Rhopalosiphum padi, a major pest of cereals and a main vector of the barley yellow dwarf virus (BYDV), in England. We have used a genotyping‐by‐sequencing approach to study whether (a) there is any underlying population genetic structure at a national and regional scale in this pest that can disperse long distances; (b) the populations evolve as a response to environmental change and selective pressures; and (c) the populations comprise anholocyclic lineages. Individual R. padi were collected using the Rothamsted Insect Survey's suction‐trap network at several sites across England between 2004 and 2016 as part of the RIS long‐term nationwide surveillance. Results identified two genetic clusters in England that mostly corresponded to a North–South division, although gene flow is ongoing between the two subpopulations. These genetic clusters do not correspond to different life cycle types, and cyclical parthenogenesis is predominant in England. Results also show that there is dispersal with gene flow across England, although there is a reduction between the northern and southern sites with the south‐western population being the most genetically differentiated. There is no evidence for isolation by distance and other factors such as primary host distribution, uncommon in the south and absent in the south‐west, could influence the dispersal patterns. Finally, results also show no evidence for the evolution of the R. padi population, and it is demographically stable despite the ongoing environmental change. These results are discussed in view of their relevance to pest management and the transmission of BYDV.Genetic diversity is the determinant for pest species’ success and vector competence. Understanding the ecological and evolutionary processes that determine the genetic diversity is fundamental to help identify the spatial scale at which pest populations are best managed. In the present study, we present the first comprehensive analysis of the genetic diversity and evolution of  Thus, we have analysed the population structure and gene flow levels and inferred the predominant mode of reproduction in the English populations. We have also analysed the proportion of gynoparae and virginoparae females collected during the autumn dispersal to confirm the genetic results. This study will be relevant to improve the management and control of cereal viruses vectored by this pest species, such as BYDV.In this study, we analysed the distribution of the genetic variation in time and space of 22.1R. padi from the RIS biological archive collected in seven locations  for 30 min at 37°C, followed by a 3 hr’ incubation with 20 µl of Proteinase K at 56°C in a shaker at 180 rpm. The DNA Micro Kit samples were instead incubated overnight at 56°C with 20 µl of Proteinase K in a shaker at 180 rpm. but without a previous incubation with RNase A. Carrier RNA was added to Buffer AL as recommended in the DNA Micro Kit manual for small samples, at a final concentration of 5 ng/µl of carrier RNA. The elution of the DNA was done with prewarmed (56°C) buffer AE, leaving the column stand for 10 min at room temperature before centrifugation; a second elution was performed using the first eluate. DNA yield was quantified using Qubit's dsDNA assay.2.3R. padi, long reads were generated using a MinION (ONT). DNA was extracted from a pool of four individuals of R. padi using Qiagen's DNeasy Blood and Tissue Kit. A total of 2.9 µg of DNA was used to prepare a genomic library following the ONT 1D ligation library protocol SQK‐LSK108. A total of 1 µg of genomic library was loaded on a FLO‐MIN107 flow cell, and the sequencing run was performed with live base calling option on using MinKNOW v1.11.5.In order to improve the available genome of R. padi PRJEB9912 and PRJEB24317 (ERR2238845–ERR223884)  , Newcastle (n = 67), Preston (n = 35), Wye (n = 15), Writtle (n = 10) and York (n = 10) during March–October in 2004, 2007, 2010, 2013 and 2016 (Table A subsample of the aphid DNA samples was genotyped to investigate the population genetic diversity of 16 Table . To incrMse I (primary digestion enzyme) and Hae III + EcoRI (secondary digestion enzymes), which were shown to provide an appropriate level of digestion and fragment size with an in silico evaluation of the Acyrthosiphon pisum genome. The library preparation and sequencing were performed following the standard Illumina pair‐end (PE) protocol. PE sequencing of 150 bp was performed on an Illumina HiSeq platform. The GBS protocol was outsourced commercially.Samples were genotyped using genome‐wide single nucleotide polymorphisms (SNPs) identified following a genomic reduced representation sequencing method . Sequencing of Writtle and York samples was done separately to those from the other four locations. At least 500 ng of genomic DNA per sample was digested with R. padi genome assembled in this study using BWA‐MEM v0.7.16.0  replicates. Parameter convergence was inspected visually. We ran the Structure simulations using a multicore computer with the R package ParallelStructure  and the geographic distance between sampling locations estimated using Google maps. Demographic events such as population expansion or bottleneck were inferred using Tajima's D  with RAxML 8.2.12  and recording the number of virginoparae and gynoparae since 1995. For this, aphids are collected alive using the Rothamsted suction trap and the method of Lowles  and gynoparae  individuals dispersing in autumn is an indirect method to estimate the proportion of anholocyclic and cyclical parthenogenetic lineages. This is because gynoparae females fly to the primary host to produce the sexual forms, while the virginoparae are flying to winter cereals and other grasses to produce females that will reproduce parthenogenetically throughout winter. The RIS has been monitoring the autumn dispersal of 33.1R. padi has been recently sequenced . The number of reads obtained that passed the quality control was 1,111,646, with a distribution of lengths ranging from 63 bp to 74,319 bp , while there are no significant differences in the sequencing results from older samples. The total number of SNPs identified with FreeBayes when all samples were analysed together was 2,287,871. The proportions of missing data per individual and per locus for the complete data set ranged from 0 to 1, reflecting the high variability in the quality of the sequencing results  resulted in 4,802 SNPs in 86 individuals (Table w 0.17 (Table FIS) was 0.42 (p = 0) (Table p < .05); however, there was no significant deviation from the Hardy–Weinberg equilibrium (HWE) at the haplotype level when the SNPs were phased. The nucleotide diversity (π and θw) was similar across all populations except for a higher π in Wye and a lower θw in York (Table FIS) observed in all sampling locations was positive and significant (Table p = 1), although 37% (Newcastle), 25% (Preston), 17% (Starcross), 13% (Wye), 30% (Writtle) and 21% (York) of individual loci deviated significantly from the HWE. The FIS levels were higher in York and Writtle, which could be because the samples from these two locations were collected during a small period of two to three weeks in late July to early August  Table . 74% of rk Table . Neverthrk Table . The meast Table  with an st Table , reducin3.4K = 2 as the most probable number of genetic groups in the R. padi population in England  within R. padi inferred by a nonhierarchical AMOVA was low but significant . The north–south genetic differentiation was further explored using analyses of the molecular variance (AMOVA) (Table FCT (0.037) was not significant (p = .1); 3.22% of the genetic variation arose between populations within geographic clusters and 93.05% occurred within populations. The hierarchical AMOVA when individuals were grouped according to the genetic clusters identified in the Structure analysis showed that 17.20% (p = .001) of the genetic variation occurred between the two clusters, while the majority  still occurred within locations.Results from the Structure analyses with the FS7 data set identified a d Figure . These td Figure , althougA) Table  A. This ST, were low to moderate and significant between the different sampled locations  except for York in the north and Writtle in the south. Mantel test did not identify a significant relationship between genetic and geographic distances between sampling locations, suggesting that the differentiation is not the effect of isolation by distance. The genetic differentiation between the north and south regions was FST = 0.05 (p = 0); when the differentiation was estimated for the genetic clusters identified by Structure, the FST was higher .The genetic differentiation levels, estimated as population pairwise Fns Table . StarcroThe estimated phylogenetic tree had little bootstrap support, and the internal branches were in general short Figure . NeverthFS for the different populations did not deviate significantly from neutrality  while the value of Fu's FS was negative and significant , indicative of a significant departure from the neutral expectation. Similarly, when these tests were run for the two identified genetic clusters, FS was negative and significant only for the genetic cluster comprising most individuals of a northern origin , while Tajima's D was not significant. A negative value of FS evidences an excess of alleles, which would be expected after a recent population expansion. Fu's FS is considered to be more sensitive to population expansion than the Tajima's D statistic and is the best performing statistic for large sample sizes, but its sensitivity to recombination may result in significant values even if no expansion has taken place  between years was low and nonsignificant  and Starcross , although it should be noted that the seasonal analyses at population level could be biased by the different number of samples, which in some cases was low. It is interesting to note that the gene diversity varied throughout the season in all locations, although the variation pattern was different for each of the locations . Instead, asexual reproduction results in an excess of heterozygotes and therefore a negative FIS, as observed in the French asexual population of R. padi  or the two southernmost (Wye and Starcross), but there was a significant differentiation between the northern and southern sites with the south‐west being the most genetically differentiated population. The two locations of York and Writtle showed high genetic differentiation with respect to all the other populations, which was unexpected given their geographic location. There was no correlation between the geographic and genetic distances between the locations, so there is no isolation by distance. This suggests that there are other factors than just geographic distance that are reducing the dispersal of individuals between the north and the south, and mostly in the south‐west. Ecological factors such as the distribution of the primary host, R. padi samples from the last 20 years in the Rothamsted Insect Survey has allowed the study of the evolution of its populations through time. The analyses have found no significant genetic differentiation between 2004 and 2016 populations, indicating no evolution of the population in Great Britain; moreover, there is no signature of demographic bottleneck or expansion either. Thus, there is no indication that climate change has affected the abundance of this pest in Britain, which is consistent with the observations of Bell et al. (Climate change is shifting the distribution and reducing the genetic diversity in many species (Balint et al., l et al. ; there iR. padi in Canada (Simon & Hebert, R. padi are a tree, the bird cherry, and the secondary host is herbaceous, cereals (Rogerson, R. padi emigrants that come from the primary host during spring colonize grasses readily and are well‐adapted, also there does not seem to be much effect of predators on the spring population of R. padi on P. padus (Dixon, R. padi in the UK and it would be expected a larger reduction in genetic diversity in the autumn dispersing population than that observed in the study. The possibility remains that a large proportion of the R. padi population remains on other secondary host plants than crops and maintains the diversity of the species, serving as the source of variability.Another aspect of temporal dynamics is the seasonal change in populations. This study has identified low but significant differentiation levels between samples collected in spring, summer and autumn, with this latter being the most differentiated. There is also a variation in the observed heterozygosity and genetic diversity from spring to autumn. Heterozygosity has been observed to decrease from the primary to the secondary host populations of s Dixon, . Therefos Dixon, , which cR. padi, being the south‐west population the most differentiated of these. This genetic differentiation cannot be explained just by geographic distance, which suggests that there are other factors that prevent complete panmixia. The dominant form of reproduction in English populations of R. padi is mostly cyclical parthenogenetic, which has an impact on the transmission of BYDV, although population monitoring is still recommended to identify any possible shift towards anholocycly. There is no evidence for long‐term demographic changes in the populations of R. padi, which is consistent with previous studies of the population dynamics of the species, indicating that environmental change and seasonal selective pressures as insecticide application will have little impact on the genetic diversity of the species. These results have direct implications in control and management of R. padi, but further studies are needed to fully understand the diversity and dynamics of the species to improve control programmes and prediction models.In conclusion, the use of genomic approaches has allowed the detection of geographic structure signature in a pest species at a national and regional scale in a region where they disperse long distances. The study has identified two genetic clusters in this aphid pest in England, which could play different roles as vectors of plant viruses. In addition, there is significant genetic differentiation across the distribution of  Click here for additional data file. Click here for additional data file. Click here for additional data file.


