Sander lucioperca L.) possesses great potential for diversifying European aquaculture. However, studies on the genetic risk of stocking natural waters with farmed individuals of this species have been limited. Even the effect of pond culture on the genetic composition of stocks with natural-origin has not yet been determined. Our study aimed to compare the genetic variability of a wild living pikeperch population, a pond cultured broodstock (originating from the wild population) and its offspring generation. We also aimed to detect the potential signs of selection using three different methods. By analyzing the molecular data with 14 microsatellite markers, we illustrated that the impact of pond culture on the genetic diversity of fish stocks is similar to hatchery rearing due to its diversity reducing effect caused by using lower effective population sizes. Although the heterozygosity was similar in all populations (Ho = 0.68–0.71), the average number of alleles and allelic richness were significantly lower in the pond cultured stocks  compared to the wild population . Despite the semi-natural conditions of the present study, we detected changing selection pressure in one of the 14 microsatellite markers.The pikeperch ( Stock enhancement of cultured species usually aims to compensate for exploitation shortages and to foster fisheries . BesidesSupplementation programs have mainly focused on populations with low effective population sizes and/or low genetic diversity . Any typBeside captive broodstock size, the other main factor is the fitness of captive individuals in the wild. The genetic diversity of wild populations enables them to respond to both natural  and anthropogenic challenges , resultiSander lucioperca L.) exhibits great potential for the diversification of European aquaculture  species  in Central-Eastern Europe, where the native pikeperch is the main piscivorous species. Although commercial fishing has been banned there since 2014, the maintenance of the pikeperch population requires regular stocking due to the high rate of angler catches. Until recently, 1–1.5 million of 3–5 cm length fingerling produced in fish ponds were stocked annually in the lake , a pond cultured broodstock (originating from the wild population) and its offspring generation. Our objectives were: (i) to describe the genetic patterns (genetic diversity and genetic structure) of the different stocks; (ii) to outline the causes that may have driven genetic patterns in pikeperch ; and (iii) to analyze how current hatchery practices might affect the management of natural populations. The broodstock had been maintained in fish ponds and the offspring generation was propagated artificially by a company that dominates the pikeperch larvae and fingerling market in Hungary.2 and a mean water depth of about 3 m, located in Hungary, Central-Eastern Europe) from anglers’ catchings  in the period of 2016–2017. BoFa Fish Farm  provided samples of the hatchery stocks (broodstock n = 40 and offspring n = 44) in the 2016 breeding season. The broodstock had been maintained with artificial propagation since 2005. The stock included approximately 70–80 individuals from which 40–50 individuals were annually selected for propagation . The other broodfish were replaced with individuals bred on the farm, resulting in overlapping generations in the broodstock. The generation time of the species was 4 years. In the experiment, all propagated brood fish were sampled and used to produce fingerlings (an “offspring” sample). We took precautions to handle the live fish in a way that minimized possible stress for them. Fin samples were collected in 96% ethanol and stored in a deep freezer (−80 °C) until ready for processing. The genomic DNA was extracted with DNeasy Blood and Tissue kits  following the extraction protocol of the manufacturer. DNA quality and quantity were checked using the Maestro Nano Drop Spectrophotometer MN-913 . The sample DNA concentration was equalized to 50 µg/ml.This study was approved by the Committee on the Ethics of Animal Experiments of the Kaposvár University (permit number: 3/2016-MÁB). Field experiments were approved by the Hungarian Ministry of Agriculture (permit number: HHgF/269-1/2015). Fin samples of wild fish were collected from Lake Balaton  with Buffer II . The final reaction conditions and temperature profiles of the multiplex reactions are presented in Amplifications were carried out in 20 µl reaction volume and polymerase chain reaction (PCR) was conducted using AmpliTaq GoldThe PCR was carried out in a Px2 Thermal Cycler . The products of the multiplex PCR reactions were pooled together and run against a Genescan 600 LIZ internal size standard  on a 3,500 Genetic Analyzer . After electrophoretic runs, PCR fragments were sized using GeneMapper version 4.0 software .IS were calculated using GenAlEx 6.5 software  (SPSS for Windows 11.5). We performed examinations of observed and expected heterozygosity over all loci, deviations from the Hardy–Weinberg equilibrium (HWE) using a Markov chain exact test  (U test with a Bonferroni correction (a significance level of 0.016).The evidence for the presence of null alleles at each locus and correction (Monte Carlo simulation (bootstrap) method) was evaluated using MICRO-CHECKER version 2.2.3 . The numsoftware . We estisoftware . The Ho : 5,000)  for each: 5,000) . Individ: 5,000) ), were aGenAlEx 6.5 software was used to perform Analysis of Molecular Variance (AMOVA) and to calculate pairwise Fst values and their significance .K, the most probable cluster number, posterior probabilities are calculated. The software uses a Bayesian clustering approach applying Markov Chain Monte Carlo (MCMC) estimation. For assessing the number of population clusters, an admixture scenario with allele frequencies correlated was chosen, the burn-in was set to 104 and the number of further MCMC ran to 105. Calculations were repeated 10 times for each K. The ΔK (a quantity based on the second order rate of change with respect to K of the likelihood function), was calculated using the STRUCTURE HARVESTER software  with MEGA6.06 software . RelatedDiscriminant Analysis of Principal Components (DAPC) using microsatellite loci and populations was performed in R environment (3.4.0) with ADEGENET 2.0.1. package  to partiThe effective population size (Ne) of the stocks was calculated in NeEstimator 2.1  using th6 simulations was used. BayeScan uses a logistic regression model that explains the observed pattern of diversity by dividing it in a locus- and population-specific component (10 (PO) > 1.5. The third method, the lnRH method, could separate selective sweeps from demographic effects, such as bottlenecks. The lnRH test calculates the logarithm to the ratio of H for each locus for a pair of populations. H is defined from the expected heterozygosity by the following equation: Loci under directional selection were expected to have lower intrapopulation variability and larger interpopulation variability than neutral loci. Three methods were used for detecting the sign of selection: LOSITAN , BayeScaomponent . We used(1−He)]2 . The lnRPerca fluviatilis (taxid: 8168) using the program discontiguous megablast with default settings. The contigs above an “E-value” significance threshold of 1 × 10−4 were further blasted against NCBI BLAST nucleotide collection database using the program megablast with default settings.As the genome sequence for pikeperch is not available in the GenBank database, the putative outlier loci were blasted against the NCBI BLAST Transcriptome Shotgun Assembly (TSA) database, limited by the organism The Microchecker did not detect evidence for large allelic dropout and the presence of null alleles was assumed only in the case of loci PflaL8  and SviL8 (in the broodstock population) due to general excess of homozygotes. PflaL8 locus was removed from the downstream analyses, but SviL8 was retained as its frequency was low (6%). Basic molecular genetic parameters are shown in o) were also high (ranging between 0.68 and 0.71) and the differences between the three groups were also not significant . The average allele numbers (NA) per loci ranged between 6.00 and 11.00. The differences between wild and broodstock  and wild and offspring populations  were significant. The allelic richness (AR) data showed significant differences between all groups, but after the Bonferroni correction, only wild-broodstock  and wild-offspring  comparisons remained significant and broodstock and offspring were not different . In the case of private allelic richness (ARp) all populations differed significantly . The offspring population showed no private alleles while the average ARp was 0.43 in the broodstock and 3.81 in the wild population. The following loci showed deviation from the HW equilibrium only in one population: PflaL3 in the broodstock population; MSL-1, MSL-5, MSL-6 and Svi-18 in the offspring population; and MSL-9 and MSL-3 in the wild population. FIS values were generally low and in the negative range, except for the wild population, but the difference between them was not significant . The averages of individual levels of multilocus heterozygosity  values were high (ranging between 0.68 and 0.72) and there were no significant differences between the three examined groups (effect size of difference (d) was 0.36, 0.18 and −0.18 between broodstock-offspring, broodstock-wild and offspring-wild groups, respectively). The observed heterozygosity values (Hzygosity  were 9.6P = 0.00006). An excess of heterozygosity compared to expectations under drift-mutation equilibrium was calculated in all of the 14 loci against the expected value of 8.23 and all of them was significant.There is significant evidence to support the hypothesis that management can alter the effective population size. The values of the effective population sizes estimated by the linkage disequilibrium method were 1,032.6 (95% CI [306.5–infinite]), 62.5 (95% CI [47.9–86.6]) and 15.1 (95% CI [12.9–17.7]) individuals in the wild, broodstock and offspring populations, respectively. The temporal method according to broodstock and offspring populations found that Ne was 31.3 (95% CI [23.0–89.9]) individuals. Unexpectedly, results from the Wilcoxon sign-rank test for heterozygosity excess performed using BOTTLENECK suggested a recent population bottleneck only in the wild  population (P < 0.05) of pairwise Fst values between population pairs. A low Fst value (0.011) was detected between broodstock and offspring while moderate values were found between wild and broodstock (0.089) and wild and offspring (0.123) populations.The AMOVA results showed that only 7.87% of the variance was found among populations indicating low/moderate levels of population differentiation. This was confirmed by the significance  = −6,037.19; ΔK = 40.499) using both neutral and outlier markers in the calculation (K was changed to K = 2 (mean LnP(K) = −4,946.13; ΔK = 71.640)  . In the DAPC analysis grouped the samples into three groups according to their origin, supporting the presence of one more distant (wild) and two partly overlapping (broodstock and offspring) groups. There was no difference between the displayed plots of DAPC based on all  or only on the neutral markers  and 2.Based on the UPGMA tree  the offsThree of the 14 loci were assigned as outliers in at least one of the methods, with the different methods identifying one, one and two outliers for LOSITAN, BayeScan and lnRH, respectively . The othPositive selection resulted in the loss of