Figures
Abstract
Amplified fragment length polymorphism (AFLP) is a powerful fingerprinting technique that is widely applied in ecological and population genetic studies. However, its routine use has been limited by high costs associated with the optimization of fluorescently labelled markers, especially for individual study systems. Here we develop a low-cost AFLP protocol that can be easily transferred between distantly related plant taxa. Three fluorescently labelled EcoRI-primers with anchors that target interspecifically conserved genomic regions were used in combination with a single non-labelled primer in our AFLP protocol. The protocol was used to genotype one gymnosperm, two monocot and three eudicot plant genera representing four invasive and four native angiosperm species (Pinus pinaster (Pinaceae), Pennisetum setaceum and Poa annua (Poaceae), Lantana camara (Verbenaceae), Bassia diffusa (Chenopodiaceae), Salvia lanceolata, Salvia africana-lutea, and Salvia africana-caerulea (Lamiaceae)). Highly polymorphic and reproducible genotypic fingerprints (between 37–144 polymorphic loci per species tested) were obtained for all taxa tested. Our single protocol was easily transferred between distantly related taxa. Measures of expected heterozygosity ranged from 0.139 to 0.196 for P. annua and from 0.168 to 0.272 for L. camara which compared well with previously published reports. In addition to ease of transferability of a single AFLP protocol, our protocol reduces costs associated with commercial kits by almost half. The use of highly conserved but abundant anchor sequences reduces the need for laborious screening for usable primers that result in polymorphic fingerprints, and appears to be the main reason for ease of transferability of our protocol between distantly related taxa.
Citation: Blignaut M, Ellis AG, Le Roux JJ (2013) Towards a Transferable and Cost-Effective Plant AFLP Protocol. PLoS ONE 8(4): e61704. https://doi.org/10.1371/journal.pone.0061704
Editor: David L. Roberts, University of Kent, United Kingdom
Received: July 26, 2012; Accepted: March 15, 2013; Published: April 16, 2013
Copyright: © 2013 Blignaut et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding was provided by South Africa’s National Research Foundation (NRF Blue Skies Programme, grant no: BS2008093000013). This work was also partly supported by the DST-NRF Centre of Excellence for Invasion Biology and the Working for Water (WfW) Programme through their collaborative project on “Research for Integrated Management of Invasive Alien Species”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Amplified Fragment Length Polymorphism (AFLP [1]) is a versatile technique for genome-wide screening of genetic diversity and can be applied to almost any organism [2]–[4]. The technique relies on detecting genetic polymorphisms through differential endonuclease restriction digestion of genomic DNA. The rapidity and large amount of data generated by this approach, as well as robustness and repeatability [3], makes AFLP analysis a commonly used tool in population genetic and ecological studies [5]. For example, a search for “amplified fragment length polymorphism” in ISI Web of Science returned more than 15000 papers, highlighting the impact of this technique since its description 17 years ago [1].
One of the biggest advantages of AFLP technology is that, unlike many other genotyping techniques, genome wide screening of genetic diversity is possible without a priori knowledge of genome sequences [4], [6]. The technique was originally used for constructing high density linkage maps [1], [7], but is widely applied today to estimate genetic diversity, assign parentage, determine population structure and reconstruct shallow phylogenies (reviewed by [2]). Moreover, AFLP analyses have also been adapted to assess epigenetic variation [8], [9] and differential gene expression [4], [10].
The AFLP procedure relies on complete digestion of genomic DNA by restriction enzymes, usually with a rare (e.g. EcoRI) and a frequent (e.g. MseI) cutter. Adapters are ligated onto the digested fragments and these fragments are then amplified with a polymerase chain reaction (PCR) [1]. Conventional detection of the fragments requires radio-labelled primers and autoradiography, but advances in capillary electrophoresis allow for rapid and high throughput fluorescent or infrared detection [3], [4] and has therefore become the standard for AFLP genotyping [11].