Quercus L.) are important models for estimating abiotic impacts on the population structure and demography of long life span tree species. In this study, we generated genetic data for 17 nuclear microsatellite loci in 29 natural populations of Quercus fabri to estimate the population genetic structure. We also integrated approximate Bayesian computation (ABC) and ecological niche analysis to infer the population differentiation processes and demographic history of this oak species. The genetic analyses indicated two genetic clusters across the 29 populations collected, where most approximately corresponded to the intraspecific differentiation among populations from western and eastern China, whereas admixed populations were mainly found in central mountains of China. The best model obtained from hierarchical ABC simulations suggested that the initial intraspecific divergence of Q. fabri potentially occurred during the late Pliocene (ca. 3.99 Ma) to form the two genetic clusters, and the admixed population group might have been generated by genetic admixture of the two differentiated groups at ca. 53.76 ka. Ecological analyses demonstrated clear differentiation among the Q. fabri population structures, and association estimations also indicated significant correlations between geography and climate with the genetic variation in this oak species. Our results suggest abiotic influences, including past climatic changes and ecological factors, might have affected the genetic differentiation and demographic history of Q. fabri in subtropical China.Oak trees ( Historical and ecological factors have left complex imprints on the genetic structure and demographic history of extant species e.g., . These fIn China, the subtropical zone covers the area from 22 to 34°N, and it is characterized as a heterogeneous environment with complex topography . It is cMore recent climatic fluctuations during the Quaternary may also have affected intraspecific differentiation because populations could have contracted into refugia during glacial periods. These climate oscillations influenced the population demography by causing range shifts and possibly driving local adaptation . PhylogeQuercus species are regarded as powerful models for studying the adaptation of forest trees to variable environments because of their wide geographical range and the large variations in the climatic and edaphic conditions in the areas that they occupy , particularly in Mexico (ca. 161 species) and Southeast Asia (ca. 130–160 species)  . Section Eurasia . Quercusal China . MoreoveQ. fabri might have contracted into refugia during the glacial period and expanded in the warm period  sampled throughout its distribution range in subtropical China. The special goals of this study were to (1) determine the genetic diversity and population structure of Q. fabri, (2) infer its intraspecific evolutionary history and evaluate the effects of geological and/or past climatic factors, and (3) test whether environment factors (geography and climate) contributed to the current distribution.In the current study, we used 17 nuclear simple sequence repeat (SSR) loci to assess the genetic variation in Q. fabri in subtropical China. At each site, mature foliage was sampled from 6 to 21 individuals spaced > 100 m apart and dried rapidly with silica gel, before storing at −10°C until DNA isolation, where 490 individuals were sampled in total  as follows: 5 min at 94°C, followed by 32 cycles at 94°C for 40 s, at the specific annealing temperature (Tm) for each marker  were obtained for each microsatellite locus with FSTAT v2.9.3 (HO), expected heterozygosity (HE), and polymorphism information content for each microsatellite locus with CERVUS v3.0.7 (Na), number of effective alleles (Ne), HO, and HE. The within-population inbreeding coefficient FIS was estimated with GENEPOP v4.2 (HO and HE) with population locations (latitude and longitude) were investigated using a simple linear regression analysis.MICROCHECKER v2.2.3  was usedT v2.9.3 . We alsoS v3.0.7 . GENALEXS v3.0.7  was emplPOP v4.2 . AssociaHO and HE for all 29 Q. fabri populations.The inverse distance weighted (IDW) interpolation function implemented in the Geographic Information System (GIS) software ArcGIS 9.3  was used to estimate the geographic patterns of R-statistics in ARLEQUIN v3.11 to infer the genetic differentiation among populations. The significance of fixation indices was tested based on 1,000 permutations for the AMOVA results  with GENALEX v6.5 , where a results .Q. fabri. Eight independent runs were performed for each K from 1 to 15 with 200,000 burn-in steps followed by 500,000 Markov chain Monte Carlo (MCMC) steps. The STRUCTURE output was analyzed and visualized by using Structure Harvester1, and the optimal number of clusters (best K) was estimated according to The STRUCTURE v2.3.4 software  was usedNm) patterns, MIGRATE-N v3.6 (M) among the groups identified by STRUCTURE. In this analysis, we used a maximum likelihood procedure and a Brownian motion microsatellite model with 10 short chains following 5,000 steps and three long chains of 50,000 iterations. We sampled every 100 steps under a constant mutation model and discarded the first 10,000 records as a burn-in.In order to infer historical gene flow  with 95% confidence intervals (95% CI) for each scenario. In order to estimate type I and type II errors in the power of model selection, we simulated 500 pseudo-observed data sets for the plausible scenarios. For the best supported scenario, we estimated the posterior distributions of all parameters and evaluating the relative median of absolute errors (RMAE) using 500 pseudo-observed data sets from the simulated data sets. We also estimated the goodness of the fit for 1,000 simulated pseudo-observed data sets by principal component analysis (PCA)  for modeQ. fabri populations with Mantel tests to evaluate the correlations between genetic and geographical/ecological distances. To evaluate the ecological distance, we selected six bioclimatic factors (we focused on the temperature and precipitation because these two factors have important effects on growth and flowering in tree species) that contributed to the phylogenetic patterns and geographic distribution of Quercus species , we also performed a partial Mantel test between the genetic differentiation and geographical distances/bioclimatic factors where the bioclimatic or geographical matrix was controlled individually. These Mantel tests were implemented in PASSaGE v2.We analyzed the isolation-by-distance (IBD) and isolation-by-environment (IBE) patterns among  species : annual IN v3.11 . The geoIN v3.11 . Correlapcnm function in the R package “vegan” and the distVincentyEllipsoid function in the R package “geosphere”  and partial RDA were performed using the R package “vegan”  to test osphere” . We seleosphere”  were appE-space) using PCA-env approach developed by D metric, which ranges from 0 (no overlap) to 1 (complete overlap)  was conducted to examine the climatic variability in the actual niches of the genetic populations across the total climatic space. Second, we estimated the partial environment space (overlap) .Q. fabri individuals, and the other genetic diversity parameters are summarized in HO: ranged from 0.380 to 0.444; HE: ranged from 0.547 to 0.609) , but the variations in HO were not significantly correlated with longitude in the species distribution (p = 0.217) .After Bonferroni correction, four SSR loci  deviated significantly from Hardy–Weinberg equilibrium in most populations due to the deficiency of homozygotes in these populations. In total, 17 SSR loci yielded 217 alleles in 490 o 0.609) . IDW anao 0.609) . Correla= 0.217) . In addiRCT = 0.10, p < 0.001), but only 9.56% of the genetic variation was partitioned. At the population level, 16.30% of the variation was partitioned among populations and 83.70% within populations  (K = 2) among Q. fabri populations based on delta K simulations  . Bayesiaulations . At K = clusters . Accordiclusters . Similarclusters .Q. fabri and then formed the “genetically mixed” population group (group 2) via genetic admixture between population groups 1 and 3. The median values of the effective population sizes for N1, N2, N3, and NA were estimated as 7.83 × 104, 6.19 × 104, 8.85 × 104, and 3.11 × 103, respectively, thereby suggesting that the effective population sizes of the current population groups underwent expansion after intraspecific divergence  presented here should be treated with caution because the estimated value is approaching the maximum threshold of prior setting (4 (90% CI = 1.53–8.29 × 104) and 6.72 × 102 (90% CI = 1.02 × 102–2.85 × 103) generations ago, respectively  and 53.76 ka (90% CI = 8.16 ka–228 ka), respectively.ABC analyses showed that scenario 6 had the highest PP and 95% CI compared with other scenarios . Analysevergence . However setting . In addi setting . Therefo setting . The estectively . If the M1→3 = 11.77 vs. M3→1 = 4.28). For the admixed population group (group 2), the estimated gene flow from group 1 and group 3 into group 2 was greater than in the opposite directions, respectively   . The est = 7.37) .Q. fabri, but the effect of IBE was greater than that of IBD  , whereas IBD was not significant . For the defined population groups, Mantel tests showed that both IBD and IBE had no significant effects on the genetic divergence of group 1 and group 2. By contrast, significant IBD was observed for population group 3  (The Mantel tests indicated that both IBD and IBE had significant effects on the genetic divergence of = 0.001) . Given t= 0.002) .df = 12, F = 1.761, Radj2 = 0.246, P = 0.001) and both partial RDA models were significant with respect to climate  and geography . The full model explained 56.91% of the total variation and partial RDA demonstrated that climate and geography explained 21.71% and 22.84% , where the first component (PC1) was mainly explained by BIO1  and the second by BIO11 (mean temperature of coldest quarter) .According to PCA-env analysis, the first two axes explained 80.85% of the total variation in the climatic conditions across the population distributions of quarter) . Multiplquarter)  showed tquarter) . Group 1Q. fabri revealed several distinctive features relative to reported patterns for other tree species distributed in subtropical China. We detected moderate nSSR gene diversity , and it was higher than the nuclear gene diversity of Populus adenopoda , HE = 0.263] . The differentiation time was similar to previous results obtained based on three chloroplast DNA regions  (Q. acutissima (2.96 Ma) (Q. variabilis (1.45 Ma) (Q. mongolica (1.38 Ma)  . Compare2.96 Ma)  but earl1.45 Ma)  and Q. m1.38 Ma) .Castanopsis fargesii    when the–228 ka) . Expansianalysis . Paleoclanalysis  had a clanalysis . TherefoQ. fabri is primarily determined by the QTP uplift (∼3.4–2.6 Ma), which is proposed to have associated with past geographical and climatic fluctuations. Our niche models produced based on the Broennimann method , mean temperature of coldest quarter (BIO11), and precipitation seasonality (BIO15) were potential ecological factors associated with intraspecific differentiation (Q. fabri population differentiation. The contributions of environmental variables to the genetic differentiation in Q. fabri were also supported by the IBE and IBD analyses. It should be noted that the spatial Mantel tests did not detect the IBE patterns within groups to a large degree, and thus the genetic structure within groups might have been less correlated with the ecological variables. However, our results suggest that the distribution of Q. fabri populations in different regions was mediated by the perturbation of abiotic environment factors, which might have influenced the genetic variation and population differentiation.Ecological processes play an important role in species differentiation . Ecologin method  suggestee niches , althouge niches . In addiQ. fabri . E-spacentiation . The temntiation . Climatentiation . Thus, iQ. fabri, which is an endemic tree species in subtropical China. Our genetic results suggest that the observed patterns of genetic variation and structure among populations of Q. fabri could be explained by a combination of both geological and climatic events in the late Pliocene/Quaternary. In addition, ecological analyses indicated clear ecological differentiation among the population structures of Q. fabri. Initial topological constraints reinforced by subsequent differential ecological adaptations may have resulted in intraspecific differentiation and the formation of genetic patterns in this oak species. Thus, heterogeneous environmental factors may have affected the spatial genetic variation, structure, and demographic history of this deciduous tree species in subtropical China.In this study, we used genetic methods and niche analyses to investigate the spatial genetic structure and demographic history of https://datadryad.org/stash/share/NCuKCzVN_yl9MikUBvD8_e41QndMma9daCr6iLe5Z_M.The original contributions presented in the study are publicly available. This data can be found here: X-DC, JY, and G-FZ designed the research. X-DC and Y-FG performed the experiments. JY, TZ, Y-MZ, XZ, and M-MJ contributed materials and data analysis. X-DC wrote the manuscript. JY, Z-HL, and G-FZ revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Austropotamobius crayfish in the light of population genetics, spatial ecology and protected areas of the Carpathians. Advancing the classical approaches, we defined ecological distances upon the rasterised river network as a surrogate of habitat resistance to migration, quantifying the deviations from the species´ suitability range for a set of relevant geospatial variables in each cell of the network. Molecular analyses revealed the populations of the two Austropotamobius crayfish species are clearly distinct, lacking hybridisation. Comparing pairs of populations, we found, in some cases, a strong disagreement regarding genetic and ecological distances, potentially due to human-mediated translocations or the geophysical phenomena of regressive erosion, which may have led to unexpected colonisation routes. Protected areas were found to offer appropriate local habitat conditions but failed to ensure connectivity. The methodology applied in this study allowed us to quantify the contribution of each geospatial  variable to the overall effect of fragmentation, and we found that water quality was the most important variable. A multilevel approach proved to reveal a better understanding of drivers behind the distribution patterns, which can lead to more adequate conservation measures.The long-term survival of a species requires, among other things, gene flow between populations. Approaches for the evaluation of fragmentation in the frame of freshwater habitats consider only a small amount of the information that combined demography and geography are currently able to provide. This study addresses two species of  Connectivity for freshwater organisms depends on the physical distance between the populations on the river path as well as the quality of habitats this path traverses3. The success of efforts to establish corridors for migration, especially in case of rare and sensitive species, is substantially dependent on the accuracy of ecological assessment methods employed5. Fragmentation is one of the most challenging issues in conservation biology because its assessment is based on the evaluation of a wide array of interlocking factors6. Current techniques for molecular analysis can accurately pinpoint the degree of separation between populations, but the tools for measuring the ecological fragmentation within hydrological networks are still underdeveloped.For species with insular distributions, conservation measures through the establishment of Protected Areas (PAs) are only partially useful in the long run, as these measures usually fail to ensure the connectivity between populations8 and pressure on freshwater systems10, the future of biodiversity is marked by the question whether PAs are able to face the next challenges in ecosystems services11. Good management of PAs should focus, among others, on minimising the loss of priority species and their habitat13 and restoring the connectivity of populations14. Many crayfish species are protected as ecologically sensitive, requiring long-term habitat stability16. The most sensitive indigenous European crayfish species in terms of ecological and conservation requirements (see e.g. ref.17) are the Austropotamobius species: the white-clawed crayfish A. pallipes sensu lato  and the stone crayfish A. torrentium . Recently, a new Austropotamobius species, namely the idle crayfish A. bihariensis20, was described by phylogenetic and taxonomic evidence. Both A. torrentium and A. bihariensis reside in the Romanian Carpathians20 and prefer habitats with clean waters in the mountainous and submountainous regions21. The range of A. torrentium19 comprises Central Europe and the Balkans, and the metapopulation in Romania is representative for this species as it consists of its most common haplogroup22. Austropotamobius bihariensis is an endemic species restricted to the Apuseni Mountains, Romania18.In the context of increasing global human demand25. Large-scale evaluations in spatial ecology require remotely computable variables rather than pointwise field measurements27. In our assessment of fragmentation, we started from the idea that, in order for two populations to exchange genes, the individuals need to travel through a sector of river with characteristics that may or may not conform to the species’ ecological requirements2. Working on a rasterised river network, we introduced ecological distances, a surrogate of habitat resistance to migration widely used in landscape ecology. The ecological distance between a pair of populations is determined by the deviation from the species suitability range in each cell of the network pathway, for a relevant set of spatial variables. Coupled with population genetics data, this allowed us to obtain a better understanding of the distribution of crayfish and the efficiency of the PAs for their conservation.Multilevel approaches are expected to reveal the relationship between species’ biology and their habitats more accuratelyA. torrentium and A. bihariensis in the Romanian Carpathians were obtained from recent published papers28, totalling 274 sites . After one segment from the last pair of walking legs from each individual was detached and stored in 96% ethanol, the animals were released back into their habitat, in compliance with national and international ethical guidelines. This study was approved by the Romanian Academy, Commission for Monuments of Nature Protection (decision no. 3131/08.04.2009), and additionally, licences were granted from the legal administrators of Natura 2000 sites in the investigated area.Sample collection for the present study did not significantly affect the wild animals (according to ref.A. torrentium ecology (e.g. refs.32). The SRTM V3 void filled 33 digital elevation model (DEM) at a 100-m resolution was processed and used for the extraction of the river network of the analysed area in ArcGIS version 10.4 . Each cell (100 × 100 m) of the river network was populated with the values of the four selected geospatial variables as follows.Four GIS-derived environmental variables (see below) were selected based on existing literature as relevant for RIVERenhancer to remotely estimate water quality by using the variable RWQ as defined by ref.34, based on land cover data extracted from remote sensing images. Considering that each cell in the river raster collects environmental influences from its upstream drainage basin, RWQ is defined using the Corine Land Cover  categories found in the catchment area of each cell in the river network grouped by anthropogenic influence potential35 and weighted by their respective areas in the catchment34. Flash-flood potential (FFP) was used to quantify the riverbed stability32; it estimates the potential for flood-related disturbance to streams by taking into account the potential drainage velocity both upstream and at the site. This variable was calculated as a product of two land-surface variables, namely slope gradient and catchment slope, derived from the DEM. The multiannual mean temperature (MMT) was extracted from WorldClim global climate layers (www.worldclim.org) raster datasets of 1-km spatial resolution, resampled at 100 m. The altitude (ALT) was extracted directly from the DEM.We used A. bihariensis appears to occupy a slightly narrower ecological niche than A. torrentium; its occupied range based on field observations was fully included in that of A. torrentium for all the considered variables, but no statistically significant differences were found between the species regarding the distribution of any of the environmental variables . For this reason, and considering the fact that until very recently18, the two Austropotamobius species were regarded as being one and the same, their records were analysed in a single dataset. The presence/absence data were used to establish the most suitable range of each variable for the species distribution. The values of these characteristics in each point of the raster river network situated outside the species suitability range imposed an “ecological cost”, i.e. a stress that the travelling individuals would have to face (see e.g. ref.36). An “ecological distance” between pairs of populations was defined by