small-sized alleles in the case of MSL-9 . To idenGFIQ01024864.1 and GFIQ01024865.1; Query cover 70%, Expect value: 9e−19; Percent of Identity: 98.36%). The further blast of these perch sequences to databases of other species resulted in an association of the sequence with Perca flavescens transthyretin precursor mRNA .The putative outlier marker sequence MSL-9 was (250 bp) BLAST to two perch transcriptomes. The sequence showed homology with two transcribed perch RNA sequences  in all but one of the cases where literature was concerned with information on the genetic diversity of pikeperch populations. The heterozygosity levels and allelic richness showed a wide range: Finnish populations He = 0.3–0.46, Ar = 3.3–5.5  and He =Perca fluviatilis) had higher genetic diversity compared to the source population, contrary to our results. Moreover, the offspring populations had similar allelic richness and heterozygosity compared to the broodstock and only the allele number decreased with 10–21% in the offspring. Data on the genetic variability of Hungarian wild and farmed pikeperch populations has been published in only two studies. In our study the genetic diversity of the wild population was higher than in both the farm broodstock and offspring populations. The allele number and the allelic richness of the offspring population was almost half that of the wild population. In spite of its higher diversity, the wild population showed the signs of a recent bottleneck event. The genetic separation of the farmed and the wild populations is already evident with neutral markers. However, DAPC analyses or the involvement of outlier locus in SRUCTURE analysis resulted in further separation of the offspring population from the broodstock. Effective population size decreased in both farm populations and offspring also showed a significant reduction compared to the broodstock. An unequal family contribution was observed within the offspring population . The 75%Oncorhynchus mykiss) involved in a supplementation program in the Hood River population. Despite a total of 40–80 fish being used as broodstock each year, the Ne of the parents estimated from the offspring generation was small . The low population size caused decreased allelic richness, increased average relatedness and substantial levels of genetic drift in hatchery fish.Broodstock population size was estimated as Ne = 67.9, which agrees with the known number of animals. However, the Ne of the offspring populations was extremely low, with 15.1 individuals (according to the LD method) or 31.3 individuals . In the present study, the decrease in genetic diversity in the farmed populations coupled with the decrease of effective population sizes suggested a strong founder effect in broodstock. This effect was more intense in the offspring population, which could be caused by several factors. The most probable is the variance in reproductive success of fish resulting in different hatching rates of eggs and/or different survival ratios of fish larvae . Other factors could be the effect of age or unbalanced sex ratio, although the effect of these factors is limited by the selection before controlled propagation. IS value was negative in three of the loci  suggesting that the founder effect or genetic drift significantly affected the offspring population. In the case of MSL-1, one of the outlier methods (LnRH) suggested selection on the locus.In addition to the reduction of Ne and genetic diversity, a significant deviation from HWE was observed in four of the 14 loci in the offspring population. Although heterozygosity was high, the FOncorhynchus mykiss). The process influences the expression of more than 700 genes involved in the adaptation to the culture environment due to the modification of the immune response or metabolism. Similar results were described by Beyond the founder effect and genetic drift, the source of genetic differentiation could be the selection of different environmental factors. In the present study, three of the 15 loci were detected as an outlier using one of the selection detection methods and one of them was supported by at least two of the three independent methods. DAPC supports the separation of the three populations independently from the inclusion of this outlier locus, but structure analysis confirms the further separation of the offspring population as an effect of this locus (MSL-9). The comparison between allele frequencies of MSL-9 in the present study and in German populations (with a range between 196 and 254 bases pairs in the dataset of Nevertheless, selection has a minor effect compared to the genetic effect of low effective population sizes. Stocking with offspring populations could pose significant genetic risks to the wild population. One of the main sources of genetic change originates in the “Ryman–Laikre” effect created by reduced effective population size of the hatchery stock compared to the original wild population. It is most aggressive if the hatchery broodstock is small, if the proportion of the released offspring is high and if the RRS of the released fish is high .According to the Ryman–Laikre model, 62.5 and 15.1 broodstock Ne assumes reproductive success of about 11.5% and 2.5%, respectively to maintain the size of the 1,032 population. Although RSS is not known in Lake Balaton, Our case study shows that the lower population size of the broodstock resulted in a reduced, but still high, genetic variability compared to that of the natural population. This variability and population size appears to be sufficient for the aquaculture production of this species, but there is still a genetic risk in supplementation of natural waters. The same parameters in the offspring population were significantly reduced. When using these individuals for both aquaculture and stock enhancement purposes, the genetic decline of the affected populations is expected. Altogether, the present study provides support for the reassessment of the suitability of pond cultured stocks in the supportive breeding of natural fish populations and it highlights the importance of the genetic monitoring of both farmed and natural populations.10.7717/peerj.8745/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.8745/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.8745/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.8745/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.8745/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.8745/supp-6Supplemental Information 6Click here for additional data file.


Anopheles funestus genetic diversity Africa-wide revealed evidences of a major division between southern Africa and elsewhere, associated with different population histories. Three genomic regions exhibited strong signatures of selective sweeps, each spanning major resistance loci . However, a sharp regional contrast was observed between populations correlating with gene flow barriers. Signatures of complex molecular evolution of resistance were detected with evidence of copy number variation, transposon insertion and a gene conversion between CYP6P9a/b paralog genes. Temporal analyses of samples before and after bed net scale up suggest that these genomic changes are driven by this control intervention. Multiple independent selective sweeps at the same locus in different parts of Africa suggests that local evolution of resistance in malaria vectors may be a greater threat than trans-regional spread of resistance haplotypes.Insecticide resistance in malaria vectors threatens to reverse recent gains in malaria control. Deciphering patterns of gene flow and resistance evolution in malaria vectors is crucial to improving control strategies and preventing malaria resurgence. A genome-wide survey of  Anopheles funestus and elucidating genomic signature of resistance evolution are crucial for designing resistance management strategies and preventing malaria resurgence. Here, we performed a genome-wide survey of An. funestus genetic diversity from across its continental range using reduced-genome representation (ddRADseq) and whole genome (PoolSeq) approaches revealing evidence of significant barriers to gene flow impacting the spread of insecticide resistance alleles. This study detected signatures of strong selective sweeps occurring in genomic regions controlling cytochrome P450-based and glutathione s-transferase metabolic resistance to insecticides in this species. Fine-scale analysis of the major pyrethroid resistance-associated genomic regions revealed complex molecular evolution with evidence of copy number variation, transposon insertion and gene conversion highlighting the risk that if this level of selection and spread of resistance continues unabated, our ability to control malaria with current interventions will be compromised.Malaria control currently relies heavily on insecticide-based vector control interventions. Unfortunately, resistance to insecticides is threatening their continued effectiveness. Metabolic resistance has the greatest operational significance, yet it remains unclear how mosquito populations evolutionarily respond to the massive selection pressure from control interventions including insecticide-treated nets. Deciphering patterns of gene flow between populations of major malaria vectors such as  Anopheles funestus is a major malaria vector throughout sub-Saharan Africa , Central , East  and Southern Africa . Clear divergence was seen between southern Africa and elsewhere  to analyse population structure in lsewhere . In STRUlsewhere . Principlsewhere , as did ulations . OverallTo extend the analysis of population structure and history to include more populations and more variant sites, we carried out pooled-template whole genome sequencing (PoolSeq) of 40 field collected female mosquitoes  for 8 populations from West (Ghana and Benin), Central , East (Uganda) and Southern Africa .An. gambiae, where “western” populations show evidence of population expansion that “eastern” populations do not [Sequence data obtained for each pool was trimmed to remove sequencing adapter and low-quality regions and filtered to remove short and unpaired reads  then alis do not , and mayAn. funestus populations across Africa, we analysed historical relationships among populations using a graph representation that allows population splits and migration events. The TreeMix analysis  and help to characterise and detect genomic regions associated with resistance. Hypothesizing that insecticide pressure is the strongest recent selection pressure upon mosquito populations, so that most major selective sweeps will be due to exposure to these insecticides, we scanned our PoolSeq samples to detect such signatures. We used the Tajima’s D values for 50 kb sliding windows across the autosomes  to scan the genome and detected multiple selective sweeps. These were seen in some populations but not in others, and some populations showed multiple selective sweeps at different loci .rp1) and sequenced on a 120 kb BAC (accession PRJEB37305) [An. funestus and An. gambiae populations [One major selective sweep, on chromosome arm 2R (on scaffold KB669169 of the reference genome assembly) was seen in Malawi, Mozambique, Uganda, DRC  and Benin . It was EB37305) . The loculations , 5, 25.An. gambiae [Another selective sweep, on chromosome arm 2L (scaffold KB669036) and seen only in Benin , encompa gambiae , 21.In addition to these, additional possible signature of selection were seen on chromosome arm 2L  and at the telomeric end of 3R  in all populations . These