Despite these obvious advantages, the initial costs associated with AFLP analysis can be high because large numbers of fluorescently labelled oligonucleotides often need to be screened in order to obtain sufficient polymorphism [11], [12]. Furthermore, although AFLP protocols are usually transferable between closely related taxa, or species within the same family (i.e. [13], [14]), a highly transferable AFLP protocol that can be applied to distantly related taxa is still lacking. To date, only a few attempts have been made to develop ‘universal’ AFLP protocols, and even these remain fairly taxon-specific, e.g. for sharks [15].
Here, our overall aim was to develop a time and cost efficient AFLP protocol for plants that can easily be transferred between distantly related taxa. Specifically, by targeting known conservative regions of plant genomes we hope to develop AFLP primers that will amplify fragments in a wide range of taxa. By targeting these regions we aim to develop a protocol that requires only a small number of fluorescently labelled selective oligonucleotides (in order to reduce costs) that consistently yield reproducible and highly polymorphic loci.
Materials and Methods
Study Species and Plant Material Collection
We collected leaf material from between 10–30 individuals representing populations of two monocots (Pennisetum setaceum and Poa annua; Poaceae), and five eudicots, Lantana camara (Verbenaceae), Bassia diffusa (Chenopodiaceae), Salvia lanceolata, Salvia africana-lutea, Salvia africana-caerulea (Lamiaceae) and Salvia hybrids of unknown parentage. The two monocots as well as L. camara were sampled in their non-native ranges, while B.diffusa populations, the three Salvia species, and Salvia hybrids were collected from their native ranges. We sampled 22 plantation individuals of Pinus pinaster (Pinaceae) in its adventive range in South Africa. All plant material was desiccated on silica gel until further use.
All necessary collection permits were obtained for the collection of native species. The Salvia spp. collection was approved by Cape Nature (permit number: 0028-AAA005–00219), and the B. diffusa collection was approved by the Cacado Municipality district in the Eastern Cape (permit number: CRO 56/12CR). The abundant invasive species (P. setaceum, P. annua and L. camara) did not require permission for collection, and where collected along public roadsides. A permit was obtained from MTO Forestry (PTY) LTD (permit number: 65105) for the collection of P. pinaster in the Jonkershoek plantation, Stellenbosch, South Africa.
DNA Extraction and AFLP Analysis
DNA was extracted either by the standard CTAB method [16] or CTAB with the addition of a 25∶24:1 phenol:chloroform:isoamylalcohol step [17]. All DNA samples were quantified using a micro-volume UV-Vis spectrophotometer (Nanodrop, Thermo Fisher Scientific, Wilmington, USA) and good quality genomic DNA (A260/280 ∼ 1.8 and A260/230∼ 2.0) was diluted to a final concentration of 100 ng/µL.
We modified the original AFLP protocol by Vos et al. [1]. For each sample, ca. 200 ng of genomic DNA was digested with 5 units of EcoRI (Fermentas, supplied by Inqaba Biotechnical Industries (PTY) LTD, Pretoria, South Africa) for 2 hours at 37°C in 2X Tango™ buffer (66 mM Tris-acetate, pH 7.9), 20 mM Mg-acetate, 132 mM K-acetate, 0.1 mg/ml BSA) in a 20 µL reaction volume. After EcoRI digestion, 5 units of TruI (isoschizomer of MseI; Fermentas) were added and the buffer concentration was again adjusted to 2X Tango™ buffer in a total volume of 30 µL. The reaction was incubated at 65°C (as recommended by the manufacturer) for 2 hours. A 10 µL ligation reaction mix was made up consisting of 1 unit T4 DNA ligase (Fermentas), 1X T4 DNA ligase buffer, 50 µM MseI adapter and 5 µM EcoRI adapter (Integrated DNA Technologies [IDT], Iowa, USA; see [4] for preparation) that targets the frequent and rare cut fragments respectively, and was added directly to the digestion reaction. The digestion-ligation reaction was incubated overnight at 4°C. Following ligation, the digestion-ligation reaction mix was diluted 1∶5 with sterile distilled water and used as template for the pre-selective PCR.
