Populus tremula, P. tremuloides) planted in plots representing either a single deme only or combinations of two, four and eight demes. The goals of this study were to explore the complex inter- and intraspecific genetic diversity of aspen and to then relate three measures for diversity  to arthropod abundance. Microsatellite and AFLP markers were used to analyze the genetic variation patterns within and between the aspen demes and deme mixtures. Large differences were observed regarding the genetic diversity within demes. An analysis of molecular variance revealed that most of the total genetic diversity was found within demes, but the genetic differentiation among demes was also high. The complex patterns of genetic diversity and differentiation resulted in large differences of the genetic variation within plots. The average diversity increased from plots with only one deme to plots with two, four, and eight demes, respectively and separated plots with and without American aspen. To test whether intra- and interspecific diversity impacts on ecosystem services, arthropod abundance was determined. Increasing genetic diversity of aspen was related to increasing abundance of arthropods. However, the relationship was mainly driven by the presence of American aspen suggesting that species identity overrode the effect of intraspecific variation of European aspen.The ecological consequences of biodiversity have become a prominent public issue. Little is known on the effect of genetic diversity on ecosystem services. Here, a diversity experiment was established with European and North American aspen ( In recent years, biodiversity is rapidly declining due to habitat loss and fragmentation driven by human activities including climate change . Thus, tPopulus tremula L.) from twelve populations, trait variation structured arthropod communities (Theory predicts associations between intraspecific (genetic) diversity and species diversity  and an imunities . Furthermunities . Overallmunities . KnowledThe manipulation of intraspecific diversity is straightforward, if a limited and known number of different genotypes is clonally propagated and planted in pure or mixed experimental plots. However, most woody plant species reproduce sexually, therefore, studies based on the observation of different clonal diversity patterns in plant populations do not reflect the intraspecific diversity present in natural ecosystems dominated by woody species and do not allow to establish links between ecological functions and intraspecific diversity. Accordingly, there is an urgent need to better understand ecological effects of different intraspecific diversity patterns in experimental populations comprising sexually produced progenies .P. tremula) and American aspen (P. tremuloides Michx.). To maximize the genetic diversity in this study progenies from seeds of single trees, population samples, and wildlings of aspen were planted. The plants originated from populations of different locations across Europe  and the US. Because the genetic variation of a particular progeny array after sexual reproduction depends on numerous factors including the number of seed and pollen parents involved in the production of the planted progenies, we used the term “deme” in its original definition for an “assemblage of taxonomically closely related individuals”  of the section Leuce : Europeaviduals”  to distividuals” , nor to viduals” .P. tremula) is included, and that the inclusion of a second species (P. tremuloides) increases the plot diversity.In the present study the demes from different locations were mixed in plots to obtain a design consisting of single demes and mixtures of two, four, and eight demes. Because of the unknown complexity of the intra-specific diversity in demes generated by open-pollination, an important goal of our study was the establishment of scales for the genetic diversity. We expected that our study design would result in increasing intra-specific diversity with an increasing number of demes in the mixture. Simple population genetic theory predicts that the diversity of a mixed plot will not be lower than the mean diversity of the demes contributing to this plot. However, the diversity of a specific combination of demes is difficult to predict, if demes differ with regard to their within-deme diversity and their differentiation from each other. This holds in particular if demes represent more than a single species as in our case. Thus, we tested whether the number of demes mixed in a plot is a proxy for its diversity. The analysis of a few hypervariable SSRs and a large number of dominant amplified fragment length polymorphisms (AFLPs) loci allows a comprehensive view on the neutral genetic diversity and differentiation . In presPopulus sp. are keystone species for a multitude of associated organisms (rganisms . In biomrganisms . HoweverThe poplar diversity experiment (POPDIV) was carried out at the Relliehausen Experimental Farm near Silberborn in the Solling Uplands, Germany . The average annual temperature is 6.9°C while average annual rainfall is 1031 mm . The site was situated on permanent, extensively grazed grassland at an altitude of 485 m above sea level. The soil of the experimental area is a shallow (40–60 cm), stony haplic Cambisol on middle Bunter  and one from North America . From May to June 2008 seeds were germinated and seedlings of most demes were cultivated in the greenhouse at 20°C and 16 h day length achieved by supplemental illumination. When reaching a height of about 5 cm, seedlings were planted individually in pots (13 × 13 cm) with nutrient-rich humus soil. Pots were placed outdoors and watered as necessary until autumn. With an average height of about 30 cm, the seedlings were out-planted in the experimental field at the end of October 2008. The aspen demes from Holstein, Germany and Bialystok, Poland, were provided by a tree nursery  as bare-rooted plants with a height of ca. 30 cm. The aspen trees from Sweden originated from natural regeneration on a clear-felled area close to Edsvalla and were collected within a privately owned forest area with permission given by the forest owner in October 2008.Eight aspen demes were used in this experiment: seven demes from Europe (Arvicola amphibious). Voles within the fenced area were decimated by traps  with permission of the lower nature conservation authority . Prior to the establishment of the poplar plants the biologically degradable herbicide Roundup  was applied, and the area was processed with a disk harrow. This is a typical treatment for plantation establishment and the herbicide dose was not toxic for arthropods . The poplar seedlings were out-planted in a randomized block design with 20 blocks, each divided into six plots . In each plot, 25 poplar seedlings from up to eight different demes were planted at four levels: (1) each deme was planted solitary with three replications (24 plots), (2) two demes were planted in combination with each other (56 plots), (3) four demes were planted in random combinations (32 plots) and (4) all demes were planted in combination (eight plots). The total number of experimental trees was 3000, and the distance between the trees was 1.5 m. To avoid edge effects each block was surrounded by a row of additional trees  that was not taken into account for the measurements and analyses.Before the planting of the poplar seedlings the experimental site (110 × 170 m) was surrounded by a knotted-wire-netting-fence . Inside the area, a second fence was installed with a height of 20 cm above-ground and reaching 70 cm below-ground to protect the plants from European Water Voles . If some seedlings were missing in these plots, additional samples were collected from plots comprising two demes . Total genomic DNA was extracted with the DNeasy Plant Minikit  according to the manufacturer’s protocol.Fresh leaves were collected from all seedlings planted in the plots comprising single demes and eight demes in the POPDIV experimental field in spring 2009 . The forward primer of each marker was labelled with 5-HEX (5′-hexachlorofluorescein) or 6-FAM (6′-carboxyfluorescein). The linkage groups of markers used in this study are known according to genetic linkage map in aspen with the exception of PTR6 (Table 2).The microsatellite markers , WPMS16 , PTR2, PR2, PTR4 , PTR5, Pnd PTR14  were use of PTR6 . The gen4)2SO4, 0.2% w/v Tween-20; Solis Biodyne), 1.0 μl of MgCl2 (25 mM), 1.0 μl of each dNTP , 1 μl S-solution (Solis Biodyne), 1 μl of each primer (5 pmol/μl) and 0.2 μl of Taq DNA polymerase . The PCR reaction was carried out in a Peltier Thermal Cycler  with the following steps: 95°C initial denaturation for 15 min followed by 35 cycles at 94°C for 1 min (denaturation), annealing for 1 min  and at 72°C for 1 min (extension). In the last cycle the extension period was increased to 10 min at 72°C. PCR products were diluted in appropriate proportions according to the band intensity on agarose gels and denatured at 95°C for 2 min in HiDiTM formamide containing a 500-bp ROX standard . Fragments were separated on ABI Prism 3100 genetic analyser . The size of the fragments was determined with the software packages GeneScan 3.7 and Genotyper 3.7 .The PCR reaction was performed in a total volume of 15 μl containing 1 μl of genomic DNA (∼10 ng), 1.5 μl of 10 × reaction buffer  and extension at 72°C for 2 min, and continued with 24 cycles with the following temperature profile: 94°C for 10 s, 56°C for 30 s and 72°C for 2 min, ending with a final extension step at 60°C for 30 min. The fragments were analyzed similarly to the SSR method described in detail above.The AFLP technique followed the protocol of P), observed and expected heterozygosity  as well as the fixation index (F) were calculated by the program genetic data analysis (GDA) version 1.1 . The number of polymorphic loci (PL), percentage of polymorphic loci (PPL) and Shannon index (I) were calculated using the program POPGEN version 1.32 p, where ip and jp is the frequency of band presence and absence, respectively  of plots with different numbers of demes were statistically significant. To test the null hypothesis for the comparison of measured and predicted diversity, the sign rank test was applied .n = 120 samples. The animals were stored in 70% ethanol. The number of collected invertebrates per plot was counted and sorted according to the categories: caterpillars, beetles, flies, and spiders. Tree height was measured. For statistical analyses rank correlations  were calculated for the relationship between He by SSR markers and the abundances of the invertebrates using the free PAST software package 2.17c2 . Most of the clones were observed in two plots comprising only this Swedish deme , suggesting an uneven distribution of the clones within the experimental plots. However, among a total of 99 plants only 23 plants were discovered that belonged to 9 clones that the contribution of clones was below 15% for this deme.We observed a maximum of two alleles at all investigated SSR loci in single individuals. Thus, we assume that all investigated plants were diploid. Nine clones were jointly identified by both SSR and AFLP markers within one Table 3). All SSR loci used in this study were moderately or highly polymorphic within all demes with at least 10 different alleles observed in the complete data set (Table 2). The average number of alleles per locus varied from 5.3 to 8.8 within the German (G8) and Swedish demes, respectively. The observed and expected heterozygosities indicated the highest diversity within the North American P. tremuloides deme. Most private alleles were also observed in this deme. The lowest diversity was estimated for the P. tremula deme G8, which was a progeny array from a single seed tree growing in Göttingen, Germany. Fixation indices (F) were calculated for all demes, although Hardy-Weinberg structures are not biologically meaningful genotypic reference structure for the single tree progenies. Mean F values were low and moderately positive for the population samples (0.083) and in the case of the single mother tree progeny G8 even