl diluted pre‐amplification product was used as a template, and 2 μM EcoRI and MseI selective primers were used. 10 pairs of primers were selected for selective amplification . The DNA samples were diluted to 10 ng/μl and stored at −20°C until further analysis. AFLP marker was carried out according to the method of Vos et al.  with theion Table. The selion Table.I) , and genetic distance were estimated using the POPGENE v1.31  and mean genetic diversity within populations (SH) were calculated using Nei's , of 20,000 and 200,000 iterations, respectively. The most likely number of genetic clusters (K) was estimated by the ΔK statistic  between pairs of runs, the average pairwise similarity (H) for the 20 replicates, and the average probability of belonging to each cluster (Q). For K = 4, the full search method with 1,000 replicates was used. A hierarchical analysis of molecular variance (AMOVA) was used to determine genetic differentiation (STFs within and among the groups. A nested AMOVA takes into consideration the four main genetic groups resulting from the Bayesian clustering with STRUCTURE (K = 4) using ARLEQUIN version 3.5.1.2  or absent (0), and the AFLP band data were transferred to a binary (1/0) data matrix for further analysis. Shannon's information index . The sampled area spanned all the known R. aureum range in China, which occurs primarily in the alpine, along an annual mean precipitation gradient from 761 to 1,096 mm and annual mean temperature gradient from −6.6°C to 0.1°C. Elevation, slope, and topographic index were derived from a 90 m digital elevation model. The dataset is provided by International Scientific & Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn). A principal component analysis (PCA) was applied to these environmental variables to examine possible correlations between ecoclimatic variables and elevation and remove redundant variables . We first identified variables correlated with each retained axis, creating groups of variables. Within each group, we kept only one (or two) variables considered to be the most pertinent in terms of local adaptation in plants. Finally, eleven factors  were chosen as representative of environment factors.To characterize environmental differences, BIOCLIM variables were obtained for each of the 36 sites by extrapolating climate data to the GPS coordinates for each population using DIVA‐GIS software  transformed to improve normality and reduce heteroscedasticity. Dissimilarity matrices of Euclidean distances were calculated among normalized environment variables using R package. A matrix of geographic distances among sites was generated from GPS coordinates with the AFLP data in R software  transformed. The genetic distance matrices of R. aureum were calculated with PopGene  . After removing duplicate records, it remained a total of 42 records of R. aureum  scaled down to a 2.5‐arcmin resolution. We used the Hadley Global Environment Model 2 (HadGEM2‐ES) as a general circulation model under two climate scenarios (IPCC‐CMIP5 RCP 26/85) to ensure the accuracy of assessment. The RCP 85 scenario represents a higher predicted greenhouse gas emission than RCP 26.To obtain the distribution of 33.1I) and Nei's genetic diversity (H) were 0.584 and 0.402, respectively. R. aureum showed high genetic diversity at the species level. I ranged from 0.014 to 0.329, and H ranged from 0.01 to 0.228 within population  were polymorphic. The overall Shannon's information index  and NW7 and NW8 populations on the U‐shaped valley of the northern slope formed the group 3. Populations L1, L2, WTE1, and WTE2 formed the group 4, and their individuals had a high level of admixture  attributed 16.8% of the global variation to differences between the 4 groups, 52.14% to among populations within groups and 31.06% to within populations .An AMOVA Table  attribut3.2r = .4871, p = .001), but between genetic distance and geographical distance were a nonsignificant correlation . When geographical factors were controlled, a partial Mantel test also showed isolation by environmental distance . Whereas environmental factors were controlled, we could not detect significant correlations between genetic differentiation and geographical distance . The MMRR analysis indicated that the environment factors had large regression coefficient, whereas the effects of geographic distance were not significant  associated with at least one of the eleven selected environmental variables.BayeScan determined 71 loci as outliers with a log9 Figure . Using t9 Figure . 42 outl3.4R. aureum , and the high level of genetic diversity was also in accordance with studies on five Rhododendron species in Qinling Mountain in China, and Shannon's information index (I) differed from 0.4286 to 0.6921, while Nei's gene diversity (H) varied from 0.2711 to 0.4989 , solar radiation, and wind, occur over short distances, resulting from obvious changes in the selection pressures of R. aureum individuals.Plant species with wide altitudinal ranges encounter different environmental conditions across the elevation gradient, which may lead to genetic variation as well as phenotypic variation among populations Forsman, . R. aureSTΦ = 0.689). Meanwhile, the high population differentiation could possibly accelerate local adaptation. Local adaptation and directional selection should have locus‐specific effects of reducing genetic diversity within populations and increasing differentiation between populations  and isolation by environment (IBE) , present, and future climate conditions. MAXENT captured well a major portion of current distribution of R. aureum. With the climate changing from the LGM to future, R. aureum decreased its future distribution range under a climatic warming scenario, especially under the Representative Concentration Pathways (RCP) 85 scenario which higher level greenhouse gases are emitted than RCP 26 in the years to come.We used MAXENT to predict the distribution of Rhododendron species generally would be negatively affected by the climatic and land‐use change, and some distribution areas of narrow‐ranging Rhododendron species would decrease or even go extinct ; formal analysis ; methodology ; writing‐original draft (lead). Xiaolong Wang: Formal analysis . Lin Li: Methodology . Jiangnan Li: Data curation . Hang Yin: Methodology . Ying Zhao: Methodology . Xia Chen: Conceptualization ; writing‐review & editing .Appendix S1Click here for additional data file.


Understanding and identifying the factors responsible for genetic differentiation is of fundamental importance for efficient utilization and conservation of traditional rice landraces. In this study, we examined the spatial genetic differentiation of 594 individuals sampled from 28 locations in Yunnan Province, China, covering a wide geographic distribution and diverse growing conditions. All 594 accessions were studied using ten unlinked target genes and 48 microsatellite loci, and the representative 108 accessions from the whole collection were sampled for resequencing.japonica and indica groups, respectively. In each group, the rice accessions could be further differentiated corresponded to their geographic locations, including three subgroups from northern, southern and middle locations. We found more obvious internal geographic structure in the japonica group than in the indica group. In the japonica group, we found that genetic and phenotypic differentiation were strongly related to geographical distance, suggesting a pattern of isolation by distance (IBD); this relationship remained highly significant when we controlled for environmental effects, where the likelihood of gene flow is inversely proportional to the distance between locations. Moreover, the gene flow also followed patterns of isolation by environment (IBE) whereby gene flow rates are higher in similar environments. We detected 314 and 216 regions had been differentially selected between Jap-N and Jap-S, Ind-N and Ind-S, respectively, and thus referred to as selection signatures for different geographic subgroups. We also observed a number of significant and interesting associations between loci and environmental factors, which implies adaptation to local environment.The genetic diversity of rice landraces was quite different geographically and exhibited a geographical decline from south to north in Yunnan, China. Population structure revealed that the rice landraces could be clearly differentiated into Our findings highlight the influence of geographical isolation and environmental heterogeneity on the pattern of the gene flow, and demonstrate that both geographical isolation and environment drives adaptive divergence play dominant roles in the genetic differentiation of the rice landraces in Yunnan, China as a result of limited dispersal.The online version contains supplementary material available at 10.1186/s12284-021-00497-6. Rice, as one of the world’s major food crops, feeds more people than any other crop species and is a staple crop for nearly all of Asia. Historically, rice yield has accounted for 40 % of the total grain output in China  are found in Yunnan, and include both indica and japonica types, those with glumes, with or without hairs, non-glutinous or glutinous, upland or lowland, naked rice, and rice with various hull grain colors  and flavors (ordinary or fragrant)  of rice landraces that cover a wide geographical area and diverse growing conditions in Yunnan province, China, and analyzed ten gene sequences and 48 SSR (simple sequence repeat) markers. Then, a core subset of the collection of 594 landraces (108 accessions) was sampled for whole-genome sequencing. Our objectives were to examine the genetic structure and differentiation in rice landraces from Yunnan province, China, and to determine the relationships with their spatial and geographical distribution. We also discuss the role of geographical isolation and environmental heterogeneity in genetic differentiation among populations of rice landraces to provide theoretical support and a scientific basis for the protection and utilization of rice landraces.