Each 15 µL pre-selective PCR reaction contained 2.5 µL of the diluted digestion-ligation reaction mix, 1 µM MseI+0 primer, 1 µM EcoRI+0 (IDT, Table 1), 1X Kapa Taq Readymix (contains 0.2 µM of each dNTP, 1.5 µM MgCl2, 0.3 unit Taq Polymerase, 1X Kapa Buffer A, KapaBiotech, Cape Town, South Africa supplied by Lasec SA, Cape Town, South Africa ). Pre-selective PCR amplification was done with an initial denaturing step of 94°C for 5 minutes, followed by 23 cycles consisting of denaturation at 94°C for 30 sec, annealing at 56°C for 30 sec, elongation at 72°C for 30 sec, and a final elongation step at 60°C for 30 minutes. Successful amplification was confirmed by running 5 µL of the PCR product on a 1% agarose gel and observing a smear between 100 and 500 bp.
Following successful amplification, pre-selective PCR products were diluted with sterile distilled PCR-grade water (1∶19 dilution) of which 5 µL was used as template for selective PCR amplification. Each 20 µL selective PCR reaction contained 0.25 µM of fluorescently-labelled EcoRI+NNN (see Table 1 for anchor and label) and 1 µM unlabelled MseI+CTT (IDT; Table 1), and 1X Kapa Taq Readymix. PCR reactions were done without a step-down PCR step [1], [4] following pre-selective PCR conditions described above but with 30 repeat cycles.
After amplification, 5 µL of each fluorescently-labelled PCR product was mixed for each DNA sample and purified using the NucleoFast Purification System (Machery-Nagel Gmbh and Co.kG, Düren, Germany). Electrophoresis was performed on the 3130×l DNA Analyser (Applied Biosystems, California, USA) with the ROX500 size standard (Applied Biosystems).
Automated fragment size calling and scoring was performed with Genemarker Version 2.2.0 (SoftGenetics, LLC, CA, USA) with the manufacturer’s default settings. The presence or absence of all fragments was confirmed manually since intensity differences between samples might result in false absences. All individuals within each species were scored in a single session to avoid manual scoring artefacts and errors. Only loci (fragments) between 100 and 450 bp were scored to decrease the possible detection of co-migrating fragments, i.e. size homoplasy [18].
Genetic diversity.
Locus-specific variability was measured with the polymorphic information content (PIC) for dominant markers [19].
For each species we generated a binary presence-absence data matrix. From this we calculated the total number of loci generated per primer pair as well as the percentage of polymorphic loci for each taxon in GenAlEx version 6.4 [20]. Expected heterozygosity (HE), under the assumption of Hardy-Weinberg equilibrium, was also calculated in GenAlEx version 6.4 [21].
Reproducibility and average peak intensity.
Reproducibility of obtained AFLP banding profiles was assessed by repeating all experimental steps on at least 10% of all individuals genotyped per population [22]. The Salvia spp. were analysed together and a single error rate was calculated for all the samples analysed. Error rates were determined as the percentage of loci that were mismatched between the replicate pairs [23]. Furthermore, the average peak intensity was calculated across all scored loci and compared across species for significant differences using a Kruskal-Wallis test in the R statistical environment [24]. Dunn’s post hoc test was performed to compare the difference in rank sum for each species in Graphpad Prism V5.01 (GraphPad Software, Inc.).
Results
Genetic Diversity
The usefulness of population genetic markers, for example, in parentage assignment and linkage studies, is measured by how informative they are (polymorphism information content (PIC) sensu [25], [26]. Even though these applications were not explored here, we determined that the PIC of each primer pair was both comparable between species and between markers (Table 2). Overall, our PIC values ranged from 0.003–0.379, where markers with PIC ≥0.3 are considered of high discriminatory value [19].