wFST estimated (using ddRADseq data) across the genome reflected the broad trend . The presence of two independent haplotypes under selection in Ghana, possibly on different inversion backgrounds, might partly explain these patterns.In comparisons between Ghana and other populations, extended regions of elevated divergence were seen on chromosome arm 2R . This isTo further characterise the selective sweep and haplotypes of the CYP6 cluster on chromosome arm 2R , we exteFST occurred near the CYP6 gene cluster (FST>0.4) than in Malawi (FST>0.3) and occurred over a broader region spanning the CYP6 cluster . The insertion was fixed in both the 2014 Malawi and 2016 Mozambique populations. It was also seen at approximately 80% frequency in a population from eastern DRC  in 2015, but absent from western DRC (Kinshasa) in 2015  contains a number of assembly gaps near and within the CYP6 cluster, making very fine-scaled analysis of sequence alignments difficult. To solve this, we re-aligned the PoolSeq data to a 120kb BAC sequence, with no gaps, containing the CYP6 cluster  [The region spanning the CYP6 cluster in the EB37305) . We usedAnopheles species but at least three cases of gene duplication have occurred in the lineage leading to An. funestus (detectable in both the FUMOZ reference genome assembly and in the FUMOZ rp1 BAC). These tandem duplications, of CYP6P3 , CYP6P4 (duplicated to form CYP6P4a and CYP6P4b) and a carboxylesterase gene, highlight the evolutionary plasticity of the locus. In addition to these among-species differences, we identified complex polymorphisms segregating within the species. As well as the 6.5 kb insertion between CYP6P9a and b described previously [The CYP6 gene cluster contains a core set of genes conserved among CYP6P9a and b seen in southern Africa  spanned all of CYP6AA1, CYP6AA2, the two carboxylesterase genes, CYP6P15F and the 3’ half of CYP6P9a. In Ghana, three tandem duplications were identified  was 60.8 kb long and contained the entire CYP6 cluster. A third (rp1:46407–52668) spanned CYP6P5 and CYP6P4a. The leftmost breakpoint occurred approximately 30bp upstream of CYP6P9b and the rightmost approximately 170bp upstream of CYP6P4a. Both genes are significantly over-expressed in this population [CYP6P9b gene, at least one copy of CYP6P4a would have a portion of the CYP6P9b promoter upstream of it and this novel promoter sequence may also contribute to the over-expression of CYP6P4a.While West and Central African populations  showed no evidence of the 6.5 kb insertion between n Africa , six difn Africa . In Benid copies ; S3C Figd copies ; S3D Figentified . One  or linkage between the different duplications. Whether the pattern of polymorphism reflects neutral standing genetic diversity at the locus or the presence of multiple selective sweeps overlaid at the same locus could not be determined from the data, though the positive Tajima’s D seen at this locus in the Ghanaian population does suggest two or more haplotypes driven to higher than expected frequencies by selection. Haplotypes containing the whole region in duplicate may also contribute to this signal. Individual whole genome sequencing in this population may resolve this.CYP6P5. The deletion (rp1:46864–49106), nested within this duplication, also spanned and fully contained CYP6P5. The result was to effectively replace the upstream promoter of CYP6P9b with that of CYP6P5 which may have profound effects upon the expression of CYP6P9b in this population (though we did not have data to test this here). By contrast, the eastern DRC  population showed no evidence of this duplication, only the 6.5 kb insertion between CYP6P9a and b seen in southern African populations .In central Africa, the western DRC (Kinshasa) population showed a duplication containing a deletion within it ; S3E FigCYP6P9a and b. This suggests that an allele not associated with a CNP or other forms of complex molecular evolution are under selection in this population.In the population from Tororo, Uganda despite evidence of a major selective sweep spanning the CYP6 gene cluster no duplications were seen and there was no evidence for the 6.5 kb insertion between CYP6P9a and lower in CYP6P9b  and CYP6P9b (n = 119) gene sequences supported our hypothesis. A multidimensional scaling (MDS) plot based on pairwise sequence divergence among the genes showed the Benin CYP6P9b genes located among CYP6P9a genes (CYP6P9a and CYP6P9b cluster separately (100% bootstrap support), separated by a long branch but the Benin CYP6P9b gene sits within the CYP6P9a cluster   . As bothAn. funestus population from Kpome, Benin also showed evidence of a selective sweep on chromosome arm 2L (GSTe2 (AFUN015809), an allele of which (GSTe2-L119F) is associated with DDT and pyrethroid resistance [GSTe3 at one end of the cluster and between GSTe6 and GSTe8 at the other end (GSTe2 could be linked to a transposon insertion upstream of the transcription start site (TSS) of GSTe2, in the 3’ un-translated region of GSTe1 (GSTe2 (GSTe6 (AFUN016008) shows evidence of partial tandem duplication  of the resistant TTT (F) allele of GSTe2-L119F reported previously  in Uganda not found in the other populations (CYP9K1 and two other genes) was duplicated, based on increased coverage depth in Uganda . This barrier to gene flow is similar to previous genetic differentiation patterns obtained from microsatellite markers [An. funestus [An. gambiae [CYP6P9a and b) is detectable in Mozambique and Malawi, where it is at or near to fixation. It is also detected at intermediate frequencies in Mikalayi in south-eastern DRC (but not in Kinshasa in western DRC) and in Tanzania. Our data suggest that it may be prevented from spreading across the rift valley in east Africa, but how it will spread across central Africa is unknown.We found population subdivision between southern Africa and elsewhere on the continent.  markers , 15. Rec markers  and the  markers  and the  markers  in contrfunestus  and An.  gambiae . Our datAn. funestus across its range. Currently, the relative contributions of isolation by geographical distance, the presence of physical geographical barriers such as the Rift Valley and genomic barriers to recombination, such as chromosomal inversions, are not clearly defined. Each of these has different consequences for the evolution of resistance. For instance, if populations are simply separated by geographical distance then one may expect resistance haplotypes to spread over time and to mix, creating multiple- and super-resistant mosquitoes that will seriously jeopardise future vector control. Conversely, barriers to gene flow, whether geographical  [Our observation of multiple, independent selective sweeps at the CYP6 locus in different regions of Africa suggests that resistance is associated with different haplotypes in different regions of Africa. There is a risk that resistance haplotypes could spread among regions and even combine to create multiple- and super-resistant mosquitoes. Therefore, it is important to understand the factors defining population structure of  valley)  or evolu valley) , may morAn. funestus across its range reported signatures of population expansion in the Western part of its range [Anopheles gambiae and Anopheles coluzzii, where large-scale whole genome sequencing suggests their populations have expanded north of the Congo basin and west of the East African Rift [A previous study of the population genetics of ts range . Our whocan Rift .CYP9K1 gene on the X chromosome. Regional differences were observed between populations, in line with patterns of gene flow observed with ddRADseq.Three genomic regions associated with insecticide resistance exhibited signatures of strong recent positive selection in different populations. These were: a cluster of CYP6 cytochrome P450 monooxygenase genes on chromosome arm 2R; a cluster of Glutathione S-Transferase epsilon genes on chromosome arm 2L; and a region containing the CYP9K1 only in an East African population (Uganda) and one associated with a GST epsilon gene cluster containing GSTe2 only in a West African population (Benin). Signatures of selection spanning a cluster of CYP6 cytochrome P450 monooxygenase genes on chromosome arm 2R were more geographically widespread, yet showed patterns specific to different regions. Extreme differentiation among populations (seen as high FST) at this locus may indicate the selection of different haplotypes in different populations. However, the same signature could also result from selection on the same mutation in all populations, with hitchhiking of different genetic backgrounds flanking this mutation resulting from recombination unique to each population. We suggest that, while the latter scenario is clearly occurring, different haplotypes are being selected for in different populations. For example, while the shape and extent of the selective sweeps differ between neighbouring Mozambique and Malawi, both contain a large insertion between CYP6P9a and CYP6P9b in the swept haplotype and both populations over-express these genes, massively so for CYP6P9a [CYP6P9a does not show the massive over-expression seen in southern Africa [Some selective sweeps were seen only in some populations: one associated with An. funestus populations are resistant to pyrethroids and carbamates [That other resistance associated CYP6 genes in the same gene cluster are differentially expressed among populations [rbamates , 33 wherrbamates , 34.rp1 pyrethroid resistance locus [Overall, our results underlined the key role played by the CYP6 gene cluster on chromosome arm 2R, previously identified as the ce locus . We founAn. gambiae and An. coluzzii [Typically, because mosquito populations are resistant to multiple different insecticides it is difficult to establish which selective sweep is driven by a specific resistance mechanism and only further functional characterisation of the genes involved will help decipher this complexity. Similar findings, of multiple, independently selected haplotypes, have been reported for coluzzii . This stDrosophila at the CYP6G1 locus driving metabolic resistance to DDT [Analysis of temporal changes in genomic diversity is a powerful method to detect selection. Previously, we used this method to show that scaled-up use of insecticide treated bed nets since 2002 in Malawi appears to have driven the selective sweep spanning CYP6 gene cluster on chromosome arm 2R . Here, we to DDT  and in ae to DDT .GSTe2 was suggested to mediate DDT resistance). The population genomic results are consistent with pyrethroids and DDT driving selective sweeps at both the CYP6 cluster the GST epsilon cluster. This is supported by the presence of a nearly fixed haplotype of the P450 CYP6P9b after gene conversion in Kpome whereas the 119F-GSTe2 haplotype associated with DDT resistance is fixed in this location [et al. also demonstrated carbamate resistance in the population. Carbamate resistance mechanisms are less well known in mosquitoes, including An. funestus. CYP6AA1 has been shown to metabolise the pyrethroids permethrin and deltamethrin, as well as the carbamate bendiocarb [CYP6AA1 appears to exist in multiple copies  in the selected CYP6 cluster haplotype in Kpome, Benin. Whether this is associated with pyrethroid and/or carbamate resistance is not known and would benefit from further study. However, increased copy number does not appear to be associated with increased levels of gene expression in Benin [CYP6P9a/b [GSTe2 [CYP9K1 [An. funestus mosquitoes resting indoors were collected from