‘True’ null allele detection in microsatellite loci: a comparison of methods, assessment of difficulties and survey of possible improvements

Abstract

Null alleles are alleles that for various reasons fail to amplify in a PCR assay. The presence of null alleles in microsatellite data is known to bias the genetic parameter estimates. Thus, efficient detection of null alleles is crucial, but the methods available for indirect null allele detection return inconsistent results. Here, our aim was to compare different methods for null allele detection, to explain their respective performance and to provide improvements. We applied several approaches to identify the 'true' null alleles based on the predictions made by five different methods, used either individually or in combination. First, we introduced simulated 'true' null alleles into 240 population data sets and applied the methods to measure their success in detecting the simulated null alleles. The single best-performing method was ML-NullFreq_frequency. Furthermore, we applied different noise reduction approaches to improve the results. For instance, by combining the results of several methods, we obtained more reliable results than using a single one. Rule-based classification was applied to identify population properties linked to the false discovery rate. Rules obtained from the classifier described which population genetic estimates and loci characteristics were linked to the success of each method. We have shown that by simulating 'true' null alleles into a population data set, we may define a null allele frequency threshold, related to a desired true or false discovery rate. Moreover, using such simulated data sets, the expected null allele homozygote frequency may be estimated independently of the equilibrium state of the population.

Ecological and historical determinants of population genetic structure and diversity in the Mediterranean shrubRosmarinus officinalis(Lamiaceae)

“…no amplification failures) were detected in any of the genotyped loci. In addition, the estimation of null allele frequencies may be inaccurate in populations with strong deviations from HWE, as in our case, where no null homozygotes have been observed (Dabrowski et al ., ). Furthermore, ongoing reproductive biology studies on six of these populations and based on large progeny arrays genotyped for this same set of microsatellite loci (unpublished results by the authors) did not reveal the presence of null alleles.…”

Broad-scale genetic patterns of New Zealand abalone, Haliotis iris, across a distribution spanning 13° latitude and major oceanic water masses

“…The discordant clustering results may be due to unidentified null alleles. Dąbrowski et al (2015) reported that MICRO-CHECKER failed to identify 33 % of the true null alleles in simulated data sets. Alternatively, very little to no population structure coupled with a small number of markers may be influencing the results of the clustering programs.…”

Null alleles are ubiquitous at microsatellite loci in the Wedge Clam (Donax trunculus)

“…The analysis yielded a close match between the expected and observed number of null homozygous. Third, we estimated the frequency of null alleles using ML-NULLFREQ (Kalinowski & Taper, 2006) which is a maximum likelihood estimator of the frequency of null alleles in a sample with or without missing data which has been shown to be the single best-performing method (Dąbrowski et al, 2015). Finally, to test the scoring’s robustness and power of resolution to depict population genetic structure of these microsatellite loci, we took advantage of the fact that it has extensively been shown, in many marine taxa, that Atlantic populations are often genetically distinct from Mediterranean populations (Patarnello, Volckaert & Castilho, 2007).…”

“…Null alleles have been associated with heterozygous deficits in many studies and several have tested their presence and effects in population structure estimates using various analytical and simulation tools (e.g., Dąbrowski et al, 2015 and references therein). However, to the best of our knowledge no study has empirically tested for its consequences in estimating differentiation using population samples separated by well characterised biogeographic barriers.…”