An analysis of the population structure and genetic diversity for any organism often depends on one or more molecular marker techniques. Nonetheless, these techniques are not absolutely reliable because of various sources of errors arising during the genotyping process. Thus, a complex analysis of genotyping error was carried out with the AFLP method in 169 samples of the oil seed plant Plukenetia volubilis L. from small isolated subpopulations in the Peruvian Amazon. Samples were collected in nine localities from the region of San Martin. Analysis was done in eight datasets with a genotyping error from 0 to 5%. Using eleven primer combinations, 102 to 275 markers were obtained according to the dataset. It was found that it is only possible to obtain the most reliable and robust results through a multiple-level filtering process. Genotyping error and software set up influence both the estimation of population structure and genetic diversity, where in our case population number (K) varied between 2-9 depending on the dataset and statistical method used. Surprisingly, discrepancies in K number were caused more by statistical approaches than by genotyping errors themselves. However, for estimation of genetic diversity, the degree of genotyping error was critical because descriptive parameters (He, FST, PLP 5%) varied substantially (by at least 25%). Due to low gene flow, P. volubilis mostly consists of small isolated subpopulations (ΦPT = 0.252-0.323) with some degree of admixture given by socio-economic connectivity among the sites; a direct link between the genetic and geographic distances was not confirmed. The study illustrates the successful application of AFLP to infer genetic structure in non-model plants.

Department of Crop Sciences and Agroforestry Faculty of Tropical AgriSciences Czech University of Life Sciences Prague Kamýcká Prague Czech Republic

Department of Genetics and Breeding Faculty of Agrobiology Food and Natural Resources Czech University of Life Sciences Prague Kamýcká Prague Czech Republic

Gene Bank Division of Crop Genetics and Breeding Crop Research Institute Drnovská Prague Czech Republic

Peruvian Amazon Research Institute Tarapoto Peru

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Guillén MD, Ruiz A, Cabo N, Chirinos R, Pascual G. Characterization of sacha inchi (Plukenetia volubilis L.) Oil by FTIR spectroscopy and 1H NMR. Comparison with linseed oil. J Am Oil Chem Soc. 2003;80: 755–762.

Gillespie LJ. A synopsis of neotropical Plukenetia (Euphorbiaceae) including two new species. Syst. Bot. 1993;18: 575–592.

Hamaker BR, Valles C, Gilman R, Hardmeier RM, Clark D, García HH, et al. Amino acid and fatty acid profiles of the inca peanut (Plukenetia volubilis L.). Cereal Chem. 1992;69: 461–463.

Gutiérrez L-F, Rosada L-M, Jiméneza Á. Chemical composition of sacha inchi (Plukenetia volubilis L.) seeds and characteristics of their lipid fraction. Grasas Aceites. 2011;62: 76–83.

Chirinos R, Zuloeta G, Pedreschi R, Mignole E, Larondelle Y, Campos D. Sacha inchi (Plukenetia volubilis): A seed source of polyunsaturated fatty acids, tocopherols, phytosterols, phenolic compounds and antioxidant capacity. Food Chem. 2013;141: 1732–1739. doi: 10.1016/j.foodchem.2013.04.078 PubMed DOI

Maurer NE, Hatta-Sakoda B, Pascual-Chagman G, Rodriguez-Saona LE. Characterization and authentication of a novel vegetable source of omega-3 fatty acids, sacha inchi (Plukenetia volubilis L.) oil. Food Chem. 2012;134: 1173–1180. doi: 10.1016/j.foodchem.2012.02.143 PubMed DOI

Fanali CH, Dugo L, Cacciola F, Beccaria M, Grasso S, Dachà M, et al. Chemical characterization of sacha inchi (Plukenetia volubilis L.) oil. J Agric Food Chem. 2011;59: 13043–13049. doi: 10.1021/jf203184y PubMed DOI