π indicates the nucleotide diversity, which was calculated for each locus. This term ranged from 0.0023 (STS22) to 0.0149 (CatA) in rice landraces, with an average of 0.0060. The haplotype diversity varied from 0.501 (Os1977) to 0.788 (GS5), with an average of 0.655. Four-gamete testing revealed that the Rm ranged from 0 to 7 in rice landraces, with an average of 3, indicating that there was low heterozygosity in the rice landraces due to high levels of inbreeding. Neutrality test values for most gene loci were not significant, except Os1977 (D = 2.856) and Ehd1 (D = 2.280), indicating that most were neutral gene loci. Based on SSR markers, a total of 629 alleles were detected in all accessions, with an average of 13.10 per locus. The average gene diversity and PIC were 0.755 and 0.725, respectively  to 0.0063 , with an average of 0.0055. The haplotype diversity varied from 0.475  to 0.671  (with an average of 0.610). Based on SSR markers, gene diversity, PIC, and heterozygosity ranged from 0.5082 to 0.7289, 0.4717 to 0.6928, and 0.0355 to 0.1264, respectively. Globally, the mean genetic diversity, PIC, and heterozygosity were 0.6729, 0.6318, and 0.0690, respectively. Correlation analysis indicated that genetic diversity was significantly negative with respect to latitude , but a significant negative correlation between the proportion of indica rice landraces in various locations and latitudes   in different locations  to 80.00 %  (with an average of 34.57 %) in the japonica group, and the frequency of H_3 varied from 5.88 %  to 66.67 %  (with an average of 21.45 %) in the indica group. Notably, there were some environment-specific haplotype which could be found only in one location, such as H_2, H_11 and H_14 in the japonica group, and H_2, H_4 and H_6 in the indica group.To further demonstrate gene flow among different populations, we estimated frequency changes of dominant haplotypes at the important gene  Figure S. The totjaponica group, 179 regions and 170 regions were identified as the most affected by selection in subgroup Jap-N (rice landraces from northern locations) and Jap-S (rice landraces from southern locations), respectively , comprising approximately 5.0 % of the rice genome, and 77 potential selected regions with an average size of 234.5 kb in Ind-S (rice landraces from southern locations), comprising approximately 4.8 % of the rice genome (Table SWe used a composite likelihood ratio method (CLR)   in Ind-N  released by Q-TARO  Fig. a, which 87) Fig. . Meanwhid-N Fig. . Whereasnes Fig. . AdditioP < 10− 10)        Table S were assc Table S, which ic Table S, reflectπ and PIC, which ranged from 0.0039 to 0.0063 and 0.4717 to 0.6928, respectively (Table SO. ruffipogon Griff.), a direct ancestor of cultivated rice, is only found in Yuanjiang, Yuxi  and Jinghong, Xishuangbanna (the southwest region of Yunnan). Gene flow between landraces and their wild relatives greatly enhances the genetic variation of rice landraces in the genetic diversity center.Owing to the wide geographical variation, diverse growing conditions, and cultural and ethnic diversity, Yunnan is acknowledged as one of the largest genetic diversity centers for rice in China and even worldwide  in different geographic locations and further demonstrated that both adaptation to local environment and environment-dependent gene flow played key roles in the genetic differentiation of rice landraces in Yunnan, China. Different environmental conditions  can affect the survival and reproduction of plants and populations; thus, divergence might be due to local adaptation , and 10–30 ng of genomic DNA. The PCR amplification profile was as follows: pre-denaturation at 94 °C for 5 min, followed by 36 cycles of denaturation at 94 °C for 30 s, annealing at 55–60 °C (depending on the primers) for 30 s, and extension at 72 °C for 1.5 min, with a final extension at 72 °C for 10 min. The PCR products were electrophoresed on 1.2 % agarose gels, and DNA fragments were excised from the gel and purified using a Tiangen Gel Extraction Kit . DNA sequencing reactions were performed using an ABI 3730 Automated Sequencer. Initially, all samples were directly sequenced. However, if the haplotypes could not be readily inferred owing to heterozygosity, the PCR products were ligated into a pGEM®-T Easy Vector (Transgen) and at least four clones were then sequenced. For heterozygous individuals, one allele sequence was randomly selected. Because Taq amplification errors can occur, when a polymorphism was found in only one accession, this accession was re-sequenced after cloning the amplified DNA fragment to verify the polymorphism.To detect genes, PCR was performed in a 25-µL volume consisting of 0.2 µM of each primer, 200 µM of each dNTP, 10 mM Tris–HCl (pH 8.3), 50 mM KCl, 1.5 mM MgClθπ) (Nei θw)  Nei  and Wattk value (from 1 to 12), using a burn-in length of 100,000, a run length of 100,000, and admixture and correlated allele frequency models. The k value was determined based on LnP(D) in the STRUCTURE output and the ad-hoc statistic Δk  (Slatkin Pairwise  Slatkin  was meas Slatkin  .indica and japonica subpopulations (see results), partial Mantel tests were also conducted separately in each subpopulation.We reduced the environmental variables using principal component analysis (PCA) based on the 24 environmental variables Table S in SAS vTo visualize the level of morphological structure across locations, we conducted a PCA of fifteen morphological traits Table S6 and ploA total of 2,771,245 high-quality SNPs of the representative 108 accessions were used for selection analyses. The sequence data were released in our previous study  , between θπ and latitude (b), between the number of alleles and latitude (c), and between gene diversity and altitude (d). Figure S4. The ΔK statistic for each given k. Figure S5. Model-based ancestries and their distribution in each location. (a) Model-based ancestry of each accession in P1 and P2; (b) distribution of model-based populations in each location. Figure S6. Correlation between the proportion of japonica rice and latitude (a) and between the proportion of indica rice and latitude (b). Figure S7. A map showing the sampled populations of rice landraces and the distribution of haplotypes. (a) and (b) show rice landraces in the japonica and indica group, respectively. Phylogenetic relationship of the haplotype based on the NJ analysis is indicated below the map. Pie charts show the proportions of the haplotypes within each county. Haplotypes are indicated by different colors. Figure S8. Functional category of cloned genes in selected regions. Figure S9. “a” to “d” depict the composite likelihood ration (CLR) value of subgroup “Jap-N” (purple), “Jap-S” (orange), “Ind-N” (dark blue), and “Ind-S” (blue), respectively, and “e” presents |z|-scores of the SNPs which were tested for associations between genetic variation and environmental gradients using latent factor mixed models (LFMM).Additional file 2: Table S1. List of samples included in the study, including their origin and group. Table S2. Summary of sequenced genes and primer sequences used in this study. Table S3. Summary of nucleotide polymorphisms and neutrality tests. Table S4. The genetic diversity of rice landraces based on 48 SSRs. Table S5. The genetic diversity of rice landraces in each population. Table S6. Putative selective-sweep regions in subgroup jap-N. Table S7. Putative selective-sweep regions in subgroup jap-S. Table S8. Putative selective-sweep regions in subgroup ind-N. Table S9. Putative selective-sweep regions in subgroup ind-S. Table S10. Cloned genes located on the selection-sweep regions. Table S11. QTLs that overlapped with selection-sweep regions. Table S12. SNPs with absolute value greater than 6 for the first three components of 24 environmental factors. Table S13. SNPs with the top 25 highest |z|-scores among those candidate genes with known functions. Table S14. Summary of SSR markers and primer sequences. Table S15. The detailed information of 24 environmental factors in each population. Table S16. Summary of agronomic traits for rice landraces in each population. The value before the slash shows the data collection from Yunnan, and the value after the slash shows the data collection from Hainan.