Our AFLP protocol yielded highly polymorphic loci and was successfully transferred between the eight species included here (data summarised in Table 2) and generated a minimum of 5.26% polymorphic loci for P. setaceum (for EcoRI-AAT NED™), and a maximum of 100% polymorphic loci for B. diffusa (for all three labelled primers). The total number of loci generated for P. setaceum (37 of which 8.11% were polymorphic overall) were the lowest, whilst we amplified a total of 144 loci in B. diffusa A previous AFLP study on P. annua reported 60% polymorphic loci out of the 226 loci analysed [27]. We amplified a total of 80 loci for P. annua of which 41.25% were polymorphic. Lantana camara and P. pinaster had an intermediate number of 52 loci (of which 71.15% were polymorphic) and 53 loci (41.51% polymorphic), respectively. The three Salvia species (S. africana-lutea, S. africana-caerulea and S. lanceolata) yielded 95 loci each of which 68.42%, 80.0% and 78.95% were polymorphic, respectively. The Salvia hybrids (of unknown parentage) yielded 99 loci of which 48.86% were polymorphic. This is the first report of AFLP fingerprints for B. diffusa, P. setaceum and the three Salvia species. The number of loci generated for the native B. diffusa and Salvia spp., are within the range of 100–150 loci for which can be used for fine-scale spatial genetic structure assessment, although the use of much larger numbers of loci (up to 250) is suggested [28]. Based on this criterion, the low and intermediate number of loci generated for P. setaceum, L. camara, P. annua and P. pinaster might be insufficient to reveal the true fine-scale population genetic structure. Typical fingerprints generated for the eight species are shown in Figure 1.
We also determined expected heterozygosity (HE), which is a measure of within-population gene diversity and is equivalent to Nei’s unbiased gene diversity (HS), as adapted for dominant markers under the assumptions of Hardy-Weinberg equilibrium and the Lynch-Milligan model [21]. Here, the overall expected heterozygosity (HE = 0.272) for the combined L. camara loci was the highest observed out of the eight species, but is lower than the range (HE: 0.336–0.848) previously reported for this species based on co-dominant microsatellites [29]. It does however fall within recently reported Nei’s gene diversity values (0.023–0.293) for L. camara [30]. For P. annua, our heterozygosity estimate (HE = 0.167) fell within the previously published range based on estimates of Nei’s unbiased gene diversity (HS = 0.152 based on AFLP markers [31] and HS = 0.245 using RAPD markers [32]). The expected heterozygosity for P. pinaster (HE = 0.147) compared well with previously published results based on 122 loci for two populations(HS = 0.159 and 0.162 respectively [33]). The average expected heterozygosity for the three Salvia species ranged between 0.259–0.277 (in the order listed in Table 2), while the Salvia hybrids showed the lowest within population diversity (HE = 0.194). The low combined expected heterozygosity for all the P. setaceum loci (HE = 0.03) is not surprising given previous reports [34], [35] that showed no genetic variation within or among populations of P. setaceum based on dominant ISSR markers, microsatellites and DNA sequencing data.
Reproducibility and Average Peak Intensity
We assessed data quality of our protocol by determining the error-rate and reproducibility of our datasets. The suggested and generally acceptable error rate for AFLP data ranges between 2–5% [12]. Here, for samples that were genotyped twice, we found the lowest average error rate (calculated with the lowest number of repeats for a marker) across all markers for P. setaceum (1.35%), and the highest error rate for B. diffusa (3.82%), and L. camara (2.84%), with intermediate values for P. annua (3.75%), Salvia spp. (2.1%), and P pinaster (2.83%). Error rates were never greater than 5% indicating that our protocol is highly reproducible across a wide variety of species representing different plant families.
In order to determine and compare the overall amplification success we compared peak intensity (a measure of data quality) between the different species and found that B. diffusa and the Salvia spp. profiles had significantly lower fluorescence intensities than the other species (Kruskal-Wallis Chi squared = 125.9, df = 8, P<0.0001, Figure 2). Compared to all other taxa, more loci were generated for B. diffusa and the Salvia sp., which likely resulted in overall reduced fluorescence.
No significant differences were found between the invasive species (dark grey). The four native species and Salvia hybrids (shown in light grey) differed significantly from all of the invasive species (with exceptions), but not from each other (Kruskal-Wallis Chi squared = 125.9, df = 8, P<0.0001). Samples with the same letters do not differ significantly, whilst different letters indicate significant differences between the species and were determined with Dunn’s post hoc tests.
Discussion
Although capillary electrophoresis has become the standard for AFLP genotyping, the costs associated with screening numerous fluorescent primers for individual taxa remain prohibitively expensive. Here we describe a modified AFLP protocol that can easily and successfully be transferred across a wide range of closely and distantly related plant taxa with high repeatability.