Crawford LA, Koscinski D, Keyghobadi N. A call for more transparent reporting of error rates: The quality of AFLP data in ecological and evolutionary research. Mol Ecol. 2012;21: 5911–5917. doi: 10.1111/mec.12069 PubMed DOI

Price DL, Casler MD. Simple regression models as a threshold for selecting AFLP loci with reduced error rates. Bioinformatics. 2012;13: 268 doi: 10.1186/1471-2105-13-268 PubMed DOI PMC

Zhang H, Hare MP. Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters. Heredity. 2012;108: 616–625. doi: 10.1038/hdy.2011.132 PubMed DOI PMC

Greene S, Kisha TJ, Yu L-X, Parra-Quijano M. Conserving Plants in Gene Banks and Nature: Investigating Complementarity with Trifolium thompsonii Morton. PLoS ONE. 2014;9:e105145 doi: 10.1371/journal.pone.0105145 PubMed DOI PMC

Gupta S, Bharalee R, Bhorali P, Das SK, Bhagawati P, Bandyopadhyay T, et al. Molecular analysis of drought tolerance in tea by cDNA-AFLP based transcript profiling. Mol Biotechnol. 2012;53: 237–248. PubMed

Montes Osorio LR, Salvador AFT, Jongschaap REE, Perez CAA, Sandoval JEB, Trindade LM, et al. High level of molecular and phenotypic biodiversity in Jatropha curcas from Central America compared to Africa, Asia and South America. BMC Plant Biol. 2014;14: 77 doi: 10.1186/1471-2229-14-77 PubMed DOI PMC

Melito S, Sias A, Petretto GL, Chessa M, Pintore G, Porceddu A. Genetic and Metabolite Diversity of Sardinian Populations of Helichrysum italicum. PLoS ONE. 2013;8: e79043 doi: 10.1371/journal.pone.0079043 PubMed DOI PMC

Meudt HD, Clarke AC. Almost Forgotten or Latest Practice? AFLP applications, analyses and advances. Trends Plant Sci. 2007;12: 106–117. doi: 10.1016/j.tplants.2007.02.001 PubMed DOI

Bensch S, Åkesson M. Ten years of AFLP in ecology and evolution: why so few animals? Mol Ecol. 2005;14: 2899–2914. doi: 10.1111/j.1365-294X.2005.02655.x PubMed DOI

Bonin A, Bellemain E, Bronken Eidesen P, Pompanon F, Brochmann C, Taberlet P. How to track and assess genotyping errors in population genetics studies. Mol Ecol. 2004;13: 3261–3273. doi: 10.1111/j.1365-294X.2004.02346.x PubMed DOI

Pompanon F, Bonin A, Bellemain E, Taberlet P. Genotyping errors: Causes, consequences and solutions. Nat Rev Genet. 2005;6: 847–859. doi: 10.1038/nrg1707 PubMed DOI

Herrmann D, Poncet BN, Manel S, Rioux D, Gielly L, Taberlet P, Gugerli F. Selection criteria for scoring amplified fragment length polymorphisms (AFLPs) positively affect the reliability of population genetic parameter estimates. Genome. 2010;53: 302–310. doi: 10.1139/g10-006 PubMed DOI

Whitlock R, Hipperson H, Mannarelli M, Butlin RK, Burke T. An objective, rapid and reproducible method for scoring AFLP peak-height data that minimizes genotyping error. Mol Ecol Resour. 2008; 8: 725–735. doi: 10.1111/j.1755-0998.2007.02073.x PubMed DOI

Arrigo N, Tuszynski JW, Ehrich D, Gerdes T, Alvarez N. Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring. BMC Bioinformatics. 2009;10: 33 doi: 10.1186/1471-2105-10-33 PubMed DOI PMC

Arthofer W. TINYFLP and TINYCAT: software for automatic peak selection and scoring of AFLP data tables. Mol Ecol Resour. 2010;10: 385–388. doi: 10.1111/j.1755-0998.2009.02751.x PubMed DOI

Caballero A, Quesada H, Rolan-Alvarez E. Impact of amplified fragment length polymorphism size homoplasy on the estimation of population genetic diversity and the detection of selective loci. Genetics. 2008;179: 539–554. doi: 10.1534/genetics.107.083246 PubMed DOI PMC

Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987;19: 11–15.

Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, et al. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 1995;23: 4407–14. PubMed PMC

Hasbún R, Iturra C, Moraga P, Wachtendorff P, Quiroga P, Valenzuela S. An efficient and reproducible protocol for production of AFLP markers in tree genomes using fluorescent capillary detection. Tree Genet. Genomes. 2012;8: 925–931.

Vekemans X, Beauwens T, Lemaire M, Roldán-Ruiz I. Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Mol Ecol. 2002;11: 139–151. PubMed

Herrmann M, Holderegger R, Van Strien MJ. Influence of parameter settings in automated scoring of AFLPs on population genetic analysis. Mol Ecol Resour. 2013;13: 128–34. doi: 10.1111/1755-0998.12033 PubMed DOI

Lambert SM, Geneva AJ, Luke Mahler D, Glor RE. Using genomic data to revisit an early example of reproductive character displacement in Haitian Anolis lizards. Mol Ecol. 2013;22: 3981–3995. doi: 10.1111/mec.12292 PubMed DOI

Bonin A, Ehrich D, Manel S. Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists. Mol Ecol. 2007;16:3737–3758. doi: 10.1111/j.1365-294X.2007.03435.x PubMed DOI

Holland B, Clarke A, Meudt H. Optimizing automated AFLP scoring parameters to improve phylogenetic resolution. Syst Biol. 2008;57: 347–366. doi: 10.1080/10635150802044037 PubMed DOI

Perrier X, Jacquemoud-Collet JP. DARwin software. 2006; Available from: http://darwin.cirad.fr/darwin. Retrieve on 11th July, 2015.

Kruskal JB. Nonmetric multidimensional scaling: A numerical method. Psychometrika. 1964;29: 115–129.

Meirmans PG. AMOVA-based clustering of population genetic data. J Hered. 2012; 103: 744–750. doi: 10.1093/jhered/ess047 PubMed DOI

Meirmans PG, Van Tienderen PH. GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004;4: 792–794.

Smouse PE, Peakall R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity. 1999;82: 561–573 PubMed

Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155: 945–959. PubMed PMC

Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics. 2003;164: 1567–1587. PubMed PMC

Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes. 2007;7: 574–578. doi: 10.1111/j.1471-8286.2007.01758.x PubMed DOI PMC

Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009;9: 1322–1332. doi: 10.1111/j.1755-0998.2009.02591.x PubMed DOI PMC

Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol. 2005;14: 2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x PubMed DOI

Coulon A, Fitzpatrick JW, Bowman R, Stith BM, Makarewich CA, Stenzler LM, et al. Congruent population structure inferred from dispersal behaviour and intensive genetic surveys of the threatened Florida scrub-jay (Aphelocoma cœrulescens). Mol Ecol. 2008;17: 1685–1701. doi: 10.1111/j.1365-294X.2008.03705.x PubMed DOI

Earl DA, von Holdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4: 359–361.

Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23: 1801–1806. doi: 10.1093/bioinformatics/btm233 PubMed DOI

Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2003;4: 137–138.

Duchesne P, Bernatchez L. AFLPOP: a computer program for simulated and real population allocation, based on AFLP data. Mol Ecol Notes. 2002;2: 380–383.