We predicted that genome-wide diversity in F2 dogs would be higher than in the Lundehund but lower than in the F1 and the Buhund, and the heterozygosity values showed the expected patterns. We also found that runs of homozygosity, extended chromosomal regions of homozygous genotypes inherited from a common ancestor, were reduced in F2 individuals compared with Lundehund individuals. Our analyses demonstrate the benefits of outcrossing but indicate that some of the acquired genetic diversity is lost following immediate backcrossing. Additional breeding among F2 crosses could therefore merit from further consideration in genetic rescue management.Augmenting the genetic diversity of small, inbred populations by the introduction of new individuals is often termed “genetic rescue”. An example is the Norwegian Lundehund, a small spitz dog with inbreeding-related health problems that is being crossed with three Nordic breeds, including the Norwegian Buhund. Conservation breeding decisions for the  small number of outcrossed individuals are vital for managing the rescue process, and we genotyped the Lundehund ( Canis lupus familiaris) being comparable to small and closed populations with a high frequency of breed-specific genetic disorders, genetic rescue by outcrossing with individuals of another breed is not a well-established practice among breeders. The main reasons for this are concerns about (1) contaminating lineage “purity”, (2) losing breed-specific phenotypic traits and (3) incorporating “new” genetic diseases from other dog breeds  [Increasing the genetic variability of small, isolated populations by the spontaneous or human-planned introduction of new individuals is often referred to as “genetic rescue” ,2,3,4. Talleles) .Nevertheless, genetic disorders are likely to be less prevalent in a dog breed if a large, and therefore diverse, gene pool is maintained. Alleles with a negative effect on fitness are present in any population but only at low frequencies due to natural selection. However, when genetic variability is reduced, the frequency of such alleles will increase, whereby genetic disorders suddenly “appear” in the gene pool . For thiC. lupus) in Isle Royal and island foxes  [One important limitation of outcrossing programs for small populations, as revealed by computer simulations of different breeding schemes, is that outcrossing followed by backcrossing to the original population might only provide a short-term rescue effect, unless outcrossing is repeated continuously . This istoralis) .Some examples of outcrossing in dog breeding are described on various web pages, although these efforts were initiated privately and are, as far as we know, not yet scientifically documented. For example, in 1973, the Pointer was crossbred to the Dalmatian by the geneticist and Dalmatian breeder Robert Schaible, because all Dalmatians worldwide tested positive for a metabolic illness called hyperuricosuria , and a lIn 2011, the Irish Kennel Club (IKC) established an outcrossing program between the Irish Red and White Setter and the Irish Setter and, in 2017, made a call to breeders worldwide to participate in the international outcrossing program to increase the genetic diversity of the Irish Red and White Setter [Fratercula arctica, hereafter puffins) on steep cliffs along the northern coast of Norway. The breed is considered a cultural heritage and has several unique phenotypic traits, such as enhanced flexibility of the neck and shoulder joints, foldable ears and extra toes (polydactyly) on both front and back legs [The Norwegian Lundehund (hereafter Lundehund) is a small spitz dog traditionally used to hunt Atlantic puffins . Therefore, the breed is not at immediate risk of extinction.Due to a major effort to rescue the breed, today, there are about 1500 Lundehund individuals in the world, and about 900 of these live in Norway (e) of the global Lundehund population was estimated to be very low based both on pedigree data [e = 13) and on molecular data [e = 28). Moreover, the Lundehund shows signs of inbreeding depression by reduced fertility due to small litter size, problems with mating behavior (probably due to inbreeding avoidance mechanisms) and low sperm quality [However, the effective population size  to start an outbreeding project in 2014, which is still ongoing ,23. The Based on behavioral and morphological traits, shared history and genetic distance, three candidate breeds for outcrossing were selected by the NKK: the Norwegian Buhund (hereafter Buhund) b, the NoThe plan is to keep the crossings with these three breeds as three separate lineages with their own studbook and to monitor them for several generations before considering their inclusion in the Lundehund population and registering them in the main Lundehund studbook. All dogs used in the project were carefully selected based on health requirements established by the NKK, and each combination of Lundehund × Buhund also had to be approved by the NKK. Because the Buhund (12–18 kg) is larger than the Lundehund (6–9 kg), the first generation of crosses was made by mating a Buhund dam with a Lundehund sire. The Buhund dam should fulfil several requirements to be included in the project, such as to have grade A or B hip dysplasia (HD), grade 0 patella luxation (dislocated kneecap), be free of hereditary eye disorders, have a good temperament and generally have good health, confirmed by a health certificate. The same requirements, except HD status, applied to the Lundehund sire. In addition, both the Buhund and the Lundehund should have proven their fertility by having had litters before. These rather strict requirements, in addition to the difficulty in finding Buhund dam owners who were willing to let their dogs participate in the outcrossing project, limited the number of crossings that could be performed. The first two litters of the Lundehund × Buhund crosses were born in 2014, and their offspring was bred back to the Lundehund . All croIn this study, we investigated four groups of dogs, namely, Lundehund, Buhund and first- (F1) and second- (F2) generation Lundehund x Buhund crosses generated by the rescue project , to compn = 12), Buhund (n = 12), first-generation crosses Lundehund × Buhund  and first-generation backcrosses F1 × Lundehund . For an overview of the relatedness among individuals, see http://www.illumina.com/Documents/products/technotes/technote_infinium_genotyping_data_analysis.pdf accessed on 10 January 2022). We screened individual profiles in PLINK v.1.90 [https://www.cog-genomics.org/plink/1.9/ accessed on 10 January 2022) and retained dogs with individual and per-SNP call rates of >90%, resulting in 111,542 autosomal SNPs (genotyping rate > 0.99) for a total of 41 dogs. The profiles of two F2 dogs did not pass the screening process and were removed, resulting in n = 12  Lundehund, n = 12  Buhund, n = 7  F1 individuals and n = 10  F2 individuals. Next, we pruned the data for loci in linkage disequilibrium (LD), with a window size of 50 SNPs, a sliding window of 5 loci and a variance inflation factor threshold of 2 (PLINK command --indep 50 5 2), resulting in 8182 SNPs (henceforth the 8K dataset). Because the conservative LD pruning substantially reduced the number of SNP loci, we also performed a second, less stringent pruning that would retain more loci for assessment of runs of homozygosity (ROHs). The second dataset was obtained by filtering as described above but with pairwise genotype associations (r2) > 0.9 (--indep-pairwise 50 5 0.9 in PLINK), which retained 34,725 loci (henceforth 34K dataset).Due to the non-invasive nature of DNA sampling , it was not necessary to apply for ethical approval of animal procedures to the Norwegian Animal Research Authority. In all cases, the collection of samples from individual dogs was approved by the dog owner. The following four dog groups were included in the study: Lundehund ; unbiased expected heterozygosity (uHE), an unbiased estimator of genetic diversity; the inbreeding coefficient (FIS); and the mean percentage of polymorphic loci (P) in GenAlEx 6.501  (definitThe cumulative curves (representing the cumulative frequency distribution) for the Ho and uHE for every dog group were plotted for all the 8182 loci investigated, and the cumulative curves were compared among the groups.We next investigated deviations from Hardy–Weinberg equilibrium (HWE) within each dog group with a statistical test in GENEPOP v4.3 .Pairwise FST values were calculated for all combinations of dog groups to determine the degree of genetic differentiation using GenAlEx, and the Fisher’s exact probability test for genic differentiation was carried out using GENEPOP. We examined ROHs per group with the 34K dataset in PLINK (--homozyg–homozyg-group) with default parameter settings, and because of the small sample size, we included ROHs found in two or more individuals. We then plotted the results per autosomal chromosome for each of the four groups to visualize the differences among groups