summing the ecological costs along the river path connecting them.As seen in Table randomForest package37, version 4.6-12, of the R software38. In total, 5,000 trees were generated, with two predictors randomly chosen for each split. The cutoff probability for discrimination between classes was determined so that predicted species prevalence would equal observed prevalence39 and set at 0.35. Variable importance was assessed by the amount that the Gini index decreased by splits over the respective predictor, averaged over all the trees . The considered predictors are of comparable importance in the model  and Hungary (https://www.protectedplanet.net/country/HU) were considered for the EU region and PAs in the Republic of Serbia (https://www.protectedplanet.net/country/RS).To assess the influence of protected areas (PAs) on the crayfish distribution pattern, the shapefiles of PAs in the region were superimposed over the river network. Sites of Community Importance in Romania  and 6 to A. bihariensis (North-Western Populations). Sample size ranged from 10 to 66 individuals per population. The DNA extraction and PCR conditions followed the protocols described by ref.40. We used a set of five polymorphic microsatellites: aas5, aas6, aas3040, ap140 and Tor11, with the following sequence: Tor11F (5′- TGCTGCCCATGATATCCTTT-3′) and Tor11R (5′-TGGGGATGGTCATTTTGTT-3′) (unpublished data).Microsatellite analysis was performed on samples from 23 populations, of which 17 belonged to We ran PCR products on a LI-COR 4300 DNA analyser platform and visualised the results using IRD Dye 700 and 800 and a 50–700-bp size standard . The program SAGA v. 3.3  was used to score the microsatellite alleles and to determine the genotypes.O and HE), number of alleles and inbreeding coefficient (FIS) for each locus and each population were calculated in GenAlEx v. 6.50241. Allelic richness (AR) was calculated using FSTAT v. 2.9.342. Deviations from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium between all pairs of loci were tested in Genepop v. 4.643, and significance was determined using 100 batches and 1,000 iterations per batch.Observed and expected heterozygosity  and the highest probability for K following ref.51 on the CLUMPAK online platform (https://clumpak.tau.ac.il)53. Individuals were assigned as belonging to a particular cluster with an assignment probability (Q) of ≥ 0.8, with individuals with an assignment probability of 0.2 < Q < 0.8 being classified as ‘admixed’55). We used two other alternative methods to Bayesian clustering algorithms in order to identify the number of clusters and to characterize the population structure. The first one is discriminant analysis of principal components (DAPC), a multivariate method to identify clusters and describe genetic structure in biological populations, implemented in ADEGENET package for R56. The optimal number of clusters KDAPC is chosen as the minimum number of clusters after which the Bayesian information criterion (BIC) increases or decreases by a negligible amount56. Results were visualized as scatter plots in ADEGENET.We carried out a Bayesian analysis in STRUCTURE v. 2.3.457 that partitions a collection of genotypes into K groups in an iterative approach, implemented in FLOCK v. 3.158. We applied 20 re-allocations for the algorithm, with 50 runs for each K and a LLOD threshold score of 0. The optimal number of clusters, KF, was determined by ad hoc “stopping” rules58.The second method is a multilocus maximum likelyhood algorithmA. torrentium and A. bihariensis, clustered in 15 groups according to the ecological cost  . Moreover, a significantly higher suitable network length was found in PAs (39.6%) than outside PAs (22.5%)  for A. torrentium and from 0.325 to 0.834 (HO) for A. bihariensis. The expected heterozygosity HE ranged from 0.5196 to 0.755 for A. torrentium and from 0.349 to 0.755 for A. bihariensis.The dataset of 411 individuals was successfully genotyped, with only 1.02% missing data. No loci showed significant linkage disequilibrium tested over all populations after Bonferroni correction. Only three populations significantly differed from HWE expectations after Bonferroni correction: N1 for loci Aas3040 and Aas5, S9 and N6 for locus Tor11. Diversity values for the five loci ranged from 0.489 to 0.802 . FreeNA on the other hand, suggested the existence of null alleles in 55 out of 115 combinations of loci and populations, with values ranging from 0.0001 to 0.1655 (N2 in locus ap1).O) and expected heterozygosity (HE) varied from 3.91 to 6.98, 0.567 to 0.850 and 0.526 to 0.785, respectively, in A. torrentium and from 3.25 to 5.35, 0.485 to 0.752 and 0.503 to 0.680, respectively, in A. bihariensis. The inbreeding coefficient (FIS) varied from − 0.203 to 0.165 for A. torrentium and from − 0.113 to 0.074 for A. bihariensis populations  versus populations from SW (i.e. A. torrentium). FST values and FST ENA values, uncorrected and corrected for null alleles, respectively calculated in FreeNA did not show any significant difference . The FST index for A. torrentium populations , observed heterozygosity . The first cluster  or were admixed (N4). In the A. torrentium populations, only S14, S10 and S15 populations had a higher Q , each representing a different cluster. Populations S6, S1, S2, S3 and S4 had all the individuals admixed, and there was a high percentage of admixed individuals in S13 (21 out of 22). The rest of the remaining populations had a proportion of admixed individuals between 28.5 and 77.8%. The two species showed little evidence of mixing.Bayesian analysis in STRUCTURE Fig.  showed t59. And although it is an elegant method from the statistic point of view, it always produces a “solution” for the given data and the solution may not be the best one60. The alterative assessment of clusters performed by DAPC revealed KDAPC = 14 inferred clusters, after 40 principal components and 4 discriminant functions were retained  = 16). A. bihariensis populations are assigned in DAPC to 5 distinct groups instead of 2 as in STRUCTURE Prob K results, with populations N1, N2, N5, N6 genetically differentiated and N3 and N4 with admixed individuals. In the A. torrentium populations the number of clusters identified in DAPC is smaller (KDAPC = 9) than in the analysis performed in STRUCTURE (Prob(K) = 14), but the results partially overlap the results performed by the Bayesian clustering aproach. We can see a separation between cluster 1 , cluster 6  and the rest of the clusters with the rest of the A. torrentium individuals.Evanno method of determining ΔK is best identify the highest level of genetic structure and may not be particularly reliable under complex evolutionary scenariosned Fig. A. The DAsee Fig. C. The nuF = 2 (with a very long plateau of 50), recognising thus the two species. When the two metapopulations were analysed separately, we obtained an optimum KF = 4 for the ones belonging to A. bihariensis and an optimum KF = 2 for the A. torrentium populations . Genetic structuring was supported when the STRUCTURE results, 2 and 13 groups, respectively (the number of clusters is lower due to admixed populations) were considered. We also observed a strong genetic structure when we took the genetic variation according to the drainage basins into account Table . Mantel A. torrentium from Austria, Switzerland and Germany (e.g. ref.61). We observed a strong genetic structuring with no hybridisation between two groups of Austropotamobius species populations, suggesting separation that occurred a very long time ago. This result is in agreement with the recent documentation that these populations no longer belong to a single species18. The separation was, most likely, driven by a tectonic displacement of the Tisza-Dacia microplate (including the current Apuseni Mountains in NW of Romania) during the early Miocene (ca. 15 Ma) (see ref.20).Generally, the results of this study revealed a moderate to high genetic diversity in comparison to other studies on populations of A. bihariensis revealed high genetic diversity and genetic structuring with up to 4 populations recognised as distinct clusters. The molecular data is supporting the argument for the endemic nature of this species. A homogenous population of A. bihariensis was found in the N1 river, presenting lower heterozygosity than other populations. The spatial analysis of this watercourse showed a large ecological distance from the rest of the A. bihariensis populations  which presumably colonised the Danube basin before and after the Pleistocene glaciations22, the genetic pattern observed in this study supports the assumption of multiple colonisation episodes in the past, which explains the legacy of high diversity and divergence between neighbouring basins and populations. Spatial analysis of the river network shows a mixture of sectors with zero ecological cost, mainly found in the upper sections of rivers, and sectors with positive ecological cost found especially on the main courses. The RWQ  was identified as the most important contributor to the ecological cost, which supports the assumption that the fragmentation pattern is a recent one, most likely resulting from anthropic development62.The strong genetic differentiation between A. torrentium belonging to three different river basins  of the highly successful invasive species78. In this context, there is a good side of the fragmented pattern found in A. torrentium and A. bihariensis populations. In the worst scenario of upcoming invasions (see e.g. refs.79), the fragmented habitats could offer “ark sites” (sensu ref.80) of isolated populations, turning into an advantage against unwanted colonisers (see e.g. refs.81).Another factor that can influence species distribution in an area is the presence of invasive species and the spread of their associated diseasesAustropotamobius species in the Carpathians. Combining ecological and genetic approaches, we provide a more complete picture. In this study, without ecological analysis, the fragmentation would have been underestimated, and also without genetic analyses, the translocations and atypical colonisation routes would not have been documented and revealed.Quantifying habitat connectivity by considering the dendritic nature of freshwater networks has been a key element in evaluating the current status in conservation of two sensitive and endangered 82 such as decreasing population trend83, the results of this study might be used to reconsider the “data deficient”83 status for A. torrentium. Moreover, for the still unrated A. bihariensis, a newly described species with few populations and a scarce gene pool, a conservation status assessment is urgently needed.In the light of IUCN Red List of Threatened Species relevant criteria Supplementary Information.


Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual’s genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.https://research.cchmc.org/mershalab/MI-MAAP/login/.MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at  Markers with large differences in allele frequencies between ancestral populations, known as ancestry informative markers (AIMs), provide genetic information useful to infer ancestry . GeneticThe task of identifying informative markers to assign individual genomic regions to correct ancestries can be difficult in admixed populations with multiple ancestral origins , 7. HencIn this study, we extend the two-way ancestry analysis into multi-way ancestry classification, and present a user-friendly web-based tool called Marker Informativeness for Multi-Ancestry Admixed Populations, MI-MAAP, to facilitate selection of informative SNPs in multi-admixed population using 1000 Genomes Project, Human Genome Diversity Project as well as user-generated data. It adopts a novel likelihood-based feature selection method, Lancaster Estimator of Independence (LEI), which has the ability to efficiently compare multiple ancestral populations using allele frequency information . LEI potm and M be the alternate and reference alleles at a marker and X =  be the three genotypes. Let Y =  be the k distinct ancestral populations and fj represents the population reference allele frequency in the jth population with cj indicating the number of individuals in the jth population. Then, under the assumption of HWE in individual populations, the expected genotype counts of genotype x1, x2, and x3 in the jth population can be expressed as n = c1 + c2 + … + ck is the total sample size, the joint probability distribution of X and Y, pij = P can be estimated as i = 1, 2, 3 and j = 1, 2, …,k. Then, the Lancaster estimator of independent (LEI) is defined as –Lancaster estimator of independence (LEI) is a maximum likelihood-based estimator of ancestry informativeness of markers using allele frequency data under Hardy-Weinberg Equilibrium (HWE). LEI can compute the quantitative score from the marker-population joint distribution and thus account for 3 or more populations simultaneously. Let X and Y, respectively. X and Y, with X and Y are independent and k = 2) and k ≥ 3).≤min3k−1  . In parMI-MAAP was built on the Django web development framework which can be run on different operating systems. PHP APIs were developed to retrieve SNP data from the local database and Python scripts were written to access them and perform feature selection calculations. Python’s Scikit-Learn library was used to conduct PCA, SVM, and RF. The combination of HTML and CSS was used to construct the user interface. In addition, jQuery Javascript library as well as jQuery plugins were used to build dynamic input forms, add interaction controls to output tables, and assist in some client-side design details. The tool was deployed on Apache httpd web server.MI-MAAP was designed as a web-based tool for analyzing the marker informativeness in multi-ancestry admixed populations. Its workflow is described in Fig. n = 84.7 million SNPs) across chromosomes are provided in Table The local database stores allele frequency data from four different public data repositories: the 1000 Genomes Project , Human GAfter obtaining population-specific SNP data from either the public database or the user uploaded file, feature selection methods will be performed. LEI is an efficient likelihood-based approach which is computed by using the reference allele frequency data to estimate the genotype count data based on the Hardy-Weinberg Equilibrium (HWE) assumption; while PCA, SVM, and RF require the individual-level genotype data. When using summary-level allele frequency data, LEI will be applied. When using user-generated individual-level genotype data, one of the feature selection methods from LEI, PCA, SVM and RF can be performed. If LEI is chosen for genotype data, estimated allele frequencies will be first calculated from the genotype data and then used to compute LEI values. Thresholds for the computation values of the selected feature selection method can be set to filter relevant SNPs. In order to select the most informative SNPs and remove redundant and uninformative SNPs, we implemented filtering criteria including spacing between markers to account the linkage disequilibrium (LD). Removal of redundant and uninformative SNPs reduces the time and genotyping costs while increasing the power for extracting the most informative panels of SNPs. MI-MAAP also provides various ways to export the output, including Excel, CSV and PDF files. The ability of LEI to determine ancestry proportion in multi-ancestry admixed populations using allele frequency data without requiring the individual-level genotype data provides a more efficient alternative to the existing computationally and time-consuming genotype-based analysis for ancestry inference. When other choices are made instead of LEI, we recommend using Random Forest or SVM. In our analyses, the PCA based approach requires a larger set of informative markers to achieve similar performance as of LEI, RF, and SVM to estimate the ancestry proportion of admixed samples. We would also like to point out that the reference panels used for the construction of an informative marker set should be representative of the samples. We suggest developing AIMs from representative populations of interest.In addition to prioritizing AIMs, MI-MAAP allows to retrieve and explore functional annotation information associated with AIMs. These attributes are grouped into eight categories: SNP information , gene information , genome and variation , gene expression , biological pathways , gene ontology , protein , and species orthologs  , 18, 19.Figure https://research.cchmc.org/mershalab/MI-MAAP/login/. In addition, a detailed documentation including instructions and how to use the tool is provided in Additional file The output page contains a table which includes the variant ID, chromosome, reference allele, allele frequencies among the used populations, and the computation values of the selected feature selection method. The output table can be ordered by any columns by clicking the sorting arrow in the column header. The table can also be downloaded to Excel, CSV or PDF files, copied to the clipboard for further use or printed directly. At the bottom of the result page, the previously selected attributes are displayed under each category. Clicking an SNP ID out of the result table redirects to a new page on which the selected attribute information is displayed in a tabulated format. These attribute data are obtained by either directly querying a local API, or providing the hyperlinks to external resources which are shown as the corresponding database logos. For detailed information, we created a fully dedicated website with navigation bar that contains links for manual, frequently asked questions, and contact information and can be accessed at The computation time depends on the number of markers and the feature selection threshold as well as the number of populations that are selected. Mining the information for large chromosomes (e.g. chromosome 1 from 1000 Genome Project - about 6.5 million SNPs) takes longer than mining the information for smaller chromosomes (e.g. chromosome 22 from 1000 Genome Project - about 1.1 million SNPs). For example, with threshold LEI > = 0.2, analyzing LEI values of SNPs in chromosome 1 for CEU, CHB and YRI takes about 32 s and gives 233,636 AIMs, while analyzing SNPs in chromosome 22 is about 6 s and gives 43,532 AIMs. With threshold LEI > = 0.8, analyzing SNPs in chromosome 1 and chromosome 22 for the same populations give an output of 1089 AIMs and 243 AIMs, which take about 26 s and 4 s, respectively. Other genotype-based methods are not scalable to a genome-wide data and cannot be directly compared.Suppose we have identified a set SNPs on chromosome 22 which we want to investigate the ancestry informativeness of the SNPs among three populations CEU (Northern and Western European), CHB  and YRI . Using MI-MAAP tool, we compute LEI measure for the desired set of SNPs. Since all SNPs are investigated, we set threshold LEI > = 0. The corresponding output table with a threshold LEI > = 0 is shown in Fig. To demonstrate how MI-MAAP can efficiently extract few AIMs from multiple ancestral populations, and correctly cluster different individuals into their respective geographic populations, the top 100 AIMs selected from 1000 Genome Project dataset were generated using LEI. The genotype data of the top 100 AIMs was examined using the principal components analysis (PCA) algorithm to capture the genetic structure. The PCA plots of the top 100 AIMs with the first and second principal components (PCs) for three continental ancestral populations, CEU (Northern and Western European), CHB  and YRI  is shown in Fig. To provide more insight into the variation of LEI values in different sets of populations, we analyzed the distribution of LEI in multiple population groups. It should be pointed out that the value of LEI is dependent on multiple factors including the number and genetic diversity among the ancestry populations, level of structures one wishes to correct and the sample size. LEI values are in the range of 0 to 1 for two populations and 0 to 2 for three or more populations. Moreover, LEI values from genetically diverse populations tend to be large while the values from similar populations tend to be smaller. When selecting the LEI threshold for AIMs, it is necessary to consider all these factors together and find the optimal LEI value can achieve the desired performance under different scenarios such as classification accuracy or admixture proportion. As a case study of LEI distribution in different sets of populations, we analyzed the distribution of LEI values and PCA clustering for two population groups, CEU, CHB and YRI, and FIN, GBR and TSI. The maximum LEI score for the diverse ancestral populations CEU-YRI-CHB is 1.21 and for the genetically similar populations FIN-GBR-TSI, the maximum LEI score is 0.52. Additional file Although a large number of SNPs can provide high-resolution genomic information for ancestry, a small but robust set of SNPs may be more desirable for certain applications including population structure and sample classification based on continental ancestry . The smaProject name: MI-MAAP.https://research.cchmc.org/mershalab/MI-MAAP/login/Project home page: https://research.cchmc.org/mershalab/MI-MAAP/manual/Project documentation page: Operating system(s): Platform independent.Programming language: Python, JavaScript, HTML, CSS, PHP.Other requirements: JavaScript enabled web browsers.License: GNU General Public License.Any restrictions to use by non-academics: GNU.Additional file 1: Table S1. Reference population with labels, sample size that are available from each public database to infer ancestry and web link.Additional file 2: Table S2. Step-by-step manual: Documentation including instructions and how to use the tool in developing ancestry SNP markers in multi-ancestry population.Additional file 3: Figure S1. Distribution of LEI scores. (A) Histogram shows the distribution of LEI scores with threshold 0.75 computed among CEU-YRI-CHB populations. (B) Histogram shows the distribution of LEI scores with threshold 0.15 computed among FIN-GBR-TSI populations.Additional file 4: Figure S2. PCA plots using different LEI thresholds. (A) PC1 vs PC2 produce clear separation of three ancestral populations CEU, YRI, and CHB genome-wide markers with LEI ≥1. (B) PC1 vs PC2 produce some separation of three closely related populations FIN, GBR, and TSI using markers with LEI > 0.15. We have used 2313 markers for the analysis. (C) Using markers with higher thresholds of LEI ≥0.25 failed to separate the three populations. Results were based on all 251 markers with LEI ≥0.25. PLINK 2 was used for the PCA analysis.


