Dealing with AFLP genotyping errors to reveal genetic structure in Plukenetia volubilis (Euphorbiaceae) in the Peruvian Amazon

Abstract

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.

Genetic diversity in wild populations of the restinga ecotype of the cashew (Anacardium occidentale) in coastal Piauí, Brazil

“…Most studies of genotyping error in dominant markers have been carried out on AFLP data. Vašek et al (2017), in a recent study, found that error rate affected descriptive parameters of diversity such as H e , %P and Φ PT more strongly than the results of Bayesian STRU CTU RE analysis. We therefore judge that our observed genotyping error (6.7%, which compares to the 5% maximum AFLP error rate used by Vašek et al 2017) is unlikely to have affected the optimal 8-group genetic structure or the relative values of the genetic diversity parameters among the wild and domesticated populations.…”

Molecular genetic diversity and mating system in sacha inchi progenies1

“…In general, AFLP markers revealed a high degree of polymorphism and proved to be a useful and reliable tool for characterizing genetic diversity in sacha inchi progenies, as also reported by Vašek et al (2017). In addition, AFLP markers were highly informative in evaluating the reproductive system of sacha inchi.…”

Population genetics and geometric morphometrics of the freshwater snail Segmentina nitida reveal cryptic sympatric species of conservation value in Europe

“…AFLP analysis can incorporate hundreds of polymorphisms across a whole genome, but it suffers from relatively high genotyping error (Zhang and Hare 2012) that can significantly affect analysis of population structure and make it impossible to determine the 'true' number of populations among a set of individuals. Mensch (2009) reports shifting AFLP peak profiles between individuals included in her study, which may have resulted in incorrect genotyping and thus the greater number of genetic lineages detected (Vašek et al 2017). It is not clear if the lineages described here coincide with any of those identified by Mensch (2009) with the genetic data from that study unavailable, but the weight of genetic evidence suggests the existence of (at least) two morphologically cryptic but reproductively isolated species within S. nitida.…”