and among chromosomes.We first sought to validate the SNP data against the previous estimates of extremely low genetic diversity for the Lundehund and higher diversity for the Buhund. The mean Ho values for the four groups varied from 0.043 (Lundehund) to 0.272 (F1) . The medOnly the Buhund showed a 25% percentile different from zero, illustrating that less than 25% of the loci were homozygotic in the Buhund. In contrast, only the Lundehund showed a 75% percentile equal to zero, indicating that more than 75% of the loci investigated were homozygotic in the Lundehund .The skewness values were all positive, ranging from 0.534 (Buhund) to 3.456 (Lundehund), whereas the kurtosis ranged from −4009.148 (Lundehund) to 680.202 (F1) . The cumE), inbreeding coefficient (FIS) and the mean percentage of polymorphic loci (P), are listed in Genetic variability parameters, including unbiased heterozygosity (uHp < 0.001) for the F1, F2 and Lundehund and significant (p < 0.05) for the Buhund. All the deviations were due to heterozygote excess as can be seen by the negative FIS values ranging from −0.420 (F1) to −0.051 (Buhund) (Deviations from HWE were found to be highly significant ((Buhund) . The P r(Buhund) .ST comparisons were highly statistically significant (p < 0.001), with values ranging from 0.055 (F1–F2 comparison) to 0.424 (Lundehund–Buhund comparison) (All the pairwise Fparison) .Runs of homozygosity (ROHs) indicate regions of the chromosome where a single genotype is contiguous. The ROH plot for the four dog groups showed, as expected, that the Lundehund had the highest number of ROHs, whereas no ROH was found in the first generation of crosses (F1) . MoreoveOur analysis suggests that the initial part of the Lundehund genetic rescue project has been successful, although further work remains to be carried out until outcrossed individuals can be officially included in the studbook of the Lundehund breed. In the F1 dogs, we observed a clear increase in unbiased heterozygosity (uHE) and the mean percentage of polymorphic loci (P) . In the E and Ho that are higher than those of the Buhund generation , because many loci that were heterozygotes in the F1 became homozygotes when backcrossing with Lundehund dogs to create the F2. If we had used the mean Ho to compare the F2 and F1, we would have observed a reduction of 0.272 − 0.153 = 0.119, which is equivalent to a reduction of 43.75%, but the medians show a more dramatic scenario with more than 50% of homozygous loci. However, there are fewer homozygous loci in the F2 compared to the Lundehund, even if the 75% upper quantile in the F2 was reduced compared to the F1 .The kurtE, P and FIS being increased in the F2, we can clearly see that backcrossing the F1 with the Lundehund reduced the genetic distance between the F2 and the parental Lundehund (FST = 0.134) compared to the distance between the F1 and the parental Lundehund (FST = 0.319). A future backcross of the F2 with the Lundehund could further reduce the genetic distance between the next generation (F3) and the parental population and result in an additional loss of genetic variability.Although genetic rescue efforts have provided successful results for various species and populations , additioThe ROH results reflect the genetic rescue effect by illustrating the differences between the Lundehund genome and that of the other groups. However, the immediate backcrossing with the F1 and the Lundehund to form the F2 resulted in the rapid re-emergence of several ROH segments. This also raises the question of whether additional crossing between F2 individuals or the possible crossing of F2 individuals from different types of crossings  could present alternative scenarios for preserving genetic diversity and reducing homozygosity in future generations.This study clearly documents the beneficial genetic effect of outcrossing a highly inbred dog population. It also documents that backcrossing the F1 generation to the parental population results in a loss of some of the desired heterozygosity achieved in the initial outcross. To preserve the characteristics of the Lundehund, the F2 dogs were made by crossing the F1 with the Lundehund and not with other F2 dogs from different lineages. While subsequent backcross of the F1 with the Buhund would have produced an F2 generation with higher levels of heterozygosity than those achieved by the F1 × Lundehund cross, our study quantifies the effect that the immediate backcross strategy had on the genetic diversity of the F2 dogs. Thus, stakeholders in the program and in future genetic rescue projects can use this data in combination with health data for the F1 and F2 animals to evaluate the effectiveness of the genetic rescue program so far. Our results indicate that additional crossbreeding would extend and augment the genetic rescue process. These include crossing among F2 dogs from the three different breeds involved in the outcrossing project  to further increase and maintain genetic diversity. A careful evaluation of the resulting phenotypes, particularly with respect to LS ,17,18,19


Vigna subterranea L. verdc.) genotypes were sampled from eleven distinct populations of different origins to explore the genetic structure, genetic inconsistency, and fixation index. The Bambara groundnut, an African underutilized legume, has the capacity to boost food and nutrition security while simultaneously addressing environmental sustainability, food availability, and economic inequalities. A set of 32 ISSRs were screened out of 96 primers based on very sharp, clear, and reproducible bands which detected a total of 510 loci with an average of 97.64% polymorphism. The average calculated value of PIC = 0.243, RP = 5.30, H = 0.285, and MI = 0.675 representing the efficiency of primer set for genetic differentiation among the genotypes. The ISSR primers revealed the number of alleles (Na = 1.97), the effective number of alleles (Ne = 1.38), Nei's genetic diversity (h = 0.248), and a moderate level of gene flow (Nm = 2.26) across the genotypes studied. The estimated Shannon’s information index (I = 0.395) indicates a high level of genetic variation exists among the accessions. Based on Nei’s genetic dissimilarity a UPMGA phylogenetic tree was constructed and grouped the entire genotypes into 3 major clusters and 6 subclusters. PCA analysis revealed that first principal component extracted maximum variation (PC1 = 13.92%) than second principal component (PC2 = 12.59%). Bayesian model-based STRUCTURE analysis assembled the genotypes into 3 (best ΔK = 3) genetic groups. The fixation-index (Fst) analysis narrated a very great genetic diversity (Fst = 0.19 to 0.40) exists within the accessions of these 3 clusters. This investigation specifies the effectiveness of the ISSR primers system for the molecular portrayal of V. subterranea genotypes that could be used for genetic diversity valuation, detection, and tagging of potential genotypes with quick, precise, and authentic measures for this crop improvement through effective breeding schemes.As a new crop in Malaysia, forty-four Bambara groundnut ( Vigna subterranea L. Verdc.) is a potential legume recently introduce in Malaysia from the African continent. It is the third most important legume in Africa after groundnut and cowpea1 popularly known as “poor man’s crop” or “women’s crop” nowadays declare as “crop for the new millennium”2. It is one of the most drought-tolerant legumes also well fitted to low fertile soil where other major crops cannot survive easily 1. Besides Africa, there is ample scope to introducing this legume in Asian countries such as Malaysia, Bangladesh, India, Thailand, and Indonesia. The major drawbacks of this crop expansion in lack of high-yielding cultivars' improved production technologies as well as limited research interest by the global scientific community3. The yield of Bambara ground is recorded as low as 68.5 to 159.9 kg ha-14, 650–850 kg ha-15 are smaller than other legumes. Although this crop has enabled to produce up to 4.0 t ha-16 whereas Redjeki7 recorded 0.7–2.0 t ha-1 and 0.38–1.6 t ha-1 reported by Khan et al.2 at the optimal growing environment. This legume has versatile usages and the ability to supply agri-based food and nutrients. It can be a potential source of cheap protein for resources-limited consumers and growers8 where animal-based protein is very expensive. Seeds of Bambara groundnut can be consumed in a fresh form as vegetables whereas dried seeds are processed as roasted snacks3. Bambara groundnut is considered as “complete food” due to the content of a significant amount of nutrients such as Carbohydrates (64.4%), Protein (23.6%), fat (6.5%), fiber (5.5%)9, and trace elements. Moreover, it possesses a countable amount of K (11.44–19.35 mg/100 g), Fe (4.9–48 mg/100 g), Na (2.9–12.0 mg/100 g), and Ca (95.8–99 mg/100 g), all this amount is favourably comparable to other consumable legumes 10. The average yield of Bambara groundnut is not remarkable yet due to the fact of the use of local landraces for cultivation. No high-yielding cultivars were released by the breeding scheme due to their very complex floral biology (cleistogamous) and anthesis period (3.00 to 5.30 am) reported by Suwanprasert et al11. Assessment of genetic diversity is the prerequisite for genetic enhancement of any crop’s species especially in an under-research crop like Bambara groundnut. 