slightly negative.Moderate to high levels of genetic diversity were observed within all demes at SSR loci . The Pearson correlation between the expected heterozygosity at SSR markers and Shannon indices at AFLPs was highly significant .The two AFLP primer pair combinations yielded 163 clear and repeatable peaks in the range of 75–492 bp. By far most private alleles were present within the North American FST values of all pairs of demes showed significant differentiation of the eight demes analyzed here by SSR markers  and AFLP markers . For SSR markers, the FST values varied from 0.031 to 0.308 with an average of 0.150, indicating that about 15% of the genetic variation was present among demes. Strong genetic differentiations were observed between the North American P. tremuloides deme and the European P. tremula demes (0.16 < FST < 0.31). Most P. tremula demes were also moderately to highly differentiated. High genetic differentiation was confirmed between the North American P. tremuloides deme and the European P. tremula demes by AFLP markers, which indicates strong genetic differentiation between different species (0.55 < FST < 0.63). Moderate genetic differentiation was also found among the P. tremula demes (0.05 < FST < 0.15).The Table 5). The AMOVA revealed high and significant differentiation among demes at both SSRs and AFLPs (P < 0.001). The AFLP markers showed a higher genetic differentiation (36.8%) compared to SSR markers (16.0%).For both markers, the AMOVA analysis at two hierarchical levels showed that most of genetic variation was present within demes . Congruent results were obtained from both markers. Large genetic distances were observed between the North American P. tremuloides deme and the European P. tremula demes resulting in clear outgrouping of the North American P. tremuloides deme from the European P. tremula demes with high bootstrap support (89% at SSRs and 100% at AFLPs). This finding confirmed that P. tremuloides represents a genetically distinct species in comparison to the European P. tremula demes.For both SSR and AFLP markers, the genetic distances between the eight demes were visualized in UPGMA dendrograms (Table 6). While the overlap was almost 100% for He, the AFLP markers slightly, but consistently overestimated the diversity (6.7%) of the 8-deme plots (Table 6).Because not all plots could be measured, the overlap of the estimation of the genetic diversity with measured values was analyzed. We predicted the diversity in 8-deme plots with measured diversity of the individual demes and compared these values with the measure diversity in the 8-deme plots. We found a strong match between the predicted and measured values . The genetic diversity within plots composed of 1, 2, or 4 demes was highly variable  because the genetic diversity within a given deme and among the demes was not homogenous (Table 3). Because the plots with eight demes contained the plants of all demes in an almost balanced manner (three plants of each of the eight deme + 1 extra plant of any deme = 25 plants), the 8-deme plots showed only very little variation . The mean genetic diversity increased with increasing number of different demes included within plots as expected. The mean diversities of plots with different number of demes were significantly different from each other based on the ANOVA test both for AFLPs and SSRs . However, it was notable that the absolute maximum of genetic diversity was present in 2-deme plots and that the Shannon indices on 2- and 4-deme plots clearly separated plots with high (>0.30) and plots with low (<0.22) indices . The reason for this separation was the presence of P. tremuloides in these combinations, which resulted in much higher genetic diversity than in plots containing only different P. tremula demes . Because P. tremuloides strongly affected the analysis, we determined the relationship between neutral genetic diversity measures and number of demes in the mixtures for plots comprising only P. tremula demes and for the 8-deme plots without the USA deme. Increasing deme diversity resulted in increasing genetic diversity . However, the increment was small, regardless of whether expected heterozygosity or Shannon indices were used as the genetic measure.The genetic diversity was calculated for all plots in our study based on the analyses of both markers. The diversity measures were grouped according to the number of demes present within the plot: 1-, 2-, 4-, and 8-demes (The invertebrate inventory in the aspen plots showed that beetles formed the largest group (mean number per plot ± SE: 11.3 ± 0.9), whereas all other groups occurred with much lower abundances . The mean total number of invertebrates on the central trees per plot was 17.0 ± 1.0 and their density amounted to 13.0 ± 0.6 animals per meter of plant height.P. tremuloides on the genetic diversity indices, rank correlation analyses were conducted for the detected invertebrate groups for all plots and additionally only for the plots without the USA deme (Table 7). When all plots were included we found that He was significantly correlated with beetle abundance (positive) and with caterpillar abundance . The relationship between He and total invertebrate abundance was also significant and positive . However, when plots with and without P. tremuloides were analyzed separately, it was apparent that invertebrate abundance was unrelated to P. tremula diversity and that the observed increase was driven by the presence of P. tremuloides . When the correlation analysis was conducted with the Shannon indices of the AFLP markers for all plots, the relationship with invertebrate abundance was also highly significant  and disappeared when P. tremuloides was removed from the analysis. Correlations were neither detected when the invertebrate groups  in plots with and without P. tremuloides were analyzed separately (Table 7).To investigate potential relationships between the genetic diversity and the abundance of invertebrates we correlated expected heterozygosity (He) with invertebrate abundance. Since our previous analysis showed a strong effect of P. tremula were included in the analysis, we found that beetles were slightly more abundant in 4-deme plots than in 1- or 2-deme plots .We furthermore tested the effect of deme diversity on invertebrate abundance. No significant differences were observed for any of the invertebrate groups in different demes when the deme USA was present. When only plots differing in the genetic diversity of Both SSR and AFLP markers are commonly used to examine genetic diversity . UsuallyWe propose that an assessment of genetic diversity effects at the ecosystem level should be based on measurements characterizing intraspecific variation independent from specific plant traits. Accordingly, estimates of neutral genetic diversity are better suited to compare the magnitude of possible effects on different traits or interactions with accompanying organisms rather than diversity estimates within genes putatively involved in specific responses. The patterns of genetic diversity and differentiation reported here can later be compared to effects of the variation within and among the same demes at functional, phenotypic traits to search for possible correlations.P. tremuloides deme had higher genetic diversity than any other deme, including the population samples G1 and S, both at SSRs and AFLPs. This might be related to the different species status of the demes: genetic diversity in P. tremuloides . As expected, the demes propagated from seeds of only one  or presumably few  seed parents showed lower genetic diversity compared to demes collected as population samples (PL) or wildlings (S). But notably this was not found for G1, which was also supposed to originate from population sampling. Overall, considerable genetic diversity and in particular high allelic multiplicity  was observed in all demes, including those originating from a single or few seed parents. Accordingly, a high diversity of pollen contributions and a large effective number of pollen parents is assumed for these trees.The seven P. tremula and, as a reference, a population sample of P. tremuloides (USA). Since material was sampled in different locations within Europe from Switzerland to Sweden, an influence of large-scale geographic variation patterns on diversity levels of the sampled material cannot be excluded. However, triploid clones, as identified in a survey of natural populations of aspen (P. tremuloides) in North America (Table 3). Furthermore, the genetic diversity observed in the Swedish deme is comparable to the diversity found in other natural populations of P. tremula in Sweden , single-tree progenies , samples from presumably few  and many  seed parents of  America , were no America  the findn Sweden . OverallFST and related measures quantifying the proportion of the total diversity due to differences among populations  and phenological traits (leaf abscission) between the two species have also been found in this experiment  was included in mixtures of two or four demes. Both the high diversity of this deme and its strong differentiation from all other demes contribute to high diversity of plots containing this deme. Plots containing all eight demes consist of three plants from seven demes and four plants from one deme. Accordingly, the diversities of these plots are similar due to only minor differences of the contribution of the demes to these plots. Strong differences are particularly pronounced for two and four deme mixtures at AFLPs  due to the higher overall differentiation of AFLPs in comparison to SSRs. The diversity of these plots is considerably higher than the diversity of plots with the same number of demes but without P. tremuloides. The highest diversities were estimated for two-deme plots containing the Swedish and the USA (P. tremuloides) deme both at SSRs and AFLPs.The genetic diversities of plots comprising a given number of demes are highly variable. Thus, the genetic diversity of plots with a given number of demes is not homogeneous. This result is due to different diversity levels of the demes planted in the experiment and their complex patterns of (pairwise) differentiation. A particularly strong effect of increasing plot diversity was regularly observed if the USA deme  and neutral genetic diversity as basis to drive ecosystem functions.We expected that instead of testing a wide range of traits to identify relationships with important ecological functions such as the abundance of the associated invertebrate fauna, “neutral” genetic diversity as an integrative index for evolutionary adaptation might be suitable to depict significant relationships. However, the correlation uncovered here for neutral genetic diversity and invertebrate abundance was only significant, if the plots with P. tremula with P. tremuloides. To address the difference between inter- and intraspecific genetic diversity future studies should include the same number of demes for different species.A better understanding of the role of intraspecific diversity for ecosystem processes and services requires the establishment of experimental populations under conditions closely resembling natural ecosystems. While it is straightforward to manipulate clonal diversity in woody species, only few forest trees regularly propagate as clones. Therefore, the manipulation of intraspecific diversity in experimental tree populations using sexually produced progenies is needed to better understand links between evolutionary and ecological processes. Our field study is one of the first experiments in this regard. Its genetic diversity was assessed ex post. Thus, the genetic diversities of the plots were unknown at the time of trial establishment and could not be modified later. A thorough assessment of genetic diversity prior to the establishment of a trial will allow an optimization of the trial design with regard to the different intraspecific diversities represented in the plots and their spatial arrangement. Our results indicate that genetic diversity within different aspen demes may exhibit significant