We determined the genetic variability, population structure, and influence of genetic factors on two parameters of fitness (body mass and reproductive ability) in roe deer females in the contact zone between the Alps and the Dinaric Mountains by utilizing microsatellite variations in 214 individuals collected throughout Slovenia, Central Europe. Spatial differences in the genetic diversity of the species can be explained by population history, different approaches to population management and/or different connectivity among subpopulations. The population genetic structure confirms the high side fidelity of roe deer, but also shows the existence of admixtures of genes among different areas. We found evidence that genetic factors, including individual heterozygosity, influence body mass, confirming that heterozygosity positively affects fitness in wild populations. However, as the effect of genetic factors is usually masked or overruled by the influence of environmental factors, i.e., availability of resources, data on the joint influence of external and intrinsic factors on fitness and other life-history traits are needed to better predict the population dynamics of targeted species, which would enable sustainable, science-based population management.Capreolus capreolus) faces a wide diversity of environmental and climatic conditions; therefore, several factors, including intrinsic ones, shape life-history traits and cause significant variability in parameters of fitness. By utilizing microsatellite variations in 214 roe deer females collected throughout Slovenia, Central Europe, we determined the genetic variability and population structure of this species in the contact zone between the Alps and the Dinaric Mountains, i.e., over a wider area where data on the genetic outlook for this—the most common and widespread European wild ungulate—have been completely lacking so far. Throughout the country, we found moderate microsatellite diversity (Ho = 0.57–0.65) in relation to the observed heterozygosity reported for other roe deer populations in Europe. Spatial differences in genetic diversity of the species in Slovenia can be explained by population history linked to varying approaches to population management and/or different connectivity among subpopulations in topographically differentiated habitats. A country-wide pattern of genetic structure is clearly defined by separation of the populations into three groups present in the following regions: (i) Southern sub-Mediterranean and Karst regions, (ii) Central Slovenia, and (iii) the Sub-Pannonian Region in the north-east. This is also confirmed by evidencing a moderate isolation by distance, especially by separating southern samples  from others. Levels of genetic differentiation vary among populations, which can be explained by the effect of natural geographical barriers or the presence of anthropogenic barriers such as urban areas and highways. In the subset of 172 yearling females, we analyzed the influence of genetic advantage  and other genetic data (reflected in the structuring of the population) on body mass and reproductive ability. We found evidence that genetic factors influence the body mass of roe deer yearling females , and the level of individual heterozygosity alone also positively affected body mass, which is in accordance with the theory that heterozygosity is commonly positively correlated with fitness in wild populations. However, we did not uncover any effect of heterozygosity on two parameters of reproductive ability , indicating that several other factors, especially environmental ones, have a predominant effect on the parameters of fitness in roe deer.Across its pan-European distribution, the European roe deer ( Capreolus capreolus) is spread throughout Europe from the Mediterranean to Scandinavia [European roe deer (ndinavia . Across ndinavia . This vandinavia ) and majndinavia ,6,7,8,9)In Slovenia, the country where we conducted this study, the results of Plis et al.  showed tThe species exhibits a similar history on the continental scale: An evident increase both in abundance and geographic distribution throughout Europe in the 20th century made roe deer a prime example of a successful species . HoweverAs a species which is widespread throughout the Europe , roe deeCervus elaphus) and 16.0% in chamois (Rupicapra rupicapra) [As roe deer are income breeders , their bpicapra) . Therefopicapra) ,32). Indpicapra) . Body mapicapra) . In Slovpicapra) .Assuming that body size (and consequently body mass) is hereditary, a phenotypic response to selection in the form of an increase in body size could be expected, but this fact is rarely observed in nature. While there is a general consensus that selection is stronger under harsh conditions , it remaWe tested the following hypotheses: (i) The genetic structure of Slovene roe deer population is determined by landscape features and is an expression of isolation by distance; and (ii) mean microsatellite heterozygosity is positively correlated with individual body mass and has an effect on the reproductive capacity of primiparous roe deer females, both suggesting that genetic features have an important influence on roe deer fitness and the population increment rate.All harvested individuals used in the study were legally hunted during the regular hunting activity prescribed by the state of Slovenia within yearly hunting management plans. We only used data on dead individuals and no animal was either shot or killed by any other means for the purposes of this research. Therefore, there was no need to seek ethical approval.In this study, 214 roe deer females (172 yearlings) from all 15 Slovene hunting management districts (HMD) were used; on the basis of the location of mortality, the studied individuals were pooled prior to analysis into 10 groups (for the purposes of this paper further considered as “populations”) based on the geographical characteristics of Slovenia . Borders2) is located in Central Europe  at the intersection of four major European geographical units: The Alps, the Pannonian Basin, the Dinaric Alps, and the Mediterranean. The climate is varied with a continental climate in the north-east, a severe alpine climate in the high mountain regions, and a sub-Mediterranean climate in the coastal region.Muscle tissue or a biopsy of the reproductive organs of female roe deer, shot or found as roadkill during the hunting season (1 September–31 December), were used. All samples were collected from 2013–2015 in 58 hunting grounds, continuously distributed throughout Slovenia, and covering the whole spectrum of environmental conditions and population characteristics of the species’ distribution in the country. Slovenia  in the study due to the following facts: (i) Roe deer females show high spatial fidelity  and haveSince all studied females were shot during regular hunting or were found as road-kill in the autumn, when roe deer females are in embryonic diapause , we deteImmediately after the culling and dissection, hunters placed the uterus (or muscular tissue in some cases) in plastic bags and kept them frozen until collection. For each specimen, the date and place of sampling, the eviscerated carcass mass , and the age group  were recorded immediately after the hunting episode see . The lowSince the body mass of yearling females in our sample set linearly increased with consecutive days in the September–December period, the eviscerated body mass of each individual was subsequently corrected (standardized) on a daily basis using a general regression model . StandaTo determine the fertility and ovulation rate  of each female, the presence and number of corpora lutea (CL) had been previously determined by ovarian dissection ,35 see . Paralle® dsDNA . In addition, the spectral curve was measured on Epoch™  using Gene5 v.1 software to check for potential impurities. We expressed the amount of DNA obtained in ng per µL of DNA in the final elution volume.Tissue samples (2 × 2 mm) were air-dried under sterile conditions to remove the ethanol and the DNA was extracted using a peqGOLD Tissue DNA Kit (S-Line)  in accordance with the manufacturer’s instructions. The DNA was eluted in the appropriate kit elution buffer, then the sample concentrations were normalized and the dilutions stored in the refrigerator at 4 °C to shorten the thaw–frost cycles. The concentration and purity of the DNA obtained in the final elution volume were measured with a 3.0 Qubit Fluorimeter  using QubitThe microsatellites (The deviation from the Hardy–Weinberg equilibrium (HWE) was calculated with the Genepop 4.2 software . The exaO) and expected (HE) heterozygosity [IS) were calculated for each population with Genetix 4.05.2 [The mean number of alleles (A), observed , and significant differences from zero FST estimates were tested again with 1000 permutations [The FreeNA program was used to estimate the global Futations .We performed discriminant analysis of principal components (DAPC), a method that allowed us to describe genetic diversity and identify genetic clusters using multi-variant methods. DAPC specifically seeks synthetic variables (the discriminant functions) that best reveal differences among groups while minimizing variation within clusters . FurtherSoftware Structure 2.3.4   and GeneSeveral methods of Bayesian clustering are available that implement the spatial analysis of genetic data . We perfWe then investigated roe deer genetic structure with the R-package, adegenet 2.0.0 , using RThe analysis of molecular variance  implemep < 0.05 was set as the level of statistical significance; in the case of stronger connections, relevant p-levels are explicitly indicated.In all statistical analyses, A sample subset of 172 yearlings  were used in this analysis. By using linear models, we analyzed effects of genetic features , and a proportion of the membership of the individual in each of the three K-groups  estimated by the Structure) on standardized body mass as dependent variable.We calculated HL as a measure of individual heterozygosity at multiple loci , using tSpatial algorithms in the program Structure assume a priori that all individuals are equally likely to be assigned to a genetic cluster, regardless of their geographical location or biological feasibility or constraint. Firstly, the Bayesian iterative algorithm randomly assigns individuals to a pre-determined number of groups, then variant frequencies are estimated in each group and individuals re-assigned to genetic groups/clusters (K) based on those frequency estimates . The individual Q-matrix presents the inferred ancestry components of each individual in each of the K clusters . We included the membership proportion  of an individual’s assignment to the most probable number of clusters (K = 3) in the linear regression model (LRM) to better represent the overall pattern of genetic variability after testing the q-values for collinearity with HL by using an accepted threshold of −0.7 in the Pearson correlation .By using the general linear model (GLM), we analyzed the effects of genetic features on fertility (presence/absence of CL) and potential reproductive output  separately, as these parameters may respond differently to various influential factors . As the A total of 14 loci were examined in 214 individuals from 10 geographic regions . All locIS  (Populations C2 (Polhograjsko and Škofjeloško hills), C3 (Dinaric Mountains), and N4 (Prekmurje) showed significant deviations from HWE based on F values)  but not ST value for ten populations was 0.017 (95% CI: 0.011–0.022) and differed significantly from zero. The pairwise FST values between populations  were also significantly different from zero  vs. C1 and C2, respectively.The global From zero . The higDAPC from the Bayesian Information Criterion suggested that there were five genetic clusters, clearly distinguishing individuals by their geographical origin (north–south gradient) and closest populations . The firThe best model of population assignment obtained from the Structure (using ΔK according to ) separatThe correlated allele frequency model in Geneland also divided ten roe deer populations into three groups , clearlyOn the other hand, AMOVA did not strongly support the three-group structuring revealed by the Structure, DAPC, and Geneland, as the among-group variance was low and not significant. However, it nevertheless supported a significant association of individual’s genotype with its geographical position, as differences among populations within groups were significant .t = 11.38, p < 0.001, R2 = 0.0056). Although this finding confirms to some extent that the geographical distance among individuals has some effect on the spatial genetic structure of roe deer in Slovenia, its influence is rather weak, explaining <1% of genetic variability among individuals : 86% of individuals belonging to the highest HL class (≥0.60) were also in the highest body mass class (≥14.0 kg), and HL distribution of individuals in very poor body condition (<10.0 kg) was skewed towards the lower HL classes, with 9% in class 0.00–0.19, 82% in 0.20–0.39, 9% in 0.40–0.59, and none in ≥0.60 (compare The mean multilocus heterozygosity (HL index) of analyzed roe deer yearlings ranged from 0.00 to 0.69  were fertile, i.e., they had ovulated (71% of them had two CL and 29% one CL), and HL distribution of non-fertile females was again skewed towards the lower HL classes, with 10% in the class 0.00–0.19, 70% in 0.20–0.39, 20% in 0.40–0.59, and none in ≥0.60 compare . In geneBy utilizing microsatellite variations in European roe deer collected throughout Slovenia we determined the genetic variability and population structure of this species in the contact zone between the Alps and the Dinaric Mountains, i.e., in the wider area of the continent where data on the genetics of this the most common and widespread European wild ungulate have been completely lacking so far.Across our sample set covering the whole distribution range of roe deer in the country, we found moderate microsatellite diversity (Ho = 0.57–0.65) in relation to the observed heterozygosity reported for other populations in Europe, where genetic diversity ranged from low , to modApart from variability in recent population abundance, spatial differences in genetic diversity can also be explained by population history and/or different connectivity among subpopulations . As far Large contiguous populations with suitable habitat connectivity in central Slovenia tend to have higher genetic diversity compared to those from north-eastern agricultural areas , where sIS values ranging from −0.061 to 0.078. Although the effect of sample variance may have an impact on the results for populations C2 and N4, where sample sizes were smaller, and can therefore have a strong influence on parameter estimation, a possible explanation for this deficit could also be the presence of the Wahlund effect, which occurs when a spatial or temporal substructure is present in the sampled population [IS value  with Fpulation . In a teIS value  were froIS value . In the ST value for the Slovenian roe deer is low (0.017) compared to populations examined in different European countries: 0.783 in Italy [ST values in our populations are understandable considering the relatively small country size , but also in view of the population history.The global Fin Italy , 0.704 iin Italy . HoweverST comparisons  vs. one of the northern (N3–Podravje and Slovenske gorice) and the southern population , revealing an isolated population structure of roe deer in this part of the Alps. As populations S1 and C1 are geographically relatively close to each other  and the closest northern population . This shows differentiation in the absence of pronounced natural geographical features and could be related to the presence of anthropogenic barriers, such as urban areas [Levels of genetic differentiation vary among populations as shown by the Fparisons . Differech other , it seeman areas  and partan areas ,23,98.ST values found within this area. Although the overall picture poses several puzzles regarding genetic diversity distribution and differentiation, the results are consistent across all the tests and support regional differentiation of Slovene roe deer populations as described above.We found a signal for the existence of isolation by distance , especiaWide-ranging habitat generalists such as different deer species are expected to exhibit low levels of population structure and a high potential for gene flow . In our A large-scale, country-wide pattern in the genetic structure of roe deer in Slovenia is clearly defined by separation of the populations in the southern sub-Mediterranean and Karst regions as well as in the sub-Pannonian Region in the north-east from the populations in Central Slovenia, which is primarily due to historical reasons ,12. The Roe deer is a typical income breeder with few available body reserves . TherefoOdocoileus virginianus) in Finland, where Brommer et al. [Capra ibex) at Gran Paradiso National Park, Italy. However, all these results are in contrast to findings that the heterozygous advantage disappears after weaning because thereafter higher heterozygosity is no longer associated with better body condition or the dispersal ability of different vertebrates [Vulpes vulpes) predation, mowing, and other agricultural machinery operations [We found that genetic factors have a significant influence on the body mass of roe deer yearling females, explaining 18.8% of its variance. By previous determination of the population’s genetic structure, we were able to divide the genetic effect into two components, the first one connected with a genetic vigour (heterozygosity) and the second one with a spatial genetic context (structuring of populations). A level of individual heterozygosity alone significantly positively affected body mass, which is in accordance with the theory that heterozygosity is commonly positively correlated with fitness in wild populations . This isr et al.  confirmer et al.  found thtebrates ,117. In tebrates  found thtebrates ,119. Howerations ,122,123,erations . Therefoerations , hence gerations , there iDespite its statistically significant effect which is particularly clear at both extremes ; 91% of individuals in poor body condition (<10 kg) had HL < 0.40), heterozygosity alone explained <5% of the total body mass variance. A more important genetic driver was connected with the population structure, particularly with the probability of being a member of the first genetic cluster (q1). Individuals with a higher q1-value  and the potential reproductive outcome , and its meaning is evident at both extremes , we could not confirm its significant effect on either of the two tested parameters. However, the population structure  had a significant effect both on fertility and on potential reproductive outcome . But in In Slovenia, the roe deer has developed a pronounced population structure in accordance with geographic features. Such a population genetic structure confirms the high side fidelity of the species, but there are nevertheless admixtures of genes among different areas (populations). Although the genetics have an influence on the fitness/vitality of individuals, this is predominantly determined by environmental factors, i.e., availability of resources. From the perspective of applied evolutionary inference, this study illustrates how important it is to understand the holistic effects of genetic and environmental factors on life-history traits when adaptive, science-based population management is required, and when decisions are based on monitoring of fitness parameters.


Off-ladder alleles were seen at several loci, and these were identified by examining their underlying nucleotide sequences. Principal component analysis (PCA) and STRUCTURE showed no genetic structure within the Kuwaiti population. However, PCA revealed a correlation between geographic and genetic distance. Finally, phylogenetic trees demonstrated a close relationship between Kuwaitis and Middle Easterners at a global level, and a recent common ancestry for Kuwait with its northern neighbours of Iraq and Iran, at a regional level.This study evaluates the forensic utility of 23 autosomal short tandem repeat markers in 400 samples from the Kuwaiti population, of which four markers  are reported for the first time for Kuwait. All the markers were shown to exhibit no deviation from Hardy–Weinberg equilibrium, nor any linkage disequilibrium between and within loci, indicating that these loci are inherited independently, and their allele frequencies can be used to estimate match probabilities in the Kuwaiti population. The low combined match probability of 7.37 × 10 Currently, forensic DNA analysis in Kuwait is carried out by the Kuwaiti Identification DNA Laboratory (KIDL) using only short tandem repeat (STR) markers, including autosomal STRs, Y-chromosome STRs (Y-STRs) and X-chromosome STRs (X-STRs). Autosomal STRs are routinely used both for identification of individuals and paternity testing, whereas Y-STRs and X-STRs are used less frequently, and only for specific scenarios.The State of Kuwait is located on the Arabian Gulf in the northwest of the Asian continent and in the heart of the Middle East. Kuwait is bordered by the Kingdom of Saudi Arabia in the south, the Republic of Iraq in the north and west, and Iran in the east, across the Persian Gulf Sea. Kuwait’s population is about 4.8 million, which includes 1.4 million Kuwaiti nationals and 3.4 million foreign nationals, according to the 2019 census 3. Although these existing STRs are efficient for analysing cases of simple relationships, more STRs are increasingly required, particularly for complex paternity cases or to increase the discrimination power in cases of partial DNA profiles and DNA mixtures.To date, few papers have been published investigating the forensic utility and genetic diversity of autosomal STR markers in the Kuwaiti population. In 2008, Alenizi and colleagues reported the allele frequencies of 15 STR loci included in the AmpFℓSTR Identifiler kit 4, three additional autosomal STRs  to increase the power of discrimination, two sex chromosome markers (Amelogenin and DYS391), and two rapidly mutating Y-STRs (DYS570 and DYS576)5. The PowerPlex Fusion 6C kit was validated by multi-laboratory evaluation following SWGDAM guidelines5.Recently, Promega launched the PowerPlex Fusion 6C kit, a six-dye kit that can amplify 27 loci, including the 20 autosomal loci in the expanded CODIS set Before utilising this kit for criminal and relationship cases in Kuwait, population and forensic statistical data for the loci in the kit must be evaluated. In this study, we aim to increase the amount of genetic data available for the Kuwaiti population, using the 23 autosomal STRs in the PowerPlex Fusion 6C kit, of which four loci  have not been reported before for Kuwait. In addition, we aim to evaluate the forensic utility of these autosomal STRs in this underrepresented region, and to investigate the utility of these markers in population genetic differentiation by examining the genetic distance between the Kuwaiti population and other global populations for which data are available.GeneMapper ID-X software version 1.4 (Thermo Fisher Scientific).Blood samples were collected on Whatman FTA cards  from 400 unrelated Kuwaiti . DNA was amplified directly, without quantification, from a 1.2 mm FTA card punch, according to the directions in the PowerPlex Fusion 6C manual, using a SureCycler 8800 thermal cycler . Detection and separation of the DNA fragments were carried out using an Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific) with the internal lane standard WEN ILS 500 and allelic ladder provided with the PowerPlex Fusion 6C kit. Genotype determination and allele calling for only the 23 autosomal loci were carried out using Arlequin statistical software version 3.5 was used to calculate allele frequencies, to test for linkage disequilibrium, and to test for deviation from the Hardy–Weinberg Equilibrium6. Forensic parameters, including the random match probability (RMP), discrimination power (DP), power of exclusion (PE), typical paternity index (TPI) and polymorphic information content (PIC), were calculated using STRAF (http://cmpg.unibe.ch/shiny/STRAF/), an online tool for STR data analysis7.Data analysis was carried out for the 23 autosomal loci only (the sex chromosomes are not included in this paper). 