5. For effective breeding in Bambara groundnut morphological alongside molecular characterization of existing germplasm or landraces becomes authoritative. A few molecular types of research has been demonstrated for this crop improvement such as SDS–polyacrylamide electrophoresis technique by Odeigah and Osanyinpeju, 12, DArT used by Olukolu et al.5, RAPD used by Rungnoi et al.1, Amadou et al.13, Massawe et al.14, and Mukakalisa et al.15, AFLP used by Massawe et al.16 and Ntundu et al.17, SSR used by Basu et al.18, Somta et al.19, Siise Aliyu and Massawe, 20, Molosiwa et al.21, Odongo et al.22, Mohammed et al.3 and all of these researchers noted the existence of greater diversity of Bambara groundnut species. Like other underutilized legumes, there is only one research has been conducted on Bambara groundnut using ISSR molecular marker by Rungnoi et al.1. For the development of new cultivars information related to genetic relatedness among the prospective parents is obligatory. Before commencing breeding, program information related to the genetic distance of parents must be discover based on both morphological and molecular approaches 23. Genetic diversity can be assessed considering both qualitative and quantitative features 16 though characterization based on morphological traits is less reliable due to influenced by environmental factors 24. Molecular marker provides more precise and authentic results to detecting variation that exists in the landraces compared to that of conventional approaches 25. Moreover, molecular markers can be used for tracing the genotypes' origin as well as be employed in plant breeding programs26. One of the effective molecular markers is ISSR (inter simple sequence repeat) that can be successfully appley to plant genome identification and characterization. The main advantage of ISSR is it produces multiple bands in the same locus, highly reproducible, and does not need any previous genomic information of the Plant27. There is no information on the use of ISSR primers in Bambara groundnut genetic diversity, as well as its botany, genetics, farming techniques, economic value addition due to the very recent introduction of this legume in Malaysia. In the present study, we used ISSR primers to measure the extent of genetic variation, genetic drift as well as genetic admixture among the existing accessions. Additionally, the findings of this research will enrich the molecular database of Bambara groundnut in Malaysian content. It also provides a basic idea of conserving and protecting the existing genotypes through optimum utilization as well as an effective breeding program for V. subterranea improvement..Bambara groundnut , Universiti Putra Malaysia (UPM), with GPS of 2°98′26.9′′N and 101°73′58.9′′E, from June to December 2020. Initially, fifteen collected accessions were undertaken the formal identification by Md Mahmudul Hasan Khan under the direct supervision of Prof. Dr. Mohd. Rafii Yusop, Director, ITAFoS, UPM, Malaysia with following the proper national and international strategies and deposited these accessions at GenBank, ITAFoS, UPM. For this study eleven accessions were used from the “ITAFoS GenBank”, UPM with the permission of the proper authority of the Institute of Tropical Agriculture and Food Security, UPM, and the study was implemented in accordance with institutional relevant guidelines and regulations. After continuous selfing, we selected a set of 44 best performing individuals of Bambara groundnut from selfed (fourth) generation (S4). The GPS location of the major growing region of each population and its code and names were listed in Table 28. Using the mortar and pestle, the presence of liquid nitrogen young healthy leaves tissues was milled into fine powder. The ground leaves tissues were transferred in a 2-mL Eppendorf tube and mixed well with 800 μl of CTAB extraction buffer  and 2 μl β-mercapto-ethanol using vortex for 5 min. The suspension was incubated at 65 °C for 60 min at 500 rpm using an electric thermo shaker with continuous gentle shaking. Afterward, an equal volume of chloroform/isoamyl alcohol, CIA (24:1 v/v), was added and the mixture was centrifuged at 4 °C temperature with 14,000 rpm for 10 min to sediment the leaf residues. Collect the supernatant, then transferred into new 1.5 ml tube and add an equal volume of ice-cold Isopropanol and incubated at 4 °C temperature for 30 min afterward centrifuge the tubes again at 14,000 rpm for 5 min to obtain the DNA pellet. To remove the RNA, the RNase enzyme (50 µg/ml) was added and incubated at 37 °C temperature for 60 min. For the purification purpose, ethanol precipitation was done by the addition of potassium acetate (5.0 M) with 2 Vol. of isopropanol at -20 °C temperature followed by shaking for 1–2 min and centrifuged at 13,000 rpm at 4 °C temperature for 10 min. After discarding the top aqueous phase, the DNA pellet was principiated and washed twice by mixing 800 μl 75% ethanol at -20 °C then centrifuged at 13,000 rpm for 10 min, discarded the ethanol, dried at room temperature. The obtained DNA pellet is dissolved in 50 μl of TE buffer . The solution with genomic DNA was measured to check the concentration and quality by the Thermo Scientific NANO DROP Lite Spectrophotometer . The quality of DNA extracted was also checked by running the electrophoresis  on a 1% agarose gel of DNA samples. To ensure DNA purity, ratio absorbance 260/280 nm and 260/230 nm of more than 1.8 were considered as a standard to proceed for the next steps. A portion of the DNA template was diluted to a final concentration of 40 ng/μl as a working sample for use. Both the stock and diluted DNAs (working sample) were stored at − 80 °C until further use.For DNA extraction 2.5 g of fresh foliar tissue from the seedling of 14 days of age, healthy and without mechanical damage were used performing the modified protocol of ZhengInitially a total of 96 ISSR primers from “Integrated DNA Technologies Inc.”, Singapore was screened, of which 32 sets of primers were preferred based on their efficacy to detect clear and sharply distinct polymorphic bands across all the 44 Bambara groundnut genotypes Table . SelecteA set of 32 ISSR primers produce a sharp and reproducible band during DNA fingerprinting of 44 Bambara groundnut accessions. Polymorphic ISSR bands were scored by using the UVIDOC software version 99.02 on the manually detecting method for the actual band sizing based on the standard weight of the DNA 100 bp Ladder. The electrophoretic profiles were coded according to the present visible and repeatable bands on the gel-electrophoresis map as “1” and absent of band at the same loci were coded as “0”.37 was used to calculate genetic diversity for each population such as percent polymorphic bands (PPB), observed number of alleles (Na), the effective number of alleles (Ne), Nei’s genetic diversity (H), Shannon’s information index (I). To analyse the genetic diversity in segmented populations, we estimated the total genetic diversity (Ht), genetic diversity within a population (Hs), Nei’s genetic differentiation index among populations (Gst), where GST as a function of within and among population heterozygosity GST = (HT − HS)/HT using POPGENE version 1.32. The amount of gene flow between populations (Nm) was calculated as per McDermott and McDonald38 population differentiation [(Nm = 0.5 (1 − Gst)/Gst], using POPGENE version 1.32. To measure the gene frequencies as well as genetic divergence between individuals of Bambara groundnut accessions were also investigated using Nie’s unbiased genetic distances matrix and genetic identities39 performing by GENALEX 6.5 software40. The analysis of PCA for the 44 V. subterranea was carried out using the same data of ISSR primers. The graphical representation based on Euclidian measures of PCA was revealed by NTSYS PC ver. 2.02; PCA biplot was generated using JMP version 16.0 from SAS program, PCA 3D, and pie chart for graphical visualization of eigenvalues and variation ratio for all PCs were illustrated by NCSS 2021 software. Moreover, the scatter matrix with density and box plot for correlation regression study among marker efficiency index  was visualized by NCSS 2021 software.For primer data analysis multiple software was used based on the accounted band profiles. Only repeatable, distinct, and well-resolved fragments were coded as presence (1) or absence (0) for each marker and presented as part of a binary matrix. POPGENE version 1.3241 and Nilkanta et al.42.Clustering was performed to determine the relative genetic distance between individuals and to check the consistency of population genetic