differences. Furthermore, mixing different demes increases the genetic diversity, however, the magnitude of this effect is small compared with the diversity present in distinct combinations. Highest diversity was present in mixtures of In this study the intraspecific diversity patterns offer unique opportunities to better understand the role of genetic variation on ecosystem processes and services, and are an interesting case to study the complexity of diversity triggered by sexual reproduction in experimental plant populations. Detailed morphological, physiological, and molecular studies on poplars and associated organisms are needed to understand impacts of intraspecific diversity on ecosystem processes and functions. However, the interpretation of the variability at these traits must rest on a solid analysis of their genetic basis.Chunxia Zhang conceived the study, participated in its design, carried out the molecular genetic studies, performed the statistical analysis and drafted the manuscript. Barbara Vornam and Kathleen Prinz participated the data analysis and helped to draft the manuscript. Katharina Volmer carried out invertebrate collection in the field, participated the statistical analysis and helped to draft the manuscript. Frauke Kleemann conceived the experiment, carried out the seeds or seedlings collection and cultivation and planted seedlings in the experimental field and helped to draft the manuscript. Lars Köhler participated in the design of the POPDIV and coordination and helped to draft the manuscript. Andrea Polle conceived and designed the POPDIV, analyzed data and drafted the manuscript. Reiner Finkeldey conceived the study, participated in its design, the data analysis and drafted the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Due to their error-prone replication, RNA viruses typically exist as a diverse population of closely related genomes, which is considered critical for their fitness and adaptive potential. Intra-host demographic fluctuations that stochastically reduce the effective size of viral populations are a challenge to maintaining genetic diversity during systemic host infection. Arthropod-borne viruses (arboviruses) traverse several anatomical barriers during infection of their arthropod vectors that are believed to impose population bottlenecks. These anatomical barriers have been associated with both maintenance of arboviral genetic diversity and alteration of the variant repertoire. Whether these patterns result from stochastic sampling (genetic drift) rather than natural selection, and/or from the influence of vector genetic heterogeneity has not been elucidated. Here, we used deep sequencing of full-length viral genomes to monitor the intra-host evolution of a wild-type dengue virus isolate during infection of several mosquito genetic backgrounds. We estimated a bottleneck size ranging from 5 to 42 founding viral genomes at initial midgut infection, irrespective of mosquito genotype, resulting in stochastic reshuffling of the variant repertoire. The observed level of genetic diversity increased following initial midgut infection but significantly differed between mosquito genetic backgrounds despite a similar initial bottleneck size. Natural selection was predominantly negative (purifying) during viral population expansion. Taken together, our results indicate that dengue virus intra-host genetic diversity in the mosquito vector is shaped by genetic drift and purifying selection, and point to a novel role for vector genetic factors in the genetic breadth of virus populations during infection. Identifying the evolutionary forces acting on arboviral populations within their arthropod vector provides novel insights into arbovirus evolution. During infection of their arthropod vectors, arthropod-borne viruses (arboviruses) such as dengue viruses traverse several anatomical barriers that are believed to cause dramatic reductions in population size. Such population bottlenecks challenge the maintenance of viral genetic diversity, which is considered critical for fitness and adaptability of arboviruses. Anatomical barriers in the vector were previously associated with both maintenance of arboviral genetic diversity and alteration of the variant repertoire. However, the relative role of random processes and natural selection, and the influence of vector genetic heterogeneity have not been elucidated. In this study, we used high-throughput sequencing to monitor dengue virus genetic diversity during infection of several genetic backgrounds of their mosquito vector. Our results show that initial infection of the vector is randomly founded by only a few tens of individual virus genomes. The overall level of viral genetic diversity generated during infection was predominantly under purifying selection but differed significantly between mosquito genetic backgrounds. Thus, in addition to random evolutionary forces and the purging of deleterious mutations that shape dengue virus genetic diversity during vector infection, our results also point to a novel role for vector genetic factors in the genetic breadth of virus populations. This muogenesis .Togaviridae, Flaviviridae, Bunyaviridae, Rhabdoviridae and Orthomyxoviridae families, the vast majority are RNA viruses, with the single known exception of a DNA arbovirus (African swine fever virus). The genetic plasticity of an RNA genome may confer arboviruses the remarkable ability to alternate between two fundamentally different hosts, and to quickly adapt to novel hosts [Arthropod-borne viruses (arboviruses) are maintained by transmission between vertebrate hosts and blood-feeding arthropods such as mosquitoes that serve as vectors. Although arboviruses span a wide range of viral taxa in the el hosts . Like otel hosts ,5 and Weel hosts –8.Arboviruses usually rely on horizontal transmission between vertebrate hosts and arthropod vectors, although vertical transmission from an infected female arthropod to her offspring may occur ,10. AfteCulex mosquitoes failed to document reductions in genetic diversity [Aedes mosquitoes that became infected by feeding on the person [Despite such population bottlenecks, arboviruses typically maintain high levels of genetic diversity during transmission by their arthropod vectors . For exaiversity . Howevere person . Over 90e person . HoweverNe), defined as the size of an idealized population that would drift at the same rate as the observed population [Ne indicates whether the evolution of a population is better described as a deterministic (selection) or stochastic (drift) process. When Ne is large, competition between variants occurs with little interference of random processes. When Ne is small, stochastic sampling of variants counteracts selection and hinders adaptation. For example, genetic drift plays a limited role during systemic infection of the plant host by cauliflower mosaic virus, as viral populations maintain an effective size of several hundreds of viral genomes [The relative strength of natural selection and genetic drift is informed by the effective population size  measure the bottleneck size during initial midgut infection of Ae. aegypti mosquitoes by DENV; (ii) monitor DENV intra-host genetic diversity during population expansion and systemic infection; and (iii) determine the influence of the vector genotype on bottleneck size and intra-host DENV genetic diversity.Our objectives were three-fold:  that was originally recovered from the serum of a clinically ill dengue patient attending Kamphaeng Phet Provincial Hospital, Thailand as previously described . InformeAedes aegypti females used in this study belonged to the 16th generation of four isofemales lines  derived from wild Ae. aegypti specimens collected in Kamphaeng Phet Province, Thailand. The lines were initiated by single mating pairs of field-caught males and females as previously described [escribed . One malad libitum and allowed to mate for 6–7 days before the experimental infection.To initiate the experiment, eggs were hatched in filtered tap water. Larvae were reared in 24×34×9 cm plastic trays at a density of about 200 larvae per tray. Adults were maintained in 30×30×30 cm screened cages under controlled insectary conditions . They were provided with cotton soaked in a 10% (m/v) sucrose solution Ae. aegypti isofemale lines used single nucleotide polymorphism (SNP) markers identified and genotyped by Restriction-site Associated DNA (RAD) sequencing [th generation of each isofemale line  and 10 females from the 1st generation of an outbred population collected in 2013 in Thep Na Korn, Kamphaeng Phet Province, Thailand  were genotyped using RAD sequencing.Genetic characterization of the quencing . Ten femNlaIII and MluCI restriction enzymes , 1× CutSmart Buffer and water for 3 hours at 37°C, without a heat-kill step. Digestion products were cleaned with 1.5× volume of Ampure XP paramagnetic beads  and ligated to the modified Illumina P1 and P2 adapters with overhangs complementary to NlaIII and MluCI cutting sites, respectively. Each mosquito was uniquely labeled with a combination of P1 and P2 barcodes of variable lengths to increase library diversity at 5’ and 3’ ends  to obtain 150-bp paired-end reads. An optimized final library concentration of 1.1 pM, spiked with 15% PhiX, was loaded onto the flow cell. Raw sequences were deposited in the NCBI Sequence Read Archive under accession number SRP075401.A previously developed bash script  was usedAe. aegypti genome  [Paired reads were aligned to the reference ry 2016)  using Bory 2016) . Parametry 2016) ,45. Firs7 states to obtain Effective Sampling Size (ESS) values >200.Phylogenetic trees were constructed using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm, implemented in the BEAST 1.8.3 package . InferenAe. aegypti females were deprived of water and sucrose for 24h prior to the infectious blood meal. The virus stock was diluted in cell culture medium  to reach an expected infectious titer of 3×106 focus-forming units (FFU) per mL. One volume of virus suspension was mixed with two volumes of freshly drawn rabbit erythrocytes washed in distilled phosphate-buffered saline (DPBS). After gentle mixing, 2.5 mL of the infectious blood meal was placed in each of several Hemotek membrane feeders  maintained at 37°C and covered with a piece of desalted porcine intestine as a membrane. Sixty μL of 0.5 M ATP were added to each feeder as a phagostimulant. Each isofemale line was allowed to feed during two rounds of 15 min on different feeders to ensure randomization of a potential feeder effect. Actual virus titer in the blood meal was measured by standard focus-forming assay in C6/36 cells [Six- to seven-day-old 36 cells . After fAt 4, 7 and 14 days post exposure (dpe), the midgut of 8–12 individuals from each isofemale line  were dissected. Midguts were homogenized individually in 140 μL of DPBS + 560 μL of QIAamp Viral RNA Mini Kit lysis buffer  during two rounds of 30 sec at 5,000 rpm in a mixer mill . At 7 and 14 dpe, the legs of midgut-dissected mosquitoes were removed and homogenized as described above. At 14 dpe, the salivary glands of the midgut- and leg-less individuals were harvested and processed as above.Total RNA was extracted from mosquito homogenates using QIAamp Viral RNA Mini Kit (Qiagen) and reverse transcribed using Transcriptor High Fidelity cDNA Synthesis Kit  and a specific reverse primer located at the 3’ end of the viral genome . PresencPCR products were purified with Agencourt AMPure XP magnetic beads (Beckman Coulter) and their concentration was measured by Quant-iT PicoGreen dsDNA fluorometric quantification (Invitrogen). Equal amounts of each amplicon were pooled by sample and brought to a final concentration of 0.2 ng/μL. Multiplexed libraries were prepared using Nextera XT DNA Library Preparation Kit (Illumina) and single-end sequenced on an Illumina NextSeq 500 platform using a high-output 75 cycles v1 kit (Illumina). Sequencing reads were demultiplexed using bcl2fastq v2.15.0 (Illumina). Raw sequences were deposited in the NCBI Sequence Read Archive under accession number SRP075335.After demultiplexing, reads