Mitigating Scoring Errors in Microsatellite Data from Wild Populations

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Current trends in microsatellite genotyping (2011, background): “Genotyping error rates can be estimated by counting Mendelian inconsistencies in parent–offspring pairs or by counting mismatches between duplicated genotypes (Bonin et al. 2004; Hoffman & Amos 2005; Pompanon et al. 2005; DeWoody et al. 2006; Johnson & Haydon 2007).”
Advances in molecular technology and their impact on fisheries genetics (2008, background): “More importantly, microsatellites suffer from a high potential for null alleles (non-amplifying alleles) and genotyping error arising from their size-based nature (Bonin et al. 2004; Pompanon et al. 2005; DeWoody et al. 2006; Broquet et al. 2007).”
Hybridisation between native Oreochromis species and introduced Nile tilapia O. niloticus in the Kafue River, Zambia (2014, methodology): “Third, we evaluated the dataset for large-allele dropout as described by Dewoody et al. (2006) by testing for an excess of small allele homozygotes by regressing FIS against the minimum, median, maximum and range of allele fragment lengths for the loci using a standard OLS linear regression model.”