differentiation. The Nei’s unbiased genetic distance was used to construct a dendrogram or phylogenetic tree for the 11 Bambara groundnut population using UPGMA (Unweighted Pair Group Arithmetic Mean) method in POPGENE program version 1.32 followed by MEGA  version 6.10 for Windows reported by Tamura et al.PIC), Resolving Power (RP), and Discriminating Power (DP), expected heterozygosity (H), and arithmetic mean heterozygosity (Havp) for each primer by program iMEC (https://irscope.shinyapps.io/iMEC/)43. This program calculates PIC using  formula PIC = 1 – Σ pi2 – Σ Σ pi2pj2 where pi and pj are the population frequency of the ith and jth allele. The first summation is over the total number of alleles, whereas the two subsequent summations denote all the i and j where i = j. EMR was calculated using Powell et al.44 formula EMR = n β, where n is the average number of fragments amplified by an individual to a specific system marker (multiplex ratio) and β is estimated from the number of polymorphic loci (np) and the number of non-polymorphic loci (nnp); β = np/(np + nnp). The resolving power (RP) of each primer was calculated as Prevost and Wilkinson45 formula; R = Σ Ib, where Ib represents the informative fragments. where Ib or band informativeness is represented on a scale of 0–1 and is defined as Ib = 1 – (2 ×|0.5 – p|), where pi is the proportion of individuals containing the ith band. Discriminating Power (DP) estimated by Tessier et al.46 as D = 1 – C; where C is the confusion probability is C = Σ ci = Σ pi Npi − 1 /N − 1 where for N individuals, C is equal to the sum of all ci for all of the patterns generated by the primer. Expected heterozygosity as per formula of H = 1 – Σ pi, where pi is the allele frequency for the ith allele, and the summation is over all available alleles. Arithmetic means heterozygosity (Havp) the formula given by Powell et al.44 is Havp = Σ Hn/np, where Hn is the heterozygosity of the polymorphic fraction of markers and the summation is over all the polymorphic loci np. To characterize the capacity of each primer to detect polymorphic loci among the genotypes, we also calculated de Marker Index (MI) for each primer as a product of PIC and EMR47.The performance of the primers was measured by calculating different parameters including polymorphic Information Content  (Table 49 to sense the patterns of population genetic structure using dominant markers due to it does not assume prior information of inbreeding and Hardy–Weinberg equilibrium even can be executed with a comparatively low population and loci50. No pre-data on population origin is necessary to determine the most likely number of populations (K) under the admixture model and correlated allele frequencies51. To estimate the best genetic unit, K value, a burn-in period of 5.0 × 104 followed by 1.0 × 106 m Markov Chain Monte Carlo (MCMC) simulations at 4 iterations52 with ten autonomous runs were performed with K value was pre-set from 1 to 1053. For this purposes the output files of structure analysis were squeezed into a single “Zip-Rar” file then upload online “Structure Harvester 0.6.93 version” (http://taylor0.biology.ucla.edu/structure)54 to determine the average Log-likelihood, Ln P(D), probability by K-graph, the most provable K value using ΔK method by Evanno et al.53 and Q value (standard Q > 0.60 < Q admixture) showing membership coefficient (%) value. This value assigned accessions to a certain population, finally allocate the accessions into a specific cluster based on the maximum (K) likelihood value was used49. The bar plot for best K was documented by STRUCTURE software: Version 248. Moreover, STRUCTURE software: Version 2 also used for calculating the fixation index (Fst), is the proportional increases of homozygosity. However, the value of the Fst index of a group of the population can range from “0” (no different) to “1” (completely different) i.e., no alleles held in common55.To infer the profile of the population structure and admixture detection, a Bayesian model clustering algorithm by STRUCTURE version 2.3.4V. subterranean L. accessions (Table ne: 1.59), Nei's gene diversity (h: 3.57), Shannon's Information Index (I: 0.536) followed by ISSR 10  while the lowest was shown by primer UBC 809 , respectively. The observed number of alleles was recorded low for the primer UBC 810 (na: 1.84). The maximum value of gene flow was calculated for primer A-856 (Nm: 3.07) followed by ISSR 856 (Nm: 2.96) while the least gene flow (Nm: 0.47) was observed in ISSR 811 with an average of 2.26 per primer  followed by ISSR 811 (0.311) whereas lowest was PIC = 0.185 for UBC 809. The heterozygosity (H) varied from 0.206 (ISSR 809) to 0.457 for ISSR 842 with a mean of 0.285 per primer. The arithmetic means of heterozygosity (Havp) ranged between 0.0002 and 0.0009 with the mean of Havp = 0.0004 per primer. An extremely conditional factor on the magnitude of primer polymorphism is the effective multiplex ratio (EMR) spanned from 1.90 (UBC 810 and ISSR 901) to 4.636 (UBC 836) with an average of 2.71 per primer. The marker index (MI) was calculated to recognize the usefulness of the ISSR primer system on Bambara groundnut which was maximum for ISSR 842 (MI = 1.37) followed by ISSR 836 (MI = 1.28) while least for UBC 835 (MI = 0.402) along with a mean of 0.675 per primer. To determine the judicious profundity of primer we calculate discriminative power (D) with a mean index of D = 0.966 and extended from 0.938 to 0.979. The highest value counted resolving power (Rp) is 9.27 for UBC 836 whereas the lowest was Rp = 3.63 for UBC 810 with an average of 5.30 per primer. A positive significant correlation  were: (a) PIC = 0.1477 + 0.1405 × MI (R2 = 0.95); (b) PIC = 0.1566 + 0.0162 × Rp (R2 = 0.96); (c) EMR = 0.2355 + 10.237 × PIC (R2 = 0.97); (d) MI = − 0.199 + 0.1648 × Rp (R2 = 0.94). The graphical contrasts, scatter matrix plots of MI, PIC, Rp, and EMR for ISSR primer were displayed in Fig. For calculating the polymorphic efficiency of individual primer . subterranea accessions under 11 populations of 4 geographical zones which spanned from a minimum of GD = 0.14 to a maximum of GD = 0.39. The topmost genetic distance (GD = 0.39) was recorded for the pair of accessions such as G10 vs G25, G10 vs G31, G10 vs G39, G13 vs G32, G13 vs G35, G15 vs G35, and G15 vs G30 from different agroecological zones of Gombe , Kwami (G13 & G15), Sokoto (G25). Although, genotype (G10), genotype , genotype (G35), and genotype (G39) having the same geographical locations but arises from three distinct populations of Cancaraki, Katawa, Giiwa, and Karu, respectively. However, the genotype (G25) comes from Maibergo the population showed less relatedness with the genotype (G10) of Roko. Typically, the accessions with low genetic distance showed extreme genetic identity and vice versa  genetic dissimilarity coefficient. The magnitude of relatedness and disparity among the accessions are demonstrated in  though having a close association with Exsokoto P4–18 (EL = 10.55). The subcluster V  accumulated seven genotypes, in which 4 accessions from Katawa  and three accessions from Maibergo . The genotype pairs of Maibergo (P9–18 vs P6–18) and Maibergo P6–18 vs Katawa P4–18 exhibited a lower degree of divergence despite their different source of collection such as Gombe and Sokoto. The subcluster VI (EL = 0.84 and NN = 79) compiled the 7 accessions of Giiwa (4 accessions) and Karu (3 accessions) from the same region of Gombe. The accession Giiwa P1–18 (EL = 10) separated from Giiwa P11–18 (EL = 8.42) but closely related with Giiwa P9–18 (EL = 9.53) and Karu P9–18 (EL = 8.82). The accession Jatau P4–18 was noted as genetically divergent from Jatau P1–18 (Gombe) and Maibergo P3–18 although these three accessions collaborate grouped into major cluster III .The genetic relationship of 44 V51 reported four clusters based on heatmap cluster analysis using ISSR binary data in Ficus deltoidei Jack.Based on Euclidian cluster distance and Ward (unsquared distances) linkage clustering method using ISSR data set illustrated three distinct groups of 44 Bambara groundnut accessions Fig. . The gen56 also known as multivariate gradient analysis. PCA is used for similarities which starts with the binary data matrix . When there are no missing data, the output of PCA and PCoA will be similar57. To lead the clustering investigation eigenvalues and total percentages of principal component case scores were used. The graphical distribution of eigenvalues, percent of genetic variation, and cumulative percent of genetic variation based on all axes (PCs) were displayed by pie chart in Fig. st nine principal components as shown in Table Ordination is a collective term for multivariate analysis which adapts a multidimensional group of data in such a way that the similar species or samples are plotted close together while the dissimilar one has placed far apart51 reported a similar type of PCA biplot-based analysis using ISSR primers in Ficus deltoidea Jack.The biplot-based