were trimmed to remove Illumina adaptor sequences using Trimmomatic v0.33  and ampli) a sequencing depth <500X or (ii) where potential sequencing or library preparation artifacts [i) a sequencing depth <500X or (ii) where potential sequencing or library preparation artifacts [Two sets of SNV markers were used for analyses of genetic diversity and natural selection. The ‘full’ marker set excluded all nucleotide positions in a given sample that had (rtifacts  were detrtifacts  were detSn) for each nucleotide site [p is the SNV minor allele frequency at the considered position, and ln(4) corresponds to maximum complexity . For individual SNVs, Sn values range from 0 to 1. For diallelic SNVs, Sn values range from 0 (no diversity) to 0.5 . For each sample, Sn was averaged over all nucleotide sites included in either the full or the conservative set of SNV markers .Genetic complexity of the viral population was estimated using normalized Shannon entropy  to 0.5 . For each sample, π was averaged over all nucleotide sites included in either the full or the conservative set of markers. This index of genetic diversity is less sensitive to low-frequency variants than Sn, due to the lack of log-transformation of the frequencies.Genetic diversity of the viral population was also estimated using nucleotide diversity at each nucleotide site :π = DD-dN/dS ratio, which is the ratio between the number of non-synonymous substitutions per non-synonymous site (dN) over the number of synonymous substitutions per synonymous site (dS) of a coding sequence, assuming synonymous substitutions are selectively neutral:Sd is the number of synonymous substitutions in the sequence, Ss is the number of synonymous sites, Nd is the number of non-synonymous substitutions in the sequence and Ns is the number of non-synonymous sites [dN/dS ratio >1 means that there is an excess of normalized number of non-synonymous substitutions relative to the normalized number of synonymous substitutions and is interpreted as evidence for positive selection . A dN/dS ratio <1 means that there is an excess of normalized number of synonymous substitutions relative to the normalized number of non-synonymous substitutions and is interpreted as evidence for negative selection . A dN/dS ratio equal to 1 is interpreted as evidence for the absence of natural selection .The magnitude and direction of natural selection were assessed using the us sites . A dN/dSdN/dS ratio was computed using the Nei-Gojobori method [Nd and Sd were calculated for each sample as the sum of SNV frequencies. Mean Nd and Sd were computed for each isofemale line at each time point and used for dN and dS calculation, respectively. Numbers of synonymous and non-synonymous sites from the initial population consensus sequence were estimated using MEGA v.7.0.16 [The i method  with sugi method . Brieflyv.7.0.16  by compuv.7.0.16 . The fulhttp://www.r-project.org/) using the packages car [Statistical analyses were performed in the statistical environment R, version 3.2.0  plots of residuals and by computing Cook’s distance to assess influence of observations. Statistically significant effects (p<0.05) of time point, mosquito genotype and their interactions were determined using type-II analysis of variance. Statistically insignificant interactions were removed from the model, subsequently repeating model validation. Statistical testing of pairwise differences between isofemale lines used the linear regression coefficients. Estimated regression coefficients were extracted and their 95% confidence intervals and p-values were calculated based on their standard errors compared to a reference level. Isofemale line A was arbitrarily chosen as the reference level.The proportion of SNVs per position, mean N) initiating the midgut infection can be computed as:p is the marker allele frequency in the initial population and p′ is the marker allele frequency in the final population [Following a published method , bottlenpulation . This mei) synonymous change at the third codon position, (ii) no significant change in mean frequency between sampling time points, (iii) SNV detected in ≥80% of the five viral input replicates , and (iv) mean frequency >0.02 in the input population. Confidence intervals of N estimates were computed using a bootstrapping procedure as described in [N. This was repeated 1,000 times to generate a distribution of N values and derive 95% confidence intervals corresponding to the 2.5 and 97.5 percentiles of the distribution.SNVs that were presumably neutral were selected based on the following set of criteria: (ribed in . Brieflyλ = 100). Initial viral population size  was drawn from a normal distribution . Bottleneck size was drawn from a normal distribution . Mean Sn and mean π were computed for all samples in the presence or the absence of a detection threshold arbitrarily set at an SNV minor allele frequency of 0.01.The effect of the initial midgut infection bottleneck on viral diversity indices was simulated in R based on 100 sampling events from an initial viral population containing 100 independent SNVs. SNV minor allele frequency was randomly drawn from an exponential distribution . These markers had a sequencing depth ≥10X per sample and were missing in <2% of samples. An outbred Ae. aegypti population from the same geographic location where the lines were created was also genotyped to provide a phylogenetic background. Phylogenetic relationships among individuals from the four isofemale lines and the outbred population were determined with a Bayesian method /mL. Assuming a blood meal size of approximately 2 μL, the infectious dose ingested by each mosquito was about 3,000 infectious viral particles. The proportion of mosquitoes that acquired a midgut infection ranged from 75 to 100% and did not differ significantly between time points or isofemale lines . Among infected mosquitoes, viral load ranged from 8.9×102 to 2.8×106 DENV genome copies per sample with no significant difference between isofemale lines at any of the time points, with the exception of lines B and CD that had significantly different viral loads (p = 0.037) in their salivary glands at 14 dpe  were exposed to DENV through an artificial blood meal at a final titer of 1.52×10le lines . The prole lines . Howevert 14 dpe .Deep sequencing of viral genomes was performed for a subset of 78 infected samples at selected time points  that werThe full set of SNV markers retained for population genetic analyses included an average of 5,843 nucleotide sites across the DENV genome, whereas a more conservative set see  was restSn   . The proportion of variable sites detected also increased following initial midgut infection (p = 0.0029) and between 0 dpe and 14 dpe (p = 0.0067). Similarly, in the conservative set of markers, mosquito infection was associated with a relative increase in viral genetic diversity following initial midgut infection, albeit more modestly due to the smaller number of markers, both when measured with normalized Shannon entropy Sn     and when= 0.003) . Viral dnfection  although= 0.008)  and when= 0.008) . The pro= 0.008) .p = 0.03; Sn: p = 0.04; π: p = 0.04) and the isofemale line  significantly influenced viral genetic diversity. Overall, DENV genetic diversity slightly decreased between 4 dpe and 7 dpe. Isofemale line A displayed significantly higher viral genetic diversity than lines B and CD, for all three indices: proportion of variable sites , Sn (p = 0.006 and p = 0.0005 respectively) and π . Similar results were obtained with the conservative set of markers. Both the time point  and the isofemale line  significantly influenced viral genetic diversity. Overall, DENV genetic diversity slightly decreased between 4 dpe and 7 dpe. Isofemale line A displayed significantly higher viral genetic diversity than lines B and CD, for all three indices: proportion of variable sites , Sn  and π .To evaluate the dynamics of DENV genetic diversity during viral population expansion in the midgut, a second series of analyses compared viral genetic diversity between 4 and 7 dpe, accounting for potential differences between mosquito genotypes. In the full set of markers, both the time point  initiating the midgut infection based on changes in the variance of their frequency between input and midgut samples , but this was not supported by our data. Likewise, the overall lack of positive selection that we observed indicates that it is unlikely to be the underlying mechanism. Alternatively, mosquito genotypes may differ in the level of balancing selection . The antiviral RNA interference (RNAi) pathway of mosquitoes was suggested to play a role in viral genetic ‘diversification’ [During DENV population expansion following initial midgut infection, natural selection was predominantly negative . Accordingly, the consensus sequence remained unchanged in most of the midgut samples. Only in the salivary glands did several SNVs reach consensus level (frequency >0.5), but with no evidence of evolutionary convergence. As was observed for West Nile virus , DENV inication’ ,77, by pication’ . Mutatioication’ . Replicaication’ ,81. Viraication’ –84.Culex mosquitoes [Interestingly, the isofemale line that displayed the lowest level of DENV genetic diversity in the midgut  was also associated with the highest prevalence and highest viral load in salivary glands. Because we did not examine viral populations in saliva samples, whether this translates in differences of virus transmission potential is unknown. It is tempting to speculate that the vector competence phenotype relates to the level of viral genetic diversity. Unfortunately, we could not compare DENV intra-host diversity in salivary glands between mosquito isofemale lines because DENV amplification was unsuccessful in two out of three lines due to low template concentration. A recent study found differences in the intra-host genetic diversity of West Nile virus among different species of squitoes . Here, wde novo mutations or were already present prior to the bottleneck at frequencies lower than the detection threshold. However, our model indicated that a change in the SNV frequency spectrum following the population bottleneck combined with a minimum detection threshold is a potential explanation to the observed increased genetic diversity following the initial bottleneck.Finally, we introduced a non-exclusive, alternative scenario to the ‘diversification’ hypothesis that may contribute to explain why the level of arboviral genetic diversity increases despite a population bottleneck. Our proposed scenario is based on the counter-intuitive idea that a strong initial population bottleneck may actually result in a higher observed level of genetic diversity if low-frequency SNVs go undetected for methodological reasons. We used a model based on stochastic simulations to illustrate the effect of a minimum detection threshold of low-frequency SNVs on observed genetic diversity. When all SNVs present were detected regardless of their frequency , mean viral genetic diversity indices decreased following a simulated population bottleneck. Conversely, mean genetic diversity indices increased when only SNVs present at a frequency >1% were successfully detected. In our empirical data, it was not possible to ascertain whether SNVs newly detected after the initial population bottleneck resulted from Taken together, our results show that DENV intra-host genetic diversity in the mosquito vector is shaped by stochastic events during initial midgut infection due to a sharp reduction in population size, followed by predominantly purifying selection during population expansion and diversification in the midgut. Differential diversification between mosquito isofemale lines indicates a genetic foundation, but the lack of convergent SNVs does not support the existence of mosquito genotype-specific directional selection. We conclude that the evolution of DENV intra-host genetic diversity in mosquitoes is not only driven by genetic drift and purifying selection, but is also modulated by vector genetic factors. Characterizing the evolutionary forces that govern arboviral genetic diversity contributes to understanding their unique biology and adaptive potential.S1 Fig(PDF)Click here for additional data file.S2 Figx-axis. (B) Each dot represents the minor allele frequency of a single SNV along the DENV reference genome indicated on the x-axis, averaged over all samples from the same time point in which the SNV was detected. Dot size corresponds to the number of samples from the same time point in which the SNV was detected. The horizontal red dashed line represents a frequency of 0.5 above which a new variant becomes the consensus sequence. SNV distributions are stratified by time point. C = capsid protein, E = envelope glycoprotein, M = membrane glycoprotein, NS1 = non-structural glycoprotein 1; NS2A = non-structural protein 2A; NS2B = non-structural protein 2B; NS3 = non-structural protein 3 (protease/helicase); NS4A = non-structural protein 4A; NS4B = non-structural protein 4B; NS5 = non-structural protein 5 (RNA-dependent RNA polymerase).