the ceilings and walls of houses using torches and aspirators between 06:00 a.m. and 12:00 p.m. Dead adult mosquitoes were transported (under DEFRA license PATH/125/2012) to the Liverpool School of Tropical Medicine for analysis. Samples collected in 2002 in Chikwawa, Malawi [Wild mosquitoes were sampled from 8 locations across the continental range of me, 2015); Ghana ; Cameroome, 2015); Uganda me, 2015); Democrame, 2015); Malawi me, 2015); Mozambime, 2015) and ZambGenomic DNA (gDNA) was extracted from individual adult female mosquitoes using either the Qiagen DNeasy Blood and Tissue kit  or the method of Livak . It was For pooled template whole genome sequencing (PoolSeq), equal quantities of gDNA were pooled from 40 individuals in all cases except Mikalayi, DRC, where due to fewer available samples, 29 individuals were pooled . Each pohttp://mosquitogenomics.org/protocols/). A minimum of 50 ng of genomic DNA for each sample was used to prepare double-digest Restriction-site Associated DNA (ddRAD) libraries, following a protocol modified from Peterson et al. [MluC1 and NlaIII  were used to digest DNA of individual mosquitoes, yielding RAD-tags of different sizes, to which short barcoded DNA adapters were ligated to enable the identification of reads belonging to each specimen. These fragments were purified, pooled and size-selected to select fragments of around 400 bp that were then amplified by PCR. The distribution of fragment sizes was checked on a BioAnalyzer  before sequencing. The library was sequenced on two lanes of an Illumina HiSeq 2000 . Sequencing was single-ended and read length was 101 bp (including an in-line barcode and restriction site). Sequencing was carried out at the Genomics Core Facility, University of California, Riverside, California.Double-digest restriction site-associated DNA sequencing (ddRADseq) was carried out following the protocol of the White lab . The distribution of coverage depth of all covered sites was calculated using samtools mpileup and custom scripts. These mpileup files were also used to identify regions of greater than expected coverage depth in order to identify copy number polymorphisms.PoolSeq sequence reads were trimmed to remove sequenced Illumina adapters (matching >3 bp at the 3’ end) using cutadapt v1.2.1  and low th centile of coverage depth were removed. These files were used to estimate pi, theta and Tajima’s D for windows of 50 kb moving in steps of 25 kb. To identify putative selective sweeps, the distribution of Tajima’s D values across all windows was calculated for each sample. Initial inspection of the data showed highly variable values on the X chromosome in some samples, so only the four autosomal chromosome arms were included in the genomic scan. Windows with Tajima’s D values in the lowest 0.1% were taken as the most likely to be under positive directional selection.Estimation of population genetic indices was carried out using popoolation v1.2.2 and popoolation2 . For estFST was calculated for each site and for windows of 50 kb moving in steps of 25 kb using ‘fst-sliding.pl’. Allele frequency differences were calculated using ‘snp-frequency-diff.pl’ and the significance of these differences calculated using ‘fisher-test.pl’. The highest 0.1% of pairwise FST values was used to identify the most divergent regions of the genome among populations.For estimation of pairwise inter-population divergence, Samtools mpileup was used to generate files from two different sample alignments, that were synchronised using the popoolation2 script ‘mpileup2sync.jar’ and subsampled to 20x coverage using ‘subsample-synchronized.pl’. Pairwise th percentile for each sample as the allele frequency estimates could be inaccurate due to low coverage or misaligned paralogous sequence, respectively.For additional analyses of allele frequencies, variant calling was carried out on the mapping quality- and duplicate-filtered alignments using SNVer version 0.5.3, with default parameters . SNPs weAn. funestus Afun1 genome assembly and sorted and deduplicated with Picard . A multiple pileup file was created with samtools (v1.9) [To define the historical relationships among populations using the PoolSeq whole genome data, reads for each population were aligned using BWA (v0.7.17-r1188)  to the As (v1.9)  mpileup s (v1.9) . Variants (v1.9) . Multials (v1.9)  and thens (v1.9)  was usedFor detailed analysis of the selective sweep and complex molecular evolution at the CYP6 gene cluster on chromosome arm 2R, PoolSeq libraries were aligned to the 120 kb rp1 BAC sequence (accession PRJEB37305) containing this gene cluster. The reason for doing this was that the scaffold in genome assembly AfunF1 that contains the CYP6 gene cluster (KB119169) contains gaps within the cluster, making detailed analysis difficult. Alignments were visually inspected and inference of duplication was based on three linked lines of evidence: (i) increased coverage depth over part of the gene cluster, indicating more than one copy of the genome region; (ii) the presence of read pairs with anomalous relative orientations and insert lengths, indicating deletion, inversion or head-to-tail tandem orientation of duplicated genome regions; (iii) the presence of multiple apparently chimeric reads that span putative breakpoints and are clipped thereafter, or not, in the alignment. (ii) and (iii) are illustrated in 265,186,694 single-end reads were produced from two Illumina HiSeq lanes . After adding index sequences to the read headers, reads were de-multiplexed using the ‘process_radtags’ script from stacks v1.34 . The de-Filtered aligned data were analysed using Stacks, version 1.34 . ‘StacksFST) was estimated using the stacks populations programme, with a minimum of 3x coverage per locus, minimum MAF of 1% and genotyped in at least 75% of individuals in each population. Pairwise AMOVA-FST was calculated, with a 50 kb window used for kernel-smoothing of FST estimates (for plotting).Pairwise population divergence  downloaded from VectorBase (www.vectorbase.org) and a sequence representing the An. funestus mitochondrial genome (GenBank accession number DQ146364.1). In order to display population genetic indices in a whole-genome context, the 1392 scaffolds of AfunF1 were ordered relative to Anopheles gambiae (assembly AgamP4) chromosomes, in order to modify the coordinates of SNPs/windows etc. to display them on a single plot. Nucmer, from MUMmer v3.0 [An. funestus (assembly AfunF1) scaffolds to Anopheles gambiae chromosomes (unplaced An. gambiae scaffolds were excluded). Nucmer alignment placed 644 of the 1392 scaffolds (46%). The total length of these was 217,255,185 bp, of a total of 225,223,604 bp (96%), the vast majority of the total genome sequence. The final file contained all 1392 scaffolds ordered according to Agam (with 2L and 3R transposed), including the unplaced scaffolds at the end.The reference sequence used comprised 1392 mer v3.0  was usedCYP6P9a and b genes, we searched the GenBank nucleotide sequence database using the text terms “CYP6P9a” and “CYP6P9b”. Spurious matches were removed to leave 280 CYP6P9a and 118 CYP6P9b sequences for analysis. A full-length FUMOZ coding sequence for each gene from VectorBase brought the total number of sequences to 400. Details of the sequences are shown in CYP6P9a (AFUN015792) and CYP6P9b (AFUN015889). All sequences were trimmed to this position. 350/400 sequences included the intron. This was trimmed from all sequences. The trimmed alignment was 1464 nucleotides long.To identify evidence of gene conversion between the paralogous CYP6P9a; 119 CYP6P9b). The MDS plot was generated in R. The Neighbour-Joining phylogeny was generated using PHYLIP [This alignment  was used to generate a multidimensional scaling (MDS) plot and a Neighbour-Joining phylogeny from pairwise divergence among all 400 sequences , PRJEB24384  and PRJEB35040 . The rp1 BAC sequence is available under study accession PRJEB37305.S1 FigEach point is Tajima’s D calculated for a 50 kb window (moving in increments of 25 kb) using PoolSeq alignments. The highest and lowest 1% and 0.1% of Tajima’s D values are shown in orange (1%) and red (0.1%). The populations are (A) Obuasi, Ghana (GHA); (B) Kpome, Benin (BEN); (C) Mibellon, Cameroon (CMR); (D) Tororo, Uganda (UGA); (E) Kinshasa, Democratic Republic of Congo (COD-K); (F) Mikalayi, DRC (COD-M); (G) Chikwawa, Malawi (MWI); (H) Palmeira, Mozambique (MOZ). The selective sweep near 31 Mbp (on 2R) is the CYP6 cluster in scaffold KB669169. That near 81 Mbp (on 2L) in Benin (panel B) is the GST epsilon cluster on scaffold KB669036. That near 88 Mbp (on 2L) in all populations is on scaffold KB668697 and that near 111 Mbp (on 3R) is on scaffold KB668905.(TIFF)Click here for additional data file.S2 FigFST calculated for a 50 kb window (moving in increments of 25 kb) using ddRADseq alignments. The highest 1% and 0.1% of FST values are shown in orange (1%) and red (0.1%). The populations are: Obuasi, Ghana (GHA); Mibellon, Cameroon (CMR); Tororo, Uganda (UGA); Chikwawa, Malawi (MWI); Kaoma, Zambia (ZMB). Compared populations are indicated on each panel with (A) showing GHA_vs_CMR, (B) is GHA_vs_UGA, (C) is GHA_vs_MWI, (D) is GHA_vs_ZMB, (E) is CMR_vs_UGA, (F) is CMR_vs_MWI, (G) is CMR_vs_ZMB, (H) is UGA_vs_MWI, (I) is UGA_vs_ZMB, (J) is MWI_vs_ZMB. The extreme genetic differentiation observed near 31 Mbp (on 2R) coincides with the CYP6 cluster on scaffold KB669169. Several regions of elevated FST between Ghana and other populations can be seen on 2R.Each point is (TIFF)Click here for additional data file.S3 FigCYP6AA1, CYP6AA2, two carboxylesterases, CYP6P15P, CYP6P9a, CYP6P9b, CYP6P5, CYP6P4a, CYP6P4b, CYP6P1, CYP6P2, CYP6AD1. Red boxes enclose identified duplications and the blue box encloses the deletion. (A) A large duplication spanning the entire gene cluster (rp1:10311–71182) and one spanning CYP6AA1 and part of CYP6AA2 (rp1:17910–24836) in Ghana. (B) A duplication spanning CYP6P5 and CYP6P4a (rp1:46407–52668) in Ghana. (C) A duplication spanning CYP6AA1 and part of CYP6AA2 (rp1:18643–25686) in Benin. (D) Duplications spanning CYP6AA1 and part of CYP6AA2 (rp1:19133–25617) and one spanning CYP6AA1, CYP6AA2, 2x carboxylesterases, CYP6P15P and partial CYP6P9a (rp1:19043–35875) in Cameroon. (E) A duplication spanning CYP6P5 (rp1:46771–50640) and containing a deletion (rp1:46864–49106) in Kinshasa, Democratic Republic of Congo.Sequence alignment views from the Integrative Genomics Viewer (IGV) showing evidence of duplications in the CYP6 gene cluster. Alignments are of PoolSeq data to the rp1 BAC (accession PRJEB37305). In each panel, the top track shows coverage depth . The middle track shows read pair alignments . The lower track shows the genes (blue), from left to right: (TIFF)Click here for additional data file.S4 FigGSTe6 shown in (E). (B) A transposon insertion upstream of GSTe2 . The target site (TTAA) is indicated. Non-grey reads indicate members of a read pair for which their mate aligns elsewhere in the genome . In the blue  gene models at the bottom, thinner parts indicate un-translated regions and thicker parts coding regions. (C) Alignment showing the 1 bp deletion (G) in Benin sequence reads relative to the FUMOZ reference (shown at the bottom). The transcription start site (TSS) of GSTe2 is indicated. (D) Relative frequencies of T (red) and C (blue) bases in codon 119 of GSTe2. GSTe2-L119F is a DDT resistance marker. Codon CTT (Leucine) is the susceptible allele and TTT  is the resistant allele, present in 98% of aligned reads in Benin, 31% in Ghana and 27% in Cameroon. The preceding codon  is also shown. (E) Evidence of duplication within GSTe6 in the Benin selected haplotype. Coverage depth is indicated at the top and aligned reads below (here shown with thin lines linking each read pair). Green reads are discordant pairs, in an orientation suggesting tandem duplication. Multi-coloured parts of reads are sections trimmed during alignment, and demarcate breakpoints .