Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods
Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in...
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ftzenodo:oai:zenodo.org:4998761 2024-09-15T18:39:52+00:00 Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods Dąbrowski, Michal J. Pilot, Malgorzata Kruczyk, Marcin Żmihorski, Michal Umer, Husen M. Gliwicz, Joanna 2013-09-20 https://doi.org/10.5061/dryad.4p41m unknown Zenodo https://doi.org/10.1111/1755-0998.12177 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.4p41m oai:zenodo.org:4998761 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode Rodentia genotypes root vole genotyping errors Microtus oeconomus Heterozygosity simulated genotypes tundra vole Arvicolidae null alleles natural populations info:eu-repo/semantics/other 2013 ftzenodo https://doi.org/10.5061/dryad.4p41m10.1111/1755-0998.12177 2024-07-25T13:36:47Z Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8 years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated data set, this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null-allele-free simulated data set, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false-positive and false-negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false-positive rate, but this approach may increase the false-negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural populations. Genotypes_data The file Genotypes_data.zip contains two folders: Simulated_genotypes, Simulated_genotypes_subpopulations and one .csv file named Microtus_oeconomus_genotypes.csv Other/Unknown Material Tundra Zenodo |
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Rodentia genotypes root vole genotyping errors Microtus oeconomus Heterozygosity simulated genotypes tundra vole Arvicolidae null alleles natural populations |
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Rodentia genotypes root vole genotyping errors Microtus oeconomus Heterozygosity simulated genotypes tundra vole Arvicolidae null alleles natural populations Dąbrowski, Michal J. Pilot, Malgorzata Kruczyk, Marcin Żmihorski, Michal Umer, Husen M. Gliwicz, Joanna Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
topic_facet |
Rodentia genotypes root vole genotyping errors Microtus oeconomus Heterozygosity simulated genotypes tundra vole Arvicolidae null alleles natural populations |
description |
Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8 years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated data set, this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null-allele-free simulated data set, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false-positive and false-negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false-positive rate, but this approach may increase the false-negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural populations. Genotypes_data The file Genotypes_data.zip contains two folders: Simulated_genotypes, Simulated_genotypes_subpopulations and one .csv file named Microtus_oeconomus_genotypes.csv |
format |
Other/Unknown Material |
author |
Dąbrowski, Michal J. Pilot, Malgorzata Kruczyk, Marcin Żmihorski, Michal Umer, Husen M. Gliwicz, Joanna |
author_facet |
Dąbrowski, Michal J. Pilot, Malgorzata Kruczyk, Marcin Żmihorski, Michal Umer, Husen M. Gliwicz, Joanna |
author_sort |
Dąbrowski, Michal J. |
title |
Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
title_short |
Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
title_full |
Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
title_fullStr |
Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
title_full_unstemmed |
Data from: Reliability assessment of null allele detection: inconsistencies between and within different methods |
title_sort |
data from: reliability assessment of null allele detection: inconsistencies between and within different methods |
publisher |
Zenodo |
publishDate |
2013 |
url |
https://doi.org/10.5061/dryad.4p41m |
genre |
Tundra |
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Tundra |
op_relation |
https://doi.org/10.1111/1755-0998.12177 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.4p41m oai:zenodo.org:4998761 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.4p41m10.1111/1755-0998.12177 |
_version_ |
1810484210081726464 |