(A) Bars represent the density of markers retained in the conservative marker set for diversity and natural selection analyses along the DENV reference genome indicated on the (PDF)Click here for additional data file.S3 Figx-axis, averaged over all samples from the same time point and isofemale line in which the SNV was detected. Dot size corresponds to the number of samples from the same time point and isofemale line in which the SNV was detected. The horizontal red dashed line represents a frequency of 0.5 above which a new variant becomes the consensus sequence. SNV distributions are stratified by time point and isofemale line. C = capsid protein, E = envelope glycoprotein, M = membrane glycoprotein, NS1 = non-structural glycoprotein 1; NS2A = non-structural protein 2A; NS2B = non-structural protein 2B; NS3 = non-structural protein 3 (protease/helicase); NS4A = non-structural protein 4A; NS4B = non-structural protein 4B; NS5 = non-structural protein 5 (RNA-dependent RNA polymerase).Each dot represents the minor allele frequency of a single SNV along the DENV reference genome indicated on the (PDF)Click here for additional data file.S4 FigLetters indicate statistically significant pairwise differences between time points.(PDF)Click here for additional data file.S5 FigSn) per site over all positions per sample. (C) Averaged nucleotide diversity (π) over all positions per sample;(A) Proportion of variable sites detected. (B) Averaged Shannon entropy ((PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(TXT)Click here for additional data file.


Genetic introgression of domesticated to wild conspecifics is of great concern to the genetic integrity and viability of the wild populations. Therefore, we need tools that can be used for monitoring unidirectional gene flow from domesticated to wild populations. A challenge to quantitation of unidirectional gene flow is that both the donor and the recipient population may be genetically substructured and that the subpopulations are subjected to genetic drift and may exchange migrants between one another. We develop a standardized method for quantifying and monitoring domesticated to wild gene flow and demonstrate its usefulness to farm and wild Atlantic salmon as a model species. The challenge of having several wild and farm populations was circumvented by in silico generating one analytical center point for farm and wild salmon, respectively. Distributions for the probability that an individual is wild were generated from individual-based analyses of observed wild and farm genotypes using STRUCTURE. We show that estimates of proportions of the genome being of domesticated origin in a particular wild population can be obtained without having a historical reference sample for the same population. The main advantages of the method presented are the standardized way in which genetic processes within and between populations are taken into account, and the individual-based analyses giving estimates for each individual independent of other individuals. The method makes use of established software, and as long as genetic markers showing generic genetic differences between domesticated and wild populations are available, it can be applied to all species with unidirectional gene flow. Results from our method are easy to interpret and understand, and will serve as a powerful tool for management, especially because there is no need for a specific historical wild reference sample. Large-scale releases of plants and animals have the potential to augment population sizes, but also to cause adverse genetic changes in wild populations such as loss of genetic variation, loss of adaptation and change of population composition and structure ide ICES . Geneticide ICES . As longide ICES . The setide ICES  enable iIn other studies, all samples of interest are commonly being analyzed collectively in STRUCTURE. However, STRUCTURE is sensitive for differences in sample size, in particular when a group of samples are genetically heterogeneous  population, caused by gene flow from a diverse source of (farm) populations that have some genetic characteristics in common. Specifically, we wanted to develop a method for (1) estimating genetic change resulting from unidirectional gene flow when genetic drift and gene flow from other wild populations are also affecting the gene pool; (2) estimating farm to wild genetic introgression in a wild population without the need of a historical reference sample from the same population; and (3) estimating the probability of single individuals being of farm or wild origin. We develop the method using Atlantic salmon as a model, but emphasize that the approach is a general one provided the necessary molecular genetic markers are at hand.Our method consists of the following steps: (1) identify a set of reference populations defining two genetically distinct groups; (2) generate a center point for each of these genetic groups; (3) genetic assignment of the reference individuals to the center points to generate a reference probability distribution for belonging to one of the two center points; (4) genetic assignment of admixed individuals to the center points to generate an admixed probability distribution; and (5) the admixed and the reference probability distributions are compared to quantitate genetic introgression.FST estimates, as implemented in GENALEX 6.0 . From the genetic assignment of reference individuals to the center points, we obtained a probability of belonging to the wild center point P(wild) for each individual. The corresponding probability of belonging to the farm center point is 1 − P(wild). Based on the individual P(wild) estimates for the reference samples, we generated probability distributions for wild and farm salmon, respectively. These probability distributions were then used for statistical testing if there had been a farm to wild gene flow, and for estimating proportions of wild genome left in admixed populations.Assignment of wild and farm samples with real (observed) genotypic data to the wild and farm center points was conducted using STRUCTURE  for admixed individuals are obtained in the exact same way as explained for reference individuals. From individual estimates of P(wild), we obtain a probability distribution for the admixed population.Estimates of P(wild) as the historical reference, and the alternative hypothesis is that the contemporary sample is admixed and has a lower mean P(wild). Before performing the analysis, the probabilities P(wild) are logit-transformed (Warton and Hui P(wild), this variance can be estimated from all the historical samples.When evaluating a contemporary sample from a population, we are interested in testing whether or not the population has experienced genetic introgression. The null hypothesis is that the contemporary sample has the same mean without a historical reference sample, the test, or confidence interval estimation, will have an additional variance component caused by the variability in mean P(wild) value between wild populations. The variance in wild population mean P(wild) values can be estimated from the available wild references, assuming that the available wild references can be considered a random sample from all wild populations.If we have a historical reference sample from a population, the two samples  can be compared directly by the test on two means, alternatively by calculating a confidence interval for the difference between the two means. When we evaluate the genetic introgression from escaped farm salmon into a wild salmon population P(wild) less than one, and farm reference populations have an average P(wild) above zero, we need to calibrate the scale accordingly. Hence, the proportion of wild genome left in an admixed population, was calculated as:Because wild reference populations have an average P(wild) estimate for the admixed population, Farmref is the average P(wild) for the farm reference samples, and Wildref is the average P(wild) for the wild reference samples.where The power of identifying first-generation hybrids between farm and wild salmon was explored by simulating mating between individuals of wild and farm origin using observed genotype data. From wild individual genotypes, one allele was randomly sampled from each locus, generating one gamete that was merged with a correspondingly generated gamete from farm individuals. Five randomly generated first-generation hybrids were selected from each pair of wild and farm populations (260 pairs × 5 = 1300). Hybrid simulations were conducted using a script written in R , and in the second generation also backcrosses of first-generation hybrids to wild (BCW) and farm (BCF), and crosses between first-generation hybrids (2GH). Thirty individuals from each group were analyzed using STRUCTURE as described above. This material enabled us to investigate a realistic outcome from two generations of interbreeding between escaped farm salmon and wild salmon, assuming 20% farm escaped salmon participating in each breeding event, and applying relative fitness components as described by Hindar et al. .The generated wild and farm center points were located in the center of the wild and farm reference population clusters, respectively Fig. . These tP(wild) serve as reference distributions for defining wild and farm salmon, respectively  estimate of 0.93 with a lower 5 percentile of 0.73, while corresponding estimates for the farm salmon was 0.07 with an upper 95 percentile of 0.33. Population-specific mean estimates of P(wild) varied between 0.88 (Orkla -84) and 0.98 (Ferga -91) for the wild populations, and between 0.018 (AG -01) and 0.18 (AG -99) for the farm populations.The estimated probabilities for the reference wild and farm populations of belonging to the wild center point ely Fig. . The wilP(wild) values from a contemporary sample can be compared to: (1) the distribution from the historical reference sample from the same population, if this exists; or (2) the distribution of values from all wild reference samples, assuming that the historical reference populations we have can be considered a random sample from all populations. Figure n = 33). If the observed mean P(wild) from the contemporary sample is below the lower dashed line, we can reject the null hypothesis of no change in mean P(wild), at a 0.05 significance level.To evaluate whether or not a wild population is significantly affected by genetic introgression of farm salmon, P(wild) for 1300 simulated first-generation hybrids between wild and farm salmon ranged from 0.01 to 0.99, with an average of 0.48  estimates between the lower 5 percentile for wild salmon (0.73) and the upper 95 percentile for farm salmon (0.33), and these can therefore be classified as admixed. Furthermore, the average estimate of P(wild) was 0.48 which in relation to the average estimate of wild salmon (0.93) and the farm salmon (0.07) corresponds to a proportion of wild genome left of 47.7%, which is close to the expected 50%.Estimates of P(wild) from these groups, conditional on expected proportions, the estimated average proportion of wild genome left was 87%, that is, close to the