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'True' null allele detection in microsatellite loci: a comparison of methods, assessment of difficulties and survey of possible improvements
Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
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2015 (English)In: Molecular Ecology Resources, ISSN 1755-098X, E-ISSN 1755-0998, Vol. 15, no 3, 477-488 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. Vol. 15, no 3, 477-488 p.
Keyword [en]
genetic diversity estimates, genotyping errors, microsatellite loci, null allele detection, NullAlleleGenerator, rough sets
National Category
Biochemistry and Molecular Biology
URN: urn:nbn:se:uu:diva-252678DOI: 10.1111/1755-0998.12326ISI: 000352653700003PubMedID: 25187238OAI: oai:DiVA.org:uu-252678DiVA: diva2:814254
Available from: 2015-05-26 Created: 2015-05-11 Last updated: 2015-05-26Bibliographically approved

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Bornelöv, Susanne
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Science for Life Laboratory, SciLifeLabDepartment of Medical Biochemistry and MicrobiologyComputational and Systems Biology
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