We tested the technique on eight species from five different plant families, representing monocots (Poaceae), eudicots (Verbenaceae, Chenopodiaceae and Lamiaceae), and one gymnosperm (Pinaceae). Our sampling encompassed taxa exhibiting variation in life history traits such as growth form and geographic range size which tend to influence the amount of genetic variation within species [36]. Also, some taxa were sampled from their introduced ranges and would thus be expected to have reduced genetic variability [37], [38], whereas others were natives. Variation in ploidy and genome size can also affect the numbers of bands observed and the quality of AFLP profiles [39], we included angiosperms with 1C genome contents that ranged from 1.4 ρg (P. setaceum, triploid) to 2.88 ρg (P. annua, tetraploid), as well as a gymnosperm (P. pinaster, diploid) with a 1C content of 28.90 ρg. Our protocol yielded polymorphic and highly reproducible AFLP fingerprints across all these taxa.
As expected for high quality AFLP markers, all our markers generated clear scorable genotypic fingerprints which were spread evenly along profiles for all species included [28]. The three primer pairs (EcoRI-labelled primers) used here were designed with three base pair anchors that target specific and conserved regions within most plant genomes, similar to sequence-specific amplified polymorphisms (SSAP, [40]). The EcoRI-ATG anchor (which also has the highest PIC value of 0.196), was designed to target gene transcription initiation regions (ATG-) which are conserved motifs (AUG) found throughout the genome within coding, intronic and intergenic regions [41]. The intermediately variable of the three primers tested here (PIC = 0.184) targets the TATA-box region (TATAA-motif) upstream from the transcription initiation motif (ATG), which is a highly conserved but rare region that has been recorded in all species investigated to date [42]. Our primer combination with the lowest PIC value (0.174) targets the more common –CAT gene motif not associated with any conserved region.
Screening for primer pairs that create sufficiently polymorphic loci requires extensive technical expertise and is expensive [11]. Although there are many commercial kits available for AFLP analysis, these also require extensive screening of different primer pairs to obtain sufficiently polymorphic loci and tend to be done in a species-specific manner. Compared to a leading commercial kit, our protocol costs approximatly half (∼7 $ US vs. 15 $ US) to perform for three labelled primer pairs per sample. It should also be noted that our protocol worked for B. diffusa for which fragment amplification failed after numerous attempts using a commercial kit. Although the number of loci for P. setaceum, P. pinaster and L. camara might not be sufficient for fine-scale genetic structure analysis (although the latter diversity indices compared well to published results [29], [30], [33]–[35]) these labelled primers were designed to target specific genome wide regions found in all living organisms. It should thus be possible to increase the number of fragments by merely adding another unlabelled Mse-NNN primer to increase the number of primer pairs at nominal cost.
In summary, we developed a cost- and time-effective AFLP protocol for large-scale high-throughput data generation that only requires three selective fluorescently-labelled primers, eliminating the need for extensive screening of suitable primer combinations, while simultaneously providing highly polymorphic and informative loci that are reproducible. Moreover, our protocol is readily transferable between distantly related plant taxa, further eliminating tedious optimization steps normally required when transferring AFLPs to new taxa. We speculate that by targeting additional regions that are known to be conserved throughout genomes as anchors for PCR primers, that our protocol could be easily adapted across all forms of life.
Acknowledgments
The authors thank Stellenbosch University’s Central Analytical Facility for their support as well as Dimitri Allastair Veldkornet (Nelson Mandela Metropolitan University) for supplying B. diffusa samples and Anton Pauw (Stellenbosch University) for Salvia samples. J.LeR acknowledges funding from the South African National Research Foundation’s Blue Skies Programme and Stellenbosch University’s Centre for Invasion Biology. We also thank David L. Roberts and two anonymous reviewers for their helpful comments on a previous draft of this manuscript.
Author Contributions
Conceived and designed the experiments: MB JLR AE. Performed the experiments: MB. Analyzed the data: MB. Contributed reagents/materials/analysis tools: JLR. Wrote the paper: MB JLR AE.