Zhivotovsky LA. Estimating population structure in diploids with multilocus dominant DNA markers. Mol Ecol. 1999;8: 907–913. PubMed

Lynch M, Milligan BG. Analysis of population genetic structure with RAPD markers. Mol Ecol. 1994;3:91–99. PubMed

Chybicki IJ, Oleksa A, Burczyk J. Increased inbreeding and strong kinship structure in Taxus baccata estimated from both AFLP and SSR data. Heredity. 2011;107:589–600. doi: 10.1038/hdy.2011.51 PubMed DOI PMC

Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics. 2012;28: 2537–2539. doi: 10.1093/bioinformatics/bts460 PubMed DOI PMC

Jombart T, Devillard S, Dufour A-B, Pontier D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity. 2008;101: 92–103. doi: 10.1038/hdy.2008.34 PubMed DOI

R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. Available from: http://www.R-project.org/.

Hadfield JD, Richardson DS, Burke T. Towards unbiased parentage assignment: combining genetic, behavioural and spatial data in a Bayesian framework. Mol Ecol. 2006;15: 3715–3730. doi: 10.1111/j.1365-294X.2006.03050.x PubMed DOI

Ley AC, Hardy OJ. Improving AFLP analysis of large-scale patterns of genetic variation–a case study with the Central African lianas Haumannia spp. (Marantaceae) showing interspecific gene flow. Mol Ecol. 2013;22: 1984–1997. doi: 10.1111/mec.12214 PubMed DOI

Caballero A, Quesada H. Homoplasy and distribution of AFLP fragments: An analysis in silico of the genome of different species. Mol Biol Evol. 2010;27:1139–1151. doi: 10.1093/molbev/msq001 PubMed DOI

Paris M, Bonnes B, Ficetola GF, Poncet BN, Després L. Amplified fragment length homoplasy: in silico analysis for model and non-model species. BMC Genomics. 2010;11: 287 doi: 10.1186/1471-2164-11-287 PubMed DOI PMC

Herrmann D, Poncet BN, Manel S, Rioux D, Gielly L, Taberlet P, Gugerli F. Selection criteria for scoring amplified fragment length polymorphisms (AFLPs) positively affect the reliability of population genetic parameter estimates. Genome. 2010;53: 302–310. doi: 10.1139/g10-006 PubMed DOI

Jombart T, Pontier D, Dufour A-B,. Genetic markers in the playground of multivariate analysis. Heredity. 2009;102: 330–341. doi: 10.1038/hdy.2008.130 PubMed DOI

Meyer RS, Purugganan MD. Evolution of crop species: genetics of domestication and diversification. Nat Rev Genet. 2013;14: 840–852. doi: 10.1038/nrg3605 PubMed DOI

Brack A. Diccionario enciclópedico de plantas útiles del Perú. Centro de estudios regionales Andinos Bartolomé de las Casas, Cusco; 1999.

Bussmann RW, Zambrana NP, Téllez C. Plukenetia carolis-vegae (Euphorbiaceae)–A new useful species from northern Peru. Econ Bot. 2013;67:387–392.

Keneni G, Bekele E, Imtiaz M, Dagne K, Getu E, Assefa F. Genetic diversity and population structure of Ethiopian chickpea (Cicer arietinum L.) germplasm accessions from different geographical origins as revealed by microsatellite markers. Plant Mol Biol Rep. 2012;30: 654–665.

Ch Reisch, Bernhardt-Römermann M. The impact of study design and life history traits on genetic variation of plants determined with AFLPs. Plant Ecol. 2014;215: 1493–1511.

Motamayor JC, Lachenaud P, da Silva e Mota JW, Loor R, Kuhn DN, Brown JS, et al. Geographic and genetic population differentiation of the Amazonian chocolate tree (Theobroma cacao L). PLoS ONE. 2008;3: e3311 doi: 10.1371/journal.pone.0003311 PubMed DOI PMC

Stout AB. The pollination of avocados. University of Florida. Agric. Expt. Sta. Bulletin. 1933;257: 44 pp.

Porcher E, Lande R. Inbreeding depression under mixed outcrossing, self-fertilization and sib-mating. BMC Mol Biol. 2016;16:105. PubMed PMC