representation of PCA showed the association of evaluated 44 accessions of Bambara groundnut along with ISSR loci loaded in the same plot. The principal component analysis assembled the total tested accessions in a diverse group based on the ISSR data set. Using the JMP version 16.0 analytical tools from the SAS program, we generated PCA sample (accessions) loading Fig. C, PCA va53 method followed by Structure harvester. The structure analysis of V. subterranea accessions was initially performed based on the maximum number of (K = 1 to 10) as the original population order displayed in Fig. 51. Nevertheless, regarding the membership likelihood (Q) > 0.60, some of the accessions showed unique standards which lead to the pure population while Q < 0.60 regarded as the admixture populations49. In the bar plot  clustering analysis using the STRUCTURE program of Evanno et al.st 58. Genetic differentiation among the accessions due to genetic structure is measured by the fixation index (Fst) using genetic polymorphism data. It is one of the most frequently applied statistics in explaining the population genetic structure59. Using the STRUCTURE program, the fixation index can be measured. Considering the best delta K value (ΔK = 3), resultant the entire genetic component is grouped into three clusters. The Structure output can be displayed as a “triangle plot” in which two clusters were plotted at two vertices and all others were at the third. In the triangle plot  while cluster 2 had Fst_2 = 0.3684  and Fst_3 = 0.3997 for cluster 3 . Bar plots can be used to further clarification of gene flow between individuals  can further be accelerated by the estimation of Flot Fig. D, indivilot Fig. C. Averague Table  for the 896 Fig.  B1 whileV. subterranea species. Usually, the efficiency of a certain primer for evaluating the population genetic structure is extremely subjected to the level of polymorphism that could be generated among the accessions. Our results represented a moderate to a higher level of genetic diversity among the studied accessions. A similar trend of diversity is very common as self-pollinated members of Bambara groundnut from the genus Vigna, recommend its medium genetic base, which is perhaps assembling of novel gene incorporation due to dynamic forces of natural selection. In this current study, the magnitude, and pattern of genetic variation within 44 V. subterranea accessions using 32 ISSR primers exposed the availability of polymorphism. We detected a total of 510 DNA fragments with an average of 15.93 bands per prime. In the present study, comparatively high percentages of polymorphism (97.64%) were observed using the ISSR primer serve as a high potential tool for genetic discrimination among the closely related V. subterranea species. Relative studies in vigna species particularly in V. subterranea based on RAPD, AFLP, SSR, DArT array, and ISSR primer systems were effectively used and reported by researchers ; Ntundu et al.17 reported a total of 49 polymorphic bands using AFLP primer; Massawe et al.14 recorded the highest 88.2% polymorphism using RAPD primers, Fatimah and Ardiarini60, noted 73.10% polymorphism using RAPD in V. subterranean. The average gene flow (Nm = 1.54) was estimated by Oumer et al.27 using ISSR whereas in our study it was recorded as Nm = 2.26.The use of a molecular marker is a very common phenomenon to investigate the population structure and genetic diversity as well as distinguishing one genotype from another as a prerequisite for pre-breeding and breeding of crops improvement. Molecular genetic diversity is very crucial as it gives a greater precise measure of polymorphism related to morphological characterizations. Most of the markers showed complete polymorphism suggesting the efficacy of these ISSR markers for the assessment of genetic variation among the 43, there are two major dimensions of genomic marker polymorphism excellency and informativeness as heterozygosity (H) and the polymorphic information content (PIC). These indices were measured based on data gained from ISSR primers using the iMEC . The range of H and PIC value for a binary or dominant marker is maximum as 0 (monomorphic) to 0.5  due to assume of two alleles per locus and both are influenced by the number and frequency of alleles61. Estimation of PIC value delivers a projection of discriminatory power of a locus by considering not only alleles numbers but also the relative frequencies of those alleles62. Polymorphic information content (PIC) is the likelihood of exposure of marker polymorphism depending on the number of detectable alleles and their frequency distribution. Moreover, the PIC index indicated better sources of variation that will assist plant breeders to assess genetic diversity and inter or intra relationships among genotypes. Resolving power (RP) is an index of the separating ability of a certain marker and an effective multiplex ratio (EMR) is a matrix which highly depends on the polymorphic extent of markers. Considering the range High PIC and H values indicate the advanced discriminatory capacity of both marker systems. MI highlights the distinctive power of the primer. A higher value of Discriminating (D) power (closer to 1) indicates a lower possibility of a mix-up between V. subterranea accessions43. There was a positive correlation was observed among PIC, RP, MI, and EMR which is supported by Kayis et al.63 and Ramzan et al.64. Most of the ISSR primers were highly polymorphic and informative, suggested for genetic discrimination analysis of this studied genus. The mean of primer efficiency index was comparatively high  and this matrix indicates the overall efficacy of the tested primers which provides exact differentiation among the accessions. The greater the RP and MI indices refer to the greater efficacy of the respective primer65. In our research, the primer ISSR 842 ; UBC 836 ; ISSR 848 ; ISSR 811 ; and ISSR 11  had moderate to high values of marker index and resolving power, suggesting these primers are extremely potential and useful for genetic discrimination of the V. subterranea accessions. These findings have harmony with the previous result reported by Oumer et al.27; Zarei and Erfani-Moghadam et al.65; Ahmed et al.61.According to Amiryousefi et al.V. subterranea accessions evaluated. On behalf of these findings, there was clear evidence by the previous observation by Mohammed et al.3 and Rungnoi et al.1 in V. subterranea. In addition to detecting the genetic distances with minute, numeric fraction values emphasize the efficiency of the ISSR primers to differentiate among the Bambara groundnut genotypes, even those that have a close relationship with each other. The findings of the current study revealed the significant genetic distinction among the V. subterranea genotypes of GD = 0.14 to 0.39 which is consistent with the similar trends of findings reported by Massawe et al.14 from GD = 0.55 to 0.92 using RAPD, Siise and Massawe20, from 0.48 to 0.90, Mohammed et al.3 from GD = 0.00 to 3.8 and Somta et al.19 from GD = 0.27 to 0.53 using SSR markers in V. subterranea. Alghamdi et al.66 revealed considerable variation among 34 Faba bean genotypes extended from 0.22 to 0.92. The genetic distance spanned from 0.08 to 1.17 among 105 Bambara groundnut genotypes noted by Odongo et al.22 using microsatellite markers, whereas Ntundu et al.17 reported genetic distance varied from GD = 0.10 to 0.68 for 100 Bambara genotypes using AFLP markers. There was high similarity covered from 0.83 to 0.94 recorded for 12 Bambara groundnut genotypes using RAPD by Fatimah and Ardiarini60. The accessions with high relatedness from two different geographical zones suggesting the involved genotypes may have the common origin and/or mechanical mixture of seeds from one agro-ecological zone to another across Nigeria. The genotypes with less relatedness indicating the presence of extreme divergence among the evaluated accessions. Typically, the genetic diversity of a population in a species is influenced by several evolutionary factors, such as geographic distance, natural selection, reproductive system, gene flow, seed dispersal as well as the center of diversity67. However, a significant extent of genetic diversity is predictable in V. subterranean accessions due to geographic dispersal of the genus but differentiation among the collected germplasm is inadequate at growing areas due to mixing of germplasms within the regions and the fact is that farmers either produce their seeds or collected seeds from unauthorized ways. As a result, the existence of close relatedness was noted among some of the accessions used in this study due to the accessions collected from similar locations or origins or names of different landraces.In our study, besides the closely associated and distant genotypes, we recorded merely similar genetic distance values with minute fractions within the populations that reflected the extent of variation presences among the 3 stated the seven clusters of 50 Bambara