Null allele, allelic dropouts or rare sex detection in clonal organisms: simulations and application to real data sets of pathogenic microbes

Abstract

BackgroundPathogens and their vectors are organisms whose ecology is often only accessible through population genetics tools based on spatio-temporal variability of molecular markers. However, molecular tools may present technical difficulties due to the masking of some alleles (allelic dropouts and/or null alleles), which tends to bias the estimation of heterozygosity and thus the inferences concerning the breeding system of the organism under study. This is especially critical in clonal organisms in which deviation from panmixia, as measured by Wright’s FIS, can, in principle, be used to infer both the extent of clonality and structure in a given population. In particular, null alleles and allelic dropouts are locus specific and likely produce high variance of Wright’s FIS across loci, as rare sex is expected to do. In this paper we propose a tool enabling to discriminate between consequences of these technical problems and those of rare sex.MethodsWe have performed various simulations of clonal and partially clonal populations. We introduce allelic dropouts and null alleles in clonal data sets and compare the results with those that exhibit increasing rates of sexual recombination. We use the narrow relationship that links Wright’s FIS to genetic diversity in purely clonal populations as assessment criterion, since this relationship disappears faster with sexual recombination than with amplification problems of certain alleles.ResultsWe show that the relevance of our criterion for detecting poorly amplified alleles depends partly on the population structure, the level of homoplasy and/or mutation rate. However, the interpretation of data becomes difficult when the number of poorly amplified alleles is above 50%. The application of this method to reinterpret published data sets of pathogenic clonal microbes (yeast and trypanosomes) confirms its usefulness and allows refining previous estimates concerning important pathogenic agents.ConclusionOur criterion of superimposing between the FIS expected under clonality and the observed FIS, is effective when amplification difficulties occur in low to moderate frequencies (20-30%).

Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites

“…Alternatively, null allele presence, caused by mutations at locus specific primer annealing sites which retard PCR amplification efficiency of particular alleles, can result in excesses of homozygotes scored at loci and hence significant deviation from Hardy-Weinberg Equilibrium in populations [ 44 ]. Allelic dropout in PCR caused by a variety of inhibitory causes can result in similar outcomes [ 45 ].…”

Local Coastal Configuration Rather Than Latitudinal Gradient Shape Clonal Diversity and Genetic Structure of Phymatolithon calcareum Maerl Beds in North European Atlantic

“…When only considering the individuals scored at every locus, results from ML-NULLFREQ (Kalinowski and Taper, 2006) showed evidence of null alleles at the loci PC-6 (null allele frequency = 20.58%) and PC-8 (null allele frequency = 24.41%). Even if missing data can have an impact on the estimation of genotypic frequencies (Gourraud et al, 2004), we chose to keep all loci in the subsequent analyses since (i) our capacity to detect distinct MLGs was limited at some sites, even when all eight microsatellite loci were included (see Supplementary Figure S1), and (ii) it has been demonstrated that including loci with low to moderate frequencies of null alleles (i.e., <20-30%) does not profoundly affect classical and Bayesian population genetic analyses of population structure (Séré et al, 2014).…”

Friends and family: A software program for identification of unrelated individuals from molecular marker data

“…Once it is observed that a marker is in fact in HWE after the removal of related individuals from the data set, the researcher should return to the full data set to investigate the causes of deviation from HWE, which could be biological or technical (Waples, ). Deviation of microsatellite markers from HWE before and after relatives have been removed is more likely a result of technical errors, such as null alleles or large allele dropout, which leads to an excess of observed homozygotes (Séré et al., ; Van Oosterhout, Hutchinson, Wills, & Shipley, ). Table shows that marker TGLA227 was not in HWE before and after relatives had been removed in five of 16 herds and across all herds.…”