(A) IGV alignment view showing the entire gene cluster. Coverage depth is indicated at the top and aligned reads below. Genes are shown at the bottom (blue) and their orientations indicated with arrows. Gene names are abbreviated (e.g. ‘e3’ is GSTe3). Genomic location on scaffold KB669036  is indicated at the top. The three vertical arrows at the top mark the positions of (from left to right) the transposon insertion shown in (B), the 1 bp deletion shown in (C) and the putative duplication in (TIFF)Click here for additional data file.S5 FigCYP9K1 (AFUN007549) and neighbouring genes on the X chromosome. The position  is on scaffold KB668367. Patterns seen in (A) Uganda; (B) Ghana; (C) Cameroon and (D) Malawi suggest a selective sweep associated with over-expression of CYP9K1 in Uganda.Kernel-smoothed minor allele frequency  and gene expression, as fragments per kilobase per million mapped reads  across (TIFF)Click here for additional data file.S6 FigCYP9K1 gene (blue shape at the lower left). The putative transcription start site (TSS) at position KB668367:175642 is indicated. The track at the top of the panel summarises coverage depth, indicating the change from high (left) to low (right). The middle track shows individual sequence reads (grey rectangles) aligned to the reference sequence. Read pairs are linked by thin lines. Dark grey and cyan reads indicate discordant read pairs where the other read of the pair is aligned to a different scaffold in the genome assembly. This pattern, seen around position KB668367:176475, along with read clipping around this position and a small peak seen in the coverage depth track, are characteristic of the presence of a transposon insertion in the sequenced reads that is absent from the reference sequence. (B) Detailed analysis of clipped reads. IGV screenshot showing reads from discordant pairs (pink and green) and clipped reads  seen left of KB668367:176470 (‘clipped 1’) and right of KB668367:176479 (‘clipped 2’) and KB668367:176606 (‘clipped 3’). (C) Nucleotide sequences clipped (blue) from reads aligned to this putative breakpoint region. The GAAATTTG motif (in bold) was present in, but not clipped from, reads aligned in both directions and is a putative target site duplication causing a small peak in coverage depth, characteristic of a sequenced transposon. Underlined regions indicate putative terminal inverted repeats.(A) Screenshot of the Integrative Genomics Viewer (IGV), showing the alignment of PoolSeq data from Uganda at and upstream of the (TIFF)Click here for additional data file.S7 Fig(A) Detection of a transposon insertion in the sequenced sample that is absent from the reference genome. The reference genome is shown in grey and sequencing reads shown as arrows. Between the two vertical dotted lines is a target sequence (in this case TTAA). Reads (in peach) align up to and including this sequence, then are clipped during alignment  as clipped sequence represents transposon rather than flanking genomic sequence. The inserted transposon is shown below . Clipped sequence can be used to search the genome and may match transposon(s) inserted elsewhere in the reference genome (shown on the right). Dark red and dark blue reads are from discordant read pairs in which one read aligns elsewhere in the genome . (B) Detection of regions present in the reference genome that are deleted from the sequenced sample. Vertical dotted lines represent breakpoints (edges of the deletion). Reads (in peach) align up to these then are clipped during alignment (in white). Clipped sequence may align to the flanking sequence on the other side of the deleted region. Dark red reads indicate a discordant read pair (pair is joined by a thin black line), fully aligned in the correct orientation but with too large a gap between them. (C) Detection of tandem duplications in the sequenced sample that exist in only one copy in the reference genome. First is a reference containing a tandem duplication . Reads align across the breakpoint  and pairs spanning the breakpoint are concordant. Second is a reference with only one copy of this duplication. If the sequenced sample contains a duplication, chimeric reads (derived from the head-to-tail breakpoint) will exist that align up to the edge of the duplicated region  and be clipped thereafter . Also, concordant read pairs spanning the head-to-tail breakpoint (in peach in the first diagram) will appear discordant when only one copy is represented in the reference genome (in dark red in the second diagram).(TIFF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


Viola uliginosa, an endangered perennial wetland species typically inhabiting sporadically flooded meadows alongside rivers/lakes. In order to estimate genomic diversity, population structure, and history, we sampled five sites in Finland, three in Estonia, and one each in Slovenia, Belarus, and Poland using genomic SNP data with double‐digest restriction site‐associated DNA sequencing (ddRAD‐seq). We found monophyletic populations, high levels of inbreeding (mean population FSNP = 0.407–0.945), low effective population sizes (Ne = 0.8–50.9), indications of past demographic expansion, and rare long‐distance dispersal. Our results are important in implementing conservation strategies for V. uliginosa, which should include founding of seed banks, ex situ cultivations, and reintroductions with individuals of proper origin, combined with continuous population monitoring and habitat management.Species occupying habitats subjected to frequent natural and/or anthropogenic changes are a challenge for conservation management. We studied one such species,  Viola uliginosa, to estimate genomic diversity, population structure, and history from genomic data. We found monophyletic populations, high levels of inbreeding, and low effective population sizes and indications of past demographic expansion and rare long‐distance dispersal. These are proposed to include the founding of seed banks, ex situ cultivations, and reintroductions with individuals of proper origin, combined with continuous population monitoring and habitat management.We investigated endangered and nonendangered populations of a violet,  Populations of such species have a high risk of extinction simply due to environmental stochasticity, and if populations are sparse, as is usually the case with endangered species, recolonizations of locally extinct populations will be unlikely. The temporal extinction–recolonization dynamics, that is, metapopulation dynamics, has been shown to depend on the number of available habitats, extinction rate, and colonization rate. However, the classic metapopulation model , which showed strong genetic structuring along bodies of water, due mainly to genetic drift , in situ, and their respective ex situ populations became highly genetically differentiated within a few decades, partly attributed not only to the effect of genetic drift, but also to unintended selection during cultivation  in Ireland  in China  and Belarus, both of which form the species’ center of distribution and from Poland and Slovenia, which form the southern and western edge populations, respectively. Thus, our sampling provides a latitudinal perspective. We collected one or two leaves per individual from each sampling site during summer 2016 and 2017 , with a special effort to sample individuals as distantly from each other as possible from each site. We sampled five sites in Finland , three in Estonia , one in Slovenia (Ljubljansko Barje N = 30), one in Belarus , and one in Poland   for further analyses (see Table We focused our sampling on extant Finnish populations of 2.2PstI and MseI (NEB) followed by a ligation step, whereby each sample was assigned to one of 48 adaptors. Pools of 48 individuals were combined and run on 1.5% agarose cartridge in the automated size‐selection technology, BluePippin (Sage Science), where 300‐bp fragments were excised. Each pool was amplified using 16 PCR cycles in 50 µl reactions containing 30 µl Phusion High‐Fidelity PCR Master Mix (Thermo Fisher Scientific), 15 µl of library DNA, and a unique indexing primer for each pool that corresponds to the standard Illumina multiplexed sequencing protocol. PCRs were performed in a Veriti 96‐Well Thermal Cycler (Life Technologies) using the following protocol: initial step heating at 98°C for 30 s, 16 cycles , followed by a final step at 72°C for 5 min. DNA libraries were quantified using the High‐Sensitivity DNA Analysis Kit in a 2100 Bioanalyzer (Agilent Technologies). Pools were combined in equimolar concentration to form a single genomic library and sequenced in one lane of a HiSeq 2500 Illumina sequencer . The demultiplexed V. uliginosa fastq data are archived in the NCBI SRA: PRJNA540749.The quantity of genomic DNA (gDNA) was determined with the PicoGreen Kit . The ddRAD‐seq library was implemented following protocols described in Peterson, Weber, Kay, Fisher, and Hoekstra  and Lee 2.3ipyrad v.0.7.23 . The demultiplexed paired‐end reads were run through PEAR , excessively undetermined, or characterized with many heterozygous sites (>8), potentially resulting from paralogs or highly repetitive genomic regions, were discarded. Additionally, we excluded all loci with excessive (>50% of samples) shared polymorphic sites as they likely represented clustering of paralogs. Up to four shared polymorphic sites per called locus were allowed to accommodate polyploid genomes. This was done, because tetraploid marker data can be indistinguishable from diploid data . In the “reference” assembly, the sequences were mapped to the whole genome of Viola pubescens  using BWA with default bwa‐mem setting  for all populations were calculated. AMOVA  quantified the proportion of variation at each organizational level based on unlinked SNP data with 1,000 permutations. All five Finnish populations were grouped into one group, the three Estonian populations into another group, and Belarusian, Polish, and Slovenian populations were retained as separate groups. Reasoning for the grouping was that Finnish and Estonian populations are separated by the Gulf of Finland, likely a strong dispersal barrier, and the Central European populations locate far apart. The grouping also reflects latitudinal locations of the study populations. Isolation by distance was tested using the Mantel test implemented in Arlequin, with Slatkin's linearized FST and natural logarithms of geographic distances.The ddrad_m95 dataset Table  was usedFSNP per individual using the equation FSNP = (O – E) / (N – E), where O is the observed number of homozygotes, E is the expected number of homozygotes , and N is the total number of genotyped loci. Boxplots between populations and FSNP values were executed with R v.3.5.2 . Samples were assigned to populations using 100,000 iterations as burn‐in prior to sampling with 100,000 iterations. The trees were constructed using 10,000 iterations and the output visualized using the fineradstructureplot.r and finestructurelibrary.r R scripts (http://cichlid.gurdon.cam.ac.uk/fineRADstructure.html).The package FineRADstructure was also used to