Use of AFLP for the study of eukaryotic pathogens affecting humans.

Amplified fragment length polymorphism (AFLP) is a genotyping technique based on PCR amplification of specific restriction fragments from a particular genome. The methodology has been extensively used in plant biology to solve a variety of scientific questions, including taxonomy, molecular epidemiology, systematics, population genetics, among many others. The AFLP share advantages and disadvantages with other types of molecular markers, being particularly useful in organisms with no previous DNA sequence knowledge. In eukaryotic pathogens, the technique has not been extensively used, although it has the potential to solve many important issues as it allows the simultaneous examination of hundreds or even thousands of polymorphic sites in the genome of the organism. Here we describe the main applications published on the use of AFLP in eukaryotic pathogens, with emphasis in species of the groups fungi, protozoa and helminths, and discuss the role of this methodology in the context of new techniques derived from the advances of the next generation sequencing.

Simple regression models as a threshold for selecting AFLP loci with reduced error rates.

BACKGROUND: Amplified fragment length polymorphism is a popular DNA marker technique that has applications in multiple fields of study. Technological improvements and decreasing costs have dramatically increased the number of markers that can be generated in an amplified fragment length polymorphism experiment. As datasets increase in size, the number of genotyping errors also increases. Error within a DNA marker dataset can result in reduced statistical power, incorrect conclusions, and decreased reproducibility. It is essential that error within a dataset be recognized and reduced where possible, while still balancing the need for genomic diversity. RESULTS: Using simple regression with a second-degree polynomial term, a model was fit to describe the relationship between locus-specific error rate and the frequency of present alleles. This model was then used to set a moving error rate threshold that varied based on the frequency of present alleles at a given locus. Loci with error rates greater than the threshold were removed from further analyses. This method of selecting loci is advantageous, as it accounts for differences in error rate between loci of varying frequencies of present alleles. An example using this method to select loci is demonstrated in an amplified fragment length polymorphism dataset generated from the North American prairie species big bluestem. Within this dataset the error rate was reduced from 12.5% to 8.8% by removal of loci with error rates greater than the defined threshold. By repeating the method on selected loci, the error rate was further reduced to 5.9%. This reduction in error resulted in a substantial increase in the amount of genetic variation attributable to regional and population variation. CONCLUSIONS: This paper demonstrates a logical and computationally simple method for selecting loci with a reduced error rate. In the context of a genetic diversity study, this method resulted in an increased ability to detect differences between populations. Further application of this locus selection method, in addition to error-reducing methodological precautions, will result in amplified fragment length polymorphism datasets with reduced error rates. This reduction in error rate should result in greater power to detect differences and increased reproducibility.

The apportionment of total genetic variation by categorical analysis of variance.

We wish to suggest the categorical analysis of variance as a means of quantifying the proportion of total genetic variation attributed to different sources of variation. This method potentially challenges researchers to rethink conclusions derived from a well-known method known as the analysis of molecular variance (AMOVA). The CATANOVA framework allows explicit definition, and estimation, of two measures of genetic differentiation. These parameters form the subject of interest in many research programmes, but are often confused with the correlation measures defined in AMOVA, which cannot be interpreted as relative contributions of particular sources of variation. Through a simulation approach, we show that under certain conditions, researchers who use AMOVA to estimate these measures of genetic differentiation may attribute more than justified amounts of total variation to population labels. Moreover, the two measures can also lead to incongruent conclusions regarding the genetic structure of the populations of interest. Fortunately, one of the two measures seems robust to variations in relative sample sizes used. Its merits are illustrated in this paper using mitochondrial haplotype and amplified fragment length polymorphism (AFLP) data.

Statistical assessment of variability of terminal restriction fragment length polymorphism analysis applied to complex microbial communities.

The variability of terminal restriction fragment polymorphism analysis applied to complex microbial communities was assessed statistically. Recent technological improvements were implemented in the successive steps of the procedure, resulting in a standardized procedure which provided a high level of reproducibility.

Amplified fragment-length polymorphism analysis.

Amplified fragment length polymorphism (AFLP) analysis is a universal polymerase chain reaction (PCR)-based DNA fingerprinting technique comprising three main stages: (i) digestion of genomic DNA with restriction endonucleases and ligation to double-stranded adaptors (each comprised of two oligonucleotides), thus creating restriction fragments with identical known adaptor sequences; (ii) specific amplification of a subset of these DNA fragments using primers (one labeled) targeting the adaptor sequences and additional selected bases within the unknown genomic DNA; and (iii) analysis of the patterns (usually automated). Differences or polymorphisms between samples are revealed by separation of the labeled fragments by electrophoresis (standard agarose, high-resolution denaturing acrylamide, or capillary gels). Comparison of banding patterns is typically achieved using dedicated fingerprinting analysis software. The advantages of AFLP analysis include the ability to use a universal protocol in combination with different restriction endonucleases and the choice of adding one or more selective nucleotides in the PCR -primers to achieve optimal results relatively quickly without prior knowledge of DNA sequences from a large variety of (micro)organisms. The method also has the potential for high-throughput and local electronic database pattern storage with relatively low cost. Disadvantages include variation in the precision of sizing of fragments, leading to suboptimal reproducibility, particularly across different platforms.

An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure.

There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genomes. Several statistical methods have been proposed to study the genetic structure using AFLPs but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F(IS). A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F(IS) and F(ST) values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.ujf-grenoble.fr/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.

Amplified fragment length polymorphism analysis of Salmonella enterica.

Amplified fragment length polymorphism (AFLP) is a powerful PCR-based fingerprinting method and has the capacity to reveal variation around the whole genome by selectively amplifying a subset of restriction fragments for comparison. The restriction fragments analyzed are small, and even mutation of 1 bp can be detected. The use of different sets of restriction enzymes or different primer combinations can generate large numbers of different AFLP fingerprints. AFLP is of particular value for studies of closely related strains, such as analysis of variation within a serovar of Salmonella enterica. We present here protocols for both radioactively labeled and fluorescent dye-labeled AFLP analyses that are also applicable to other bacterial species. Fluorescent AFLP has proved to be reproducible and capable of standardization.