expectation. The average estimate was the same from a sample size of 30 compared to a sample size of 100, but with a broader confidence interval from a sample of 30 individuals  compared with a sample size of 100 (79–93%).Offspring from experimental crossing between wild and farm salmon were also analyzed, with all possible admixed groups that can be generated after two generations of interbreeding with escaped farm salmon. A realistic scenario of genetic introgression from escaped farm salmon was simulated for two generations of 20% escaped farm salmon in the spawning population, whereby expected proportions of the pure wild (0.706), hybrid groups , and pure farm (0.004) salmon was obtained with an overall expected proportion of wild genome left of 89%. From resampling (1000 times) of individuals with estimates of We have developed a standardized method for estimating unidirectional introgression, using Atlantic salmon as a model organism. In doing this, we wanted to utilize the power of the recently developed set of SNP markers that collectively differentiate between wild and farm salmon irrespective of the population of origin  estimates from all historical samples, and/or from the historical populations with the highest and lowest P(wild) estimates, to give a possible range of the level of genetic introgression. To evaluate the significance of the estimates, the lower 5 percentile of expected estimates, conditional on sample size, can be used. The lower 5 percentile can either be generated from the overall sample reference distribution . One third of the first-generation hybrids had intermediate P(wild) estimates and were unlikely to be of either pure farm or pure wild origin. In an admixed population containing two or more generations of hybrids and backcrosses, it will become increasingly difficult to classify single individuals as pure wild or pure farm.We tested the precision of the method by analyzing in silico generated first-generation hybrids between wild and farm salmon, and by analyzing offspring from experimental crossing between farm and wild salmon. Results from these analyses convincingly showed that the method has a high precision in quantifying genetic introgression of farm salmon at the population level. First-generation hybrids gave a large range of estimates of The method presented here is generic and applicable to all species for which there is a set of genetic markers that can differentiate between wild and domesticated individuals irrespectively of population of origin. The complexity and/or possibilities for finding generic genetic differences is expected to vary between species and depends on the wild origin of the domesticated populations, number of different breeding populations, number of generations in captivity, effective population size (genetic drift), strength of artificial selection, etc. In addition, farm salmon, and other domesticated species, are constantly changing genetically from directional selection and genetic drift, and new farm populations may be constructed. This makes tracing of domesticated individuals a moving target, which may require updates of samples of the domesticated populations. At the same time, the available genetic tools are expected to improve, both in terms of genetic markers and analytical methods. In order to make the transition between the development of new genetic markers and the application of these as effective as possible, it is important to have a dynamic analytical platform, allowing for new samples to be included and new markers to be applied. We believe the method presented here will serve this purpose.


The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program.The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives.Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users. This paper uses stochastic simulation to evaluate designs for initiating maize pre-breeding programs that harness polygenic variation from landrace populations for later incorporation into elite maize breeding populations. Today’s elite maize germplasms have lower genetic variance than progenitor populations –3, becauSuch a reduced genetic variance limits the potential to breed for new market demands, new pathogens, and changing environments –11. ThesExtensive genetic variation is available in the diverse maize landrace populations around the globe –3 as a rhttp://seedsofdiscovery.org) funded mostly by the Mexican government through the Sustainable Modernization of Traditional Agriculture program . SeeD aims to identify and enable use of favorable variation from landraces to develop bridging germplasm with 75 % or more elite and 25 % or less landrace genome . This resource provides one foundation for harnessing favorable variation from landraces.A recent initiative to characterize and use a part of the untapped variation in landraces is Seeds of Discovery  with favorable polygenic variation . Such anDespite the high potential value of genomic selection and forward breeding applications, there are many unknowns about how to initiate a pre-breeding program to optimize the outcome within the economic and logistical constraints. This is especially challenging in SeeD because of the vast genetic diversity addressed. An important opportunity for SeeD is the recent deployment of affordable high-throughput genomic tools for plant breeding , 26, whii)the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces,ii)genetic parameters of landraces and phenotypes of interest, andiii)logistical factors related to the size and management of a pre-breeding program.This study aimed to evaluate the proposed designs to initiate a pre-breeding program within the SeeD initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. The design factors included:A pre-breeding program that would harness polygenic variation from landraces can be initiated in many possible ways. This research evaluated a preselected set of designs proposed within SeeD and quantified the effect of different design factors on the quality of the resulting pre-bridging germplasm Fig. .GBS) technology , true breeding values (TBV), and phenotypes  genomes of landraces and the elite hybrid  for testcross performance inferred from the collected genomic and phenotypic information. This phase was conducted in three stages that involved i) generating landrace × elite training population, ii) selecting the best landraces, and iii) selecting the best seeds within the selected landraces. The first stage involved generating the training population by first growing one, three, or five sampled seeds from each of the 3,000 landraces, and then crossing them with the elite hybrid to get a population of 3,000, 9,000, or 15,000 testcrosses, respectively. The obtained testcross phenotypes were regressed on the corresponding landrace seed marker genotypes using ridge regression to train a genomic prediction equation [The aim of the discovery phase was to discover the best landraces and the best seeds within these landraces to initiate the pre-bridging germplasm. Ranking of landraces and seeds within a landrace was based on their estimated breeding value ,ii.Ne) of 1,000 or 100,000), the diversity within landrace accessions (inbreeding coefficient (F) of 0.3 or 0.9), and the heritability of the selection trait (h2 =0.25 or h2 =0.50) andgenetic parameters - the diversity of the founding population (effective population size (iii.x@10K) or low-coverage GBS with 100,000 markers (GBS1x@100K)), number of phenotyped seeds per landrace in the discovery phase , the number of selected landrace (40 or 80), the number of tested seeds per selected landrace , and the number of phenotyped synthetic seeds for retraining in the improvement phase .logistical factors - the genotyping platform , and high genetic diversity within landrace accessions (F = 0.3). The simulation output and the accompanying easy to use R code [It was hypothesized that the approach of initiating the pre-bridging germplasm would substantially affect the genetic merit and uniqueness of such a germplasm and successful initiation of the pre-breeding program. Therefore, the analysis focused on the approach to initiate the pre-bridging germplasm as a main effect and on the interaction with the genetic parameters of landraces and phenotypes. These parameters described a range of possible landraces from which a pre-breeding program might aim to harness polygenic variation. Once the effect of the three approaches was determined, the most promising approach was analyzed in detail. The detailed analysis assessed the effect of different design factors on genetic merit in the final stage of the discovery and improvement phases for a subset of scenarios that are relevant for SeeD, i.e., a very diverse founding population , when the diversity within landrace accessions was high (F = 0.3) or when the heritability was high (h2 = 0.50).The influence of both diversity of founding populations and heritability did not show strong interactions .Ne = 1,000), the diversity within landrace accessions was low (F = 0.3), and the heritability was high (h2 = 0.50), while it was −0.54 when the diversity of the founding population was high , the diversity within landrace accessions was high (F = 0.9), and the heritability was low (h2 = 0.25) . However, the mixing of divergent genomes caused the mean heterozygosity to increase substantially in the selected landrace × elite seeds or in the first stage of synthetic seeds with the Landrace and LandraceDH approaches.Some LandraceElite scenarios had comparable heterozygosity to the Landrace and LandraceDH approaches, due to inaccurate selection caused by the lack of retraining the prediction equation. The interaction between the approach to initiate pre-bridging germplasm, the diversity of the founding population, and diversity within landrace accessions affected heterozygosity  in comparison with smaller numbers of GBS markers with higher coverage (GBS10x@10K). This suggests that initiation and improvement of pre-bridging germplasms should be done with more GBS markers even if they have lower accuracy of called genotypes.The tested design factors affected the genetic merit of pre-bridging germplasm at the end of the discovery and improvement phases Table . Higher The number of phenotyped seeds per landrace also affected the genetic merit of pre-bridging germplasm. The mean genetic merit with one, three, and five seeds per landrace were 0.035, 0.042, and 0.040, respectively by the end of the discovery phase and 0.109, 0.117, and 0.116, respectively by the end of the improvement phase. The number of selected landraces and the number of tested seeds per selected landrace affected the end of the discovery phase, but this effect was diluted by the end of the improvement phase. Selecting more landraces initiated a pre-bridging germplasm with lower genetic merit , while testing more seeds per landrace increased the intensity of selection and initiated a pre-bridging germplasm with higher genetic merit . The number of phenotyped synthetic seeds in the improvement phase affected the genetic merit of resulting germplasm. When the synthetic population was unphenotyped and the retraining was omitted the resulting germplasm had low mean genetic merit (0.069). Phenotyping at least 20 synthetic seeds and retraining the prediction equation increased the genetic merit of the final pre-bridging germplasm to 0.116. Increasing the number of phenotyped synthetic seeds to 40 or 60 increased the genetic merit of the final pre-bridging germplasm to 0.131 or 0.139, respectively.x coverage, test more than 10 seeds from 40 selected landraces. The observed differences in genetic merit of the selected seeds are the result of differences in selection intensity and accuracy of genomic prediction when selecting landraces and seeds within these landraces  and selecting the best seeds from the selected landraces (the third stage), but the setting was different. In the discovery phase the genetic diversity was high and the training population was large , while in the improvement phase the genetic diversity was reduced due to selection and the training population