investigate the genetic structure in p‐distances with heterozygous ambiguities averaged and normalized with 1,000 bootstrap replicates (>75% shown). This method uses aspects of Neighbor‐Joining  for the contemporary and historical samples were calculated using the bias‐corrected measure of linkage disequilibrium  for Poland, Slovenia, and Belarus of 10–1,000, and for other populations of 10–100, ancient population sizes of 10–10,000 for all and divergence times (t) of 10–1,000 for all. We ran 200,000 simulations with this scenario and used the 2,000 sets closest to the observed data for parameter estimation.History was further evaluated using the program DIYABC v.2.0.3  to 0.023 . The three sites with lowest diversity values were all from Finland  , Matsalu , and Ridala  had parts of their genomes assigned to the same cluster with individuals from Sastamala (Finland). Similarly, individuals from Belarus were partially assigned to the same cluster as individuals from Poland . The FineRADstructure analysis showed a clear separation of the southern  from northern (Finland and Estonia) populations and also weak structuring within the northern populations  and 11.85% from differentiation among groups (FCT = 0.119), with a total FST of 0.492 , with 22.2% of the geographic distance determined by the genetic distance.AMOVA and pairwise 92 Table . Pairwis02 Table . Mantel D values and small raggedness with p‐values < .05, suggesting past population expansion  and Cyanea (0.0012) populations , Dendrobium densiflorum (0.106), and Rhynchostylis retusa (0.113). Levels similar to our study were found in Cymbidium aloifolium (0.014)  and varied between populations located in the central or peripheral parts of the species ranges  were at an intermediate level compared with previous studies on other plant species using ddRAD sequencing. For example, much lower levels were found in endangered Hawaiian lobelioid V. grayi  compared with Estonian populations (average 0.201) and among the eastern and southern European populations from Slovenia, Belarus, and Poland (average 0.454). The latter are separated by 530–1,890 km, yet their FST values were smaller than between Finnish populations. This can be explained by that occasional gene flow among the southern populations may be more likely than among the Finnish populations. Most likely V. uliginosa has never been common in Finland, as it requires a rare, eutrophic, and flooded habitat and thus gene flow seldom, if ever occurs. Only 17 sites have been recorded since 1851, and all but six are now extinct. The six extant populations have existed for a range of time with the oldest being Vihti (1851) and the youngest at Tohmajärvi (discovered in 1999). The present sizes of the populations vary between tens and thousands of rosettes , strongly suggesting that occasional outbreeding reduced homozygosity of otherwise very homogeneous populations. In vitro experiments revealed that a high percentage of seeds actually germinate . However, DIYABC results strongly supported divergence of Estonian populations more or less simultaneously about 550 years BP and the Finnish populations more recently, about 100 years BP (95% CI: 94.4–140). The generation time used for these time estimates is adopted from other Viola species (9.44 years, calculated from Eckstein et al., The phylogenetic tree constructed using the maximum‐likelihood method shows mixing of Estonian and Finnish populations with the more southern populations located closer to the outgroup (Finnish populations are phylogenetically very close to the Estonian populations, which are also geographically close. The pattern is supported by mixing of these populations in the phylogenetic tree Figure , branchi4.3V. uliginosa in Finland. Three of the existing populations can be considered abundant, each with thousands of individuals (Hanko, Kökar and Tohmajärvi, Ranta et al., IUCN guidelines for reintroductions state that “Founders should show characteristics based on genetic provenance, and on morphology, physiology and behaviour that are assessed as appropriate through comparison with the original or any remaining wild populations” (IUCN/SSC, None declared.L. Kvist conceived and designed the study, performed some data analysis, and drafted the manuscript; K.M. Lee carried out the molecular laboratory work, performed the bioinformatics and data analysis, and drafted the manuscript; P. Ranta, J. Saarikivi, L. Kutnar, B. Vreš, and M. Dzhus collected field samples and drafted the manuscript; and M. Mutanen conceived the study and drafted the manuscript. All authors gave final approval for publication. Click here for additional data file.


Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm ‘package’ that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding. Over recent years, numerous multi-parent populations (MPPs) have been successfully developed in crops  populations, and only about six further generations of inbreeding in self-fertilising species are needed to make recombinant inbred lines (RILs) whose genomes are fixed. (2) Their high power to detect QTL because all allele frequencies are typically close to the optimal value of 50%. (3) The low rate of linkage disequilibrium decay within chromosomes. There are normally only one or two recombinants per chromosome arm (inbreeding a RIL only adds about one observable recombinant per arm) meaning only a few hundred genotyped markers are needed to map QTL.Bi-parental populations, derived from crosses between two inbred lines, have been the standard design for genetic mapping in crops. There are three key advantages to bi-parental populations: (1) the relative simplicity of their construction. Just two generations are needed for FHowever, bi-parental populations have two principal disadvantages: the lack of mapping precision, which stems from limited effective recombination occurring during population development, and low genetic diversity, which is due to the genetic bottleneck caused by the choice of two founders. This may limit the number of QTL captured as no more than two alleles segregate at any locus. Consequently, around a decade ago, a second generation of experimental mapping populations, initially utilising additional crossing generations in a bi-parental but eventually inter-crossing multiple parents (MPPs), was developed to address these issues.2 generation for further generations prior to genetic mapping, effectively increasing the mapping precision  design. These capture additional recombination via crossing the Fn Dudley . DespiteA. thaliana parents  and multi-parent advanced generation inter-cross (MAGIC) populations. NAM population construction involves a series of crosses between a recurrent founder line and a number of alternative founders Fig. . NAMs caCajanus cajan and chickpea, Cicer arietinum, MAGIC populations with whole-genome sequence data, Pandey et al. Helianthus annuus, Matias Dominguez, personal communication and Triticum uratu, MDA, personal communication). Where multiple MAGIC and/or NAM populations for the same crop are available, these are usually descended from different founders  derived from an explicit set of founders, preferably highly inbred lines or varieties that reproduce faithfully, (b) produced through experimental crosses that minimise selection and (c) composed of a large number of recombined individuals that are analysed together as a population. Philosophically, MPPs are established for long-term durability rather than short-term expediency. Bi-parental crosses are very efficient for mapping genetic variants that are known or suspected to vary between parents. MPPs are typically made with greater agnosticism about target traits and/or causal variants. Figure Founder selection is a key issue in MPP design because it determines the pool of genetic variation that segregates in the population. In Fig. Diverse, trait-specific and region-specific MPPs are useful in different scenarios. In general, those MPPs developed to maximise segregating genetic variation are rich in novel allelic combinations from the diverse founders. This makes them a permanent resource to analyse the genetic basis of complex traits in different environments. However, diverse MPPs are potentially less useful for the direct use of their germplasm in pre-breeding because undesirable alleles may segregate in the progeny  × (C × D)] has come through a different funnel to one derived from [(A × C) × (B × D)]. The number of possible funnels is often large: a population with 8 founders has 315 possible funnels if reciprocal crosses are ignored , interconnected MAGIC populations have been developed that expand diversity while maintaining the closed MAGIC mating design . Thus, power only depends on Nf: if the fraction of variance explained by a QTL, f, is halved but the sample size doubled then the power is unchanged. For inbred lines, the variance explained by a bi-allelic QTL with minor allele frequency π is2 crosses. Questions of dominance do not arise in RILs because all loci should be homozygous. The formula neatly separates the underlying biological effect of an allele, π. Assuming that an allele has the same biological effect, We next summarise the key characteristics of MPP population designs, using theoretical results on power and recombination. There is a well-established general theory to calculate the power to detect a QTL segregating in any population . The probability r descend from the same founder in each RIL isThere is no equivalent general theory that gives a confidence interval for a QTL as a function of the sample size, QTL effect size, genetic map, etc. The most relevant result for MPPs is from Broman , who derr is small, then n-parent MAGIC resembles that in a two-parent population except that it is scaled by p values of association that are within a set range compared with the strongest association. This has been shown to give reliable estimates of confidence intervals provided it is calibrated appropriately for each experimental population  platforms have revolutionised genetics by providing a genotyping technology which can in theory assay every base of an organism’s genome ; RNAseq evidence showed that the effects of most of these mutations were skipped by subtle changes in splicing. Moreover, structural rearrangements revealed by de novo assembly may be significant. For example, gene expression may be perturbed by the change in regulatory context .There are two main applications of NGS to MPPs. First, de novo assembly of reference genomes for each of the founders, where the development of improved DNA sequencing, algorithms and new data types now allow the construction of chromosome-level genome assemblies  genotyping arrays and Kompetitive Allele Specific PCR (KASP) assays only assess known alleles at specifically designed loci and therefore tend to miss most rare variants, and even common alleles absent from the samples used to develop the assays  WGS of each RIL. For example, using 476 A primary use of crop MPPs is to map QTL for agronomic traits to relatively narrow genetic intervals across genetically diverse backgrounds. To date, many traits have been mapped in MPPs, ranging from pathogen resistance to flavour profile. Table One purpose of mapping QTL is to understand the genetic basis of traits and to identify markers that can be used in breeding programmes. In applied settings, key factors influencing whether QTL are useful are the size and stability of the effect across environments and genetic backgrounds, and the phenotyping effort required