References
- 1. Vos P, Hogers R, Bleeker M, Reijans M, Van de Lee T, et al. (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acid Research 23: 4407–4414.
- 2. Bensch S, Akesson M (2005) Ten years of AFLP in ecology and evolution: why so few animals? Molecular Ecology 14: 2899–2914.
- 3. Meudt HM, Clarke AC (2007) Almost forgotten or latest practice? AFLP applications, analysis and advances. Trends in Plant Science 12: 106–117.
- 4. Vuylsteke M, Peleman J, Van Eijk MJT (2007) AFLP technology for DNA fingerprinting. Nature protocols 2: 1387–1398.
- 5. Mueller U, Wolfenbarger L (1999) AFLP genotyping and fingerprinting. Trends in Ecology & Evolutionvolution 14: 389–394.
- 6. Schlötterer C (2004) The evolution of molecular markers–just a matter of fashion? Nature reviews Genetics 5: 63–69.
- 7. Vuylsteke M, Mank R, Antonise R, Bastiaans E, Senior ML, et al. (1999) Two high-density AFLP ® linkage maps of Zea mays L.: analysis of distribution of AFLP markers. TAG Theoretical and Applied Genetics 99: 921–935.
- 8. Reyna-López GE, Simpson J, Ruiz-Herrera J (1997) Differences in DNA methylation patterns are detectable during the dimorphic transition of fungi by amplification of restriction polymorphisms. Molecular & General Genetics 253: 703–710.
- 9.
Xiong LZ, Xu CG, Maroof MAS (1999) Patterns of cytosine methylation in an elite rice hybrid and its parental lines, detected by a methylation-sensitive ampli ® cation polymorphism technique: 439–446.
- 10. Bachem CW, Van der Hoeven RS, De Bruijn SM, Vreugdenhil D, Zabeau M, et al. (1996) Visualization of differential gene expression using a novel method of RNA fingerprinting based on AFLP: analysis of gene expression during potato tuber development. The Plant journal: for cell and molecular biology 9: 745–753.
- 11. Stölting KN, Clarke AC, Meudt HM, Blankenhorn WU, Wilson AB (2011) Cost-effective fluorescent amplified fragment length polymorphism (AFLP) analyses using a three primer system. Molecular ecology resourcescology resources 11: 494–502.
- 12. Bonin A, Pompanon F, Taberlet P (2005) Use of amplified fragment length polymorphism (AFLP) markers in surveys of vertebrate diversity. Methods in Enzymology 395: 145–161.
- 13. Shaheen N, Pearce SR, Khan MA, Mahmood T, Yasmin G, et al. (2010) AFLP mediated genetic diversity of malvaceae species. Journal of Medicinal Plants 4: 148–154.
- 14. Després L, Gielly L, Redoutet B, Taberlet P (2003) Using AFLP to resolve phylogenetic relationships in a morphologically diversified plant species complex when nuclear and chloroplast sequences fail to reveal variability. Molecular Phylogenetics and Evolution 27: 185–196.
- 15. Zenger KR, Stow AJ, Peddemors V, Briscoe D A, Harcourt RG (2006) Widespread utility of highly informative AFLP molecular markers across divergent shark species. The Journal of heredity 97: 607–611 .
- 16. Doyle JJ, Doyle JL (1987) A rapid total DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin 19: 11–15.
- 17. Haque S, Ashraf N, Awal A, Sarker RH, Begum S, et al. (2004) Method for Quality DNA Isolation from Different Parts of Jute Plant: Corchorus capsularis L. and C. olitorius L. Plant Tissue Culture. 14: 143–148.
- 18. Vekemans X, Beauwens T, Lemaire M, Roldán-Ruiz I (2002) Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Molecular ecology 11: 139–151.
- 19. De Riek J, Calsyn E, Everaert I, Van Bockstaele E, De Loose M (2001) AFLP based alternatives for the assessment of Distinctness, Uniformity and Stability of sugar beet varieties. Genetics and Breeding 103: 1254–1265.
- 20. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288–295.
- 21. Lynch M, Milligan BG (1994) Analysis of population genetic structure with RAPD markers. Molecular Ecology 3: 91–99.