was smaller .In the sixth stage, selecting the best third-generation seeds, and in the seventh stage, selecting the best fourth-generation seeds, the accuracies with the three approaches were similar.x coverage at 10,000 markers (GBS10x@10K) or with 1x coverage at 100,000 markers (GBS1x@100K) and the results showed that more markers with lower coverage gave higher accuracies in every stage of a pre-breeding program. This is an expected result because a large number of markers is required to capture the large genetic diversity in landraces [x) genotype data can deliver accurate genomic predictions at low cost, as observed in [Accuracy and the achieved genetic merit were affected by many pre-breeding program design factors evaluated in this study. Beside the approach to initiate pre-bridging germplasm and the stage of the pre-breeding program, there were also genetic and logistical factors. The genetic parameters affected accuracy in line with the theory , 58 – acandraces . The accandraces , 62, 63.erved in , 65.The effect of number of phenotyped seeds per landrace accession in the training set was in line with expectations with larger training populations leading to higher accuracies, but with large diminishing returns. Taking into account the costs it seems that of phenotyping only one seedling per accession might be enough as long as the total size of the training population enables accurate genomic predictions. It is unlikely that of phenotyping just one seedling per accession will represent a sizeable proportion of variation within each landrace, but this might provide some overlap between accessions to cover a fair share of total variation. The simulation suggests that a sizeable proportion of total variation was captured, as genomic predictions were accurate even for a setting with high diversity. However, these results might be too optimistic as the simulation of landraces was likely too simplistic .The number of selected best landraces and the number of tested seeds per selected landrace increased response to selection with more intense selection as expected. However, this effect did not extend to the last stage of the simulation. This can be explained by the fact that increased intensity of selection reduced diversity, which in turn reduced the long-term response to selection. This result indicates that pre-breeding programs might benefit by using optimum contribution selection, which balances the short-term response to selection and the loss of genetic diversity to maximize the long-term response to selection , 67–69. Several design factors and their interactions affected the quality of resulting pre-bridging germplasm. To create easy to follow guidelines in such a setting we used decision trees. A decision tree is estimated from the variation of a modeled variable (for example the genetic merit of germplasm) in relation to other variables (for example design factors) that might influence the modeled variable on its own or through a complicated interaction , 39. TheThe developed decision trees suggest that only a small number of the design factors evaluated in this study significantly affect the resulting pre-bridging germplasm, in particular the amount of collected phenotypes in the improvement phase, heritability, and genotyping platform. This study evaluated only a small subset of possible designs to initiate a pre-breeding program with a preselected subset of scenarios proposed within SeeD. Many more design factors than could have been evaluated in this study are likely to influence pre-breeding programs.The success of a pre-breeding program that aims to harness favorable polygenic variation from landraces will be largely affected by the approaches used to initiate the program. In particular interesting landrace haplotypes harboring polygenic variation should not be introgressed into elite backgrounds too early because the subsequent improvement will favor the elite haplotypes and limit the distinctness of resulting germplasm. Early introgression is feasible for loci with large effects, but not for polygenic loci. For the latter the focus should be put on genomic selection to increase frequencies of favorable alleles and with this bridge the gap between the landrace and elite germplasms. Pre-breeding programs are affected by many factors, whose effects can be summarized with decision trees to provide easy to follow decision support systems.The simulation outputs supporting the results of this article are available in the Additional file


The importance of plant genetic diversity (PGD) is now being recognized as a specific area since explodingpopulation with urbanization and decreasing cultivable lands are the critical factors contributing to food insecurity in developing world.Agricultural scientists realized that PGD can be captured and stored in the form of plant genetic resources(PGR) such as gene bank, DNA library, and so forth, in the biorepository which preserve genetic material for long period. However, conserved PGR must be utilized for crop improvement in order to meet future global challenges in relation to foodand nutritional security. This paper comprehensively reviews four important areas; (i) the significance of plant genetic diversity (PGD) and PGR especiallyon agriculturally important crops (mostly field crops); (ii) risk associated with narrowing thegenetic base of current commercial cultivars and climate change; (iii) analysis of existing PGD analytical methods inpregenomic and genomic era; and (iv) modern tools available for PGD analysis in postgenomic era. This discussionbenefits the plant scientist community in order to use the new methods and technology for better and rapid assessment,for utilization of germplasm from gene banks to their applied breeding programs. With the advent of new biotechnologicaltechniques, this process of genetic manipulation is now being accelerated and carried out with more precision  and fast-track manner than the classical breeding techniques. It is also to note that gene banks lookinto several issues in order to improve levels of germplasm distribution and its utilization, duplication of plant identity, andaccess to database, for prebreeding activities. Since plant breeding research and cultivar development are integral componentsof improving food production, therefore, availability of and access to diverse genetic sources will ensure that the global foodproduction network becomes more sustainable. The pros and cons of the basic and advanced statistical tools available formeasuring genetic diversity are briefly discussed and their source links (mostly) were provided to get easy access; thus,it improves the understanding of tools and its practical applicability to the researchers. Diversity in plant genetic resources (PGR) provides opportunity for plant breeders to develop new and improved cultivars with desirable characteristics, which include both farmer-preferred traits  and breeders preferred traits . From the very beginning of agriculture, natural genetic variability has been exploited within crop species to meet subsistence food requirement, and now it is being focused to surplus food for growing populations. In the middle of 1960s developing countries like India experienced the green revolution by meeting food demand with help of high-yielding and fertilizer responsive dwarf hybrids/varieties especially in wheat and rice . These phttp://www.cigar.org/center/index.html.) The Food and Agriculture Organization (FAO) supported the International Treaty on Plant Genetic Resources (ITPGR) and UN supported the Convention on Biological Diversity (CBD) which are the international agreements that recognize the important role of genetic diversity conservation. Such treaty still plays in current and future food production as one of the major supremo  where Ne is the effective population size, usually less than N, the actual population size. Thus a population of Ne = 10 individuals loses 5% of its heterozygosity per generation. This indicates that severe bottlenecks degrade heterozygosity and genetic diversity [It is well known that inbreeding is the most common phenomena in cross-pollinated crops, and in small outcross populations it has resulted in deleterious effects and loss of fitness of the population due to recombination between undesirable genes . In natural population too, severe reductions in population size, the so-callediversity . TherefoThe most profound and direct impacts of climate change over previous decade and the next few decades will surely be on agriculture and food security. The effects of climate change will also depend on current production conditions. The area where already being obstructed by other stresses, such as pollution and will likely to have more adverse impact by changing climate. Food production systems rely on highly selected cultivars under better endowed environments but it might be increasingly vulnerable to climate change impacts such as pest and disease spread. If food production levels decreases over the year, there will be huge pressure to cultivate the crops under marginal lands or implement unsustainable practices that, over the long-term, degrade lands and resources and adversely impact biodiversity on and near agricultural areas. In fact, such situations have already been experienced by most of the developing countries. These changes have been seen to cause a decrease in the variability of those genetic loci  controlling physical and phenotypic responses to changing climate . TherefoTorreya taxifolia) is currently one of the rarest conifer species in North America. But in the early Holocene , when conditions in southeastern North America were cooler and wetter than today, the species was probably widespread. The reasons for that are not completely understood, but T. taxifolia failed to migrate towards the northward as climate changed during the Holocene. Today, it is restricted to a few locations in the Apalachicola River Basin in southern Georgia and the Florida panhandle. As the T. taxifolia story illustrates, once plant species are pushed into marginal habitat at the limitations of their physiological tolerance, they may enter an extinction vortex, a downward cycle of small populations, and so on [The population can also migrate across the landscape over generations. By contrast, populations that have a narrow range of genotypes and are more phenotypically uniform may merely fail to survive and reproduce at all as the conditions become less locally favorable. Such populations are more likely to become extirpated , and in extreme cases the entire plant species may end up at risk of extinction. For example, the Florida Yew  morphological, (ii) biochemical characterization/evaluation , in the pregenomic era, and (iii) DNA (or molecular) marker analysis especially single nucleotide polymorphism (SNPs) in postgenomic era. Markers can exhibit similar modes of inheritance, as we observe for any other traits, that is, dominant/recessive or codominant. If the genetic pattern of homozygotes can be distinguished from that of heterozygotes, then a marker is said to be codominant. Generally codominant markers are more informative than the dominant markers.Morphological markers are based on visually accessible traits such as flower color, seed shape, growth habits, and pigmentation, and it does not require expensive technology but large tracts of land area are often required for these field experiments, making it possibly more expensive than molecular assessment in western (developed) countries and equally expensive in Asian and Middle East (developing) countries considering the labour cost and availability. These marker traits are often susceptible to phenotypic plasticity; conversely, this allows assessment of diversity in the presence of environmental variation which cannot be neglected from the genotypic variation. These types of markers are still having advantage and they are mandatory for distinguishing the adult plants from their genetic contamination in the field, for example, spiny seeds, bristled panicle, and flower/leaf color variants.Second type of genetic marker is