to assess the trait directly. While causal markers reduce the potential for linkage drag  during marker-assisted selection, nearby markers that tag the causal allele can be used instead. Similarly, studies of general patterns of genetic architecture do not require gene-level QTL mapping. For other theoretical and some applied applications , validation of causal variants may be required. Thus, the research goals should generally determine the appropriate experimental design  and analysis , as reviewed elsewhere Bernardo , 2016.A. thaliana accessions  that uses a panel of existing lines. As noted above, relative to bi-parental populations, MPPs are a more general tool within which a wider variety of traits segregate due to the increased genetic variation Fig. . ComparePhotoperiod-1 gene could be identified as a key regulator of paired spikelet development   . Analysis of spikelet architecture in the UK winter wheat eight-way population enabled identification of further allelic diversity for TB1. Mapping using this population demonstrated that the control of the paired spikelet formation by TB1 also occurred in winter wheat and allowed identification of a new allele of TB1 involved in the regulation of paired spikelet formation and height on chromosome 4B (TB-B1)  in its native environment  but reduce its fitness when transplanted to the non-native environment—a type of evolutionary trade-off . In chickpea, RILs from MAGIC populations developed at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) have been directly released as new varieties, as well as used as donors in commercial breeding programmes . From a 16-founder wheat MAGIC population developed at NIAB, 24 MAGIC RILs have been selected for inclusion in commercial breeding programmes on the basis of their yield, ear weight and protein content . Thus, the collection of phenotypic data across several agronomically important traits in MPPs facilitates the identification of promising lines.There are several examples where MAGIC germplasm has been used for pre-breeding or released in their own right as a variety. Li et al. , 2014 repersonal communication). In cotton, Thyssen et al.  and grey leaf spot resistance (R2 ~ 0.25) in a large maize NAM. Islam et al. The germplasm of the RILs and their founders, free of intellectual property constraints.(ii)A publicly accessible database of agronomically important phenotype data for the founders and the descendent RILs, with a system for adding further phenotypic data.(iii)Complete de novo assemblies of the founders with catalogued genetic variation  and gene models confirmed using RNAseq.(iv)The genome mosaics of the RILs, with founder annotations and variants projected and integrated software for mixed model GWAS at both SNP and haplotype level.(v)Demonstration that the population can be used for genomic prediction, the identification of likely causal variants underlying QTL and marker development for marker-assisted selection.MPPs integrate extensive genotype, phenotype and germplasm resources to provide enduring and general tools that advance theoretical knowledge and support breeding. MPP designs have allowed rich genomic information to be gathered for large populations in a cost-effective way. Furthermore, MPPs have proven to have useful applications in crop breeding. To fully realise their potential, we advocate thinking of MPPs as a package, ideally comprising:Most crop MPPs currently only have a subset of these resources available. However, the continued enrichment of MPPs with open-access phenotypic and genotypic resources will enhance their power as an enduring and growing genetic toolbox to address crop improvement.


Individual and population susceptibilities to disease remain a murky area of investigation, clouded by past bias based on ideological differences and wars. The current SARS-CoV-2 pandemic, the largest in living memory, brought this matter to forefront as the disparity in disease burden became apparent. A timeline analysis of the pandemic revealed the presence of country clusters that display a marked preponderance of disease among populations carrying the ancestry marker R1b1b2, notably associated with both infection and mortality. This marker is a relic of past human expansions from western Asia and subsequently Europe and the rest of the world, which may have been accompanied by peculiar biological events rendering these populations vulnerable to SARS-CoV-2. There is also the history of the population itself, including migration, admixture, culture, and other non-biological determinants.During epidemics, the human phenotypes of interest are infection, clinical disease, morbidity, and especially death. The current coronavirus disease (SARS-CoV-2) pandemic is associated with this range of phenotypes and the existence of marked human clusters of infection and particularly death. Populations, people within populations, and individuals obviously vary in their propensity to develop clinical symptoms, including death. In fact, epidemics without associated mortality are of little epidemiological value and in many cases may pass unnoticed. Populations are products of genetic history, that is, the history of a group’s genes upon interaction with various environments, including viral onslaughts and other environmental and genetic interactions4.Genetic backgrounds pertain mainly to ancestry. When seeking markers of ancestry, the choice is usually either mitochondrial DNA or the Y chromosome, as they are both spared from the shuffling impact of recombination. However, given the unique history of males within the context of male-driven migration, they make a better marker of population structure and global human ancestry clustersPopulations as evolutionary units were rather cumbersome to define in the post-World War II era, in terms of both health and disease. In many cases, this was aggravated by a lingering tendency to adopt archaic terms for human groupings  mostly based on race myths. Entering the era of chronic and noninfectious diseases, also in the aftermath of World War II, heralded a departure from universal paradigms of health into one that accommodates human differences, currently best demonstrated in the arena of pharmacogenomics. Infectious diseases, however, remained aloof to these new approaches, as pathogens are widely believed to affect humans equally, despite the existence of human disease clusters, familial differences, and traits. The current SARS-CoV-2 pandemic raises several questions concerning the possible contribution of human genetic variation to the epidemiology of the disease and the extent to which it could explain the current disparities in disease burden across countries and communities.www.worldmeters.info) for 95 countries as of 29 April, 31 May, 25 June, and 1 July 2020 (Supplementary tables: www.worldmeters.info). Explaining complex phenomena such as the current pandemic in the biological and environmental dimensions is a cumbersome task and may often require equally complex analytical models. Regression analysis is perhaps the most convenient tool to capture the relative weight of multiple contributors to a complex phenomenon. As the main disease clusters included countries that are mainly in temperate and cold areas, suggesting temperature as an environmental factor potentially contributing to the epidemiology of the outbreak, multiple regression analysis of temperature readings against disease records by country from World Bank Data Catalog was carried out, and results are shown below. Given the current viral diversity and strain variations, neither temperature nor viral diversity seems to be able to adequately explain the current global trends in the pandemic. One of the foremost candidates naturally becomes the genetic background of the populations in the affected countries. To examine the relationship between ancestry and the clinical outcome of SARS-CoV-2, several ancestry markers were investigated, and the most prevalent and informative were included in subsequent analysis of populations affected by the pandemic. Haplotype frequencies were obtained from Y-chromosome haplogroup databases (https://en.wikipedia.org/wiki/Y-DNAhaplogroups_in_populations/http://thegeneticatlas.com/World_Y-DNA.htm) and the available literature8. For correlation and regression analysis, Python Software in the Statistical Package was used, Python Language Reference, version 3 (available at http://www.python.Org).Data from the current SARS-CoV-2 pandemic ). The haplotype is believed to have originated in western Asia, specifically around the present area of Iran, which was coincidentally one of the primary foci for the first high fatality numbers in the pandemic. In the 33 countries recording between a thousand and 10,000 cases and a mortality index of 3.6, the R1b1b2 haplotype was still up to 40% in 22 countries, which was seen as a result of recent European expansion into the Americas and Northern Africa. Among thirty countries where case numbers were below a thousand and the mortality index was 3.1, most are in Africa, Asia, or South America, with lower frequencies of R1b1b2 that reach 0% in 11 countries. Overall, linear regression based on normal, Poisson, and polynomial distribution models for the correlation of R1b1b2 with both infection and mortality had the following values: 0.68 and 0.47, respectively. The coefficient for death to cases with R1b1b2 was 1.0927, 9. However, the link in this analysis remains a tag for population history. Interestingly, such a tagging approach was reported for the risk of coronary artery disease by Charchar and his colleagues, who studied 3233 biologically unrelated British men to investigate heart disease risk; an odds ratio of 1.75  was reported between coronary artery disease and men carrying the haplotype R1b1b22. We adopted a similar tagging haplotype approach earlier during the quest to understand the genetic basis of susceptibility to visceral leishmaniasis (VL) and to reveal hidden structures within seemingly homogenous populations3. A stratification of the target population based on Y chromosome haplogroups increased the likelihood of linkage to an impressive LOD score of 5.656, defining a susceptibility locus associated with carriers of haplogroup A1b1b2b formerly A-M13 A3b2; interestingly, this is the most common Y chromosome haplogroup among Nilotics of southern Sudan and other Nilo-Saharan speakers, who happen to be the most vulnerable population to VL in East Africa and worldwide10.The association with R1b1b2 is obviously noncausal, although recently collected evidence suggests that the Y chromosome influences immune and inflammatory responses in men, translating into genetically programmed susceptibility to diseases with a strong immune component11.Population markers are not necessarily a reflection of ethnic identity. For example, African American, Caribbean and South American ethnic populations were found to carry substantial amounts of the R1b1b2 haplotype and its subclades. In some instances, the Y chromosome of Europeans has almost completely replaced the native male contribution to the gene poolth of November (Supplementary Figure 12.The events took place in time and space for ancestral carriers of R1b1b2 that rendered them vulnerable to SARS-CoV-2 infection and its severe pathology remain to be studied. Possible clues might be collected from the analysis of genomes/exomes of patients expressing the clinical outcome of such susceptibility loci. These loci might prove to be present at similar or higher frequencies in other populations that are phylogenetically divergent yet susceptible but show a high mortality phenotype, such as Indonesia and the Philippines, as outliers with no reported frequencies of R1b1b2. Even there, fatality was significantly lower when the ratio was corrected against the country population size. Similarly, in countries such as Japan and South Korea, mortality was 1.94 and 2.2, respectively, even without correction for the country population size. Interestingly, these ratios remained approximately the same, and the correlation with R1b1b2 even grew stronger over time when the same dataset was analyzed at the end of May, 25th of June, 1st of July and 18The claim that the SARS-CoV-2 pandemic has yet to take its toll in Africa is largely speculative if not unfounded. Whether Africa’s population demographic pyramid, climate, degree of crowdedness, genetic background, natural or cross immunity and other confounders/contributors might have hindered or slowed the progression of SARS-CoV-2 remains to be studied. Anecdotes and forecasts of a pandemic on the loose with some countries peaking, and others waiting to strike, are embedded in a universal health paradigm that does not cater to human variations in disease susceptibility, a fact modern biomedical sciences is recognizing and embracing progressively and more than ever.Supplementary Figure2Supplementry Figure 1Table