9. Newly presented markers therefore must be assessed for the presence of any population structure, to avoid calculation of forensic parameters using inaccurate allele frequencies taken from the total population, rather than the relevant subpopulation. Stratification also negatively impacts discrimination power, because the chance of random individuals possessing similar genotypes is higher within a subpopulation, than within the total population10. Two methods were therefore used to detect genetic structure in the population, principal component analysis (PCA), and a Bayesian-based method implemented in STRUCTURE version 2.3.413.Countries in the Arabian Peninsula, including Kuwait, have a high rate of consanguineous marriage, which causes differential distribution of alleles among families and tribes, resulting in population genetic stratificationK = 3) based on the donor’s surname. It has previously been found that the Kuwaiti population is mainly composed of settlers coming from three different regions: the Arabian Peninsula (from Saudi Arabia), the desert (representing nomadic tribes), and Persian countries (mainly from Iran)17. On this basis, the samples were categorised into three groups: KW-1 (n = 162) representing individuals originating from the Arabian Peninsula, KW-2 (n = 163), which consists of those coming from Persian countries and Iraq (north), and KW-3 (n = 75) composed of Bedouin individuals coming from nomadic tribes. PCA was carried out on allele frequencies at the 23 autosomal STR loci for the different population groups KW-1–KW-3 using R software33 and visualised using the factoextra package34.In order to demonstrate whether these two methods were able to cluster the samples into their real subpopulations, each sample was categorised into one of three ancestral subgroups (STRUCTURE (a Bayesian-based approach) takes a range of numbers of populations (K) in order to calculate the proportion of the genome of each individual in the sample originating from each inferred population11. STRUCTURE software calculates the likelihood of the data (X) for range of K values, and the true number of K is determined by the maximal value of Ln P(X|K). However, it was found by Evanno et al.18 that the maximal value does not always provide the correct number of K in the data. Instead, the maximal value of the rate of change (Delta K) in the Ln P(X|K) between successive K values accurately infers the true number of genetic clusters in the data18. As such, both Ln P(X|K) and Delta K at each K were calculated and reported. STRUCTURE was run without population information, as recommended in the STRUCTURE documentation, in order to check whether the results approximately agreed with the separation of samples into their subgroups. Thus, the predefined groups (KW-1 to KW-3) were only included as a population label rather than as prior information for the analysis. The parameters for the analysis were set as follows: ‘admixture’ and ‘correlated allele frequencies’ models using 100,000 Markov Chain Monte Carlo (MCMC) steps for each run, with the first 100,000 discarded as a burn-in, and the inferred number of K was set from 1 to 10. At each K, the analysis was repeated five times in order to test the results for consistency. The results were visualised using CLUMPAK 19, and the best K was calculated using STRUCTURE HARVESTER (available at http://taylor0.biology.ucla.edu/structureHarvester/)20.In contrast to PCA, which is an unsupervised clustering algorithm, popSTR (http://spsmart.cesga.es/popstr.php)23. Data from Lebanon (LEB) and an Indian (IND) population from Madhya Pradesh typed for the 23 autosomal loci24 were also included in the analysis. Genetic distance was also assessed at a regional level using allele frequencies for the 13 of the 23 autosomal loci  that are shared between the data reported in this study and other studies of Kuwait and neighbouring counties: Kuwait (KW13 and KW22), Iran (IRN25 and IRN12), Saudi Arabia (SA26 and SA127), Qatar (QAT28), Oman (OMN29), Yemen (YEM29), United Arab Emirates (UAE30), Bahrain (BAH31), and Iraq (IRQ32 and IRQ12). PCA analysis was conducted using R software33 and visualised using the factoextra package34.To assess the genetic distance between the Kuwaiti sample and other global populations, PCA was conducted based on allele frequencies of the 23 autosomal loci for 57 global populations grouped into seven continental regions: Africa (AFR), America (AMR), Central and South Asia (C_S_ASIA), the Middle East (ME), Europe (EUR) and East Asia (E_ASIA). Data for the global populations were obtained from the HGDP-CEPH Human Genome Diversity Panel (HGDP-CEPH) using the online forensic STR frequency browser, DA) based on Nei et al.35, which were calculated from the allele frequencies of the populations using POPTREE2 software36. The type of phylogenetic trees used were Neighbour-joining (NJ) trees, constructed using Mega X software version 10.0.537.In addition to the PCA, we studied the genetic relationship between the Kuwaiti samples and the other populations at both the continental and regional levels, using phylogenetic trees. These trees were constructed using pairwise genetic distances  was granted by the Department of Pure and Applied Chemistry Ethics Committee. Written, informed consent was obtained from all participants prior to sampling.39, SE33 was the most discriminative locus in the Kuwaiti population, having 45 different alleles (PIC = 0.945). In contrast, TPOX was the least discriminative locus, with only eight different alleles (PIC = 0.616). The calculated combined match probability (CMP) was 7.37 × 10–30, meaning that the probability of observing two identical profiles for the 23 autosomal loci in the Kuwaiti population was 1 in 1.36 × 1029 The TPI ranged between 1.439 (TPOX) and 8.333 (SE33), and the combined PE was > 99.9999%. These high values indicate the usefulness of the PowerPlex Fusion 6C kit for both human identification and paternity testing in the Kuwaiti population.Full PowerPlex Fusion 6C STR profiles were recovered from blood samples taken from 400 Kuwaiti individuals. Table p < 0.05). However, after Bonferroni correction of the significance level using the number of tests (0.05/253 = 0.000198), none of the pairs of loci showed significant linkage disequilibrium, indicating that all loci are statistically independent. Therefore, their allele frequencies can be multiplied together to estimate match probabilities in the Kuwaiti population.No significant deviation from the expectations of the Hardy–Weinberg Equilibrium was detected at any locus in the Kuwaiti genotypic data, therefore, the PowerPlex Fusion 6C autosomal STR alleles are independent and can be used to estimate allele frequencies from their genotype frequencies. Association between alleles at all possible pairwise combinations of loci was evaluated using the linkage disequilibrium test. Significant linkage disequilibrium was detected between 22  pairs of loci  alleles, and were observed in 13 samples. These samples were re-amplified for confirmation and all OL alleles were confirmed. OL alleles were observed at the PentaE , PentaD , D22S1045 , SE33 , and D18S51  loci. The samples were previously sequenced using the Verogen ForenSeq DNA Signature Prep kit (manuscript in preparation), and these data were examined to determine whether the undesignated alleles at the PentaE, PentaD, D22S1045 and SE33 loci could be identified; the repeat structure sequences from this dataset are shown in Table GeneMapper software.Alleles that could not be identified using the 40), except for the PentaD 11.2 allele, which is a novel allele not reported before in the literature.All of the identified alleles have been reported previously in the STRBase database 10. Despite the fact that, in this study, no significant deviation from the expectations of the Hardy–Weinberg Equilibrium was detected between the markers, indicating that there is no genetic stratification, it is useful to assess the markers to see if they reveal any genetic clusters within the data. To achieve this, PCA was carried out on the DNA profiles obtained from the Kuwaiti samples for the 23 autosomal PowerPlex Fusion 6C markers. PCA is an unsupervised clustering method that does not require any prior information about the ancestral origin of the samples. Simply, it clusters the samples based on their similarities to each other, forming homogenous clusters of individuals that can be seen on a PCA plot. As expected, the PCA plot  populations, and then uses this information to assign individuals into population subgroups18. The analysis was run without population information, and the mean log likelihood across five repeated runs of the analysis for each value of K (from 2 to 10) was estimated. The results showed inconsistency in estimating the log likelihood at K = 5 and over, which is indicated by the high standard deviation (SD), as presented in Supplementary Figure 18, the most likely inferred value of K was 7, as this is the number of populations at which the highest Delta K value was recorded , six American (N = 551), nine Central and South Asian (N = 202), four Middle Eastern (N = 160), 11 European (N = 2135), and 17 East Asian (N = 227) populations. An Indian population N = 374) and a Lebanese population (N = 505) were also typed for the 23 loci, thus were added to the analysis. Both PCA and phylogenetic analyses were carried out, and the resulting plots characterise the genetic differentiation between populations. The distribution of the populations on the PCA plot . It is therefore not possible to determine whether sampling from different sub-populations could explain why, in contrast to our sample, these two Kuwaiti samples cluster more closely with the Saudi Arabian sample than the samples from Iran and Iraq. Overall, it can be seen that the allele frequencies of the 23 autosomal markers in the PowerPlex Fusion 6C kit can be successfully used to separate both geographically distant global populations and closely related populations on the basis of their genetic distance, making them a good choice for detecting genetic differentiation between populations.In this study, 30% of individuals declared their origins as being from the north (Iraq and Iran), 39% from the south region , and 24% had parents of different origin (admixed). Therefore, the closer genetic relationship of our samples to the northern region might be due to the presence of these individuals. There is no information available about the population of origin for the samples collected in the two previous Kuwaiti studies (KW1STRUCTURE and PCA analysis show no signature of genetic structuring of the Kuwaiti population into subpopulations. Comparison of the Kuwaiti population to other global populations indicates that Kuwait clusters with other Middle Eastern populations, and shows a close relationship with Iran and Iraq, suggesting that they may share common ancestry.This study evaluated the forensic utility of the 23 autosomal STR loci included in the Promega PowerPlex Fusion 6C kit for the Kuwaiti population. Among these loci, D10S1248, D22S1045, D2S441 and SE33 are reported for the first time for Kuwait. The genetic data indicate that these 23 autosomal STRs are highly polymorphic in the Kuwaiti population and are of high value for human identification and paternity testing. Supplementary Information