called biochemical markers, allelic variants of enzymes called isozymes that are detected by electrophoresis and specific staining. Isozyme markers are codominant in nature. They detect diversity at functional gene level and have simple inheritance. It requires only small amounts of plant material for its detection. However, only a limited number of enzymes markers are available and these enzymes are not alone but it has complex structural and special problems; thus, the resolution of genetic diversity is limited to explore.The third and most widely used genetic marker type is molecular markers, comprising a large variety of DNA molecular markers, which can be employed for analysis of genetic and molecular variation. These markers can detect the variation that arises from deletion, duplication, inversion, and/or insertion in the chromosomes. Such markers themselves do not affect the phenotype of the traits of interest because they are located only near or linked to genes controlling the traits. These markers are inherited both in dominant and codominant patterns. Different markers have different genetic qualities . A molecular marker can be defined as a genomic locus, detected through probe or specific starter (primer) which, in virtue of its presence, distinguishes unequivocally the chromosomic trait which it represents as well as the flanking regions at the 3′ and 5′ extremity . MoleculA comprehensive study of the molecular genetic variation present in germplasm would be useful for determining whether morphologically based taxonomic classifications reveal patterns of genomic differentiation. This can also provide information on the population structure, allelic richness, and diversity parameters of germplasm to help breeders to use genetic resources with less prebreeding activities for cultivar development more effectively. Now germplasm characterization based on molecular markers has gained importance due to the speedy and quality of data generated. For the readers benefit, the availability of different DNA markers acronyms is given in Abbreviations section.DNA (or molecular) markers are the most widely used type of marker predominantly due to their abundance. They arise from different classes of DNA mutations such as substitution mutations (point mutations), rearrangements (insertions or deletions), or errors in replication of tandemly repeated DNA . These mMolecular markers may be broadly divided into three classes based on the method of their detection: hybridization-based, polymerase chain reaction- (PCR-) based, and DNA sequence-based. Restriction fragment length polymorphisms (RFLPs) are hybridization-based markers developed first in human-based genetic study during 1980s , 29 and n, where n refers to the total number of repeats that ranges between 10 and 100. These markers often present high levels of inter- and intraspecific polymorphism, particularly when tandem repeats number is 10 or greater [Microsatellites  are also greater . PCR rea3 staining, autoradiography, or fluorescent detection systems. Agarose gels  with ethidium bromide (EBr) can also be used when differences in allele size among samples are larger than 10 bp. However, the establishment of microsatellite primers from scratch for a new species presents a considerable technical challenge. Several protocols have been developed [PCR fragments are usually separated on polyacrylamide gels in combination with AgNOeveloped , 45–47 aeveloped –50. The eveloped , most wieveloped .An alternative source of SSRs development is development of expressed sequence tag- (EST-) based SSRs using EST databases has been utilized –58. WithSingle nucleotide polymorphisms (SNPs) are DNA sequence variations that occur when a single nucleotide  in the genome sequence is changed, that is, single nucleotide variations in genome sequence of individuals of a population. These polymorphisms are single-base substitutions between sequences. SNPs occur more frequently than any other type of markers and are very near to or even within the gene of interest. SNPs are the most abundant in the genomes of the majority of organisms, including plants, and are widely dispersed throughout genomes with a variable distribution among species. SNPs can be identified by using either microarrays or DHPLC (denaturing high-performance liquid chromatography) machines. They are used for a wide range of purposes, including rapid identification of crop cultivars and construction of ultrahigh-density genetic maps. They provide valuable markers for the study of agronomic or adaptive traits in plant species, using strategies based on genetic mapping or association genetics studies.http://www.diversityarrays.com/.A DArT marker is a segment of genomic DNA, the presence of which is polymorphic in a defined genomic representation. A DArT was developed to provide a practical and cost-effective whole genome fingerprinting tool. This method provides high throughput and low cost data production. It is independent from DNA sequence; that is, the discovery of polymorphic DArT markers and their scoring in subsequent analysis does not require any DNA sequence data. The detail of methodology for DArT is described by Jaccoud et al.  and Sema Arabidopsis [To identify the polymorphic markers, a complexity reduction method is applied on the metagenome, a pool of genomes representing the germplasm of interest. The genomic representation obtained from this pool is then cloned and individual inserts are arrayed on a microarray resulting in a “discovery array.” Labelled genomic representations prepared from the individual genomes included in the pool are hybridized to the discovery array. Polymorphic clones (DArT markers) show variable hybridization signal intensities for different individuals. These clones are subsequently assembled into a “genotyping array” for routine genotyping. DArT is one of the recently developed molecular techniques and it has been used in rice , wheat , 67, 68,bidopsis , cassavabidopsis , pigeon-bidopsis , and so DArT markers can be used as any other genetic marker. With DArT, comprehensive genome profiles are becoming affordable regardless of the molecular information available for the crop. DArT genome profiles are very useful for characterization of germplasm collections, QTL mapping, reliable and precise phenotyping, and so forth. However, DArT technique involves several steps, including preparation of genomic representation for the target species, cloning, data management, and analysis, requiring dedicated software such as DArTsoft and DArTdb. DArT markers are primarily dominant (present or absent) or differences in intensity, which limits its value in some application .DNA sequencing is the determination of the order of the nucleotide bases, A (adenine), G (guanine), C (cytosine), and T (thymine), present in a target molecule of DNA. DNA sequencing technology has played a pivotal role in the advancement of molecular biology . Next geThe Roche/454 FLX, based on sequencing-by-synthesis with pyrophosphate chemistry, was developed by 454 Life Sciences and was the first next generation sequencing platform available on the market . The SolIt is essential to know the different ways that the data generated by molecular techniques can be analyzed before their application to diversity studies. Two main types of analysis are generally followed: (i) analysis of genetic relationships among samples and (ii) calculation of population genetics parameters (in particular diversity and its partitioning at different levels). The analysis of genetic relationships among samples starts with the construction of a matrix, sample × sample pair-wise genetic distance (or similarities).NL), (ii) Jaccard's [J), (iii) simple matching coefficient (GDSM) [MR). Genetic distance determined by the above measures can be estimated as follows: N11 is the number of bands/alleles present in both individuals; N00 is number of bands/alleles absent in both individuals; N10 is the number of bands/alleles present only in the individual i; N01 is the number of bands/alleles present only in the individual j; and N represents the total number of bands/alleles. Readers are requested to read Mohammadi and Prasanna [The advent and explorations of molecular genetics led to a better definition of Euclidean distance to mean a quantitative measure of genetic difference calculated between individuals, populations, or species at DNA sequence level or allele frequency level. Genetic distance and/or similarity between two genotypes, populations, or individuals may be calculated by various statistical measures depending on the data set. The commonly used measures of genetic distance (GD) or genetic similarity (GS) are (i) Nei and Li's  coefficiaccard's  coefficit (GDSM) , and  matrix, namely, principal coordinate analysis (PCA) and dendrogram . PCA is used to produce a 2 or 3 dimensional scatter plot of the samples such that the distances among the samples in the plot reflect the genetic distances among them with a minimum of distortion. Another approach is to produce a dendrogram (or tree diagram), that is, grouping of samples together in clusters that are more genetically similar to each other than to samples in other clusters. Different algorithms were used for clustering, but some of the more widely used ones include unweighted pair group method with arithmetic averages (UPGMA), neighbour-joining method, and Ward's method .The molecular data can be scored in presence/absence matrices manually or with the aid of specific software. However, because these techniques are based on the incorporation of genomic elements in the primer sets or else target specific regions in the genome, biases affecting the evaluation process can occur. Although many recently developed targeting methods detect large numbers of polymorphisms, not many studies to date have utilized them, largely due to their unfamiliarity. In many cases the drawbacks are unknown. These mainly affect the analysis of the banding patterns produced, largely depending on the nature of the methods and whether they generate dominant or codominant markers. We presented a brief description of common/basic statistical approaches and its principle with the pros and cons of each method for measuring genetic diversity and it is given in Many software programs are available for assessing genetic diversity; however, most of them are freely available through source link to internet and corresponding institute web links are given in Agriculturist has been realized that diverse plant genetic resources are priceless assets for humankind which cannot be lost. Such materials increasingly required to accessible for feeding a burgeoning world population in future (>9 billion in 2050). Presence of genetic variability in crops is essential for its further improvement by providing options for the breeders to develop new varieties and hybrids. This can be achieved through phenotypic and molecular characterization of PGR. Sometimes, large size of germplasm may limit their use in breeding. This may be overcome by developing and using subsets like core and minicore collection representing the diversity of the entire collection of the species. Molecular markers are indispensable tools for measuring the diversity of plant species. Low assay cost, affordable hardware, throughput, convenience, and ease of assay development and automation are important factors when choosing a technology. Now with the high throughput molecular marker technologies ensuring speed and quality of data generated, it is possible to characterize the larger number of germplasm with limited time and resources. Next generation sequencing reduced the cost and time required for sequencing the whole genome. Many software packages are available for assessing phenotypic and molecular diversity parameters that increased the efficiency of germplasm curators and, plant breeders to speed up the crop improvement. Therefore, we believe that this paper provides useful and contemporary information at one place; thus, it improves the understanding of tools for graduate students and also practical applicability to the researchers.