Pseudorasbora parva) across Europe and East Asia. We RAD‐sequenced 301 specimens from sixteen populations and three distinct within‐catchment invaded regions as well as two locations in the native range. With 13,785 single nucleotide polymorphisms, we provide conclusive evidence for a genome‐wide signature of two distinct invasion events, in Slovakia and Turkey, each originating from a specific area in the native range. A third invaded area, in France, appears to be the result of dispersal within the invasive range. Few loci showed signs of selection, the vast majority of which being identified in the Slovakian region. Functional annotation suggests that faster early stage development, resistance to pollution and immunocompetence contribute to the invasion success of the local habitats. By showing that populations in the invasive range have different evolutionary histories, our study reinforces the idea that populations, rather than species, are the units to consider in invasion biology.Facilitated by the intensification of global trading, the introduction and dispersal of species to areas in which they are historically non‐native is nowadays common. From an evolutionary standpoint, invasions are paradoxical: not only non‐native environments could be different from native ones for which introduced individuals would be ill‐adapted, but also small founding population size should be associated with reduced adaptive potential. As such, biological invasions are considered valuable real‐time evolutionary experiments. Here, we investigated the population structure and adaptive potential of the highly invasive topmouth gudgeon ( We hypothesize that successful establishment was facilitated by the same loci evolving in parallel across non‐native regions.22.1A total of 301 topmouth gudgeon were collected for this study. Samples were distributed across four geographic areas, one in the native range and three in the invasive range, resulting in a total of 16 locations, hereafter populations. In the native range, we used fish collected from the two major catchments in China: the Yangtze (CN‐Yan) and Yellow river (CN‐Yel). Fish from these locations had their mitochondrial DNA partially sequenced and showed highly distinct lineages  following the manufacturer's instructions. Library preparation, sequencing and bioinformatic processing of raw reads  was performed at the GeT‐PlaGe core facility, INRA Toulouse, France. Libraries preparation followed Baird et al. . Librari2.3stacks v1.48  would influence the number of catalogue loci. The objective was to achieve a balance between under merging stacks due to low m and M and over merging stacks due to high m and M , number of private alleles (pA) and number of variant sites (vS) in stacks. To account for possible confounding effects linked to individual coverages . Models were corrected for the use of sequential sum of squares analyses using drop1. Absolute values of pA and vS were standardized for the depth of coverage at each population, and log transformed to normalize the distribution prior to statistical analyses.We calculated the observed heterozygosity ‐China (Yangtze river); Group II Slovakia‐Turkey‐France‐China (Yellow River) and China (Yangtze river); Group III European‐Asian; Group IV Slovakia and Turkey‐ France‐China (Yellow River) and China (Yangtze); Group V Slovakia and China (Yellow River) – Turkey and China (Yangtze river) – France; Group VI Slovakia and China (Yangtze River) – Turkey and China (Yellow river) – France. Pairwise population differentiations were also analysed in arlequin .To reconstruct the pathway of the topmouth gudgeon's introduction, investigate invasive range expansion and identify candidate loci associated with invasion success, we determined the genetic structure across the species’ spatial distribution. Population structure was investigated by a multifold approach. We started by visually inspecting the distribution of pairwise faststructure . Best‐fit number of clusters were verified by Bayesian Information criteria (BIC). DAPC is implemented in the r package adegenet  to clarify sublevels of population structure. While DAPC is a multivariate method that maximizes genetic differentiation between predefined groups, we rather explored the versatility of Jombart, . Upon id2.6newhybrids v1.1  between lineages as present in the native range. The objective was to test whether hybridization can contribute to the successful colonization in the invasive range. This was done with the software 2.7outflank v0.2 which separates the variance caused by (a) the existence of a finite real number of demes, (b) sampling a finite number of individuals per deme and (c) spatial selection from a spatially heterogeneous selection on a specific locus  are possibly affected by the presence of outliers. Hence, candidate loci whose allelic frequencies covary with environmental factor would be suggestive of selection. Environmental correlations were investigated on Slovakia's regional SNP panel and performed in bayenv2  were used per individual. After inspection of coverage and number of loci, we kept the combination m = 4, M = 5 and maintaining M = N as parameters . Coverage metrics divided by region and range are presented in Figures A total of 301 fishes were used in this study. On average, 1.3 × 10 Figures  and S2. 3.2F = 9.80; df1,14; p = .01) but a higher observed heterozygosity  and number of variant sites  than those in the native range : More specifically, Chinese populations showed on average, higher number of private alleles . Regarding observed heterozygosity, Slovakian populations harboured, on average, higher observed heterozygosity than those in China (Tukey HSD: p = .006). Lastly, Slovakian populations showed the highest number of polymorphic sites .Allelic divergence between tested range pairs revealed that Slovakian populations and that of its putative source, the Yellow River, were more genetically similar than any other native/non‐native pairs and that French populations were more similar to those of the Yellow River rather than those of the Yangtze Figure . DiscrimFST showed that the higher degree of differentiation occurs among regions of the non‐native range . Noteworthy, the two populations from China were significantly different from one another . This differentiation at the native range carried into the invasive range. This was confirmed by the clustering dendrogram, where two highly differentiated branches, each harbouring a Chinese population and either one or two regions from the non‐native range, stem from a common ancestry . Within Slovakia, the Vah river populations revealed to be less divergent from that of the Yellow river, although differentiation was not significant.Comparing pairwise differences between putative native sources and respective sinks in the invasive range revealed that locations within Turkey diverge more from Yangtze river than the Slovakian ones do from the Yellow river  form the first line, while all individuals from Turkey and the Yangtze (Pure 2) belong to the second group. Interestingly, the large majority of individuals collected in France belong to Pure 1 genomic background and showed between 5%–75% of their genomes to have arisen from backcrosses. Noteworthy was a single individual exhibiting the genomic make‐up of a F1 Figure .3.4outflank detected a total of 19 candidate loci under selection in Slovakian populations, three in Turkish populations and none in the French populations. The allelic frequencies of a total of 30 loci correlated with river speed (for a BF > 10), one of which overlapped with outflank.Data processing of Slovakian populations and subsequent match against common catalogue retrieved a total of 7,403 SNPs, the Turkish populations retrieved a total of 6,460 SNPs and the French populations retrieved a total of 4,556 SNPs. Inferences of candidate loci under selection via the two different procedures gave different quantitative and qualitative results. 3.5Ho between neutral and candidate loci across ranges and regions revealed a model including region fits better than a model including range as variable (ANOVA: (model range vs model region)F = 15.60; df1,3; p < .001, RSS modelregion = 1E−7, modelrange = 5E−7). Thus, a model with regions was preferred and proven to overall explain 71% of Ho variation . Furthermore, it revealed a negative and significant effect of candidate loci in Slovakia   Table , indicat3.6K = 2 as the model complexity maximizing marginal likelihood scores. AMOVA suggested that Slovakian, French and Turkish populations formed a single group while the Chinese populations of the Yellow river and of the Yangtze formed two independent groups . DAPC analyses were less conclusive than those of neutral loci. Plotting the principal components for K = 3 , codfish (Gadus morhua), zebrafish (Danio rerio) and the stickleback (Gasterosteus aculeatus)  introduced in North America from European native regions , whose invasive populations in Florida were traced back to eight genetically distinct native sources   in Hawaii  in Australia  in the Mediterranean  (Carreras et al., No evidence for fine‐scale population structure was found with candidate loci. The role of putative candidates in defining fine‐scale population structure is probably hampered by the relatively young age of invasive populations. While recent works suggest that candidate loci increase the resolution at which population structure can be detected, i.e., hake (In conclusion, the detection of occurrence and spread of biological invaders are dependent on human observation and documentation. Population genetics provide temporal and spatial depth to those observations and enable the characterization of invasion pathways, dispersal and establishment in the new range (Estoup & Guillemaud, M.B.S. performed research, analysed the data and wrote the manuscript; S.B., J.C., and R.E.G. designed research and contributed with data; A.S.T., E.Z. contributed with data; C.E. designed research, assisted with analyses and writing the manuscript. S.B., J.C., and C.E. received funding for this research from BIODIVERSA. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.