- 22. Bonin A, Bellemain E, Bronken Eidesen P, Pompanon F, Brochmann C, et al. (2004) How to track and assess genotyping errors in population genetics studies. Molecular ecology 13: 3261–3273.
- 23. Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nature reviews Genetics 6: 847–859.
- 24.
R Development Core Team (2011) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
- 25. Da Y, VanRaden PM, Ron M, Beever JE, Paszek A, et al. (1999) Standardization and conversion of marker polymorphism measures. Animal biotechnology 10: 25–35.
- 26. Mateescu RG, Zhang Z, Tsai K, Phavaphutanon J, Burton-Wurster NI, et al. (2005) Analysis of allele fidelity, polymorphic information content, and density of microsatellites in a genome-wide screening for hip dysplasia in a crossbreed pedigree. The Journal of heredity 96: 847–853.
- 27. Chwedorzewska KJ (2008) Poa annua L. in Antarctic: searching for the source of introduction. Cellular and Molecular Biology Letters 28: 263–268.
- 28. Bonin A, Ehrich D, Manel S (2007) Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists. Molecular ecology 16: 3737–3758.
- 29.
Ray A, Sumangala RC, Ravikanth G, Uma Shaanker R, Quader S (2011) Isolation and characterization of polymorphic microsatellite loci from the invasive plant Lantana camara L. Conservation Genetics Resources: 13–15.
- 30.
Vardien W, Richardson DM, Foxcroft LC, Wilson JRU, Le Roux JJ (2013) Management history determines gene flow in a prominent invader. Ecography: no–no. doi:10.1111/j.1600-0587.2012.00120.x.
- 31. Chen X, Li N, Shen L, Li Y (2003) Genetic structure along a gaseous organic pollution gradient: a case study with Poa annua L. 124: 449–455 .
- 32. Mengistu LW, Mueller-Warrant GW, Barker RE (2000) Genetic diversity of Poa annua in western Oregon grass seed crops. Theoretical and Applied Genetics 101: 70–79.
- 33. Mariette S, Chagné D, Lézier C, Pastuszka P, Raffin a, et al. (2001) Genetic diversity within and among Pinus pinaster populations: comparison between AFLP and microsatellite markers. Heredity 86: 469–479.
- 34. Le Roux JJ, Wieczorek AM, Wright MG, Tran CT (2007) Super-genotype: global monoclonality defies the odds of nature. PloS one 2: e590.
- 35. Poulin J, Weller S, Sakai A (2005) Genetic diversity does not affect the invasiveness of fountain grass (\emph{Pennisetum setaceum}) in Arizona, California and Hawaii. Diversity and Distributions 11: 241–247.
- 36. Hamrick J, Godt M (1996) Effects of life history traits on genetic diversity in plant species. Philosophical Transactions of the Royal Society B Biological Sciences 351: 1291–1298.
- 37. Dlugosch KM, Parker IM (2007) Molecular and quantitative trait variation across the native range of the invasive species Hypericum canariense: evidence for ancient patterns of colonization via pre-adaptation? Molecular ecology 16: 4269–4283.
- 38. Le Roux J, Wieczorek AM (2009) Molecular systematic and population genetics of biological invasions: towards a better understanding of invasive species management. Annals of Applied Biology 154: 1–17.
- 39. Fay MF, Cowan R, Leitch IJ (2005) The Effects of Nuclear DNA Content (C-value ) on the Quality and Utility of AFLP Fingerprints. Annals of Botany 95: 237–246.
- 40. Van Tienderen PH, De Haan AA, Van Der Linden CG, Vosman B (2002) Biodiversity assessment using markers for ecologically important traits. 17: 577–582.
- 41. Lu Y, Curtiss J, Miranda D, Hughs E, Zhang J (2008) ATG-anchored AFLP (ATG-AFLP) analysis in cotton. Plant Cell Reports 27: 1645–1653.
- 42. Reineke AR, Bornberg-Bauer E, Gu J (2011) Evolutionary divergence and limits of conserved non-coding sequence detection in plant genomes. Nucleic acids research 39: 6029–6043.