groundnut species and Odongo et al.22 reported three clusters of 105 Bambara groundnut genotypes using SSR primers. Conversely, the genomic grouping of Bambara groundnut accessions related to geographical distribution based on RAPDs and AFLP reported by Amadou et al.13 and Ntundu et al.17, respectively. Generally, the genotypes were positioned more closely in our generated phylogenetic tree suggesting that they were genetically more similar having identical genes. On the other hand, accessions that possess the distant group suggesting that they were genetically dissimilar even though they come from the same population as well as similar origins. This circumstance may prompt by some factors such as the mixture of seeds, mating system, natural selection, spontaneous mutation, additionally the local farmer produces their seeds or purchase from neighboring markets. A similar trend of the result was proposed in their study of Bambara groundnut by Massawe et al.14 using RAPD, Somta et al.19 using SSR, Mukakalisa et al.15 using RAPD, Olukolu et al.5 using DArT and Rungnoi et al.1 using RAPD, ISSR markers. Fatimah and Ardiarini60, grouped 12 accessions of V. subterranea into two clusters based on similarity indices using RAPD markers.Based on the ISSR banding profile we discovered three major clusters with six subclusters of the accessions evaluated in this study. The findings in our study illustrated the efficacy of ISSR markers partitions the accessions into closely related genetic groups than another marker system. The major cluster I and II occupied the maximum number of accessions whereas the smallest cluster was major cluster III which had only three genotypes from different origins. The accessions that positioned the same cluster with different regions of collection, reflecting a close genetic association despite their diverse origins. Our finding was validated by Mohammed et al.68. The principal component analysis is a method of data reduction where correlated variables are grouped and separated from others with low or no correlation. The principal component analysis is a method to explore and to visualize similarities or dissimilarities of data and assigns for each sample place in a low-dimensional space, e.g., as a 2D or 3D graphic. PCA attempts to discover the principal axes through a matrix of eigenanalysis and eigenvectors. Eigenvalues are usually ranked from the greatest to the least. The first eigenvalue is often called the "dominant" or "leading" eigenvalue. Eigenvalues are also often called "latent values". The values recorded in principal axes of PCA arebeing advocated in the similar trends of result noted by Arolu et al.69. In our study, the PCA analysis showed maximum variation captured by PC1 (13.92%) and PC2 (12.59%) which is supported by Rungnoi et al.1 and stated 90.3% variation led by the first three PCs using ISSR and RAPD in PCoA analysis of V. subterranea; Odongo et al.22 zanalyzed PCoA and concluded that 84.30% of total variability spanned by the first three PCs using SSR primer in V. subterranea which is higher than our findings; Molosiwa et al.21 noted 37.3% (1st two PCs) and 19.5% (1st two PCs) of the total variation for DArT and SSR, respectively for PCoA analysis in V. subterranea. Kaur et al.30 accounted 81.13% (1st 10 PCs), 61.75% (1st 5 PCs), and 46.17% (1st 3 PCs) variation in 23 V. radiata genotypes using RAPD, ISSR and SSR primers. Based on the standard Shannon diversity index (range from 1.5 to 3.5) noted by Khan et al.70, our calculated values indicate the presence of a moderate to high extent of genetic diversity among the accessions.The result of PCA is generally explained in terms of component scores and loadings49 as the purity standard other 9 accessions were highly complex, indicating these accessions were genetically admixture. The real fact of this mixture is either more introduction of accessions from different origins or amalgamation into breeding or natural selection which leads to increased heterozygosity. The current finding is consistent with the similar trend of results reported by Rungnoi et al.1 estimated ΔK = 2 using ISSR and RAPD in 363 Bambara groundnut genotypes while Olukolu et al.5 reported ΔK = 4 using DArT assay of 40 Bambara groundnut genotypes. Additionally, other researchers had the parallel statement such as Wu et al.49 found ΔK = 3 using ISSR; Nilkanta et al.42 found ΔK = 3 using ISSR; Zarei and Erfani-Moghadam et al.65 found ΔK = 3 using SCoT; Barbosa et al.71 found ΔK = 3 using ISSR; Zimisuhara et al.51 found ΔK = 2 using ISSR; Li and Zhang72 found ΔK = 2 using ISSR.The population structure of Bambara groundnut individuals assessed by Bayesian admixture analysis indicated 3 clusters consistent with four agro-ecological regions. Our findings of STRUCTURE analysis were in a similar trend with the genotypic relatedness revealed by UPMGA clustering resulted in entire accessions into three distinct clusters. Out of 44 accessions, 35 were comparatively pure according to Q > 0.60V. subterranea and the phenomenon of genetic drift or gene flow among the accessions was detected to some extent. Fixation index (Fst) estimation helps to know how different a group of populations from each other. High Fst implies a considerable degree of differentiation among populations. Fst values can range from 0 to 1, where 0 means complete sharing of genetic material (two population can interbreeding freely) or panmictic population and 1 means all genetic variation is explained by the population structure, and that the two populations do not share any genetic diversity, or the populations are fixed73. A standard scale of fixation index is Fst < 0.05 = little genetic difference; Fst = 0.05–0.15 = moderate genetic difference; Fst = 0.15–0.25 = great genetic difference; Fst > 0.25 = very great genetic difference established by Hartl and Clark,71. Moreover, Fst > 0.15 = significant differentiation and Fst < 0.05 = insignificant differentiation reported by Frankham et al.74. Our estimated Fst was 0.1896, 0.3684, and 0.3997 for cluster 1, cluster 2, and cluster 3, respectively. Considering the above scale, suggested that the population under cluster 1 showed great genetic differentiation whereas the population under cluster 2 and 3 showed very great genetic diversity. Frequent gene flow led to a low level of genetic differentiation with a small genetic distance among them. Oppositely, low gene flow governs the plant's adaptation to different growing regions influencing the higher level of genetic differentiation with greater genetic distance. Genetic enhancement of crops depends on the extent of genetic differentiation among accessions. The currently estimated fixation index using ISSR is higher than the report published by Kumar et al.75 Fst = 0.17, and Kimaro et al.76 while lower as compared to Fst = 0.94 reported by Kassa et al.77.The structure analysis further leads to the sharp emergence of three genetic groups of V. subterranea genotypes using ISSR primers in Malaysia. Genetic relatedness and population structure are crucial for plant breeding schemes for this crop improvement as well as its conservation. Considering this intent, to conduct this study, ISSR primer was used and exhibited a moderate to high level of efficiency in assessing genetic differentiation and genetic structure in V. subterranea populations. The amplification of many polymorphic loci indicated the used set of ISSR primers have the potential to the assessment of genetic diversity among the existing accessions. However, the combination and a large number of molecular markers (dominant and co-dominant) to further assessment of genetic variation is highly advocated. In terms of diversity indices and genetic relationships, a significant proportion of variation was accounted for among the evaluated accessions and the diverse genotypes are suggested to use in a breeding system. Oppositely, the genotypes with low average diversity indicated the potential risk of declining genetic variation due to limited genetic basis, which alarming or enlightening the implication of biodiversity, assembling, and conserving their wild genetic resources. Moreover, the Structure, PCA, UPMGA, and Nei’s analysis divulged the entire accessions into three distinct genetic components based on ISSR amplified genomic data sets. Furthermore, fixation index (Fst) and genetic structure with admixture analysis revealed the persistent genetic drift among the gene pool of V. subterranea accessions. Typically, this investigation provides an initial scientific basis of genetic data for this crop enhancement and conservation policies in the future. The result of this study will assist in more accurate portrayal, classification, preservation, and maximum utilization of genetic resources and may have real implications in future breeding schemes to broaden the genetic diversity of V. subterranea species.This is the forerunner initiative on the valuation of genetic differentiation and population structure of Supplementary Information.